ARCHIVES
Tumor Cell Deformability
in the Metastatic Cascade
MASSACHUSETTS INSTITUTE
OF TECHNOLOLGY
JUN 3 0 2015
LIBRARIES
by
Josephine W. (Shaw) Bagnall
Bioengineering, B.S.,
Electrical Engineering and Computer Science, B.S.,
University of California, Berkeley (2007)
Submitted to the Department of Biological Engineering
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Biological Engineering
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
June 2015
0 Massachusetts Institute of Technology 2015. All rights reserved.
Auth or..........................................
Signature redacted
-...
Depgrtment of Biological Engineering
May 1, 2015
Certified by............................
Signature redacted ......
Scott R. Manalis
Professor of Biological and Mechanical Engineering
Thesis Supervisor
Accepted by..................................
Signature redacted
Forest M. White
Chair, Department Committee on Graduate Theses
Thesis Committee Members in Favor of Defense:
Professor Alan J. Grodzinsky
Professor Richard 0. Hynes
Professor Scott R. Manalis
2
Tumor Cell Deformability in the Metastatic Cascade
by
Josephine W. (Shaw) Bagnall
Submitted to the Department of Biological Engineering
on May 1, 2015, in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy in Biological Engineering
Abstract
During the process of metastasis, tumor cells must undergo changes that enable them to detach
from a tumor, exit surrounding tissue during intravasation, enter into circulation and eventually
stop at a distant site for extravasation. Here, we measure the physical changes in the deformability
of tumor cells, indicated by the length of time required to pass through a microfluidic constriction
in a suspended microchannel resonator (SMR), as related to different stages of the metastatic
process-particularly, in an epithelial-mesenchymal transition (EMT) and existence in the
circulation.
We find that a mesenchymal population of murine tumor cells (MMTV-PyMT) that had
undergone a spontaneous EMT at the primary tumor site were more deformable than the parental
population of epithelial cells. In contrast, MMTV-PyMT and Ep5 murine breast carcinoma cells
that had received signaling from platelets to undergo an epithelial-mesenchymal-like transition
maintained the same deformability or became less deformable, respectively. In all cases, however,
epithelial and mesenchymal tumor cells both take much longer to pass through a constriction than
typical blood cells, as confirmed by examining various human cancer cell lines (H1975, SKBR-3,
MDA-MB231, PC3-9). Using a syngeneic mouse tumor model, we find that cells that are able to
exit a tumor and enter the circulation are not required to be particularly more deformable than the
cells initially injected into the mouse. However, in a limited study of prostate cancer patients,
various circulating tumor cells (CTCs) can pass through a constriction quickly because some are
relatively small in size, while others are more deformable than typical tumor cell lines and more
mechanically similar to blood cells.
Nonetheless, due to the ambiguity in cell identity when a heterogeneous sample like blood
is assessed by the SMR, there was a need to correlate each cell's precision biophysical
measurement to its molecular expression. I thus developed a technique whereby cells can be sorted
off-chip based on their passage time and/or buoyant mass characteristics, and collected into a 96well plate. The proof-of-principle is demonstrated by sorting and collecting cells from cell linespiked blood samples as well as a metastatic prostate cancer patient blood sample, classifying them
by their surface protein expression and relating them to distinct SMR signal trajectories. Taken
together, our results provide impetus for further studies on the mechanical properties of CTCs as
well as the future utilization of this platform for other types of biophysical-molecular
characterizations.
Thesis Supervisor: Scott R. Manalis
Title: Professor of Biological and Mechanical Engineering
3
4
Acknowledgements
I would like to thank my advisor, Professor Scott Manalis, for the guidance and mentorship he
provided to me over the last five and a half years. His enthusiastic vision with regard to our
research and ability to see beyond existing hurdles was often what I needed in order to balance my
innate tendency toward cynicism and to persevere. He additionally gave me the opportunity not
only to develop my research skills, but to learn how to establish, maintain, and cultivate
collaborative relationships with other researchers. I have been privileged to be under his direction,
and am greatly appreciative.
Similarly, I would like to thank my thesis committee members: Professor Richard Hynes
and Professor Alan Grodzinsky. Professor Hynes was always flexible in meeting with me
whenever I came by, and always graciously entertained questions from a bioengineer. Every
meeting with him turned out to be immensely helpful in shaping the direction of my experimental
studies. The collaborations I had with his laboratory were some of the most fruitful collaborations
of which I have been a part. Professor Grodzinsky, who has been the chair of my thesis committee,
provided helpful suggestions and always caused me to think about fundamental questions of
importance as related to my research. In addition to scientific advice, he also always gave me
practical suggestions in directing my path to finish the doctorate degree.
I would also like to thank Sangwon Byun, with whom I worked closely in the last few
years. He brought me up to speed with the constriction SMR and trained me in meticulous tissue
culture techniques. We also shared the ups and downs of a difficult project, spending many hours
in the lab running experiments, doing fluorescence imaging, and having deep discussions
regarding the significance of our research. Our seamless coordination in sharing the work enabled
us to attain to what we did in our research, and I am extremely grateful to have been working
alongside such a great researcher. I would additionally like to thank all the other members of the
Manalis Lab, of whom I only have space to name a few. I would like to thank Sungmin Son for
guiding me when I first joined the lab and teaching me some crucial components in microfluidic
device design, Kris Payer for his expertise in microfabrication, Scott Knudsen for excellent ideas
and making the lab run as smoothly as it does, Vivian Hecht for running density measurements as
well as providing moral support with a female perspective, Selim Olcum for making everything
work, Nate Cermak for phenomenal LabVIEW codes, Mark Stevens and Rob Kimmerling for
tissue culture related advice, and many more whom I have not named-thank you all for the helpful
discussions and for making the lab a great place to work.
Next, I would also like to thank all of my collaborators, only some of whom I can
particularly mention here. Shahinoor Begum was one of my most helpful and responsive
collaborators, who, in spite of her busyness, was always willing to help. She truly does her work
efficiently and effectively. I also appreciate Dr. Shannon Stott for taking the time to coordinate
everything for us at MGH, and always providing helpful insights in our collaboration. I am also
5
grateful for her spending time to teach me how to identify circulating tumor cells and neutrophils
under the fluorescence microscope, which is something I would have never known how to do
otherwise.
Finally, much support came from outside of my research community, including classmates,
family, and my Christian community. I would like to thank all of my classmates in Biological
Engineering with whom I started this journey together. I especially would like to thank Kara
Huang, Jing Ge, and Nirmala Paudel for their friendship and support through the years. I would
like to thank my father, Haur Shaw, for encouraging me to attend graduate school, even when I
told him as a teenager that getting a PhD was "crazy" and that I would never do it. I also thank my
mother, Sandra Shaw, who supported me no matter what I did or didn't want to do. I would like
to thank my siblings, Abraham and Phebe, for being patterns to me since my youth. I am also
tremendously grateful to my wonderful husband, Kevin Bagnall, who has been a constant support
and encouragement, caring for me in every way possible: physically, psychologically, and
spiritually. I thank all those in my Christian community, with whom I have been on an eternal,
unseen journey, for keeping me rooted in the true meaning of human life. I especially thank Maya
Hartono who ensured the completion of my thesis proposal and has seen me through all manner
of circumstances. Most importantly, I thank my God and Savior, the Lord Jesus Christ, from whom
are all things, through whom are all things, and to whom are all things.
6
Contents
1
Introduction
17
1.1
17
1.2
Cancer metastasis...................................................................................................
1.1.1
Epithelial-mesenchymal transition...............................................................
1.1.2
Circulating tumor cells.................................................................................19
Single-cell deformability .......................................................................................
17
21
21
1.2.1
Existing methods for measuring single-cell deformability ..........................
1.2.2
The suspended microchannel resonator (SMR) with constriction................ 22
1.2.2.1 Single-cell buoyant mass and passage time through a constriction ....... 23
1.2.2.2 Entry and transit velocities revealing relative contributions of
deformability and surface friction ...................................................
24
1.2.2.3 Cancer cells reveal differences in relative changes in entry and transit
. . 28
velo cities .....................................................................................
1.3
Measuring tumor cell deformability with the SMR .................................................
2 Tumor Cell Deformability Changes in an Epithelial-Mesenchymal Transition
29
31
2 .1
Intro d uctio n ................................................................................................................
31
2.2
Spontaneous EMT .................................................................................................
31
2.3
Platelet-induced EMT-like transition .....................................................................
34
2 .4
D iscu ssio n ..................................................................................................................
39
2.5
Materials and methods ............................................................................................
40
40
2.5.1
Cell deformability and size measurements.................................................
2.5.2
Calculating passage time ratio, removing secondary population of cells .......... 41
2.5.3
Cell culture and preparation .......................................................................
43
2.5.4
Platelet preparation and treatment of cells ...................................................
43
3 Passage Time Differences between Tumor Cells and Blood Cells in Circulation
45
3 .1
Intro du ctio n ................................................................................................................
45
3.2
Blood cells versus tumor cell lines ..........................................................................
46
3.3
Circulating tumor cells in a mouse model ..............................................................
46
7
3.4
Circulating tumor cells in metastatic prostate cancer patients .................................
54
3 .5
D iscu ssio n ..................................................................................................................
57
3.6
M aterials and methods ............................................................................................
58
3.6.1
Single cell passage time and size measurements ..........................................
58
3.6.2
Cluster analysis of passage time data...........................................................
59
3.6.3
Cell culture and preparation ........................................................................
59
3.6.4
Blood cell preparation .................................................................................
59
3.6.5
M ouse CTC generation and preparation .....................................................
60
3.6.6
Human patient CTC SMR measurements and staining.................................
60
3.6.7
Fluorescence imaging of CTCs....................................................................
60
3.6.8
Converting imaged cell diameter to buoyant mass ........................................
61
4 Biophysical-Molecular Correlation with the Suspended Microchannel Resonator
63
4.1
Introduction ................................................................................................................
63
4.2
M aterials and methods .............................................................................................
65
4.2.1
Suspended microchannel resonator for buoyant mass and passage time
measurements ............................................................................................
65
4.2.2
Cell culture .................................................................................................
65
4.2.3
Blood cell preparation for initial test ..........................................................
65
4.2.4
Healthy donor and cancer patient blood samples processed in CTC-iChip and
4.2.5
Cell sorting method for SM R ......................................................................
66
4.2.6
Fluorescence imaging..................................................................................
66
4.3
Results and discussion ............................................................................................
67
4.4
Conclusion .................................................................................................................
75
5 Additional Project: A Microfluidic "Baby Machine" for Cell Synchronization
77
5 .1
Ab stract ......................................................................................................................
77
5.2
Introduction ................................................................................................................
77
5.3
M aterials and methods.............................................................................................79
5.3.1
Device description......................................................................................
79
5.3.2
Device fabrication......................................................................................
80
8
System assembly........................................................................................
81
5.3.4 Device operation ........................................................................................
81
5.3.3
5.4
5 .5
5.3.5
Cell count and cell cycle analysis ..............................................................
5.3.6
Cell culture ..............................................................................................
82
. . 82
Results and discussion ............................................................................................
5.4.1
Working principles......................................................................................
83
5.4.2
Cell loading and viability ............................................................................
83
5.4.3
Yield and synchrony ...................................................................................
86
C o n clu sio n s ................................................................................................................
A. I Further characterizations of SMR constriction measurements .................................
91
A. 1.1
Supplementary figures for chapter 1 ............................................................
91
A.1.2
Effects of applied pressure on passage time of cells....................................
93
A.1.3
Effects of varying constriction dimensions .................................................
94
SMR platform development and variations for different types of on-chip imaging and
95
m easurem ent ..............................................................................................................
A.2.1
Combined imaging and optical lever system...............................................
A.2.2 Addition of PMT sensor for fluorescence detection .....................................
95
97
Variations of SMR constriction designs......................................................
98
Preliminary study of small cell lung cancer ...............................................................
100
A.2.3
A.3
88
91
Appendix
A.2
83
A.4 Further studies on tumor cells in circulation..............................................................101
A.5
Agarose bead measurement for potential stiffness standardization ............................ 103
A.6 Further surface characterizations of cells ..................................................................
105
A.7 Characterizing heterogeneity in single-cell biophysical properties using SMR passage
113
tim e m easurem ents ...................................................................................................
A.8 Characterizing the effect of Mena and MenalNV on the stiffness of cancer cells ......... 118
121
Bibliography
9
10
List of Figures
Simplified schematic of the metastatic cascade ............................................................
17
1-2 Epithelial-m esenchym al transition.................................................................................
18
Schematic diagram of the instrument and data extracted from the measurement .......
23
1-1
1-3
25
1-4 Extracting entry and transit velocities from single cell measurement ...........................
1-5
Changes in the passage time of H 1975 cells after perturbing either deformability or
m icrochannel surface charges .......................................................................................
26
1-6
Changes in the entry and transit velocities of H1975 cells after perturbing either
deformability or microchannel surface charge...............................................................27
1-7
Entry and transit velocities of cancer cell lines having different metastatic potentials ....... 29
2-1
Measuring the deformability of tumor cells undergoing an EMT .................................
32
2-2 Deformability changes in a spontaneous EMT ............................................................
33
2-3
Calculating passage time ratio .....................................................................................
34
2-4
Deformability changes in a platelet-induced EMT-like transition in Ep5 cells .............. 35
2-5
Deformability changes in a platelet-induced EMT-like transition in EpCAMhYFPO cells.37
2-6
Passage time ratios (mesenchymal:epithelial) for all replicates .....................................
2-7
Passage time versus volume for platelet-treated Ep5 cells and platelet-treated
E pCA M hY FP cells.....................................................................................................
. 39
2-8
Entry and transit velocities .........................................................................................
42
2-9
Removal of putative doublets for passage time ratio analysis ........................................
43
3-1
Measuring the mechanical properties of tumor cells and blood cells in circulation........45
3-2
Passage time and buoyant mass of blood cells versus epithelial and mesenchymal
ce ll line s ...........................................................................................................................
47
3-3
H 1650 cells spiked into a blood cell sample.................................................................
48
3-4
M ouse CTC deform ability ............................................................................................
50
3-5
FACS does not affect passage time characteristic of 4T]-ZSGreen cells......................51
3-6
Clustering analysis of mouse CTC passage times .........................................................
3-7
Passage time versus buoyant mass characteristics of murine tumor cell lines ................ 53
11
38
52
3-8
Hum an patient CTC deform ability...............................................................................
55
3-9
C T C d iam eters .................................................................................................................
56
3-10 SM R density measurem ents of PC3 cells......................................................................
57
4-1
Cell sorting method for biophysical-molecular correlation...........................................64
4-2
Healthy donor versus metastatic prostate cancer patient blood sample .........................
4-3
Validation of tumor cell detection and collection in 96-well plate.................................69
4-4
C ell line-spiked blood sam ples .....................................................................................
71
4-5
SMR peak shape correlation with immunofluorescence staining ...................................
73
4-6
SMR measurement correlation with quantified immunofluorescence levels ..................
74
4-7
Metastatic prostate cancer patient blood sample...........................................................75
5-1
Synchronization device design and operation ..............................................................
80
5-2
Loading cells onto the device .......................................................................................
84
5-3
Cells grow ing and dividing on the device ......................................................................
85
68
5-4 Y ield and synchrony ......................................................................................................
5-5
C ell separation tim e.....................................................................................................
A-i
Effects of latrunculin B and nocodozole on MEF cells.................................................91
87
. 88
A-2 Effect of PEG versus PLL surfaces tested with various cell lines .................................
-3
I ressure vai
effLect sII
uin passage Lme aIIU pea I Lime uf L0121
1el.s
92
.................
A-4 Effects of varying the constriction dimensions with different applied pressures ............ 94
A-5 SMR system with imaging and optical lever through same objective ............................
96
A-6 PM T addition for fluorescence read-out.........................................................................
97
A-7
Example of peak shape from device Gen4-312 ............................................................
99
A-8 CAD layout of alternating 4ptm and 6ptm constrictions .................................................
99
A-9 Passage time versus buoyant mass for SCLC cell lines and mouse CTCs........................100
A-10 Passage time versus buoyant mass of tumor cells circulating in blood.............................101
A-Il Entry and transit velocities versus buoyant mass of tumor cells circulating in blood ....... 102
A-12 Bright-field image of broken agarose bead .....................................................................
103
A-13 SMR passage time versus buoyant mass measurements of agarose beads........................104
12
A-14 Heparinase-treated and untreated TMet cells in PBS.........................................................106
A-15 Passage time of heparinase-treated and untreated TMet cells in DMEM medium......107
A-16 LLC cells having been in trypLE for different amounts of time ......................................
107
A- 17 Untreated (control) and heparinase-treated LLC cells .....................................................
109
A- 18 LLC cells treated with trypsin, PBS, or OptiMEM..........................................................110
A-19 Comparison of SMR peak shapes between cells in different subpopulations ................... 110
A-20 U ntreated and sialidase-treated LLC cells.......................................................................1
1
A-21 SMR peak shapes for untreated and sialidase-treated LLC cells within different
subp o p u latio n s ...............................................................................................................
112
A-22 SMR measurements of wild-type and lamin A/C knock-out MEFs .................................
114
A-23 Offset in passage time for MEF cells after treatment with LatB or Noc...........................1 14
A-24 Passage times of Lmna +/+ MEFs treated with LatB compared with those of Lmna-/M E F s .............................................................................................................................
1 15
A-25 Trajectories of SMR peaks reveals cells with a distinct ("bumpy") peak pattern ............. 115
A-26 More "bumpy" peaks are observed with Lmna +/+ than Lmna -/- MEFs after LatB
treatm ent ........................................................................................................................
1 16
A -27 H eterogeneity in passage tim es.......................................................................................116
A-28 Treating FL5.12 cells with LatB induces a decrease in passage time and heterogeneity ..116
A-29 High throughput cellular phenotyping (HTPC) on FL5.12 cells ......................................
117
A-30 Passage time vs. buoyant mass of control, Mena, and MenaNV cells ...............................
118
A-31 Volumes of control, Mena, and MenaINV cells measured by a Coulter counter ................ 118
A-32 Entry and transit velocities of control, Mena, and MenaNV cells.....................................119
A-33 Passage time vs. volume instead of buoyant mass...........................................................119
13
14
List of Tables
4-1
Healthy donor blood samples measured in an SMR ................................................
69
4-2
Collected measurement events from cell line-spiked samples .................................
72
A-1
SMR constriction device designs............................................................................98
A -2 L ist of experim ents...................................................................................................
15
106
16
Chapter 1
Introduction
1.1
Cancer metastasis
Metastasis involves a cascade of events, where carcinoma cells undergo molecular and cellular
changes as they journey away from the primary tumor and travel to a distant site to colonize a new
tumor [1-3]. During this journey, cells that were once embedded within a tumor detach and
undergo potential elastic deformation to penetrate surrounding tissue, including endothelial cell
junctions, to intravasate into the lymphatic or circulatory system (Fig. 1-1). As tumor cells circulate
in the bloodstream as circulating tumor cells (CTCs), they squeeze through small capillaries and
endure harsh flow conditions, leaving only a fraction ofthem to survive and adhere at a distant site
for subsequent extravasation [4]. Then, among those that are able to extravasate into surrounding
tissue, depending on the conditions of the microenvironment as well as traits acquired by the
carcinoma cells themselves, some advance to seed and colonize new metastatic lesions [4,5].
Tumor
00
0
~0
AT!
ET
WBC
0
00
Detach
Intravasation
Seed tumor
Extravasation
c)
CTC
Fig. 1-1. Simplified schematic of the metastatic cascade
Cells within a primary tumor undergo changes such as an epithelial-mesenchymal transition (EMT),
detach from the tumor, intravasate into circulation, where they become circulating tumor cells (CTCs).
Then, some CTCs are able to extravasate, potentially undergo a mesenchymal-epithelial transition
(MET), and either reseed the original tumor or seed a new tumor at a metastatic site.
1.1.1 Epithelial-mesenchymal transition
During the metastatic process, it is thought that tumor cells undergo cell-biological changes, such
as progression through an epithelial-mesenchymal transition (EMT). In the course of this program,
epithelial cells tend to become less differentiated, reducing intercellular adhesion, and acquire
17
mesenchymal traits, such as increased motility and invasiveness (Fig. 1-2) [6-10]. The phenotypic
change frequently involves the loss of E-cadherin, an adhesion molecule, and often includes an
increase in mesenchymal markers, such as N-cadherin and vimentin [7,8]. Such transitions
underlie epithelial cell plasticity and enable several developmental processes in vivo.
E ithefial censs
FAP
SraiI
FTS
-'r
ndein
'IC-cdrrn
zo- 1
Entctn
Mesenchymal phenotype
Intermediate phenotypes
as cells transition
Epithelial phenotype
Mesenchymal
-
Sluq
I
Cv~ke~~tn VI
osmopiav
mi2i
fan'y
Progressive loss of epithelial markers
and gain of mesenchymal
'-an~
W
markers
SIPI
A-SMA
It
~kTwist
B cad'nli
Goosecoid
nif
LEF-1
FOXC2
rmR21
9511
gn
dfn1
-Synr
IR10
m1
Fig. 1-2. Epithelial-mesenchymal transition
An EMT involves cell-biological changes, where an epithelial cell undergoes a progressive loss of
epithelial markers and gain of mesenchymal markers, rendering the cell less differentiated and more
invasive. This figure is adapted from Ref. [7].
EMT has already been known to play key roles during embryonic development, including
embryo implantation as well as gastrulation [11]. It has also been shown to occur during tissue
repair and inflammation, where inflammatory stress induces epithelial cells to advance to various
degrees of EMT, or "partial EMTs" [7]. More recently, however, EMT has gained increasing
attention due to its association to human diseases, including the onset and progression of
carcinomas [6-8]. For example, it has been shown in a pancreatic ductal adenocarcinoma mouse
model that EMT precedes frank tumorigenesis [12]. In humans, mesenchymal markers have been
identified in carcinoma biopsies [13]. Additionally, a recent study has discovered CTCs in breast
cancer patients that exhibited a range of epithelial to mesenchymal expression; indeed, in one case
study, the breast cancer patients exhibited a higher fraction of CTCs with mesenchymal markers
when the disease was actively progressing [14]. Furthermore, EMT has been linked to cancer stem
cells and increased therapeutic resistance [6,15].
The phenotypic transition can be induced by environmental cues such as growth factors,
cytokines, certain physiological conditions, and other interactions in the tumor microenvironment.
One of the most well characterized inducers of EMT is the transforming growth factor-P (TGFp)
family of cytokines. TGFP is known to effect several pathways, including phosphorylation of
SMAD transcription factors, which can regulate cell polarity, tight junction formation, and can
associate with Zeb proteins to repress E-cadherin expression [6,15]. Notch signaling can also
induce EMT through the nuclear factor-KB (NFKB) pathway. NFKB regulates production of
18
vascular endothelial growth factor (VEGF), which aids in angiogenesis, and matrix
metalloproteinases (MMPs), which can degrade basement membrane and extracellular matrix
components to aid in tumor cell invasion and metastasis [16-18]. In addition to various signaling
mechanisms, the physiological condition of hypoxia can also induce EMT in tumors via various
signaling mechanisms (HIFla, SNAIl, TWISTI, NFKB) [19]. Moreover, the tumor
microenvironment, as dictated by tumor-stromal cell interactions can also trigger EMT in
carcinoma cells. Indeed, multiple EMT markers have been found at the tumor-stroma interface
[13,20]. Last, the direct contact between tumor cells and platelets induces an epithelialmesenchymal-like (EMT-like) transition, involving signaling along both the TGFP and NFKB
pathways [21-25]. Thus, various triggers may work together in increasing tumor cell invasiveness
and malignancy.
1.1.2 Circulating tumor cells
Carcinoma cells that have escaped into circulation, known as CTCs, have drawn increasing interest
in recent years due to their potential in cancer prognosis as well as the information they hold
regarding a patient's tumors [26,27]. As some tumors are difficult or unfeasible to biopsy, it would
be beneficial if CTCs from a simple blood draw could give doctors a window into the progression
of a patient's tumors as well as their genetic mutations over time. Such information would aid in
determining more appropriate and personalized therapies. In spite of rapidly advancing research,
still much is unknown about CTCs, and although there are some standards in place, an optimized
and adequate set of properties by which to identify them has not yet been fully determined. Even
with current methods, such as the FDA (Food and Drug Administration) approved CellSearch
system (Veridex), which selects cells from blood based on the surface expression of epithelial cell
adhesion molecule (EpCAM), CTCs are very rare in the blood (5 CTCs are considered significant
in 7.5mL blood) and not trivial to isolate. In fact, in one mL ofblood there are billions of red blood
cells and millions of white blood cells, yet only a few, tens, or at most thousands, of CTCs. Thus,
scientists and engineers have been developing a variety of methods to isolate CTCs based on a
number of cellular and molecular properties.
The most commonly used methods for CTC isolation are based upon antibody detection of
cell surface antigens. Since epithelial cells express EpCAM, whereas blood cells do not, EpCAM
is used to enrich CTCs from blood samples. One platform utilizing this strategy is the previously
mentioned CellSearch system, which employs ferrofluid nanoparticles coated with anti-EpCAM
antibodies to capture the cells, and then stains them for cytokeratin and CD45 to distinguish
between epithelial cells and leukocytes [28,29]. Other examples include microfluidic devices that
are coated with anti-EpCAM antibody, where captured CTCs either remain adhered or are released
from the chip, and can be stained for other epithelial markers for further imaging analysis [30-32].
Although the number of cells captured based on EpCAM expression have been shown to possess
prognostic value for some cancers, it is not known what role these EpCAM expressing cells have
19
in metastasis and whether another non-EpCAM expressing population of CTCs may provide
additional information [33,34].
In fact, recent reports have shown that some breast cancer cell lines do not express EpCAM
and are thus not captured by EpCAM-based isolation techniques [35-37]. Furthermore, in a study
involving an autochthonous pancreatic ductal adenocarcinoma mouse model, CTCs were found to
maintain a mesenchymal phenotype and bear stem cell properties [12]. As mentioned previously,
a case study in breast cancer has also shown CTCs bearing mesenchymal characteristics [14]. In
order to avoid biases in positively selecting for surface markers, other methods have been
employed, such as nucleic-acid base detection. An increased level of DNA in the plasma, as
measured by quantitative PCR, has been shown to correlate to cancer patients over healthy
individuals, and may correspond to the presence of CTCs [38-40]. However, the origin of such
DNA is unclear and may produce false positive findings due to the shedding of dying cells directly
from tumors, exosomes, and non-malignant epithelial cells released from an invasive procedure
[28,29]. In addition, physical methods have been used to isolate CTCs, such as separating cells by
size. Two examples include a filtration system known as Isolation by Size of Epithelial Tumor
cells (ISET, Rarecells) [41], and dean flow fractionation, which involves a spiral channel
employing centrifugal forces [42]. However, distinguishing between sizes does not provide
sufficient specificity toward the cells being retained since small CTCs (similar in size to most
leukocytes) may be lost, while large leukocytes may be enriched for [29,31]. Last, negative
depletion is a method by which white blood cells are removed by anti-CD45 antibodies, thereby
enriching the blood sample for CTCs [43]. One platform is the CTC-iChip, which removes red
blood cells by size-dependent deterministic lateral displacement and removes white blood cells by
labeling them with magnetic beads, targeting CD45 and CD15 [44,45]. Although in general,
negative depletion methods may leave impurities in the enriched CTC samples, they may prove to
be one of the least biased methods for isolating CTCs [28,46].
Currently, only the enumeration of CTCs (largely based on EpCAM expression) has been
recognized to have prognostic value in particular carcinomas. Current efforts have mostly been
focusing on molecular analyses of CTCs, including genetic and protein expression, as they begin
to uncover their heterogeneity and potential function in metastasis. Genetic alterations, such as in
chromosomal translocations, transcriptional profile variations, and mutations in the exome have
been demonstrated, and may provide insight into the diversity of tumor cells as well as mutations
occurring at the tumor sites [47-50]. Analysis of protein expression, often evaluated by
immunofluorescence or immunohistochemistry, has included assessment of a proliferative index
by Ki-67, which increase has been correlated to resistance to certain prostate cancer therapies [51].
Another protein of interest is HER2, which may be an important indicator of HER2 status changes
during breast cancer progression [50]. Also, androgen receptor (AR) signaling in prostate cancer,
based on expression levels of prostate-specific antigen (PSA) and prostate-specific membrane
antigen (PSMA), has been shown to relate to patient survival [52]. Although much effort is
underway to study the molecular characteristics of CTCs and their implications, not much has been
studied from a holistic biophysical point of view. While specific genes and proteins may be
20
involved in particular cancers or their subtypes, it is possible that the biophysical properties of
cells, such as their deformability, may be more generally applicable across multiple cancer types.
Although mechanical differences between metastatic and nonmetastatic cell lines have been
demonstrated, only few precision measurements have been made on patient CTCs, with little
implication on what properties may be necessary for them to adapt to the circulation [53-56].
1.2
Single-cell deformability
For decades, scientist have been intrigued by the physical characteristics of living cells, including
their stiffness or deformability [57]. Using various methods, they established that cell
deformability can identify changes in cell phenotype, including those in differentiation and
malignancy [55,58-60]. Mechanical changes have even been discovered to provide diagnostic
value in oral cancer [56]. From these studies, it has been found that often, more malignant cells
tend to be more deformable than nonmalignant cells.
1.2.1 Existing methods for measuring single-cell deformability
There are now copious methods by which one can measure the deformability of single cells [61].
Some techniques probe a portion of a cell or its surface, such as atomic force microscopy (AFM)
and microrheo logical methods. AFM consists of a sharp tip at the end of a cantilever, which can
apply small forces (pN to pN) to locally deform a cell's surface. The depth of indentation relative
to the applied force provides information regarding the local elasticity (Young's modulus) of the
cell [62-64]. Meanwhile, microrheology involves the tracking of particles or beads that are either
adhered to a cell's surface or introduced within the cell. Based on their motion due to an applied
magnetic field or to Brownian motion, viscoelastic properties of local regions of a cell can be
determined [65-69].
Other techniques for measuring single-cell deformability, including optical stretching,
hydrodynamic deformation, micropipette aspiration, and microfluidic constriction devices, apply
a stress to the entire cell holistically. To accurately measure cell deformability, each of these
methods must also be able to account for cell size. The optical stretcher consists oftwo laser beams,
which apply a stretching surface force to a cell that is placed between them. The change in shape
experienced by the cell over a given time is recorded as the compliance of the cell, having been
shown to be indicative of cytoskeletal strength [70,71]. Then, rather than using laser beams,
hydrodynamic stretching applies a stretching force to the cell with fluid flow. This high throughput
method (2,000 cells/s) captures images of the deformation of each cell, and based on the cell shape,
obtains a metric for cell deformability and size [58,72]. Hydrodynamic shear stress has also been
used to assess cell mechanics in a high throughput fashion (100 cells/s) [73]. Next, micropipette
aspiration involves suctioning a cell either partially or fully into a micropipette. Observation of
changes in a cell's geometry provides insight into its elastic and viscoelastic properties [57,74].
Finally, there are also microfluidic constriction devices, which, similar to micropipette aspiration,
21
consist of a small orifice through which cells must pass by deformation. The throughput of
microfluidic devices, however, tends to be higher than that of micropipette aspiration due to its
automated cell loading capabilities. The amount of time the cell takes to pass through the
constriction can be considered as a metric for cell deformability, and has been shown to relate to
cytoskeletal structure [59,75-77]. One such microfluidic device is the suspended microchannel
resonator (SMR), which has the ability to determine the size of each cell as well as parse out the
relative contributions of deformability and friction to the passage of a cell through the constricted
channel, as described in the following section [59].
1.2.2 The suspended microchannel resonator (SMR) with constriction*
[T]he SMR consists of a hollow microchannel embedded in a silicon cantilever, whose resonant
frequency is detected by the deflection of a laser beam [78]. [We have integrated a constriction
near the apex of a SMR as shown in Fig. 1-3A.] In each experiment, a solution with cells that are
denser than the surrounding fluid is flowed into the device. Once a cell enters the SMR, the
resonant frequency is lowered by an amount that depends on its buoyant mass and position away
from the resonator base (Fig. 1-3B) [78]. By tracking the resonant frequency as a cell traverses
through the SMR, the position of its center of mass along the channel and its buoyant mass can be
measured with a precision near 100 nm and 1 pg, respectively, for a cell that weighs ~100 pg and
travels through the SMR in -1 s. As a result, a cell's buoyant mass, passage time, velocity upon
entering the constriction (entry velocity), and velocity as it transits through the constriction (transit
velocity) can be extracted with a throughput of a few thousand cells per hour. Buoyant mass, which
is the metric we use in this study for cell size, is defined by the product of the cell's volume and
its density difference from the surrounding fluid.
Using the SMR with the integrated constriction, we demonstrate that precise, single-cell
buoyant mass measurements in conjunction with passage time information enable the distinction
between cell lines bearing different physical characteristics. More specifically, these combined
measurements reveal differences between cell lines arising from blood and epithelial tissue, as
well as between cell lines having varying metastatic potential. To assess factors affecting cell
passage through the constriction, we further show that entry and transit velocity measurements
enable us to identify the relative importance of deformability and surface friction, respectively.
Changing the deformability of the cell by perturbing its cytoskeleton primarily alters the entry
velocity, whereas changing the surface friction by immobilizing positive charges on the
constriction's walls primarily alters the transit velocity. To demonstrate the insight that these
parameters provide, we compare the properties of both mouse and human cancer cell lines having
* This section as well as its subsections are individual excerpts taken directly from a manuscript, the writing to
which I contributed heavily: Byun, S., Son, S., Amodei, D., Cermak, N., Shaw, J. et al. "Characterizing
deformability and surface friction of cancer cells." PNAS. 2013. 110(19):7580-5. Author contributions are listed in
the publication. Changes to the published text include reference numbers, figure numbers, and text included in
brackets.
22
known metastatic potentials. When accounting for cell buoyant mass, we find that cells possessing
higher metastatic potential exhibit faster entry velocities than cells with lower metastatic potential.
However, in some cases, the increase in transit velocities associated with faster entry velocities
was considerably greater than expected, suggesting that reduced friction may be a factor in
enabling invasive cancer cells to efficiently squeeze through tight spaces.
A
B
2000
-
5
Passage time
-. 1980
'
Entry
1960
1940.
Transit
2
0.0
0.2
0.3
Time (s)
10-
C
0.1
Jos'*
E
0.1.
0..
0.001
50
100
150 200 250
Buoyant mass (pg)
Fig. 1-3. Schematic diagram of the instrument and data extracted from the measurement
A) Suspended microchannel resonator (SMR) with a constriction (6 [Lm wide, 15 pm deep, and 50 pm
long) located at the apex. A cell passing through an embedded microfluidic channel is deformed as it
flows into the constriction. Numbers 1-5 indicate different positions within the microchannel to
demonstrate the trajectory of a cell flowing inside the channel. B) The resonant frequency response of
the SMR as the cell passes through the microfluidic channel. The numbers 1-5 correspond to the
position of the cell in the cantilever, as marked in A. The height of the peak corresponds to the buoyant
mass of the cell (1-+ 2). The cell slows down as it deforms to enter the constriction (entry), and then
speeds up as it travels through the constriction (transit). The passage time corresponds to the sum of
the entry and transit times (3 -+ 4). C) Power law dependence of passage time versus buoyant mass for
the H1975 cell line (n = 967). Measurements were acquired with a PEG-coated channel surface and
using a pressure drop of 1.8 psi.
1.2.2.1 Single-cell buoyant mass and passage time through a constriction
We first measured the buoyant mass and passage times of hundreds of single cells from a human
lung adenocarcinoma cell line, H1975 (Fig. 1-3C), to validate our method. As expected, a cell's
passage time through the constriction has a power law relationship to its buoyant mass [79].
23
Interestingly, the range of passage times spans nearly four orders of magnitude as it changes with
cell buoyant mass. In addition, the passage time of cells having the same buoyant mass varies by
up to an order of magnitude, suggesting the influence of other effects. Compared with other
approaches for measuring cell size and passage time through a constriction [75,77,80,81], our
results show a thousand-fold larger range in passage time across the population and a nearly 10fold larger variation in passage time for cells of similar buoyant mass. One possible explanation
for this discrepancy is that, because the passage times in these previous studies were typically on
the millisecond scale and below [77,80], the relatively higher strain rates diminished the range in
the mechanical properties. Although it seems surprising that cells of similar buoyant mass could
have such a significant variation in passage time, time lapse microscopy of a cell passing through
multiple constrictions in existing literature [82] suggests that, in addition to inherent biological
variation, the orientation of the cell upon entry into the constriction could give rise to this spread.
1.2.2.2 Entry and transit velocities revealing relative contributions of deformability and
surface friction
To assess the components that govern passage time, the entry and transit velocities were extracted
from the SMR measurements. The passage of each cell through the constricted microchannel
comprises the initial deformation of the cell to enter the constriction as well as the subsequent
transit of the cell through the constriction. The velocity of each cell during its entry and transit can
be obtained by monitoring the position of its center of mass within the microchannel (Fig. 1-4 A
and B), as given by the resonant frequency shift of the cantilever [83]. Both entry and transit
velocities have a power law dependence on the buoyant mass, similar to the passage time, but these
velocities decrease with increasing buoyant mass (Fig. 1-4C). Also, for a given cell, the entry
velocity is slower than the transit velocity. However, the difference between the entry and transit
ve itie
6se prooce
fr smaller
beas mle e+srqiels
]elVs ime+to efrm
10
13 106.3
V1J'
flUI
L
I
.4L'JI
0111"11VI'
VV113,
V%%CLLI3%' 3111U411%11
I..'%113
1%14LII11%
1%13a
L1111%,
LM
UJLU1
111
into the constriction in comparison with larger cells. Hence, in complement with passage time
information, the entry and transit velocities can provide more insight into cellular properties
governing the passage through the constriction.
Next, the effects of deformability and surface friction on the passage of cells through the
constriction were assessed by measuring H1975 cells under two separate conditions-having
perturbed its cytoskeleton with latrunculin B (LatB), and having coated the microchannel surface
with positively charged poly-L-lysine (PLL) [Fig. 1-5, 1-6]. First, as expected, the treatment with
LatB decreased the passage time ofthe cells (Fig. 1-5), corresponding to the LatB-induced increase
in cell deformability by the disruption of actin filaments [84]. Furthermore, the LatB treatment
increased both entry and transit velocities, with the relative increase in entry velocity being greater
than that of transit velocity (Fig. 1-6 A-C). Although the small change in transit velocity indicates
that a change in cell stiffness can influence the friction because the normal force of the cell against
the channel wall is altered [85], the greater change in entry velocity is consistent with the fact that
perturbing the viscoelastic properties of the cell affects the passage most when the deformation of
24
the cell is critical, i.e., at the entry rather than during transit. A similar behavior was observed when
MEF cells were treated with nocodazole (Fig. A-1), further demonstrating that a cytoskeletal
deformability change in the cell corresponds to a larger shift in entry velocity than transit velocity.
A
B
0.4
C
100
MEntry velocity
100-
N
MTransit velocity
10_
.
10
n
-0.6
-
Transit velocity
1
00
.
~0.8
> 0.
ransit Entry
Entry
1.0
Entry vetocity
.
0
CL00.
0.2
0.1
Time (s)
0.8
0.9
Position (normalized)
> 0.1-;
0.01
50
100 150 260 250
Buoyant mass (pg)
Fig. 1-4. Extracting entry and transit velocities from single cell measurement
A) The resonant frequency response (positions 3-5 in Fig. 1-3B) is converted to the normalized
position of the cell in the cantilever and plotted versus time. The length of the cantilever was
normalized to 1 to represent the cell's position, where 1 and 0 correspond to the tip and base,
respectively. B) Cell velocity is obtained by taking the time derivative of the normalized position.
Entry and transit velocities are extracted at specific locations that correspond to the entrance and the
inside of the constriction, respectively. C) Entry (green) and transit (orange) velocities versus buoyant
mass for the data set from Fig. 1-3C.
Coating the microchannel constriction with PLL increased the passage time of the cells
(Fig. 1-5B), corresponding to the increased interaction of the negatively charged cell surface with
the positively charged PLL-coated channel surface than with the neutral PLL-graft-PEG-coated
surface. The PLL coating decreased both the entry and transit velocities, but with a larger change
in transit velocity (Fig. 1-6 D-F). Although the surface interaction during the deformation at the
constriction entry was nontrivial, the effect was greatest when the contact area between the cell
and the channel wall was maximum, i.e., during the cell's transit through the constriction. The
transit velocity thus represents a friction measurement that encapsulates effects contributing to the
normal force exerted by the cell on the channel wall, as well as interactions between the surface of
the cell and the channel. The experiment involving a PLL surface coating demonstrates that
enhancing surface interactions, such as those governed by charge, alters the transit velocity more
than entry velocity. Similar results were obtained for various other cell lines, such as HCC827,
TMet, and Tnonet (Fig. A-2). Hence, even though deformability and friction are not completely
orthogonal in our measurements, the relative differences in entry and transit velocities provide a
metric for the relative importance of deformability and friction during the passage of a cell through
a constriction.
25
B
10-
10I EUntreated
I
*
I1-
0.1-
0
(V
(V
.50 I
r
00
5
i-
!c:
lb
, **.
aI-
0.01-
9
0
so
*
0.01-
1
CD 0.1
0)
AID
I
Q.
0
Oe A~aaiA
E
(n
*
.
1-
E
'I,
0)
(V
a
MPEG
LatB
(V
(V
*PLL
*
A
is.
0.001
100
150
200
250
Buoyant mass (pg)
50
V
100
150
200
250
Buoyant mass (pg)
Fig. 1-5. Changes in the passage time of H1975 cells after perturbing either deformability or
microchannel surface charges
A) Passage time versus buoyant mass for H 1975 untreated (blue) and treated with latrunculin B (LatB)
(red). Treatment with LatB decreases the passage time. B) Passage time versus buoyant mass for H1975
for a microchannel surface coated with positively charged poly-L-lysine (PLL) (blue) and neutral PEG
(red). Coating with PLL increases the passage time. The data in this figure match with those of Fig. 1-
6.
26
A
*Untreated
wLatB
-100.
10.
C
1
.* ..
*.'
6
*.A
.
1.
*
0
1
-
S ..
T
Cu 3.
Cu3
.C.
2.
-. -W .. z.
0.1
0.1.
Untreated vs. LatB
4.
*4' 10.
0
0
>T
mUntreated
mLatB
100
.
.
>
B
CU
W 0.01 1
50
100
150 200250
I. 0.01
Buoyant mass (pg)
mPLL
mPEG
.,*
100
Buoyant mass (pg)
*PLL
mPEG
C'
10.
.
*'
1
> 0'.
> 0.1.
0
*
.0.01
.
150 200250
Buoyant mass (pg)
5
PLL vs. PEG
F
5
3
0 CD
CC 0 2.
V
W4
C0.01.-
100
I ransix
velocity
4-
I-
50
eniry
velocity
*.
1.
W
0.
150 200250
E
*
10.*
5
16o
150 200250
Buoyant mass (pg)
0.
entry
Transit
velocity
velocity
Fig. 1-6. Changes in the entry and transit velocities of H 1975 cells after perturbing either deformability
or microchannel surface charge
A) Entry velocity and B) transit velocity versus buoyant mass for H 1975 untreated (blue, n = 843) and
treated with LatB (red, n = 907, 5 pg/mL for 30 min) measured in a PEG-coated channel. Treatment
with LatB decreases the passage time of H1975 and induces a larger shift in entry velocity than transit
velocity. C) A ratio of velocities from the two conditions was calculated as in Ref [59]. Changing the
deformability of the cell by perturbing its actin cytoskeleton induces a 3.8-fold increase in the entry
velocity, and only a 1.5-fold increase in the transit velocity. D) Entry velocity and E) transit velocity
versus buoyant mass for H1975 cells passing through a microchannel whose surface is coated with
positively charged PLL (blue, n = 345) or neutral PEG (red, n = 649). PLL increases the passage time
and results in a greater shift in transit velocity than entry velocity. F) Changing the surface friction
from PEG to PLL caused entry velocity to decrease 2.3-fold, and transit velocity to decrease 4.7-fold.
Error bars represent 95% confidence intervals. All measurements were acquired using a pressure drop
of 1.8 psi. [For an explanation of calculating ratios of velocities, see Fig. 2-3 and Ref [59].]
27
1.2.2.3 Cancer cells reveal differences in relative changes in entry and transit velocities
We also found that the relative importance of the cell's deformability and surface friction to its
passage through the constriction could be a unique indicator of cell state. The deformation and
friction properties of cancer cells having different known metastatic potentials were characterized
by entry and transit velocities on a PEG-coated channel (Fig. 1-7). In addition to having shorter
passage times [59], all three cell lines with higher metastatic potential exhibit faster entry and
transit velocities. Interestingly, the relative differences in entry and transit velocities from each
pair showed distinct patterns. Expressing the Nkx2-1 transcription factor in TMet led to a change
primarily in entry velocity, which is similar to what we observed in altering the cytoskeletal
deformability of the cells with LatB (Fig. 1-6C). In contrast, TMet versus TnonMet showed significant
differences in both entry and transit velocities, suggesting that changes in friction account for more
of the difference between this pair of cells than between the pair with a single genetic alteration
(TMet versus Tmet-Nkx2-1). H1975 versus HCC827 also demonstrated that a considerable change
in the transit velocity is associated with the entry velocity. These examples suggest that reduced
friction may play a role in enabling invasive cancer cells to effectively squeeze through tight
spaces. It is possible that such changes in friction may be related to differences in factors
contributing to the normal force of the cell on the channel wall, such as cell relaxation times.
Additionally, the observed changes may be caused by glycocalyx characteristics, such as the
expression of particular cell surface molecules like sialic acid, because it is already known that
increased expression of sialic acid may be accompanied with a higher metastatic potential in cancer
cells [86].
28
TMet:T met-Nkx2-1
3.0- UT.: Tnonet
2.0-
(P =
0
*H1 975: HCC827
(D
*
A
1
0.048)
1
Z 2.5-
o
00
2.0
o
1.0-
~1.5-
w
1.0
0.5
VE
T
E
T
T
VE
Met
Tm.-Nkx2-1
Met:
TnMet
H1975:
HCC827
Fig. 1-7. Entry and transit velocities of cancer cell lines having different metastatic potentials
Three pairs of cancer cell lines having different metastatic potentials (TMet versus TMet-Nkx2-1, red;
TMet versus TnonMet, green; and HI 975 versus HCC827, blue) were compared by measuring the changes
in entry velocity (VE) and transit velocity (VT) with a PEG-coated channel surface. A) Ratio of VE and
ratio of VT for three pairs of cancer cell lines. VE and VT ratios connected by a line represent one
replicate. In contrast to TMet versus Tmet-Nkx2-1, TMet versus Tnonmet and H1975 versus HCC827 show
that a significant change in transit velocity is associated with a change in entry velocity, suggesting
that the role of friction is more significant in those pairs. TMet versus TMet-Nkx2-1, TMet versus Tnonmet,
and H1975 versus HCC827 were repeated from different cultures three, six, and three times,
respectively. Error bars represent 95% confidence intervals. B) For each measurement, the ratio of VE
divided by the ratio of VT is shown, which confirms that the proportional change in VE relative to VT
was significantly different among the three pairs (*p < 0.05, Mann-Whitney-Wilcoxon test).
Measurements were acquired using a pressure drop of 0.9 psi for the mouse cell lines (TMet, TMet-AkX21, Tnonmet) and a higher drop of 1.8 psi for the human cell lines (H1975, HCC827) to account for their
larger size.
1.3
Measuring tumor cell deformability with the SMR
As we have shown, the SMR can precisely weigh single cells and record their position along the
channel. This enables us to decouple a cell's size from its passage time through a constriction, as
a measure of its deformability. Furthermore, the velocities of the cell, both as it enters the
constriction and as it transits through after the initial deformation, can provide additional
information regarding the relative significance between deformability and surface friction. The
aforementioned demonstration ofthe technique involves comparing cell lines of varying metastatic
potential. In the following chapters, we utilize the SMR to further unveil deformability
characteristics specifically when carcinoma cells undergo EMT as well as when they are in
circulation, comparing their properties to those of other cells naturally existing in circulation.
These precise mechanical measurements of tumor cells aim to provide a physical perspective and
understanding to cancer cell mechanics in various stages of the metastatic cascade.
29
30
Chapter 2
Tumor Cell Deformability Changes in an EpithelialMesenchymal Transition
2.1
Introduction
Over the past decade, the importance and role of the EMT program in the onset and progression
of carcinomas have become increasingly clear [6-8]. Mesenchymal markers have been identified
both in carcinoma biopsies as well as in circulating tumor cells during disease progression [13,14].
Furthermore, it has been found that direct contact with platelets induces an epithelialmesenchymal-like (EMT-like) transition in tumor cells, rendering them more highly metastatic
[21-25]. Thus, it is of interest to determine physical changes that occur concomitantly with these
cell-biological changes that enable carcinoma cells to enhance their malignancy both in the
primary tumor site as well as in circulation [87,88]. Previous studies of the mechanical properties
of cells have employed various methods to probe cancer cells from cell lines or body fluids,
demonstrating that highly metastatic cells are often more deformable than weakly metastatic cells
[55,58,59,72,89-95]. Here, we utilize an SMR with a 6pLm-wide constriction to measure active
physical changes displayed by carcinoma cells after they become more malignant, focusing
specifically on how EMT affects their deformability as given by their passage time (see 2.5.1).
We investigated two cell lines-one representing a spontaneous EMT that arose at the primary
tumor site, and the other induced to undergo an EMT-like transition by direct contact with platelets
(Fig. 2-1).
2.2
Spontaneous EMT*
The deformability change in a spontaneous EMT was measured in a murine tumor cell line derived
from a mammary carcinoma of a SnailYFP/; MMTV-PyMT animal. In this cell line, a YFP reporter
is directly controlled by the endogenous promoter of the EMT-inducing transcription factor Snail,
which allows fractionation of the cells according to Snail expression levels. This cell line was
sorted using fluorescence-activated cell sorting (FACS) into an epithelial population
(EpCAMhiYFP'o) and a spontaneously arising mesenchymal (EpCAMOYFPhi) population. After
sorting, the cells were propagated in monolayer culture for a few days, and differences in epithelial
* This work was done in collaboration with Dr. Xin Ye of the laboratory of Prof. Robert A. Weinberg. Xin provided
the cell line and performed FACS to sort the cells. Vivian C. Hecht of the Manalis Laboratory performed single-cell
density measurements.
31
and mesenchymal morphologies were confirmed by phase-contrast microscopy (Fig. 2-2A). The
two cell populations were then trypsinized and measured separately in the SMR.
Measure Passage Time
and Buo ant Mass
EMT
rWBC
RBCq
TO CTCEMT-like
Platelets .0
,
Suspended Microchannel
Resonator (SMR)
Fig. 2-1. Measuring the deformability of tumor cells undergoing an EMT
Carcinoma cells have been found to undergo a spontaneous EMT at the site of the primary tumor, and
have also been found to undergo an EMT-like transition when in direct contact with platelets, which
they encounter in circulation. Here, we measure tumor cells that undergo a spontaneous or plateletinduced EMT using an SMR with a constriction. (EMT = epithelial- mesenchymal transition, RBC =
red blood cell, WBC = white blood cell, CTC = circulating tumor cell, EMT-like = epithelialmesenchymal-like transition)
The measurement reveals that for cells of the same size, the cells of the mesenchymal
population generally pass through the constriction faster than do those of the epithelial population
(Fig. 2-2B). As previously described [59], lines were fit to each data set such that the difference in
y-intercepts determines the ratio of the passage time of the mesenchymal population to that of the
epithelial population, given the same cell size (Fig. 2-3, 2.5.2). In addition, for such close
comparisons, buoyant mass values were converted to volume by measuring the density of the cells
in the SMR ( 2.5.1). On average, based on three replicates, the passage time ratio values indicate
that the EpCAM'OYFPhi cells passed through the constriction 1.6-fold faster than the
3
EpCAMhiYFP* cells, for those having a volume greater than 1000 pm . As expected, the
EpCAMIOYFPhi population of cells was found to be more than two-orders of magnitude more
tumorigenic than the EpCAMhiYFPO when injected into mice (Ye and Weinberg, unpublished).
This result is consistent with studies by other methods demonstrating that often, more
mesenchymal or malignant cell types are more deformable [55,89].
32
A
2 Days after FACS
B
EpCAMIOYFPhi, N=699
0 EpCAMhiYFPO, N=615
o
100
00
0000
:E
0
100
~*
1
LU
0
0~0
U-
0
0
0~
LU
0.01
0.001..................6600
100
1000
)
Volume (pm3
Fig. 2-2. Deformability changes in a spontaneous EMT
A) FACS sorted epithelial (EpCAMhiYFPO) and mesenchymal (EpCAM'OYFPhi) populations of the
MMTV-PyMT tumor cell line were cultured for two days to confirm differences in morphology prior
to measurement in the SMR. Based on phase contrast microscopy, the mesenchymal population has
decreased intercellular adhesion and is more elongated compared to the epithelial population, as
expected. B) Passage time is plotted against volume, comparing the epithelial and mesenchymal
subpopulations of the MMTV-PyMT cell line.
33
100
0
EpCAM *YFPh, N=624
0
EpCAM hiYFPO, N=552
- 0
100o~O00 .
'~~~
0
E1A
0 .* 160
E
0 0o0
0.1 -
0.01 *.
0
.*
0
g[Pass. Time (E)
A = log Pass. Time
(M
0.001-1
)
Volume (pm 3
Fig. 2-3. Calculating passage time ratio
3
A zoomed-in region of data from Fig. 2-2B with volumes greater than 1000 m , demonstrating a
calculation of the passage time ratio, which is found by taking the difference in y-intercepts (A) as the
exponent of 10 ( 2.5.2). In the case shown, the intercept offset is significantly different from zero (p <
2x1 0-16).
2.3
Platelet-induced EMT-like transition*
In addition to spontaneous EMTs, previous work has shown that direct signaling between platelets
and cancer cells can induce the cells to undergo an EMT-like transition [24]. Thus, after one day
of co-culture with platelets, carcinoma cells already exhibited increased expression of EMTrelated, and prometastatic genes. Following an additional day of co-culture, carcinoma cells,
having the majority of the platelets washed away during sample preparation, generated an
increased number of lung metastases after tail-vein injection [24].
Using the same cell line and technique as published [24], Ep5 murine breast carcinoma
cells were cultured with platelets for two days in order to induce the transition, as confirmed by
decreased intercellular adhesion and elongated morphology (Fig. 2-4A). While one culture plate
of cells was treated with platelets, two control plates were treated with an equal amount of buffer.
Cells from all plates were then washed and measured separately in the SMR to compare any
changes in deformability between the platelet-treated and buffer-treated cells, using the
deformability difference between the two buffer-treated plates as a control (Fig. 2-4B). For each
replicate, the platelet-treated cells took a longer time to pass through the constriction than control
* This work was done in collaboration with Shahinoor Begum, who performed the platelet treatment of the cells and
Prof. Richard 0. Hynes, who provided guidance to the experimental design. The work was based on a manuscript
from the laboratory of Prof. Hynes: Labelle, M., Begum, S., Hynes, R.O. "Direct signaling between platelets and
cancer cells induces an epithelial-mesenchymal-like transition." Cancer Cell. 2011;20:576-590. Vivian C. Hecht
performed single-cell density measurements in the Manalis Laboratory.
34
A
:3
C.
icos
0)
.4-J
I-
B
Control vs. Control
C
Platelet-treated vs. Control
* Buffer-treated 0, N=1040
* Buffer-treated 1, N=1047
o
*
Buffer-treated 1, N=1 047
Platelet-treated 1, N=720
1
1
10
0
0
* 0
@0
*
.
0
0
00
0=
0.1
0)0
0.10
aue(m)
C0.1
0
0
0-.030
1090oo
Vom (pm3W4
Fig. 2-4. Deformability changes in a platelet-induced EMT-like transition in Ep5 cells
A) Phase contrast micrographs of control Ep5 cells (treated with buffer) and Ep5 cells treated with
platelets. Cells were treated on day 0 after the images were taken. Subsequent images were taken on
the following two days to observe morphological changes in the platelet-treated cells undergoing an
EMT-like transition. B) Two control plates of Ep5 cells that had been treated with buffer were
measured in the SMR. The difference in passage time versus volume between these two buffer-treated
samples serves as a control for identifying significant differences in other cell line comparisons. C)
SMR measurements of the epithelial buffer-treated Ep5 cells and the mesenchymal-like platelet-treated
Ep5 cells after platelet incubation for two days.
35
cells (Fig. 2-4C). On average, for cells between 800 ptm3 and 3500 pm 3, the platelet-treated cells
required a 1.5-fold longer time than the control cells to pass through the constriction.
In addition, to characterize the effect of direct contact with platelets on the MMTV-PyMT
cell line that can spontaneously undergo an EMT ( 2.2), the epithelial subpopulation
(EpCAMhiYFPO) was treated with platelets. After two days of co-incubation with platelets, the
EpCAMhiYFP'o did in fact undergo a morphological change suggestive of an EMT-like transition,
as they became more elongated and had somewhat decreased intercellular adhesion (Fig. 2-5A).
Although in some replicates the platelet-treated cells had a slightly higher passage time than the
buffer-treated cells, their change was not significantly different when compared to the passage
time differences between two sets of buffer-treated control cells (Fig. 2-5B, C). Nonetheless, the
passage time of the mesenchymal-like, platelet-treated population was certainly unlike that of the
spontaneously arising mesenchymal population (EpCAMIOYFPhi, 2.2), in that the cells did not
become more deformable than the founding epithelial population.
The passage time ratio of all replicates are plotted together for comparison in Fig. 2-6.
Interestingly, different sets of control EpCAMhYFPO cells varied more from one another than did
various sets of control Ep5 cells, possibly due to the composition of each cell type and the dynamic
nature of the MMTV-PyMT cells that are known to undergo a spontaneous EMT or to technical
variation. The deformability changes in tumor cells caused by signaling from platelet interactions
have not been measured previously, and the potential decreased deformability brought about by
this interaction differs from most studies referenced earlier that indicate that more malignant cells
are often more deformable. Instead, as postulated by others, the decreased deformability or
prevented increase in deformability may enhance hematogenous dissemination by augmenting
survival under shear stress conditions [94,96,97]. In fact, the absolute values of the passage times
for the Ep5 cells and the EpCAMhYFPO cells treated with platelets are not far from each other
(Fig. 2-7), corroborating the idea that there may be an ideal stiffness, rather than a relative change
in stiffness, for the cells to attain to for survival in circulation [97]. Furthermore, a decrease in
deformability or maintenance of a certain stiffness may increase the ability of the cells to lodge or
be retained longer in microcirculation [25].
36
A
Dav 0
Day 1
Day 2
I
0
7&
U
CL
LU
0
"&
11
LL a-
LI.'
B
C
Control vs. Control
100,
o
Buffer-treated 0, N=374
0
Buffer-treated 1, N=598
10 0
Platelet-treated vs. Control
Buffer-treated 1, N-598
Platelet-treated, N=796
10
0*
o0
0
0
ttl 0.1,
A0.1
0
04b
0.01,
0.01
0001
1000
Volume (jim 3)
1030
10000
1 )00
Volume (pm 3
10000
)
0.001100
0*0
CL
Co
Fig. 2-5. Deformability changes in a platelet-induced EMT-like transition in EpCAMIYFPO cells
A) Phase contrast micrographs of control EpCAMhiYFPO cells (treated with buffer) and
EpCAMhiYFPO cells treated with platelets. Cells were treated on day 0 after the images were taken.
Subsequent images were taken on the following two days to observe morphological changes in the
platelet-treated cells undergoing an EMT-like transition. B) Two control plates of EpCAMIYFP cells
that had been treated with buffer were measured in the SMR. The difference in passage time versus
volume between these two buffer-treated samples serves as a control for identifying significant
differences in other cell line comparisons. C) SMR measurements of the epithelial buffer-treated
EpCAMhJYFPO cells and the mesenchymal-like platelet-treated EpCAMYFPO cells after platelet
incubation for two days.
37
1.8-
Ep5
MMTV-PyMT
1.6-
o
1.4M
Less deformable
-
1.2
M
1.0-
Less deformable
mj
4i
0.8-Nwvnge
0.60.4More deformable
I
More deformable
I
I
I
I
Platelet
EpCAMIOYFPhi
Control
Control Ep5 Platelet Ep5
(No EMT)
(EMT-like) EpCAMhiYFPIO EpCAMhiYFPIO (Spontaneous
(No EMT)
(EMT-like)
EMT)
Fig. 2-6. Passage time ratios (mesenchymal:epithelial) for all replicates
Passage time ratios were calculated for three replicates of each control Ep5 experiment (different plates
of buffer-treated Ep5 cells compared to one another: Control Ep5, No EMT), three replicates of the
buffer-treated versus platelet-treated Ep5 comparisons (Platelet Ep5, EMT-like), three replicates of
each control EpCAMhYFP* experiment (different plates of buffer-treated EpCAMhYFP* cells
compared to one another: Control EpCAMhYFPO, No EMT), three replicates of the buffer-treated
versus platelet-treated EpCAMYFPO comparisons (Platelet EpCAMhYFPo, EMT-like), and three
replicates of the MMTV-PyMT epithelial versus mesenchymal comparison (EpCAMOYFPhi,
Spontaneous EMT). Each passage time ratio represents the passage time of the mesenchymal
population to that of the epithelial population. For the Ep5 cells, the population of cells having a larger
volume (Fig. 2-4) is presumed to be doublets that did not dissociate after trypsinization, since those
cells have twice the volume of the majority of cells, and hence were not included in the linear fits when
determining the passage time ratio ( 2.5.2). In comparing Ep5 platelet-treated to buffer-treated cells
as well as in comparing EpCAMIOYFPh to EpCAMhYFP" cells, the y-intercepts of the linear fits were
found to be significantly different with p < 3.4x1 0-16. The error bars for each point corresponds to the
95% confidence interval of the passage time ratio based on the intercept offsets of the linear fits. The
dark gray shaded regions are based on the extent of change seen in control experiments, and are
symmetric regions drawn about a passage time ratio of 1.0, indicating no change in passage time
characteristics for each given cell type.
38
. Platelet EpCAMhiYFPIO, N=796
A Platelet Ep5, N=720
10
0
0
*
100
*04
%
0
0.001.
0.001
)
Volume (pim 3
Fig. 2-7. Passage time versus volume for platelet-treated Ep5 cells and platelet-treated EpCAMiYFPIO
cells
The passage time versus volume characteristics of platelet-treated Ep5 cells are similar to those of
platelet-treated EpCAMiYFPIO cells. The data here are the same as in Fig. 2-4C and 2-5C.
2.4
Discussion
Based on the passage time measurements of single-cells, it is evident that cell deformability may
change dynamically with molecular changes in an EMT. The measurements made on MMTVPyMT cells that underwent a spontaneous EMT match previous literature showing that more
malignant cells tend to be more deformable. However, we demonstrate here that Ep5 cells that
were co-cultured with platelets and thus subject to TGFp and NF-KB signaling, become less
deformable after an EMT-like transition. Furthermore, the MMTV-PyMT cells that became more
deformable in a spontaneous EMT, did not significantly alter their deformability when induced to
undergo an EMT-like transition by direct contact with platelets. Although these findings are
dissimilar to many previous studies, it does correspond to another study that suggests a wide range
of tumor cell deformability, where a highly metastatic subpopulation of melanoma and renal
cancer cells were shown to be stiffer than their less metastatic counterparts [94].
The fact that different mesenchymal cancer cell populations were found here to be more
deformable, less deformable, or similarly deformable to their corresponding epithelial populations
may relate to the extent oftheir EMT, especially since the cells undergo cytoskeletal reorganization
during the phenotypic shift [98,99]. For example, the platelet-treated Ep5 and EpCAMiYFPO cells
may be undergoing an EMT-like transition, but have not reached a stage where they are as
mesenchymal in phenotype as the spontaneously arising EpCAMl*YFPhi MMTV-PyMT cells.
Moreover, cells in different stages ofthe metastatic cascade may benefit from being stiffer or softer
depending on the mechanism of their malignancy. More specifically, while a tumor cell is at the
39
primary site, becoming more deformable may allow it to better escape the tumor; whereas once a
tumor cell is already in circulation and binds to platelets, becoming stiffer or maintaining a certain
stiffness may enable the cell to attain to an optimal physical condition that resists damage in a
condition of shear stress, enhancing its viability in circulation [96,97]. In addition, becoming less
deformable may augment the ability of tumor cells to lodge in a capillary at a distant site, or to be
retained longer at the site to allow for subsequent extravasation [24,25,94]. In fact, it has already
been shown that that platelet interaction with tumor cells does not necessarily increase in the initial
arrest of the cells, but does enhance metastatic tumor formation [24,25,94].
2.5
Materials and methods
2.5.1 Cell deformability and size measurements
An SMR having a 6 pm wide, 15 pm deep, and 50 pm long constriction was used to measure the
buoyant mass, as a metric for size, as well as the passage time, as a metric for deformability, of
each single cell as previously described ( 1.2.2) [59]. The channel walls were coated with PEG (1
mg/mL; PLL(20)-g[3.5]-PEG(2); Surface Technology) for all experiments in this study. The
positively-charged PLL portion of the polymer adsorbs to the negatively-charged silicon dioxide
surface, while the inert PEG portion comes into contact with cells being measured. All SMR data
were processed in MATLAB as previously described [59].
In addition to deformability, the passage time measurement is also influenced by such
factors as cell size [59]. Therefore, in all analyses of deformability, cells of the same size were
compared. For measurements having small changes in passage times, such as in an EMT, the
buoyant mass of each cell was converted to volume, since the volume of the cell is more
consistently related to passage time than to buoyant mass. The single-cell densities of each cell
tvne were measured
n each div of exneriment isinr the SMR ns nrevinisy dpcr-ihed [] (IM The
density of the culture medium in which the cells were measured was determined by the resonant
frequency of the SMR. Then, the volume of the cell was determined by the ratio of the buoyant
mass to the difference in the average cell density (typical interquartile range: 0.003-0.007 g/mL)
and the fluid density.
Moreover, the relative contribution of surface friction to the passage time can be accounted
for by the cell's velocity as it transits through the constriction (transit velocity), after fully
deforming into its entrance [59]. However, the velocity of the cell upon entering the constriction
(entry velocity) is typically slower than the transit velocity, suggesting that the passage time is
mostly dictated by the time the cell spends deforming at the entrance of the constriction, rather
than transiting through afterwards (Fig. 2-8 A,B). The transit velocity of cells undergoing an EMT
was found to change by the same amount as the entry velocity, indicating that alterations in the
frictional component affect but do not dominate the differences in passage times (Fig. 2-8 C, D).
Hence, in the data presented here, we consider changes in passage times to represent changes in
deformability.
40
2.5.2 Calculating passage time ratio, removing secondary population of cells
The passage time ratios, as shown in Fig. 2-3 and Fig. 2-6, were calculated as previously described
[59]. In brief, the passage time versus volume data were plotted on a log-log scale. A line was fit
to each data set, having the same slope, but variable intercepts. The difference between the two
intercepts corresponds to the logio of the ratio of the passage times, which is then converted to the
actual passage time ratio by exponentiation.
To obtain accurate linear fits, the data were considered within its linear region (on a loglog scale) containing the majority of the cells for analysis, eliminating small particles or debris.
Thus, the lines were fit for Ep5 cells having volumes between 800 and 3500 piM 3, and for MMTVPyMT cells greater than 1000 pm 3 . As evident in Fig. 2-4 and noted in the caption Fig. 2-6, the
Ep5 buffer-treated and platelet-treated cells have a second population of cells presumed to be
doublets due to their being twice the volume of the majority of the cells in the population. To
obtain accurate linear fits, the second population of cells was removed by excluding cells below a
given line roughly parallel to the major axis of the second population of cells. The same line was
used to remove the second population on both the platelet-treated and buffer-treated cells for all
three replicates of the experiment. Fig. 2-9 shows an example of the removal of the larger
population of cells.
41
B
A
100j 0e.-
.0
1001
.a
11
0
0
00
~0
0.11
S0.1,
00
as
00
-P.
C
WI
-0.01,
0.01
' EpCAMIOYFPhi, N=639
- EpCAMhiYFPO, N=554.
0.001. - EpCAMhiYFPIO, N=554
i600
Volume (pm 3
C
1WOOO
)
Volume (pm 3
)
16oo
.
*
0.0011
D
2.5-
1.5
0
><I
.
>1.5-
1.0
-
Entry
Transit
EpCAMhiYFPIo:
EpCAMIOYFPhi
Eniry
Transit
0.5
EpCAMhiYFPIO:
EpCAMIOYFPhi
EP5-Platelet:
EP5-Buffer
EP5-Platelet:
EP5-Buffer
Fig. 2-8. Entry and transit velocities
A) Entry velocity versus volume for EpCAMhiYFPO and EpCAMOYFPhi cells. B) Transit velocity
versus volume for EpCAMhYFPl* and EpCAMIOYFPI cells. C) Entry and transit velocity ratios for
each replicate of experiment are shown, comparing EpCAMhYFPO to EpCAMIOYFP' as well as
comparing Ep5 cells treated with platelets to those treated with buffer. The velocity ratios are
calculated in the same manner as the passage time ratios described in the 2.5.2. The entry and transit
velocities change similarly, with neither change dominating the other. D) For each measurement, the
entry velocity ratio was divided by the transit velocity ratio to more clearly compare the contribution
of each in both cell lines having undergone an EMT or EMT-like transition. There is no significant
difference between the ratios for the two cases. Measurements made for each of these panels are the
same as those presented in Fig. 2-2 and 2-4.
42
o
o
Buffer-treated, N=1047
Platelet-treated, N=720
109
0.
0.0110
0.001.
100
,
100
, ,,
o
3
.
.
4~~(
Volume (pm3)
Fig. 2-9. Removal of putative doublets for passage time ratio analysis
As an example, the same data from Fig. 2-4C is shown here. To determine the passage time ratio
between the two data sets, the shaded region was used to remove the second, larger population of cells,
which was presumed to be doublets due to their size. The same shaded region was used to remove the
secondary population of cells from all data sets with Ep5 cells.
2.5.3 Cell culture and preparation
The MMTV-PyMT tumor cell line was derived from a mammary carcinoma of a SnailYFP/+.
MMTV-PyMT animal. The cells were cultivated in DMEM/F 12 (1:1) (Invitrogen 11320)
supplemented with 5% adult bovine serum (Sigma B9433), non-essential amino acids (Invitrogen
11140), and Pen/Strep (Invitrogen 15070). They were then stained with anti-EpCAM antibody
directly conjugated to allophycocyanin (APC) fluorophore (Ebioscience 17-5791-82), and sorted
by FACS based on EpCAM and Snail YFP eXpression. The cells were then cultured for 2 or 4 days
(passaged once) and then trypsinized (0.08% Trypsin-EDTA), resuspended in culture medium, and
measured in the SMR. Bp5 cells were cultured in Dulbecco's Modified Eagle's Medium (DMEM),
10% fetal calf serum (FCS), 1% penicillin/streptomycin and 2 mM L-glutamine and obtained as
described previously [24]. Cells were detached from the plate using 0.25% Trypsin-EDTA. Cells
were maintained in incubators at 370 C with 5% C02.
2.5.4 Platelet preparation and treatment of cells
Platelets were prepared and Ep5 cells were co-cultured as previously described [24].
EpCAMhiYFPIO cells were co-cultured with platelets in the same way, but having 3% adult bovine
serum in the medium since the cells would not survive without serum. EpCAMiYFP* cells were
treated with platelets within a week of being sorted by FACS to ensure relatively high purity in
the epithelial
phenotype.
Platelets were resuspended
in
PIPES
buffer
(1,443
piperazinediethanesulfonic acid, Sigma-Aldrich), and at least 500 million platelets were used to
treat each 6-cm dish of cells. Buffer-treated cells were treated with the same volume of PIPES
buffer, but without platelets. When the cells were ready to be measured in the SMR, they were
washed with PBS, trypsinized, and resuspended in culture medium. After washing and
trypsinizing, the cells were expected to be largely free of bound platelets, as confirmed in a
previous study [24].
44
Chapter 3
Passage Time Differences between Tumor Cells and Blood
Cells in Circulation
3.1
Introduction
As tumor cells escape the primary tumor and intravasate into circulation, their environment
changes entirely-from living with other carcinoma cells in a tissue to fluid flow conditions, where
they endure shear stress and encounter narrow capillaries [1]. Thus, we measure the deformability
of tumor cells, noting their distinctions from blood cells. We further investigate whether an
environment like the circulatory system may passively select for certain types of cells based on
their physical ability to deform and pass through a constricted space, as measured by an SMR (Fig.
3-1). Finally, we measure the deformability of circulating tumor cells CTCs from a limited number
of cancer patients. Taken together, our results provide a physical perspective to complement and
enrich the current understanding of the metastatic progression of carcinomas.
Measure Passage Time
and Buovant Mass
Tumor Cells
EMT
RBC(wBC
EMT-like
Platelets
t
Suspended Microchannel
Resonator (SMR)
0
Fig. 3-1. Measuring the mechanical properties of tumor cells and blood cells in circulation
A schematic diagram demonstrating the context of circulating tumor cells and blood cells in the
metastatic cascade as well as their measurement in an SMR with a constriction (EMT = epithelialmesenchymal transition, RBC = red blood cell, WBC = white blood cell, CTC = circulating tumor cell,
EMT-like = epithelial-mesenchymal-like transition)
45
3.2
Blood cells versus tumor cell lines
To begin probing the biophysical differences between cells that naturally live and survive in
circulation versus ones that typically live within in a tissue parenchyma, we first compared the
passage time and buoyant mass properties of tumor cell lines versus blood cells. Fig. 3-2 shows
distinct size and deformability characteristics between these two types of cells, where the small
differences between mesenchymal and epithelial tumor cell phenotypes (Ep5 [Buffer-treated], Ep5
[platelet-treated], EpCAMhiYFPO, EpCAMIOYFPhi, as presented in Chapter 2) pale in comparison
to the dramatic differences between the tumor cell types and blood cells. Indeed, blood cells have
passage times on the order of a few milliseconds, whereas tumor cells can have passage times as
long as a few seconds. Included in the various tumor cell lines measured are a lung cancer cell line
(H1975), two breast cancer lines (MDA-MB231 and SKBR-3), and a prostate cancer line (PC39). Meanwhile, the types of blood cells measured include human erythrocytes, peripheral blood
mononuclear cells (PBMCs), polymorphonuclear (PMN) leukocytes, L1210 (mouse lymphoblast
cell line), and primary mouse (BALB/c) leukocytes ( 3.6).
As is already known, the majority of blood cells are smaller than most of the tumor cells
in culture. However, even for cells of comparable size, as indicated by their buoyant mass, blood
cells have decidedly faster passage times than do tumor cells. This distinction holds true for
passage time measurements at two different flow rates, as portrayed by the similarity between Fig.
3-2A and B. For each flow condition, agglomerative clustering was employed on the median of
the logio values of the passage times for binned buoyant masses, given a set range of buoyant
masses where the different cell types overlapped ( 3.6.2). The unsupervised algorithm
demonstrates that the data naturally fall into two separate groups: tumor cells and blood cells (Fig.
3-2C, D). Moreover, when a lung cancer cell line (H1650) was spiked into enriched PBMCs, they
maintained their distinction from the blood cells, where the number of cells having longer passage
times corresponded to the number of tumor cells spiked into the sample (Fig. 3-3) [59]. It is also
worth noting that both cell types were measured in a suspended state; thus even after trypsinization
and resuspension in culture medium, the tumor cells maintain a marked physical distinction from
blood cells. Given such a contrast, the question arises as to whether the circulatory system requires
cells to possess a specific deformability characteristic in order for them to intravasate and remain
in circulation.
3.3
Circulating tumor cells in a mouse model*
To assess tumor cells circulating in vivo, we injected one million 4T1-ZSGreen (a murine
mammary carcinoma line stably transfected to express ZSGreen) cells via tail vein into syngeneic
BALB/c mouse hosts. 4T1 cells are known to express epithelial markers such as E-cadherin, while
also expressing Twist, an EMT-related transcription factor [101,102]. In addition, they are known
* This work was done in collaboration with Shahinoor Begum, who cultured cells and performed all mouse work, in
the laboratory of Prof. Richard 0. Hynes, who provided guidance to the experimental design.
46
A
B
EP5 (Buffer-treated
,
E PS (Platelet-treavcl,
N=720
N=1047
H1975, N=622
MDA-MB-231, N=578
-4
3
0
EpCAMhYFPO, N=615
EpCAM~oYFPh1, N699.
SKBR, N=539
PC3-9, N542
Red Blood Cells, N=1 175
Human PBMC, N=2245
Red Jlood Cells, N=1205
Human PBMC, Nt2314
L1210, N=1043
PMN Leukocyles, N=1765
L1210, N=1321
Mouse WBCs, N=39,849
Ej
Jm
BuoyantjMass (pg)
C
D
Passage Time (s)
0,0032
~
EpCAMh'YFPIC
EP5 (Platelet)
Passage lime (s)
. 02 j3
0.0032 0.01
0.032
0.1
0.32
SKBR
umfor
EpCAMIOYFPhI
Tumor
H1975
MDA-MB-231
EPS (Buffer)
PC3-9
L1210
L1210
Human PBMC
u
PMN Leukocytes
Human PBMC
Mouse WBCs
34.8 47.0
Buoyant Mass (pg)
63.3
85.3 115.0 15!
Buoyant Mass (pg)
Fig. 3-2. Passage time and buoyant mass of blood cells versus epithelial and mesenchymal cell lines
Passage time versus buoyant mass plots demonstrate the distinct difference between tumor cell lines
and blood cells, regardless of the epithelial or mesenchymal phenotype of the tumor cells. A) The same
murine cell lines (Ep5, EpCAM'IYFPO, EpCAM'OYFPS) from Fig. 2-2 and 2-4 are shown here in
comparison with blood cells: human red blood cells, human peripheral blood mononuclear cells
(PBMCs), human polymorphonuclear (PMN) leukocytes, and a mouse lymphoblast cell line (L1210),
measured under the same flow conditions (0.9 psi applied pressure). B) Four human cancer cell lines
corresponding to lung (H1975), breast (MDA-MB-231, SKBR), and prostate cancers (PC3-9)
compared to blood cells: human red blood cells, human PBMC, mouse white blood cells (WBCs) and
L1210 cells, under faster flow conditions (1.5 psi applied pressure) portray that the same distinction in
passage time profiles. For (A) and (B), the dotted grid lines on the (continued on the following page)
47
Fig. 3-2 continued: X-axis are at 10 pg and IOOpg, while dotted grid lines on the Y-axis are at 0.001
s, 0.01 s, 0.1 s, 1 s, 10 s, and 100 s. Also, the color of the scatter plots corresponds to the density of
data points, with red being the highest and blue being the lowest density. C) The passage time data in
(A) for buoyant masses between 30 pg and 150 pg are placed into 6 bins based on buoyant mass (log
scale). The median of the log10 values of the passage times within each bin is shown in the heat map,
where color assignment is on a log scale. The passage times are grouped using agglomerative
clustering, with the dendrogram shown to the right of the heat map. The reported buoyant mass bin
centers and passage time values are converted from log10 values. D) Similar to (C), the passage time
data in (B) for buoyant masses between 30 pg and 180 pg are plotted in a heat map of passage time
values, showing a dendrogram of the clustering to the right.
B
2000-
10
eBlood cells spiked with H1650
eBlood cells
1 *H1650
~52
1980-
81960
N1940-000
0.1
0.0
(2 194019200.001
O5
-.
1
1.0
,
~
0.01
IL
0.0
.
-
9
-
-
A
5
.u..
e
16o
Buoyant mass (pg)
Time (s)
Fig. 3-3. H 1650 cells spiked into a blood cell sample
A) SMR frequency signals showing some cells passing quickly, and others passing slowly through the
constriction ( 1.2.2). B) Passage time versus buoyant mass for blood cells (human PBMC, n = 2832),
H1650 cells (n = 404), and blood cells spiked with H1650 cells (n = 10810). For the leukocytes spiked
with HI 650 cells, there is a subpopulation whose passage time and buoyant mass properties correspond
to the H1650 cells alone. Measurements were acquired with an applied pressure of 1.5 psi. This figure
was adapted from Ref. [59].
to be migratory, invasive, and highly metastatic [101,102]. After one week, metastatic lesions had
formed in the lungs (Fig. 3-4A) and blood was retrieved from the mouse via cardiac puncture.
Tumor cells were then sorted from the blood using FACS, based on their ZSGreen expression and
subsequently measured in the SMR. The measured cells from the blood were compared to the 4T1
cells that had been injected seven days prior (Fig. 3-4B, C), an aliquot of which had been kept in
culture and run through FACS as a control (Fig. 3-5).
Tumor cells found to persist in the blood seven days after initial intravenous injection are
expected to be cells that had detached from initially formed metastatic tumors and re-entered
circulation, since the originally injected cells should have all been removed from circulation within
seven days, if not within a few minutes [103,104]. In each of three replicates of the experiment,
over 97% of the initially injected cells maintained in culture, spanned the same range of passage
times as the tumor cells that were able to enter into the blood stream from the metastatic lesions.
48
In fact, for the one replicate having more than 40 CTCs in the size region of interest (40 pg to 120
pg), the distribution of passage times for CTCs is the same as that of the original 4T1 control cell
line (p = 0.412, Fig 3-4C). Therefore, although the cells may transiently change their deformability
during intravasation, our measurement reveals that there is not a strict deformability requirement
that the cells must meet in order to enter circulation. Additionally, both the CTCs and the control
cell line have higher median passage times (1.7- and 1.8-fold, respectively) than healthy BALB/c
leuckoytes, with the passage time distributions of the tumor cells being significantly different from
that of the blood cells.
Due to the small number of CTCs in other replicates, rather than assessing the distribution
of passage times, agglomerative clustering of median passage times was employed as in Fig. 3-2,
combining the data from all replicates (Fig. 3-6A, B). As a result, the mouse CTCs cluster with
the control 4T1 cells and not with the leukocytes (Fig. 3-6A). In addition, the CTCs also cluster
with other murine cell lines, such as Ep5, EpCAMhiYFPO, EpCAMIOYFPhi, and B 1 6F 10 melanoma
cells (measured under the same flow conditions) instead of grouping with blood cells, including
L1210, human PBMCs, and healthy BALB/c leukocytes (Fig. 3-6B). The data from individual
replicates also separately cluster in the same fashion, grouping themselves with other tumor cells
rather than blood cells (Fig. 3-6C, D). For reference, passage time versus buoyant mass scatter
plots for the additional cell lines are shown in Fig. 3-7. Hence, cells that intravasate into the blood
stream do not necessarily need to be as deformable as blood cells. It is noteworthy that based on
the passage time characteristic alone, not taking into account any molecular differences, a cell
having almost any measureable passage time from the original 4T1 population could intravasate
into the blood stream, even though its passage time can be 10-fold higher than that of typical
leukocytes.
49
A
Left Lung
B
1
4T1 Control after F ACS (N=1007)
Fluorescent Cells in Mouse Blood (N=140)
0.1-
A
A
U'
U'
a-
0.017
A
A
A
A
A AkAA
A
100
10
Buoyant Mass (pg)
2
p = 1.13 x 10-
C
0.1-
0
p = 8.73 x1 0-13 1
E
I- 0.01tU
t'
a
M~
1E-3-
4T1 Control
(N=896)
Mouse CTCs Mouse WBCs
(N=61)
(N=40)
Fig. 3-4. Mouse CTC deformability
A) Mouse lungs were imaged seven days after tail vein injection of one million 4T1 -ZSGreen cells to
confirm presence of metastatic lesions. B) Mouse blood, after lysing the erythrocytes, was run through
FACS to sort out tumor cells having green fluorescence, and then measured in the SMR. The red
triangles indicate each signal detected by the SMR while measuring CTCs. 4T1-ZSGreen control cells
kept in culture after the initial injection were run through FACS for a comparison, and are shown as
gray points in the background. C) The distributions of passage times for the 4T1 -ZSGreen control cells,
CTCs, and BALB/c leukocytes (same data as in Fig. 3-2B) are plotted for cells with a buoyant mass
ranging from 40 pg to 120 pg, eliminating debris, aggregates, or stray blood cells sorted during FACS
while focusing on cells large enough to interact with the constriction. The control cell line and the
CTCs have similar passage time distributions (p = 0.412).
50
A
1
B
4T1 After F ACS (N=1008)
4T1 Before FACS (N=916)
4T 1After FACS5(N=1 008)
Before FACS (N--916)*
0
04T1
P%
0
Yi
0.1
0
0000
0
001
0
Oo4
W 0.01I0%
~
In
tA 0.01
M~
Ir
- -E
1
10
50
100
Buoyant Mass (pg)
100
Buoyant Mass (pg)
150
Fig. 3-5. FACS does not affect passage time characteristic of 4T1 -ZSGreen cells
A) The effect of FACS on the passage time versus buoyant mass profile of 4T1 -ZSGreen cells was
determined by comparing an aliquot of cells that had been run through FACS and one that had not. B)
A zoomed-in region of the data in (A), showing linear fits that were made to the two data sets. The
passage time ratio between the cells after FACS and before FACS is 1.02, which is in the range of
having no change as measured by the SMR (Fig. 2-6). The intercept offset between the linear fits to
the two data sets is not significantly different (p = 0.233).
51
B
A
U.uu 10 U.UUn
U.iuu U.uvo
EpCAMhIYFPIo
(N=152)
EpCAMIoYFPhi
(N=204)
(N=288)
mor
M(Nse16)
Blood
Mouse WBCs
(N=40)
44.6
55.6
693
86.3
1075
(N=569)
Mouse CTCs
(N=1 16)
L1210
(N=957)
HumanPBMC
(N=1 55)
Mouse WBCs
(N=0)
C
0.0016
lood
lo
55.6
44.6
Buoyant Mass (pg)
4T1 Control
umor
EP5
4T1 Control
(N=2066)
693
86.3
107.5
Buoyant Mass (pg)
D
Passage Time (s)
0.01
0.0040
0.0016
Passage Time (s)
0.01 0.016
0.0040
EpCAMhYFP o
(N=152)
1
(N=629)EpCAM'OYFPhi
Mouse CTC 3
(N=35)
(N=204)
EPS
(N=288)
mr
4T1 Control 3
(N=940)B16F10
4T1 Control 2
(N=896)
(N=569)
Mouse(N=61
Mouse CTC 2
(N=61)
Mouse CTC 1
(N=20)
1210
--
Mouse CT 1
Blood
Mouse WBC
(N=40)
44.6
55.6
693
863
1075
PBMC
Human
55)
(N=1
Mouse WBC
(N=40)
Blood
44.6
55.6
693
863
Buoyant Mass (pg)
107.5
Buoyant Mass (pg)
Fig. 3-6. Clustering analysis of mouse CTC passage times
A) Data from three replicates of the mouse CTC experiment are pooled to compare passage times in
the given mass range. The heat map colors correspond to the passage time value, with the color
assignments on a log scale. The binned passage time values were grouped by agglomerative clustering,
with the dendrogram shown to the right of the heat map. B) CTC data from three replicates are pooled
and compared to murine tumor cell lines other than the control 4T1 cell line, including Ep5,
EpCAMhIYFPO, EpCAMOYFPw, and B16FI0, evaluated under the same flow conditions (1.5 psi
applied pressure). Also included for comparison are the blood cells from Fig. 3-2B. The passage times
are plotted in the heat map and the dendrogram from clustering analysis is shown on the right. C) Data
from each replicate are used separately in the agglomerative clustering analysis. Each replicate clusters
together with control 4T1 cells, and not with BALB/c leukocytes. D) Each replicate of the mouse CTCs
clusters together with murine tumor cell lines other than 4T1, rather than blood cells. For all panels,
the buoyant mass range was taken from 40 pg to 120 pg, and the reported buoyant mass bin centers
and passage time values are converted from loglo values.
52
A
E P5, N~f623
816F10, N=831
EPCAMhWYPI!O, N=31 1
EpCAM4YFPO, N=486
Q)
E
(0
IA
0~
1P.~
a
Buoyant Mass (pg)
W,
'I
Fig. 3-7. Passage time versus buoyant mass characteristics of murine tumor cell lines
The mouse tumor cell lines were measured with an applied pressure of 1.5 psi, which is the same
condition as the measurements of mouse CTCs in the main text (Fig. 3-4). Plots are on a log-log scale,
with an X-axis ranging from 3 pg to 600 pg, and a Y-axis ranging from 0.008 s to 300 s. Dotted grid
lines on the X-axis are at 10 pg and 100pg, while dotted grid lines on the Y-axis are at 0.001 s, 0.01 s,
0.1 s, 1 s, 10 s, and 100 s. Colors correspond to the density of data points, with red being the highest
and blue being the lowest density.
53
3.4
Circulating tumor cells in metastatic prostate cancer patients*
We subsequently turned to human patient samples to understand whether tumor cells in cancer
patients also maintain a passage time in the circulation distinct from that of blood cells. Blood
samples from metastatic prostate cancer patients were processed with the CTC-iChip at the
Massachusetts General Hospital, depleting the samples of the majority of red blood cells and white
blood cells as previously described [44,45]. The two samples processed having a significant
number of CTCs are presented here (Fig. 3-8). As expected due to residual blood cells in the
samples, most of the cells passed through the SMR's constriction quickly, doing so without any
apparent hindrance. However, to verify whether CTCs were also measured, all cells were collected
from the output of the SMR and stained with DAPI for the nucleus, EpCAM to indicate a tumor
cell, and counterstained for CD45 as a marker for leukocytes. To then determine whether there
were CTCs that passed through the SMR's constriction with a timescale comparable to that of
blood cells (< 0.01 s), we compared the number of CTCs (DAPI positive, EpCAM positive, and
CD45 negative) to the number of long passage time events (> 0.01 s). For both patients, there were
more CTCs than long passage time events (1 more for Patient 1, 34 more for Patient 2), indicating
that CTCs can behave like blood cells in terms of passage properties.
However, since it is known that CTCs can be small [31,44] and that most cells below 50
pg pass through the SMR's constriction in less than 0.01 s regardless if they are deformable blood
cells or potentially stiffer adherent cells placed into suspension (1.5 psi applied pressure, Fig. 32B), estimates were made of the buoyant mass of each CTC based on the imaged diameter ( 3.6.8,
Fig. 3-9 and Fig. 3-10). The buoyant mass of each CTC was not known directly since although
they were measured by the SMR, the identity of each individual cell was not inherently known
during the measurement. From the buoyant mass conversion, many CTCs were in fact small
compared to the cells prepared from cultured cancer cell lines and measured previously (Fig. 3-2).
For this reason, these CTCs were below the sensitivity range of the SMR to definitively compare
their deformability to that of blood cells. However, there were some large CTCs that would be
expected to have passage times greater than 0.01 s if they were mechanically similar to those
deriving from a tumor cell line. Based on a combination of all the human tumor cell line data in
Fig. 3-2B, a tumor cell between 50 pg and 100 pg has a probability of 0.58 of having a passage
time greater than 0.01 s, and a tumor cell greater than 100 pg has a probability of 0.99 of having a
passage time greater than 0.01 s. For Patient 1, there were 3 CTCs greater than 100 pg, while there
was only one SMR measurement having a passage time greater than 0.0 1 s. If the CTCs are like
* This work was done in collaboration with Dr. Sangwon Byun in the Manalis Laboratory, who performed an equal
share of work in measuring patient blood samples in a separate SMR, as we at times operated two different SMRs to
measure the same sample. We also collaborated with Dr. David T. Miyamoto, Dr. Shannon L. Stott and Dr .Mehmet
Tohner at the Massachusetts General Hospital (MGH). Dr. Stott helped to direct the study, enabled the acquisition of
patient blood samples, and provided guidance for identifying CTCs by immunofluorescence staining. Dr. Miyamoto
also aided in experimental design, coordinating the acquisition of metastatic prostate cancer blood samples and
providing guidance in sample preparation based on his prostate cancer expertise. Katherine Broderick and Wooseok
Kim, also at MGH, performed blood sample preparation in the CTC-iChip prior to SMR measurement.
54
A
1
D
Prostate Cancer Patient 1
1 Prostate Cancer Patient 2
N=96,904
N=14,853
:
0.01Z
0.01.
040 %
0
0.001.
100
B
E
N=10
CTC-
*
10
,.,
40+
N=35
CTC-
100
10
Converted Buoyant Mass (pg)
EpCAM/
Comnosite DAPI
CD45
CDH1 1
C
160
10
Buoyant Mass (pg)
Buoyant Mass (pg)
**
*
10
Vi41 ~
100
Converted Buoyant Mass (pg)
F
Comoosite
CTC
CTC
CTC
CTC
WBC
WBC
DAPI
CD45
EDCAM
Fig. 3-8. Human patient CTC deformability
A) SMR measurement of prostate cancer Patient I blood sample, after having been processed in the
CTC-iChip [44,45]. B) Based on fluorescence images, the diameter of each CTC was converted to an
approximate buoyant mass value to visualize where they fall among the data measured by the SMR as
shown in (A). C) A sampling of fluorescence images of CTCs and a white blood cell (WBC) taken
from the same prostate cancer patient sample after it was measured in the SMR. D) SMR measurement
of prostate cancer Patient 2 blood sample, after having been processed in the CTC-iChip. E) Based on
fluorescence images, the diameter of each CTC was converted to an approximate buoyant mass value.
F) A sampling of fluorescence images of CTCs and a WBC from the same patient sample. False color
overlays were applied for composite fluorescence images.
55
the tumor cell lines, the probability of2 ofthe 3 CTCs having passage time below 0.01 s is 0.0003.
Similarly, for Patient 2, there were 5 CTCs between 50 and I OOpg, and the probability of all of
them having passage times faster than 0.01 s to match the SMR measurements is 0.013. Thus, it is
unlikely that all of the larger CTCs in these patient samples are physically similar to the tumor cell
lines for cells of the same size; rather many of them more closely resembled blood cells in their
ability to pass through the constriction quickly.
26-
22-
I
=L
18-
14-
4.
10-
Patient 1
Patient 2
N=10
Fig. 3-9. CTC diameters
CTC diameters were determined as the maximum Feret's diameter of the EpCAM fluorescence signal
for each cell.
56
1.081.071.061.051.041.031.02
PC3 (N=391)
Fig. 3-10. SMR density measurements of PC3 cells.*
The density distribution of PC3 cells measured by the SMR was used to convert CTC diameters to
buoyant masses.
3.5
Discussion
In considering tumor cells in circulation, we have found that in spite of deformability changes due
to an EMT, tumor cells in general (whether having an epithelial or mesenchymal phenotype) have
much longer passage times than blood cells. However, based on the mouse model presented, there
does not seem to be a specific deformability necessary for the tumor cells to exit the initially
formed metastases and enter circulation. More specifically, the tumor cells that enter circulation
from the metastatic lesions are not particularly more deformable than the originally injected
population of tumor cells. This observation is consistent with the fact that during invasion and
intravasation, the surrounding extracellular matrix can be proteolytically degraded [105] and
endothelial cell junctions may be weakened [106], possibly allowing tumor cells to pass through
without requiring them to be especially deformable.
In human prostate cancer patients, however, the throughput limitations of the SMR device
required patients to have high CTC levels. Based on two patient samples having CTCs, many of
their CTCs were able to pass through the constriction in less than 0.01 s, as fast as blood cells. Part
of this phenomenon is due to the small size of many CTCs. However, if CTCs were mechanically
similar to the various tumor cell lines we studied, it would be unlikely for the larger CTCs to also
have a fast passage time as was seen in the SMR measurements. Thus, while the mouse model
demonstrates that blood-cell-like deformability is not necessary for tumor cells to intravasate and
enter circulation, limited human patient samples show that some CTCs in circulation may in fact
Single-cell density data were acquired by Vivian Hecht in the Manalis Laboratory.
57
find it advantageous to be small in size or to be as flexible as blood cells to rapidly pass through a
constricted channel.
Based on these findings, one conceivable hypothesis regarding metastasis could be that
many tumor cells having a broad range of biophysical characteristics intravasate into circulation,
but only a small (potentially more deformable) subpopulation persists in circulation, while others
lodge at distant sites for metastasis. Another possibility is that the primary tumors of different
patients may be quite disparate and may evolve both in the size and deformability of their cells,
shedding CTCs that are representative of biophysical changes in the primary tumor. Certainly, the
limited data presented here would need to be much expanded and developed to verify such
hypotheses. However, due to the low throughput of the SMR (45 L/hr) combined with the rarity
of CTCs, further enhancements to the platform would need to be implemented to make a more
complete study feasible. In addition, based on the SMR measurements in total, there may exist a
few relatively stiff CTCs that take a long time to pass through a constriction; however, it would be
necessary to isolate cells with long passage time events from the SMR device in real-time to be
definitive. A new method developed for this purpose will be discussed in the following chapter
(Chapter 4), and data from Patient 2 will be revisited. Fully recognizing the limitations in the
presented study, here we demonstrate an initial biophysical measurement of clinical samples
revealing the ability of some prostate cancer CTCs to pass through a narrow constriction quickly
either by being small in size or by becoming as deformable as blood cells. Future modifications to
the SMR platform, such as increased throughput via parallelization as well as smaller constriction
sizes, may be able to further probe the deformability of CTCs and more definitively define whether
they are in fact mechanically indistinguishable from blood cells.
3.6
Materials and methods
3.6.1 Single cell passage time and size measurements
A suspended microchannel resonator (SMR) having a 6 ptm wide, 15 pm deep, and 50 pm long
constriction was used to measure the buoyant mass, as a metric for size, as well as the passage
time, as a metric for deformability, of each single cell as previously described ( 1.2.2) [59]. The
channel walls were coated with PEG (I mg/mL; PLL(20)-g[3.5]-PEG(2); Surface Technology) for
all experiments in this study. The positively-charged PLL portion of the polymer adsorbs to the
negatively-charged silicon dioxide surface, while the inert PEG portion comes into contact with
cells being measured. All SMR data were processed in MATLAB as previously described [59],
and passage time density scatter plots were created using a modified version of the dscatter
function (MathWorks File Exchange) [72]. Here, due to the large distinction in passage times
between blood cells and tumor cells, along with the unfeasibility of measuring the single-cell
density of a cell type as rare as CTCs, buoyant mass was used as a metric for cell size, and was not
converted to volume.
58
3.6.2 Cluster analysis of passage time data
For data compared by clustering analysis, logio values of the buoyant mass and passage time data
were used, both in binning buoyant mass values and determining median passage times. Thus, the
passage time value reported for each buoyant mass bin is the median of the logio values of the
passage times converted back to an actual time by taking it as an exponent of 10. The reported
buoyant mass bin centers were also converted from logio values by exponentiation. The
agglomerative hierarchical clustering analysis was performed using MATLAB, and plots were
created using the clustergram function.
3.6.3 Cell culture and preparation
Details concerning Ep5, EpCAMhYFPO, and EpCAMIOYFPhi cell lines are delineated in 2.5.3.
B16F1O cells were cultured in Dulbecco's Modified Eagle's Medium (DMEM), 10% fetal calf
serum (FCS), 1% penicillin/streptomycin and 2 mM L-glutamine. 4T1 cells were stably transfected
to express ZSGreen as previously described [107], and were cultured in DMEM, 10% fetal bovine
serum (FBS), 1% penicillin/streptomycin, 2 mM L-glutamine, 1% non-essential amino acids.
MDA-MB23 1, SKBR3, and PC3-9 cell lines were obtained and cultured as described previously
[44]. H1975 and H 1650 cells were obtained previously [108], and cultured in RPMI supplemented
with 10% FBS, 1% sodium pyruvate, and 100 IU of penicillin and 100 gg/mL streptomycin. L1210
cells (a gift from Dr. Marc Kirschner) were maintained in suspension in RPMI with 10% fetal
bovine serum (FBS), 1% penicillin/streptomysin, 25 mM HEPES. Cells were maintained in
incubators at 370 C with 5% C02.
3.6.4 Blood cell preparation
Whole human blood was purchased from Research Blood Components, LLC (Brighton, MA).
Mononuclear
cells were separated using Histopaque-1077
(Sigma-Aldrich
10771).
Polymorphonuclear lymphocytes were enriched by using Lympholyte-poly (Cedarlane CL5070).
Red blood cells were measured by diluting whole blood in PBS with 1% (w/v) Kolliphor P188
(Sigma-Aldrich), since the concentration of red blood cells is orders of magnitude higher than that
of other cell types. The Kolliphor nonionic copolymer-based buffer reduces nonspecific cell
attachment to tubing and channel walls [45].
Healthy BALB/c mouse blood was obtained via cardiac puncture. Red blood cells were
removed by lysis (150 mM NH4Cl (8.02 g/L), 10 mM KHCO3 (1.00 g/L), 0.1 mM Na2EDTA,
pH 7.23) or Histopaque-1077. To isolate live leukocytes and ensure removal of clots and platelets,
the remaining cell solution was stained for CD45 (Biolegend 103121) and CD41 (Biolegend
101319) for 15 min at room temperature after pre-incubation with Fc block (Biolegend 101319).
DAPI (Life Technologies D1306) was also used to stain dead cells. After resuspending the cells
in PBS with 0.5%FBS and 2mM EDTA, cells were sorted using fluorescence-activated cell sorting
(FACS) and the CD45hi/CD41 O/DAPIO cells were collected for analysis in the SMR.
59
3.6.5 Mouse CTC generation and preparation
Balb/c mice were injected via tail vein with 1 million 4T1-ZSGreen cells in 100 1 iL of Hank's
Balanced Salt Solution (HBSS, Life Technologies). After seven days, blood was collected by
cardiac puncture (left ventricle) into 400 pL of citrate dextrose solution (ACD; 38 mM citric acid,
75 mM trisodium citrate, 100 mM dextrose). Fluorescent images were taken of the lung to verify
tumor formation. Meanwhile, red blood cells were lysed using lysis buffer for 2 minutes on ice.
Cells were then resuspended in phosphate buffered saline (PBS) with 0.2% FBS and prepared for
FACS. Cells were collected into culture medium, filtered with a 70 pm mesh, and subsequently
measured in the SMR.
3.6.6 Human patient CTC SMR measurements and staining
Patients with a diagnosis of prostate cancer provided informed consent to an Institutional Review
Board approved protocol (DF/HCC 05-300) to allow donation of blood for this study. Patient blood
samples were prepared at the Massachusetts General Hospital (MGH), where negative depletion
was performed using the CTC-iChip as previously described [44,45]. Samples were maintained at
room temperature in PBS with 1% Kolliphor P188 during SMR measurement. For Patient 1,
measured cells were placed in a 24-well glass bottom plate coated with Cell-tak (BD Biosciences
354240). Paraformaldehyde (PFA) was then added to each well to reach a final concentration of
4% in each well (PFA was made fresh from 16% PFA, Electron Microscopy Sciences 15710). The
well-plate was then spun at 821 rcf for 10 min to ensure adherence of the cells. After over 30 min
of fixation, the cells were permeabilized with 1% Igepal (Sigma-Aldrich), rinsed, and incubated
with 3% bovine serum albumin (BSA, Fisher) and 5% mouse serum (Jackson ImmunoResearch
015-000-120) in PBS. Cells were then stained with anti-Cadherin-1 1 and anti-EpCAM antibodies
both conjugated to Alexa Fluor 488 (R&D Systems FAB17901G, Cell Signaling Technologies
5198S), anti-CD45 conjugated to PE-CF594 (BD Biosciences 562279), and DAPI (Life
Technologies D1306). The cells were then rinsed and stored in PBS for fluorescence imaging. For
Patient 2, after passing through the CTC-iChip, cells were stained with anti-EpCAM (Cell
Signaling Technologies 7139) and anti-CD45 (Invitrogen MHCD4520) antibodies. Stained, live
cells were then measured in the SMR and collected in a 24 well plate. PFA was added to a final
concentration of 4%, along with DAPI (1.43 pM final concentration) for nuclear staining. Based
on the concentration of the samples during negative depletion, an equivalent of approximately I
mL of blood from Patient 1 was measured, and an equivalent of 1.6 mL of blood from Patient 2
was measured in the SMR.
3.6.7 Fluorescence imaging of CTCs
Fluorescence imaging was done on a Nikon Ti inverted microscope (Swanson Biotechnology
Center Microscopy Core Facility). All imaging of CTCs was performed at 40x magnification.
Images were taken on a Photometrics CoolSnap HQ camera. The diameter of each cell was found
60
by taking the maximum Feret's diameter of the fluorescence EpCAM signal after background
subtraction using ImageJ.
3.6.8 Converting imaged cell diameter to buoyant mass
The PC3 prostate cancer cell line was used to estimate the single-cell density of prostate cancer
CTCs. PC3 cells were stained for EpCAM and resuspended in PBS with 1% Kolliphor for SMR
measurement as would typically be done with patient samples. The single-cell densities of one
aliquot of cells were measured in the SMR, while another aliquot was placed in a 24-well plate
where they were fixed in 4% paraformaldehyde (PFA) and imaged. From image processing, we
found that the maximum Feret's diameter determined by ImageJ software corresponded well to
the volume measured by the SMR. The maximum Feret's diameter found for each CTC is shown
in Fig. 3-9. Since the number of CTCs was low and the density distribution of the PC3 cells in 1%
Kolliphor buffer was found to be quite spread compared to typical density measurements
(interquartile range: 0.017 g/mL, Fig. 3-10), instead of using one set density value to convert all
of the CTC diameters to buoyant mass, a simulation was used to randomly assign a density value
from the measured distribution to each CTC. Buoyant mass was then calculated by the following
equation:
4
Buoyant mass = 4TR 3(Pcell - Pf luid)
3
where R corresponds to the radius of the imaged cell, pcell is the density of the cell, and pfluid is
the density of the buffer used for measurement. The result is plotted in Fig. 3-8B and 3-8E. To
determine the number of cells that fell in the buoyant mass range of 50 pg to 100 pg, or greater
than 100 pg, the simulation was repeated 1000 times. The average number of cells in each buoyant
mass range of interest was calculated and reported in the main text.
61
62
Chapter 4
Biophysical-Molecular Correlation with the Suspended
Microchannel Resonator*
4.1
Introduction
Mechanical properties of single cells have drawn increasing attention in their ability to identify
changes in cell phenotype, including those in differentiation and malignancy, and even to provide
diagnostic value [55,56,58-60]. Various techniques used to probe the deformability of single cells
include micropipette aspiration [57,74], atomic force microscopy [62,63], microrheology [65-68],
optical stretching [70,71], hydrodynamic deformation [72,73], and microfluidic constriction
devices [59,75-77]. One such microfluidic device is the suspended microchannel resonator
(SMR), which, based on the resonance frequency of the sensor, records the position of the center
of mass of a cell with up to sub-micron precision as it passes through the constriction ( 1.2.2, Fig.
4-1A, B). The resonance frequency signal enables the measurement of the passage time of a cell
through the constriction, and can be used to parse out such information as the relative contributions
of deformability and surface friction [59]. In addition to the position of the cell, the resonance
frequency changes are indicative of the buoyant mass of a cell, as a metric of its size [59].
In measuring biophysical properties of cells with an unknown identity, it is vitally
important to have molecular validation, not only to identify the cells being measured, but to gain
insight into deeper molecular underpinnings of the biophysical observations and the cell to cell
variation. Although some precise single-cell measurement techniques lend themselves more easily
than others to optical imaging simultaneously with the biophysical measurement [72,92,109-115]
until recently, few of these methods have demonstrated the ability to isolate particular cells of
interest for downstream molecular study [116]. Meanwhile, passive sorting and bulk filtration
methods have been developed, where a general population of cells is collected based on its
deformability [42,117-121]. Examples include mechanical filters, microfluidic margination, and
inertial microfluidic devices [42,117-121]. However, using these techniques, precise single-cell
deformability properties remain unknown. The variation in molecular characteristics cannot be
correlated to the deformability metric of each cell.
* This work was done in collaboration with Dr. Sangwon Byun, who helped with SMR measurements of healthy
donor and patient blood samples. LabVIEW code used for cell detection was adapted based on code originally
written by Nathan Cermak in the Manalis Laboratory. Dr. David T. Miyamoto, Dr. Shannon L. Stott, and Dr.
Mehmet Toner at the Massachusetts General Hospital helped coordinate acquisition of healthy donor and patient
blood samples, and provided guidance for the biological aspects of the study. Katherine Broderick and Wooseok
Kim helped to process blood samples in the CTC-iChip prior to SMR measurement.
63
A Suspended Microchannel
Resonator (SMR)
C
Cell Sorting Technique for
Biophysical-Molecular Correlation
1) Stain cell
2) Measure in SMR
4) Collect in
96-well plate
B SMR Measurement Signal
-198
Correlation
Passage Time
E)
3
2-
3) Detect long passage
5) Fluorescence
microscopy
time in real-time &
\Foc Buoyant Mass
stop flow in SMR
0:2 ' 0:3'
':1
Time (s)
Fig. 4-1. Cell sorting method for biophysical-molecular correlation
A) A diagram demonstrating a cell passing through a suspended microchannel resonator (SMR),
squeezing through a constriction. B) An example of the raw resonance frequency signal obtained from
a typical cultured tumor cell (an H 1650 lung cancer cell in this case) as it transits through the SMR
sensor. The height of the frequency shift is proportional to the buoyant mass of the cell, while the width
of peak, as it transits from position 3 to position 4, corresponds to the passage time of the cell through
the constriction. The numbers in (B) correspond to the position of the cell as labeled in (A). (A) and
(B) are similar to Fig. 1-3, and are repeated here for convenience. C) A schematic diagram of the cell
collection technique, showing that cells are stained and measured in an SMR. Once the software detects
a long passage time, it stops the fluid flow in the device, allowing for subsequent collection of the cell
off-chip, into a 96-well plate for downstream fluorescence microscopy.
190
-
Here, we present a cell sorting method for the SMR to correlate the single-cell biophysical
measurement with the molecular expression of each cell of interest. In particular, we demonstrate
this technique in characterizing tumor cells in blood, with a view to better understand the physical
properties of circulating tumor cells (CTCs). Although previous studies indicate that epithelial
cancer cell lines tend to take longer to pass through constrictions than do blood cells (see 3.2)
[41,59], the rarity of CTCs has caused studies regarding the physical properties of actual CTCs to
remain sparse [53,95]. Nonetheless, a technique such as the CTC-iChip, which concentrates CTCs
by eliminating the majority of erythrocytes and leukocytes, enables the SMR to measure CTCs in
spite of its limited throughput (-45L/hr). Using this combination of techniques, some cancer
patient blood samples were found to have more cells with long passage times (> lOins) than did
healthy donor blood samples. However, long passage time events may be created by debris,
aggregates, or atypical blood cells within the sample. As a proof of principle, we address this
limitation in measuring tumor cells in processed blood samples, by collecting each long passage
64
time event off-chip and correlating the biophysical SMR measurement to immunofluorescence
images.
4.2
Materials and methods
4.2.1 Suspended microchannel resonator for buoyant mass and passage time
measurements
As described in 1.2.2, a suspended microchannel resonator (SMR) having a 6 ptm wide, 15 ptm
deep, and 50 tm long constriction was used to measure the buoyant mass (as a metric for size) and
passage time (as a metric for deformability) of single cells [59]. The resonance frequency of the
cantilever sensor, determined by the buoyant mass and position of the cell, is recorded as each cell
passes through the sensor (Fig. 4-lA, B). This enables the assessment of the shape of each cell's
signal, including the cell's velocity as it deforms into the entrance and when it transits through the
constriction ( 1.2.2) [59].
All data analysis was performed in MATLAB as described previously [59], using the
dscatter function (MathWorks File Exchange) for Fig. 4-2 [72]. The position of the cells in the
cantilever sensor used in Fig. 4-5, were calculated as described previously, where a value of -1
corresponds to the tip and 0 corresponds to the base of the cantilever ( 1.2.2) [59,83]. Also,
normalized time is the time recorded by the sensor divided by the total length of time the cell takes
to pass through it, such that time 0 is when the cell is at the tip of the cantilever and time 1 is when
the cell exits the cantilever sensor.
4.2.2 Cell culture
H1650 cells were obtained and cultured as previously described [31]. PC3 and LNCaP cells were
obtained from the American Type Culture Collection. LNCaP cells were cultured in RPMI (1640)
with 10% fetal bovine serum (FBS). PC3 cells were cultured in DMEM/F-12 medium containing
10% FBS.
4.2.3 Blood cell preparation for initial test
Human blood was purchased from Research Blood Components, LLC (Brighton, MA).
Mononuclear cells were enriched using Histopaque-1077 (Sigma-Aldrich) and resuspended in 1%
(w/v) Kolliphor P188 (Sigma-Aldrich) in phosphate buffered saline (PBS). For the H1650-spiked
sample, H1650 cells were added to the enriched mononuclear leukocytes, resulting in a final
concentration of 600,000 cells/mL. The sample was then stained with anti-Cadherin-1 1 and anti-
EpCAM antibodies both conjugated to Alexa Fluor 488 (R&D Systems FAB17901G, Cell
Signaling Technologies 5198S), and anti-CD45 conjugated to PE-CF594 (BD Biosciences
562279).
65
4.2.4 Healthy donor and cancer patient blood samples processed in CTCiChip and stained prior to SMR measurement
Healthy donor blood was obtained from healthy volunteers under an Institutional Review Board
(IRB) approved protocol. Patients having a diagnosis of prostate cancer provided informed consent
to a separate IRB-approved protocol (DF/HCC 05-300) to allow blood donation for the study.
Blood samples were processed through the CTC-iChip in negative selection mode at the
Massachusetts General Hospital (MGH), as previously described [44,45]. Briefly, the CTC-iChip
removes most of the erythrocytes and leukocytes from the blood, leaving an enriched tumor cell
sample. For LNCaP- and PC3-spiked samples, the cultured cells were placed into whole blood
prior to processing in the CTC-iChip. After tumor cell enrichment, spiked samples and patient
samples (other than Patient 3) were then stained with anti-EpCAM (Cell Signaling Technologies
7139) and anti-CD45 (Invitrogen MHCD4520) antibodies. Stained, live samples were then
measured in the SMR, while maintained at room temperature in 1% (w/v) Kolliphor P188 in PBS.
4.2.5 Cell sorting method for SMR
Software was written in LABVIEW to determine the passage time of each cell in real-time, during
transit through the SMR. For passage times greater than a user-defined threshold of 10 ms, the
computer-controlled pressure regulators automatically stops the flow in the device (Fig. 4-1C).*
For Figure 4-3B only, images of the cells were taken in the exit channel of the SMR immediately
after the flow was halted. After successful cell capture was validated, it was deemed no longer
necessary to image cells in the SMR output channel. The output tubing is then flushed with 1%
Kolliphor in PBS into a 96-well plate, collecting the cells in a 50 pL volume. The cells having fast
passage times that did not trigger a collection response were all collected in a 24-well plate for
imaging together at the end of the experiment. After collection from the SMR, paraformaldehyde
(PFA, made fresh from 16% PFA, Electron Microscopy Sciences 15710) was added to a final
concentration of 4%, along with DAPI (Life Technologies D1306, 1.43 ptM final concentration)
for nuclear staining.
4.2.6 Fluorescence imaging
For immunofluorescence imaging in the output channel of the SMR, images were acquired on an
Edmund Optics monochrome CMOS camera (EO-1312M). After cell collection fluorescence
imaging was carried out on a Nikon Ti inverted microscope at either 20x or 40x magnification
(Swanson Biotechnology Center Microscopy Core Facility). The EpCAM expression intensity
levels were quantified from 40x images. Since the depth of field is small (-I pm), the fluorescence
* The software has been updated to trigger the flow to stop based on a user-defined range of buoyant masses
(frequency shifts) and a user-defined range of passage times simultaneously. However, this functionality has not yet
been fully tested.
66
intensity values were normalized by the cross-sectional area of each cell. For future biological
studies, a larger depth of field or deconvolution microscopy (involving images of multiple zsections ofthe cell) should be used to capture the fluorescence expression ofthe entire cell. Images
were acquired using a Photometrics CoolSnap HQ camera, and images were processed using
ImageJ.
4.3
Results and discussion
Healthy donor as well as metastatic cancer patient blood samples were measured in an SMR after
processing in the CTC-iChip, in order to identify whether there was a difference between their
passage time and buoyant mass characteristics, and if so, to specify a gating region for cell
collection (Fig. 4-2) [44]. In general, the healthy donor blood cells had passage times faster than
10 ms, except in some cases where a few smaller cells (< 50pg) had longer passage times (> 10
ms) possibly due to debris or stickiness from platelet aggregation (Table 4-1). Cancer patient blood
samples were prepared in the same manner and compared to healthy donor samples (Fig. 4-2B).
Since it was notable that some patient samples had cells with longer passage times, it was of
interest to see whether the cells that were larger than 50pg and had passage times longer than 10
ms were in fact CTCs. Hence, we developed and implemented the cell sorting technique to collect
cells having longer passage times and classify each cell based on its surface protein expression.
First, to validate the cell sorting technique, H 1650 cells (human lung cancer cell line) were
spiked into enriched mononuclear blood cells and stained for EpCAM/CDH1 1 to denote tumor
cells, and CD45 to counterstain for leukocytes. The sample was then measured in the SMR (Fig.
4-3A). The flow in the device was automatically halted by computer-controlled pressure regulators
when a long passage time was detected by custom software. To ensure that each long passage time
event was indeed triggered by a tumor cell, once the flow was paused in the device, the cell was
imaged in the output channel of the SMR prior off-chip collection (Fig. 4-3B). Due to the thickness
and curvature of the glass on the SMR's exit channel, only low quality images were obtained, but
the staining was apparent and verified that each of the long passage times were caused by
individual tumor cells. Then, to obtain higher quality images and to provide functionality for future
downstream applications, each cell that had been imaged in the device was subsequently sorted
off-chip into a separate well of a 96-well plate, with each well having a final volume of 50 pL. As
revealed by fluorescence microscopy of the well plate, one tumor cell was found in each well,
indicating that each tumor cell seen in the SMR channel was successfully captured off-chip (Fig.
4-3C). Note that in addition to the individual tumor cells, there were some leukocytes in a few of
the wells, due to the fact that if some leukocytes pass through the constriction just prior to the
tumor cell and have not exited the tubing of the device, they may be flushed into the 96-well plate
together with the detected tumor cell. Thus, the purity of the collection depends upon the
concentration of cells in the sample, which can be adjusted if higher purity is necessary. In many
applications, an enrichment of the tumor cells may be sufficient, such as to establish a correlation
between the molecular staining of the tumor cell to its biophysical passage time and buoyant mass
67
signature. These results demonstrate that we can successfully collect cells based on specific
physical properties and observe their particular molecular expression.
A
Healthy Donor
1 N=5,509
.E 0.1.
0- 0.01
B 0.001.
10
_
__
_
_
_
__
_
_
_
10
_
__
_
Buoyant Mass (pg)
_
_
_
_
Prostate Cancer Patient 3
1 N=204,314
1=
UI
0-
0.001.1
10
160
Buoyant Mass (pg)
Fig. 4-2. Healthy donor versus metastatic prostate cancer patient blood sample
A) Passage time vs. buoyant mass measurements of a healthy donor blood sample, after having been
depleted of most of the erythrocytes and leukocytes. An equivalent volume of -280 pL of blood was
measured. B) Passage time vs. buoyant mass measurements of a metastatic prostate cancer patient
blood sample, having been depleted of most of the erythrocytes and leukocytes. An equivalent volume
of -360 ptL of blood was measured. The difference in the number of cells measured was due to different
efficiencies of the preprocessing steps in depleting the erythrocytes and leukocytes from the samples
( 4.2.4). Colors correspond to the density of data points, with red being the highest and blue being the
lowest density.
68
Table 4-1. Healthy donor blood samples measured in an SMR
Equivalent
Total
Number of SMR Peaks with Passage Time >
Subjects
Blood Volume
Number of
0.01s
(pL)
360
200
280
400
400
Healthy 1
Healthy 2
Healthy 3
Healthy 4
Healthy 5
A
SMR Peaks
2791
4688
5509
3261
15977
All
0
0
0
2
1
> 50pg
0
0
0
0
0
I
> I0Opg
0
0
0
0
0
B Images Taken in SMR Output Channel
Prior to Collection in Well-Plate
SMR Measurement
Triggering Cell Collection
Bright EpCAM/ CD45
field CDH11
,
10 . Well 1
* Well 2
* Well 3
.Well 4
I
Collected To:
Well 1
Other measured
cells
%-
0.1
Well 2
*
6
0.01
1E-3
.
.
Well 3
'
4A
16
10b0
10
Buoyant Mass (pg)
C
Well 4
Fluorescence Microscopy After Cell Collection
Cell Counts
Well 1
Well 2
Well 3
Well 4
EpCAM/CDH11 only
1
1
1
1
CD45 only
0
2
2
3
WBC
Fig. 4-3. Validation of tumor cell detection and collection in 96-well plate
A) Passage time vs. buoyant mass measurements of H1650 cells spiked into enriched mononuclear
blood cells. The cells included in the boxed region (> 50pg buoyant mass & > 0.01 s passage time)
were imaged on-chip as well as collected off-chip for additional verification. B) Images taken on-chip,
in the exit channel of the SMR, immediately after custom software detected a long passage time signal.
It was confirmed that all detected signals falling into the region of interest corresponded to a tumor
cell by immunofluorescence imaging. C) Each detected H1650 cell was successfully collected off-chip
in a 96-well plate. Based on additional immunofluorescence imaging of each well, a tumor cell was
identified in each well (EpCAM/CDH1 I' and CD45-), along with a few leukocytes (CD45' and
EpCAM/CDH II).
69
Next, we further tested the sorting method by characterizing human prostate cancer cells
(LNCaP and PC3) spiked into whole blood. The spiked samples were processed through the CTCiChip to enrich for tumor cells ( 4.2.4), stained for cell surface markers EpCAM and CD45, and
measured in the SMR (Fig. 4-4). Cells that had passage times longer than 10 ms were sorted into
a 96-well plate. Although we particularly focused on cells larger than 50 pg, a few of the smaller
cells causing long passage times were also collected for image analysis. As listed in Table 4-2, for
the spiked LNCaP cells, four of the five wells collected in the region of interest corresponded to a
LNCaP cell. For the spiked PC3 cells, seven out of the nine wells collected corresponded to at
least one PC3 cell. Two of the wells contained two PC3 cells, possibly because one of the two had
a faster passage time due to being smaller in size or to being more deformable. In both cell lines,
there were some tumor cells having passage times faster than 10 ms, and were not collected since
they could not be clearly distinguished from blood cells.
Moreover, this technique enables the details of each SMR signal to be related to the protein
expression in each cell. Thus, not only the passage time and buoyant mass, but the resonant
frequency response denoting the position of the cell's center of mass is recorded, as dictated by
the biophysical properties of the cell as it passes through the sensor (Fig. 4-5A, 1.2.2, 4.2.1). It
is interesting to note the relationship between the SMR peak shapes to the imaged cells (Fig. 45B). For both wells L2 and L3, corresponding to LNCaP cells, the signal obtained from the SMR
corresponds to the canonical shape for a tumor cell (Fig. 4-1B) [59]. In contrast, wells L5 and L6
corresponding to debris, had SMR peak shapes that look markedly different, where the particle
spends most of the time adjusting itself to go through the entrance of the constriction. It is possible
that debris is introduced into the sample during the various pre-processing steps. Last, well L7
contained an activated neutrophil, and its corresponding SMR peak shape had distinct features of
the cell passing through the constriction, unlike other cells having the same buoyant mass. Perhaps
this may be indicative of the polymorphonuclear shape of the nucleus altering its position as it
transits through the constriction as well as altering its buoyant mass.
Indeed, hierarchical clustering analysis of the positions of the measured cells or particles
as they pass through the constriction over time (normalized, 1.2.2, 4.2.1) demonstrates that
particles of similar nature, as identified by fluorescence imaging, can cluster together by the shapes
of their SMR signals (Fig. 4-5C,D). The signals corresponding to LNCaP cells cluster together,
whereas the signals corresponding to debris cluster separately, and the activated neutrophil falls in
between. Although a more thorough and systematic study would need to be done to identify the
causes of each particular detail of the SMR peak shape, the newly developed method relating each
peak to a corresponding cell may help elucidate such details. Cataloging such SMR signals may
eventually allow us to identify particles being measured in the device without the need for
downstream imaging.
70
A
Spiked
LNCaP
L5
N= 2,426
L6*
.I
SL7
F_
L3
L4.
.
0.01,
L2.
or ---MM
a
0
.
*0
% 0
A
0
1E-310
100
Buoyant Mass (pg)
B
Spiked
PC3
N= 660
CL 0.1-
:~
.
. *
%;w
-
-g
W Roe0g
*
P5
P
.. P4 . P2
.P3
P9 p7
P8 .P.. e p
In
10
10
Buoyant Mass (pg)
Fig. 4-4. Cell line-spiked blood samples
A) A blood sample spiked with LNCaP cells was measured in the SMR. The labeled measurement
events were collected off-chip (LI-L7). B) A blood sample spiked with PC3 cells was measured in the
SMR. The labeled measurement events were collected off-chip (P1 -P9). In both panels, the dotted area
corresponds to the region of interest (> 50 pg and > 0.01 s).
71
Table 4-2. Collected measurement events from cell line-spiked blood samples
Spiked
Well Label of
# Cells
Well Content Description
Cells
Collected Peak
LNCaP LI
2
1 DAPI-CD45-EpCAM (LNCaP)
I DAPI'CD45'EpCAM- (Unidentified WBC)
PC3
L2
1
DAPI'CD45-EpCAM' (LNCaP)
L3
I
DAPI'CD45-EpCAM' (LNCaP)
L4
1
DAPI'CD45-EpCAM' (LNCaP)
L5
0
Debris (Not in region of interest)
L6
0
Debris (Not in region of interest)
L7
1
DAPI'CD45-EpCAM- (Activated Neutrophil)
P1
1
DAPI'CD45-EpCAM t (PC3)
P2
2
2 DAPI'CD45-EpCAM' (PC3)
P3
1
DAPI'CD45-EpCAM' (PC3)
P4
0
Debris
P5
1
DAPI'CD45-EpCAM' (PC3)
P6
1
DAPI'CD45-EpCAM' (PC3)
P7
1
DAPI'CD45-EpCAM-
P8
0
Debris
Note that for cells to be positive for CD45 or EpCAM, the cell had to have a clear ring of fluorescence,
indicating surface expression of the protein. If no clear ring of fluorescence was present at the boundary
of the cell, general fluorescence was considered to be background, non-specific fluorescence.
WBC = white blood cell
72
A
200B
2000
DAPI
2DIC
19W.
CD45 EpCAM Composite
L2
1960
20
1920
6
o.i 0.02
L2
19201
0
0.03
2000
0.02
0.04
2000
196C-
1.0
0.
2.0
.
L76
[62
2JU.2
-2000
1994
1998
WBC
L7
1980
0
0.02 0.04 0.06
0.0
91
Time (s)
C -0.
-LI
~L2
-L3- L4
-L5
-0.6. - L6
-0
--
Median
Position
D
L5
--
-0.5
L
-0.55
L6
-0.6
L7
-0-65
0-
-0.7
-0.75
C--0.8
4
0
L4
-0.8
-0,85
L2
-0.9
-0.95
Li
-1.0
q' q
0.'7 0.8 0.9
Normalized Time
0.0 0:1 0.2 0.3 0.4 05 0.6
1.0
C
4C4
C! I
t
! C UJ (P (PJ N ' C C q Cq N C
OO00000000000000000
Normalized Time
Fig. 4-5. SMR peak shape correlation with immunofluorescence staining
A) Raw SMR frequency signal versus time acquired for each measurement event, demonstrating
different peak shapes for different types of cells or particles being measured. Labels correspond to
those in (B) as well as in Fig. 4-4A. Note that the axes are different for each peak for better visualization
of the details of each peak shape. B) Immunofluorescence images of cells corresponding different peak
shapes from (A), with L2 and L3 being LNCaP cells, L7 being an activated neutrophil, and WBC being
a typical white blood cell. Note that the intensity of EpCAM for L3 was reduced to avoid saturation,
as it was much more intense than all other imaged cells. C) The position of the cell or particle in the
SMR as it passes through the constriction (-1 corresponds to the tip, and 0 corresponds to the base of
the cantilever) is plotted versus normalized time (0 corresponds to when the cell reaches the tip of the
cantilever and I corresponds to the time the cell exits from the cantilever) for each of the SMR signals
that were collected for visual analysis. D) The median positions of each measured signal within each
bin of normalized time are plotted as a heat map. A dendrogram derived from hierarchical clustering
analysis is shown to the right of the heat map, demonstrating that the shape of the SMR signals can be
categorized into different groups, potentially related to the type of particle measured, as identified by
fluorescence imaging.
73
10 Normalized Fluorescence
Buoyant Mass (pg)
Intensity (a.u./lm 2)
*
96.
L
159
(,-
20
-
0
389 mesrmn
38
-. 01
21.01
199.0ewese
00
0
s
e
Bo)n
0.001.i
50
152.9
163
-
as3e.6re
M0ss (p)8
II
100,
1 d5
0.001
0025300
111
0.0
s
.
0.01
251
97
0
-
*122.1
113
F
w~ *,
400106.8
2167
0.1
1......0
Passage Time (s)
B uoya nt Ma ss (pg)
Fig. 4-6. SMR measurement correlation with quantified immunofluorescence levels
A) Passage time versus buoyant mass for H 1650 cells stained for EpCAM. The color corresponds to
the quantified level of fluorescence intensity of the EpCAM stain for each cell. The fluorescence
intensity is normalized by the cross-sectional area of the imaged cell. B) The same data from (A)
plotted differently, showing EpCAM fluorescence intensity versus passage time, and having the
buoyant mass encoded in color.
Furthermore, this technique also allows us to compare the relative expression of surface
proteins of each cell to their passage times and buoyant masses. As a proof of principle, H1650
cells that were stained for EpCAM were sorted into separate wells of a 96-well plate and imaged.
The fluorescence images were quantified as an indication of relative levels of EpCAM expression
(Fig. 4-6). Thus, the additional dimension of single-cell relative surface protein expression level
can be added to each single-cell biophysical SMR measurement. Such relationships may provide
meaningful information regarding the correlation between molecular and physical traits of single
cells as well as the differences between subtypes of cells (such as more mesenchymal or more
malignant) within a given cell population.
Finally, to test the biophysical-molecular correlation method on primary patient samples,
a limited number of metastatic prostate cancer patient blood samples (n = 7) were measured,
following the same protocol. Five samples were determined to have no CTCs. Of the two samples
that were confirmed to contain CTCs, only one sample (Patient 4) had a cell in the gating region
of interest (Fig. 4-7). This cell was collected and was found in fact to be a CTC based on its
EpCAM'CD45-DAPI' signature, indicating that there can be CTCs in circulation that are stiff,
having long passage times. The rest of the measured sample was collected and pooled together in
a 24-well plate at the end of the measurement and no CTC was found. Interestingly, as discussed
in Chapter 3, there are other cases in which some CTCs in circulation have passage times faster
than 10 ms. Patient 2 in Chapter 3 was assessed using the sorting method presented here, and was
found to have 35 CTCs with fast passage times (<10ms) and none with long passage times.
However, this method has enabled us to validate that in some patient blood samples, long passage
times greater than 50pg may still indeed correspond to CTCs.
74
CTC
B
A
Prostate Cancer Patient 4
N=60,449
WBC
Composite
-1990
.1970
DAPI
1950
-
o 1
19301
CD45
2U 19 10
0.
Time
0.001
1010
2 '
.
4
EpCAM
Buoyant Mas s (pg)
Fig. 4-7. Metastatic prostate cancer patient blood sample
A) Passage time versus buoyant mass measurements of a metastatic prostate cancer patient blood
sample. An equivalent total of 1.8 mL of blood was measured. B) One cell that fell in the region of
interest that corresponded to a tumor cell-like frequency signal in the SMR as well was collected offchip and imaged. Fluorescence images indicate that the SMR measurement correlated to a CTC
(EpCAM positive, CD45 negative). Images of a white blood cell (WBC) from the remainder of the
sample is shown for comparison.
4.4
Conclusion
We have demonstrated the proof-of-principle that we can detect and collect individual cells of
interest after measurement in the SMR, using tumor cell-spiked blood samples as well as a
metastatic prostate cancer patient sample. After collection, we performed immunofluorescence
imaging on the cells and correlated them to the biophysical measurement made by the SMR. Not
only did we measure the passage time and buoyant mass of each cell, but the SMR signal shape
held additional information in identifying the type of cell or contaminating particle in the sample.
In addition to identifying the types of cells being measured, we provide evidence that the SMR
measurement can be correlated to molecular signatures such as relative EpCAM expression
measured by fluorescence microscopy. Finally, we demonstrated the ability to identify and collect
a CTC based on its stiff mechanical properties relative to its surrounding blood cells. Although
there is no clinical relevance to this study since it is based on one patient sample, high throughput
systems that exploit arrays of SMR may enable clinical studies that utilize mechanical biomarkers
based on cell size and passage time. Nevertheless, the examples presented here demonstrate the
ability to correlate SMR measurements to molecular information from individual cells. Because
cells can now be sorted off-chip based on their physical characteristics, in the future this technique
can be extended for other downstream assays, including analysis of DNA or RNA of cells of
interest. Moreover, as the SMR platform has already demonstrated the ability to measure multiple
physical parameters, the technique may be developed further to collect cells based on their buoyant
mass, growth rate, or density [78,100,122]. The demonstrated cell sorting technique, correlating
75
biophysical to molecular properties, will enable future studies to elucidate how physical changes
relate to specific fundamental molecular changes within single cells.
76
Chapter 5
Additional Project: A Microfluidic "Baby Machine" for Cell
Synchronization*
5.1
Abstract
Common techniques used to synchronize eukaryotic cells in the cell cycle often impose metabolic
stress on the cells or physically select for size rather than age. To address these deficiencies, a
minimally perturbing method known as the "baby machine" was developed previously. In the
technique, suspension cells are attached to a membrane, and as the cells divide, the newborn cells
are eluted to produce a synchronous population of cells in the GI phase of the cell cycle. However,
the existing "baby machine" is only suitable for cells which can be chemically attached to a
surface. Here, we present a microfluidic "baby machine" in which cells are held onto a surface by
pressure differences rather than chemical attachment. As a result, our method can in principle be
used to synchronize a variety cell types, including cells which may have weak or unknown surface
attachment chemistries. We validate our microfluidic "baby machine" by using it to produce a
synchronous population of newborn L1210 mouse lymphocytic leukemia cells in GI phase.
5.2
Introduction
Synchronization of cells in the cell cycle is imperative in the study of many cellular and molecular
processes. It specifically allows for the study of molecular regulatory mechanisms and genetic
expression throughout the cell cycle, which has facilitated the discovery of biomarkers for cancer
prognosis and drug [123-125]. In addition, since cells have different molecular compositions and
distinct responses to stimuli while in different phases of the cell cycle, synchronization methods
are used to reduce biological noise in experiments involving populations of cells.
The most common techniques for synchronizing eukaryotic cells involve the addition or
depletion of certain compounds that stall the cells at a particular phase in the cell cycle. The cells
are then released to continue progression of the division cycle by the removal of the drug or readdition of growth factors. For example, in serum starvation, removing serum from a growing
culture can cause the cells to enter a quiescent state, and upon the reintroduction of serum, the cells
reenter the cell division cycle. However, serum starvation has been found to reduce cell survival
and increase DNA fragmentation, and reentry into the cell cycle is often not synchronous
. This work has been published as a journal article: Shaw, J., Payer, K., Son, S., Grover, W.H., Manalis, S.R. "A
microfluidic 'baby machine' for cell synchronization." Lab on a Chip. 2012. 12:2656-2663. Changes to the
published text include reference numbers, figure numbers, and text included in brackets.
77
[126,127]. Similarly, DNA synthesis inhibitors such as excess thymidine and hydroxyurea stall
cells in S phase, while microtubule polymerization inhibitors such as nocodozole and colcemid
stall cells in mitosis[128]. Nonetheless, such compounds have unwanted side-effects, including
activating the DNA damage response, influencing RNA synthesis, and inducing apoptosis,
especially at higher concentrations [129-133]. Although these batch chemical methods may align
cells to a certain feature of the cell cycle, it is known that such drugs perturb the growth and
progression of internal molecular machinery. For instance, even while DNA synthesis is inhibited,
other cellular processes such as transcription and translation still continue, creating a growth
imbalance in the cell [134]. Thus, mitotic and replication inhibitors place cells in nonphysiological
states, and even though synchronization with them may be valid for some experiments, the extent
of their usefulness may be limited [127,135-137]. The resulting growth imbalance may
particularly confound studies regarding cell growth, metabolism, and their relation to the cell
cycle. Indeed, it has even been found that protein expression in chemically synchronized cells does
not match that of unperturbed cells [138]. Hence, the applicability of results from cells obtained
by batch chemical methods to normal, unperturbed cells remains questionable.
Another common way to synchronize cells is physically extracting a subpopulation of cells
with similar properties, rather than coercing the entire culture to converge at the chosen
characteristic. One such technique is counterflow centrifugal elutriation, which uses centrifugal
force balanced by a centripetal flow of fluid in a specially designed centrifuge rotor to separate the
cells by their sedimentation velocity, which is dependent upon cell size and shape [139-141].
Although this method successfully isolates different sizes of cells, with the smallest cells assumed
to be in GI phase, it exposes them to harsh conditions, such as a 683 x g rotor speed at 20'C for
4-5 hours [141]. More recently, cells have been separated by size in microfluidic devices using
dielectrophoresis, acoustophoresis, hydrophoresis, hydrodynamic filtration, or inertial forces
[142-146]. However, the size of the cell does not necessarily indicate its age, so the resulting
similarly-sized cells may still have a range of ages due to inherent biological variation. Cells may
also be sorted based upon DNA content using flow cytometry, but the use of DNA-staining dyes
may cause mutagenicity and affect cell [128,134]. Furthermore, a synchronous batch of Gl cells
selected based on DNA content rather than a particular event in the cell cycle could have an age
range spanning the length of G1, which can occupy over 40% of the eukaryotic cell cycle,
depending on the cell type [147]. In addition, both centrifugal elutriation and flow cytometry
require costly equipment.
A simpler and less perturbing method for synchronizing cells is mitotic shake-off. Adherent
cells that grow in a monolayer culture often become spherical during mitosis, at which point they
can be detached by shaking or agitation. Since mitosis comprises only a short portion of the cell
cycle, sometimes the shake-off method is used in conjunction with chemical synchronization
methods to increase yields; this introduces the same concerns mentioned previously with chemical
techniques [129,148,149]. Moreover, the mitotic shake-off method is only possible for adherent,
monolayer cell cultures [128,134].
78
In the 1960s, Helmstetter et al. developed the membrane elution method of
synchronization, colloquially known as the "baby machine" [150]. In this method, cells are bound
to a membrane, and as they divide, one cell remains adhered while the other is eluted. This yields
a population of cells synchronized at the beginning of G 1 phase. The technique was first used with
bacteria, and was later customized for mammalian cells in 2002 and improved upon in 2006 [150152]. Although the "baby machine" is arguably the least perturbing method of cell
synchronization, it has not been widely adopted. Furthermore, the technique depends on the ability
of cells to bind to membranes coated with concanavalin A, poly-D-lysine, etc.[151]. Cells with
weak or unknown binding ability would thus be difficult or impossible to synchronize using the
"baby machine."
Here we present a technique in which a pressure differential is used to adhere thousands of
cells to a surface, while allowing for constant perfusion of culture medium as well as elution of
newborn cells. This technique requires no specific cell surface chemistry and should be amenable
to virtually any suspension cell type. As a proof-of-principle, we fabricated and tested microfluidic
devices that include an array of 2-tm diameter trapping holes onto which cells can be loaded. We
demonstrate that L 1210 mouse lymphocytic leukemia cells load into the device, grow and divide
on chip, and elute a synchronous population of cells in GI phase.
5.3
Materials and methods
5.3.1 Device description
The silicon and glass microfluidic "baby machine" consists of an introduction channel that is
bifurcated twice and feeds into the main cell capture chamber. This chamber is 4 mm wide and 5
mm long, and houses 2109 trapping holes onto which cells can be captured (Fig. 5-A). The holes
are staggered and spaced 45 ptm from one another, and are placed at least 1.75 mm away from the
entrances and exits ofthe chamber to allow a uniform flow profile to develop. The dimensions and
spacing were chosen to minimize the variation in pressures and stresses experienced by the cells
in different regions of the capture chamber. The small pressure drop horizontally across the
chamber also allows the vertical trapping pressures to be relatively uniform and sensitively
controlled for optimal operation.
Four access ports provide fluidic access to the device: inlet, bypass, backside, and outlet.
The inlet is where fluid is introduced, the bypass allows for quick rinsing away of excess cells, the
backside enables control of the vertical pressure drop across the trapping holes, and the outlet is
where the eluted newborn cells are collected. An external fluid valve placed in-line with the bypass
tubing is opened during rinsing and closed during perfusion. Although separate pressure regulators
can manipulate the pressure at each port, we found that successful operation of the device could
be achieved by using only one regulator at the inlet, while leaving the other ports at atmospheric
pressure. Finer pressure balances were achieved by adjusting tubing resistances and fluid levels in
79
the vials. These fine-tuned parameters generally remain unchanged from one experiment to the
next.
A)
B)
Inlet
hchamber
Pressu re
utlet
Bypass
TOp view
45pm
}
Glass
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. -
. kid
MON,
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0 Hi,
Side view
Collect
0
Synchronous
Asynchronous
0-*
Backside
2 (m diameter via hole
Fig. 5-1. Synchronization device design and operation
A) Image of the synchronization device illuminated from the backside, showing branching fluidic
channels that lead into a cell capture chamber with 2109 2-pm diameter holes onto which cells can be
loaded. In the schematic diagram, the side view is an illustration of the cross section along the dotted
line in the top view. The channels are etched into the top glass wafer, and the 2 pm holes and fluidic
ports are etched into the bottom SOI wafer. B) Operation of the device is controlled entirely by pressure
differences. A positive pressure applied at the inlet creates both a pressure differential in the vertical
direction across the holes to capture cells, as well as a pressure drop in the horizontal direction to
generate lateral flow for the collection of daughter cells. An asynchronous population of cells is fed
into the device and captured onto the holes, and with a constant slow perfusion of cell culture medium,
the cells grow within the device. As they divide, the newborn cells, which are not anchored to trapping
holes, are carried away in the fluid flow and eluted into a collection chamber. The eluted cells then
compose a synchronous population of cells that has just completed cytokinesis and entered the G1
phase of the cell division cycle.
5.3.2 Device fabrication
The devices were fabricated using standard silicon and glass microfabrication techniques. The top
layer consists of a 500-jim thick, 6-inch glass wafer (Bullen), with fluidic channels etched into it
at two depths: 80 jm for the cell capture chamber and 30 pm for all other channels. The bottom
of the devices is a 6-inch silicon on insulator (SOI) wafer (Ultrasil Corporation), having a 15 pm
device layer and a 0.5-2 jim thick buried oxide layer. The 2-jim diameter trapping holes as well as
the fluidic access ports were etched through the device layer using deep reactive ion etching
(DRIE). After patterning the backside, the SOI was mounted onto a dummy wafer and DRIE was
used to etch a reservoir below the trapping holes and open up the fluidic access ports from the
80
bottom. The exposed oxide was then stripped in 25% HF to fully release the trapping holes and
fluidic access ports. Finally, the glass and silicon wafers were anodically bonded.
5.3.3 System assembly
The silicon-glass device is set in a polytetrafluoroethylene (PTFE) gasket with perfluoroelastomer
o-rings, while the entire setting is clamped between two aluminum plates to create a fluidic seal.
The aluminum plates have openings cut out for microscope viewing from above and fluidic
-
connections from below. The aluminum clamp then resides in a copper setting that enables
temperature control via a recirculating water bath (Thermo Scientific). In all experiments, the
temperature of the device was maintained at 37*C.
To introduce fluid into the device, computer-controlled electronic pressure regulators
(Proportion Air) were used to pressurize glass vials to which the fluidic tubing was connected
through PTFE septa. The tubing that connected the vials to the device were made of fluorinated
ethylene propylene (IDEX Health and Science), and were of varying inner diameters (0.15 mm
0.41 mm), chosen to balance the resistances of the fluid flow. The outlet tubing was also kept short
to minimize transit time for the cell to leave the device and enter the collection chamber.
5.3.4 Device operation
In preparation for cell synchronization, the device is first filled with ethanol to ensure flow through
all ofthe trapping holes and to rid the system of air bubbles. Next, it is rinsed with purified, filtered
water (Milli-Q) for at least one hour with an input pressure of 37 kPa; this cleans the surface and
purges any remaining air bubbles. To reduce surface fouling, the channels are then treated with
poly(L-lysine)-graft-poly(ethylene glycol) co-polymer (Surface Technology, Switzerland) at a
concentration of approximately 0.5 mg/mL in phosphate buffered saline (PBS) for 30 minutes. The
device is again rinsed with water and then primed with fresh cell culture medium at 37*C.
Next, to load cells into the device, the pressure regulator at the inlet is set to 3.4 kPa. After
the cells are loaded, fresh, pre-warmed media is placed at the inlet and the excess cells are rinsed
away. Then, the pressure at the inlet is reduced to approximately 0.6 kPa; the resulting lateral flow
rate perfuses nutrients to the cells while imparting a minimum amount of shear stress. Using a
plane Poiseuille flow approximation in the main chamber, the highest shear stress experienced by
the cells during the transient loading of the device is 0.02 Pa (0.2 dyne/cm 2 ), while the shear stress
during long term perfusion is 0.003 Pa (0.03 dyne/cm 2 ); both of these values are well below
physiological shear stresses [153,154]. The perfusion rate is 40 iL/hr and the flow in the device is
laminar (Re ~ 0.003-0.02 in the main chamber).
To ensure that insecurely docked cells do not detach from the device and contaminate the
output, the collection of cells does not begin until after over an hour of perfusion. For the
experiments presented here, we collected a new fraction of cells in a fixative every 12 hours to
validate the count and synchrony of the output. Since the cells are fixed upon exiting the device,
81
the synchrony of the output is maintained for the duration of cell collection. However, in an
experiment where one wished to use the live, synchronized G 1 cells outputted by the device, the
collection duration would be dictated by the desired number of cells and degree of synchrony: a
short collection would yield a smaller number of cells with very similar ages, and a longer
collection would yield a larger number of cells with increased age variability. Afterwards, the
device can be washed using 10% bleach or piranha (3:1 [v/v] H2SO 4 :H2 02), rinsed thoroughly with
water, and reused.
5.3.5 Cell count and cell cycle analysis
To assay the throughput and synchrony of the output from the device, cells were eluted into a
formaldehyde fixative solution and, at 12-hour intervals, were counted and stained with DAPI.
Since the fixative solution becomes more dilute as the cells elute, the initial solution contained 6%
formaldehyde (Sigma-Aldrich F1635) in I x PBS, such that the final concentration of
formaldehyde after 12 hours of collection would be 4%. For every 12-hour fraction of fixed output
from the device, the number of cells in a 40-pL portion was counted manually using bright field
microscopy. The determined concentration was then used to extrapolate the total number of cells
eluted over the 12-hour period.
After a portion of the eluted cells were counted, the rest were stained using 3 piM DAPI
(Invitrogen D1306) with 0.1% Triton X-100 (Sigma-Aldrich T-8787). Following 20 minutes of
incubation with the dye, the cell solution was centrifuged at 300 x g for 6 minutes and resuspended
in 20 pL of I x PBS. For the data presented, the cells were imaged on a glass slide using a lOx
objective (Nikon, CFI Plan Fluor) on an upright Nikon microscope. To validate this method,
replicate experiments were imaged using deconvolution fluorescence microscopy (enhanced
additive method) with a 20x objective (Olympus, UApo340) on an Applied Precision DeltaVision
1pectris IIIg~
yste1,
wh11
ylilUdU s111iIl
resUIL sIIUL (notshow) [155]. The1C pIULrsS UI
obtaining the fluorescence intensities ofthe cells in each image was automated using a macro script
for ImageJ. The results were then analyzed using scripts written in MATLAB and further validated
manually to ensure that only cells, and not dust or debris, were being used as data.
5.3.6 Cell culture
L1210 mouse lymphocytic leukemia cells were cultured in Leibovitz L-15 medium (Invitrogen
21083027) supplemented with 10% fetal bovine serum (Invitrogen 16000), 0.4% glucose (SigmaAldrich G8769) and 1% penicillin/streptomycin (Invitrogen 151400). They were grown in 25 cm2
culture flasks at 37'C in an atmosphere containing 5% carbon dioxide. Cells were seeded at a
concentration of around 5 x 104 cells/mL and diluted into fresh medium every two days. The
concentration of cells used during experiments was between 2 x 105 and 4 x 105 cells/mL.
82
5.4
Results and discussion
5.4.1 Working principles
The synchronization device is designed such that pressures alone control both the adherence of
cells onto the device and the elution of Gl cells out from the device. Similar to the original "baby
machine," an asynchronous population of cells is loaded into the device, and a synchronous
population of newborn cells is collected at the outlet. However, as shown in Fig. 1b, instead of
using surface chemistries to trap cells, the microfluidic "baby machine" captures cells by the
application of a vertical pressure differential across 2 pim diameter trapping holes. With a constant
slow perfusion of cell culture medium in the horizontal direction, the cells grow within the device.
When the cells divide, the newborn cells that are not anchored to a trapping hole are carried away
by the fluid flow and eluted into a collection chamber. The eluted cells compose a synchronous
population of cells that has just completed cytokinesis and has entered the G 1 phase of the cell
division cycle.
5.4.2 Cell loading and viability
We first verified that the cells could be loaded onto the trapping holes without being squeezed
through or sheared off of them during the constant perfusion of culture medium. L1210 cells were
introduced into the device and observed via microscopy. Bright field images (Fig. 5-2A) show that
the cells can indeed fill the device, with most holes trapping a single cell. Since the loading
mechanism is an active pressure differential rather than a passive settling probability, a large
fraction (60% - 80%) of the imaged holes is occupied by cells after rinsing away insecurelycaptured cells. In addition, the fraction of occupied holes generally increases over time as newborn
cells are caught onto empty neighboring holes (Fig. 5-2B). However, not all of the holes end up
capturing a cell because some may be obstructed by debris. The steady fraction of loaded holes
achieved over time indicates that cells rarely come unstuck from the holes, preventing
contamination of the eluted cells with non-newborn cells.
The loaded cells then remain on the device to grow and divide over time, as shown in Fig.
5-3A [see Supplemental movie http://www.rsc.org/suppdata/lc/c2/c2lc40277g/c2lc40277g.avi].
Although only a subset of images is shown, cells were observed to grow and divide on chip for the
complete duration of the experiment (3 days). Next, to assess the health of the cells growing in the
device, the mean interdivision time of the cells seen in time-lapse images was compared to the cell
doubling time in culture. The interdivision time was taken as the time from the beginning of one
cytokinesis to the beginning of the next cytokinesis. Figures 5-3B and C display the interdivision
times of two consecutive division cycles for cells docked on the trapping holes. The mean
interdivision times of 10.8 hours (between the first and second divisions) and 10.6 hours (between
the second and third divisions) are comparable to the 10-11 hour doubling time observed in culture,
and also agree well with the times given in previous studies [151,156]. The reduction in the
reported number of second interdivision times is mainly due to some cells dividing while not being
83
imaged, and other cells slipping through the trapping holes either because of the pliability of the
cells [examined more carefully in Chapters 1-4] or differences in hole sizes caused by variations
in the microfabrication process. The results indicate that cells can grow and divide well on chip
under the constant perfusion of media.
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36
Fig. 5-2. Loading cells onto the device
A) Bright field image of a section of the main channel loaded with L1210 cells. Shown here are 245
cells on 304 holes (80% loaded). B) Fraction of occupied holes over time. Initially, after insecurelytrapped cells are rinsed away, 64% of the imaged holes are occupied by cells. Over time, however,
newborn cells are captured by nearby empty holes, such that eventually over 90% of the holes are
occupied.
84
A)
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Fig. 5-3. Cells growing and dividing on the device
A) Time-lapse images of five cells growing and dividing on the device. The time denoted below each
image indicates elapsed time after the device had been loaded with cells and rinsed to remove any
unattached cells. Fluid flow is from left to right. Colored dotted circles track cytokinesis followed by
separation and release of a newly divided cell. Note that the bottom left cell (circled in blue) divides
twice within the given time frame. B) Distribution of the first interdivision time of cells growing in the
device, counted from the initial signs of pinching during the first division to the initial signs of pinching
during the second division, as captured by time-lapse images (5 min./frame). N = 150 cells, median =
10.8 hrs, mean = 10.9 hrs, standard dev. = 1.5 hrs. B) Distribution of the second interdivision time,
taken as the time between the second and third division using the same method as in A). N = 135 cells,
median = 10.4 hrs, mean = 10.6 hrs, standard dev. = 1.3 hrs. The mean interdivision times through both
divisions correspond well to the observed doubling time of the cells grown in bulk culture (10-11 hrs).
85
5.4.3 Yield and synchrony
After validating cell growth in the device, the output of the microfluidic "baby machine" was
assessed to determine the yield and synchrony of the eluted cells. Figure 4a depicts the number of
cells collected at each 12-hour time point. The initial increase in cell count between the first two
time points corresponds with the fact that not all of the trapping holes are filled with cells initially,
and as the trapped cells divide, some of the newborn cells are caught onto nearby vacant holes
rather than being eluted from the device (Fig. 5-2B). Then, from 24 to 36 hours, the number of
cells outputted plateaus at ~1000 cells every 12 hours. Based on the number of cells in the device
and their doubling time, the output may be lower than expected. Although not affecting the health
of the cells, causes for lower yield may include imprecision in estimating the total number of cells
on the device and in the collection chamber, as well as loss of cells in the gasket interface.
Furthermore, some cells slip through the trapping holes after having been docked, allowing
newborn cells to occupy the recently vacated holes, which then lowers the overall yield of the
device. In spite of this, the output reaches a steady value and the cells are not only growing in the
device, but also dividing and producing new cells for at least 36 hours of device operation.
Also at each 12-hour time point, the fixed fractions of eluted cells were stained with DAPI
to evaluate the distribution of DNA content. In Fig. 5-4B, the cells' fluorescence intensities were
normalized such that the GI DNA content distribution has a modal value of 1.0 unit. Based on the
distribution of the control data taken from an asynchronous population of cells, a threshold was
set at 1.2 units, corresponding to one standard deviation above the mean of a Gaussian curve fitted
to the G 1 peak. Cells with a fluorescence intensity less than this value (indicated by the shaded
regions in Fig. 5-4B) are considered to be in GI phase, while values above the threshold correspond
to cells in S/G2/M. The G2/M peak falls at an intensity less than 2.0 units, most likely due to
chromatin packing during G2 and formaldehyde induced cross-linking that may have hindered the
access ofthe dye to DNA binding sites [157]. For an asynchronous, exponentially-growing control
population, 52% of the cells have a DAPI intensity of less than 1.2 units (and thus are in Gl).
Within the first 36 hours of operation, a purity of up to 83% of the cells having GI DNA content
was obtained from our microfluidic "baby machine."
Next, a Kolmogorov-Smirnov test was carried out to compare the fraction at each time
point to the control population. The null hypothesis was rejected each time, having p-values of
0.026, 9.1x0-10, and 1.8x10-4 at the 12-, 24-, and 36-hour time points, respectively. Hence, the
cells being outputted from the microfluidic "baby machine" have a significantly different DNA
content distribution than the asynchronous control population. From the overlaid kernel density
estimates shown in Fig. 5-4C, it is clear that at all of the time points the probability density of the
GI peak increased, while the G2/M shoulder diminished compared to the asynchronous
population, suggesting a largely synchronous population of cells in GI.
86
A) 1200-
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Fluorescence intensity (a.u.)
Fig. 5-4. Yield and synchrony
A) Yield from the device over time. Synchronized cells were eluted into a formaldehyde fixative and
fractions were collected and counted every 12 hours. Note that the system produces a steady output
from 24 to 36 hours. B) DAPI fluorescence intensity distributions for each 12-hour fraction. The
control population is an asynchronous population of cells taken from bulk culture. At each time point
a threshold of 1.2 fluorescence intensity units is set to estimate the fraction of cells corresponding to
GI DNA content, as indicated by the percentages written in each shaded region. N = 677, 41, 128, 144
cells, for the asynchronous, 12-, 24-, and 36-hour time points, respectively. C) Kernel density estimates
of DAPI intensity distributions for each time point, overlaid with that of the asynchronous control. The
dotted vertical line marks the 1.2 unit threshold. In all cases, the fluorescence intensity data are
normalized such that the G1 modal value is at 1.0 units. The right shoulder in the control data,
corresponding to DNA content of cells in G2/M, is clearly diminished for the fractions taken at the
output of the device. Also note the increased probability density of the GI peaks of the device output
(dashed lines) compared to the asynchronous population (solid line), indicating synchrony in G1.
87
Additional information regarding the age of the cells was garnered from bright field timelapse images. The elapsed time from the appearance of a cleavage furrow during cytokinesis until
the cells fully separate and one disappears from the field of view was calculated, and the
distribution ofthese times is shown in Fig. 5-5. The median amount of time for cells to separate is
1.6 hours. Note that this time includes the time for cytokinesis. Here, 83% of the cells separated
within 4 hours of beginning cytokinesis. This means that at least 83% of cells were collected in
GI phase, since GI phase (not including cytokinesis time) for L1210 cells should be 4-5 hours
according to data reported in literature [158]. The similarity between the fraction of Gi cells
estimated by separation time and that obtained by DAPI staining indicates that the purity of
synchrony is partly affected by inherent biological variation in cytokinesis and cell separation.
This variation would also affect previous baby machines, which have been shown to maintain
synchrony over many divisions [151]. In summary, according to both DAPI staining and separation
times calculated from time-lapse imaging, over 80% of the eluted cells are in GI phase.
0.470.40
0C 0.270
0.20U-
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0.00
0
2
8
6
4
Separation time (hrs.)
10
Fig. 5-5. Cell separation time
Time for a cell to separate from its sister cell after morphological signs of a cleavage furrow are evident,
taken from time lapse images (5 min./frame). N = 150 cells, median = 1.6 hrs, mean = 2.2 hrs, standard
dev. = 2.0 hrs. 83% of the cells fully detached from its sister cell within 4 hours, including time for
cytokinesis.
5.5
Conclusions
The presented cell synchronization device serves to demonstrate the principle of a pressurecontrolled baby machine. Rather than using surface chemistries to adhere cells to a membrane, the
microfluidic device successfully uses pressure differences across trapping holes and fluidic ports
88
to capture cells on the device, allow them to grow and divide, and elute newborn cells that have
entered the GI phase of the cell cycle. On a device with 2109 holes, up to 1000 cells are eluted
every 12 hours, with a purity of up to over 80% of the collected cells being in G.
Although the device presented here is functional, a few deficiencies will need to be
addressed in future versions of the microfluidic "baby machine." First, it was difficult to confirm
the clearing of all the trapping holes while cleaning the device before reuse. Obstructed holes may
hinder proper loading of cells and lower the overall yield of the device, although not necessarily
altering its synchronizing functionality. Second, even after lengthy device preparation times, it was
difficult to ensure the removal of all air bubbles beneath the trapping holes. Over long runs, these
bubbles can expand and dislodge loaded cells. Third, beyond 36 hours of operation, aggregates of
cells can form in the trapping chamber and occasionally detach from the surface, releasing nonnewborn cells from the device. In spite of this, the current 36-hour window of operation may be
sufficient for many biological experiments, which require only a single synchronized batch of cells
and not a continuous supply.
The current yield of the synchronization device is sufficient as a demonstration and
possibly for transcriptomic analysis, but would need to be greatly increased for investigations on
the level of protein expression. To this end, it should be feasible to stack these 2-dimensional
devices into a 3-dimensional array of trapping holes, thereby vastly increasing the yield of the
device. Hence, there remains further development to improve the operation and increase the yield
of the microfluidic "baby machine." As a demonstrated concept, however, since the presented
technique avoids damaging chemicals, selects for age rather than size, and does not depend on
surface properties of cells, it may prove to be a less perturbing and more versatile way to
synchronize cells.
89
90
Appendix
A.1
Further characterizations of SMR constriction measurements
A.1.1 Supplementary figures for chapter 1*
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250
Buoyant mass (pg)
Fig. A-1. Effects of latrunculin B and nocodozole on MEF cells
Effects of latrunculin B (LatB) (5 pg/mL, 30 min) and nocodazole (Noc) (1 pg/mL, 30 min) were tested
with MEF cells and compared with untreated control. (A) Treating with LatB (red bars) and Noe (green
bars) both resulted in a relatively larger increase in the entry velocity than the transit velocity but LatB
induced greater change than Noc. Error bars represent 95% confidence intervals. (B) LatB and Noc
both induced a decrease in the passage time but the extent of the change was greater with LatB
(untreated, blue, n = 570; LatB, red, n = 494; Noc, green, n = 534). Measurements were acquired with
a PEG-coated channel surface and using a pressure drop of 1.35 psi.
*Data here were acquired by Dr. Sangwon Byun. These figures were taken from a manuscript that has been
published: Byun, S., Son, S., Amodei, D., Cermak, N., Shaw, J. et al. "Characterizing deformability and surface
friction of cancer cells." PNAS. 2013. 110(19):7580-5. Author contributions are listed in the publication. Figure
numbers have been altered to match the text of this thesis.
91
B
TMt
TMet
MPLL
-
10- MPEG
-i
.
3E.
3-
(1)
0...*
-- O.
-
A
.-
..
W 0.1CUU
00
Ix
0O
TnonMe
D
MPLL
MPEG
10-
a. 0
0
Lii
CU
o.4 3 -S Entry velocity
Transit velocity
0.01
.1
150 260 250
100
- . mass
.C.' (pg)..
Buoyant
50
ED
0
01
. *D*oe
2-
HCC827
F
HCC827
E
g10 *PLL*
4Ca
cc
W
PG
0.1-
0.0Entry velocity
Transit velocity
50
:
'
-S
*
.2> a.-
r
'5$
3-
.9D
TMt
'
C
150 260 250
100
Buoyant mass (pg)
50
Transit velocity
*
Entry velocity
..
100 mass150
(pg) 200 250
Buoyant
Fig. A-2. Effect of PEG versus PLL surfaces tested with various cell lines
Effect of PEG versus PLL surfaces was tested with various cell lines: TMet (A and B; n = 396 for PLL,
n
=
572 for PEG), TnonMet (C and D; n
220 for PLL, n
320 for PEG), and HCC827 (E and F; n
=
403 for PLL, n = 488 for PEG). For all three cell lines, PLL-coated microchannel induced a greater
change in the transit velocity than the entry velocity (A, C, and E) as well as an increase in the passage
time (B, D, and F). The distinct changes in entry and transit velocities caused by PEG versus PLL
surfaces were consistent with those observed with Hi 975 cells (Fig. 1-6). Error bars represent 95%
confidence intervals. Measurements were acquired using a pressure drop of 0.9 psi for the mouse cell
lines (TMet, TnonMet) and 1.8 psi for the human cell line (HCC827).
92
A.1.2 Effects of applied pressure on passage time of cells
The pressure applied across the SMR device was varied to observe the changes in peak rise times
and passage times (Fig. A-3). Peak rise times correspond to the amount of time the cell takes to
traverse from the base of the cantilever to the tip of the cantilever without a constriction. This time
should correspond to the linear velocity of the cells, and should thus vary proportionally with the
flow rate. For all cells, the peak rise time varies linearly with the amount of applied pressure (Fig.
A-3A). In contrast, the passage time may or may not vary linearly with the applied pressure (Fig.
A-3B). For smaller cells, which are more similar to the size of the constriction, the passage times
vary linearly; however, the larger cells seem to deviate from this pattern (e.g. cells that are 100 pg
can pass through the constriction in -0.002 s at 1.5 psi, but take ~0.2 s at 0.15 psi). This effect may
be due to the shear-thinning properties of the cells [159].
A
0.1
.0.s psi
B
- 0.45 psi
*
0.9 psi
1.5 psi
.
-
*
0.15 psi
1- * 0.45 psi
, 0.9 psi
1.5 psi
.
0.1
(U 0.01a
*
0.01
U
20
100
1020
Buoyant Mass (pg)
Buoyant Mass (pg)
Fig. A-3. Pressure variation effects on passage time and peak rise time of L 210 cells
A) The peak rise time vs. buoyant mass of L1210 cells having different applied pressures in the SMR
device. Peak rise times correlate to the velocity of fluid flow in the cantilever. B) Passage time vs.
buoyant mass of L1210 cells for the same measurements as in (A).
93
A.1.3 Effects of varying constriction dimensions
Passage time versus buoyant mass characteristics were compared for cells measured in a 4 pm x
100 pm constriction and those measured in a 6 pm x 50 pm constriction (Fig. A-4). Both
constrictions were 15 pm deep. The cell lines measured include L1210 cells, a murine lymphoblast
cell line grown in suspension, and Tmet cells, a murine lung cancer line grown in adherent
monolayer culture.
6 jim (W) x 50 jim (L) Constriction*
4 pm (W) x 100 pm (L) Constriction
A-----
-L1210
(N=329, 4um 1,5PS0)
---Tmet (N=191, 4um, 1.5psi)
-
B
--
-
*
1A
L1210 (N=1002, 6um, 0.45psi)
Tmet(N=798, 6um 0.45psi)
10
I-
0.1-
00
0
0.01
0.011
0.001
0.00101
10
10
10 DO
100
C
-
100
1000
Buoyant Mass (pg)
Buoyant Mass (pg)_
D
L1210(N=1196,4ur, 5.4psi)
Tmet(N=761, 4um, 5.4psi)
L1210(N=1419, 6um, 1.5psi)
Tmet (N=1268, 6um, 1.5psi)
101
10
i*
M M0.1
In
Ii.
_001
.2
0.011
0.01
U)
i
10
10
1000
Buoyant Mas s (pg)
0.001,
10
10010
Buoyant Mass (pg)
Fig. A-4. Effects of varying the constriction dimensions with different applied pressures
A) L1210 and TMet cells measured in a 4 jim x 100 pm constriction at a flow rate of 15 pL/hr. B) L1210
and TMet cells measured in a 6 pm x 50 jim constriction at a flow rate of 15 pL/hr. C) L1210 and TMet
cells measured in a 4 pm x 100 jm constriction at a flow rate of 40 pL/hr. D) L 1210 and TMet cells
measured in a 6 pm x 50 pm constriction at a flow rate of 40 pL/hr. *Note: The 6 pm constriction was
found to be eroding, so the width is likely to have been larger than 6 jim.
94
A few results suggest potential for future studies. First, as expected, the distinction between
TMet versus L1210 cells was maintained at a higher volumetric flow rate for the smaller constriction
width. This indicates that epithelial tumor cells may be able to be distinguished from blood cells
with a higher throughput in a 4 pm constriction device than in a 6 gm constriction device.
Interestingly, at the lower flow rate and with a 4 pm constriction, the L 1210 cells show a kink in
the passage time versus buoyant mass trend (Fig. A-4A), similar to what is seen in the TMet cells
in the 6 pm constriction. The location of the kink likely depends on the ratio of the cell diameter
to the constriction width. Furthermore, using the 4 pm-wide constriction at a slower flow rate (Fig.
A-4A), the L1210 cells larger than -50 pg seem to have the same passage time versus buoyant
mass characteristic as the TMet cells. This is extremely fascinating, as it may suggest that under
certain shear stress conditions, epithelial cells and blood cells have the same mechanical properties
in spite of their diverse origins. To be conclusive, this would need to be further investigated.
A.2
SMR platform development and variations for different types of
on-chip imaging and measurement
A.2.1 Combined imaging and optical lever system
During the time I began to work with the SMR, most members of the lab were using an optical
lever readout system that supplied a laser beam to the underside of the cantilever, while using a
separate optical system (a purchased microscope) to image the SMR from its topside. Although at
the time, many had spoken of the possibility of combining the optics for both the optical lever and
microscope in order to simplify the system, I was one of the first in the lab (working with Nate
Cermak) to implement and build a system in this fashion in 2011 (Fig. A-5). This system is much
more cost effective than purchasing a microscope. However, because the objective is shared
between the read-out system and the imaging, it is difficult to use if the study being undertaken
requires particular imaging specifications. For example, when I needed to detect a fluorescence
signal from a cell passing through the cantilever and wanted to use a higher magnification, or
wanted to image the cell in a different part of the SMR channel, it was difficult to implement. For
applications requiring dynamic or altered imaging, it proved more feasible to use the purchased
microscope system separate from the optical lever-read out system, described in more detail in the
subsequent section.
95
A
m
r
Camera
Mirror
Mirror
Tube Lens
(can reduce image if desired)
rror
tional
600 Short pass
Bleo
50:50 (or 30:70)
Bam Splitter
E
L
Bea
(op
al)
Expander (optional)
s
fMr
SMR
Objective
(lox) 605 LP Dichroic
Polarizing Beam
I
Splitter
/
(635nm Band pass) Retarder:
Lamda/4
B
1Drive CirCu ti
Polarizing
A '.
Beam Splitter
Solenoid Valves
Quarter Wa
Platp
Len s
Pressurized Vials
Fig. A-5. SMR system with imaging and optical lever through same objective
A) A schematic diagram of a system combining imaging of the SMR with optical lever read out through
the same objective. B) A photograph of the actual built system.
96
A.2.2 Addition of PMT sensor for fluorescence detection
Based on systems built by previous members of the lab, such as Sungmin Son and Amneet Gulati
[122], I added a PMT sensor to a microscope to detect fluorescence signals from cells passing
through the SMR (Fig. A-6). The optical lever read-out system was applied through the underside
of the cantilever, based on a system built by Wesley Weng, a former graduate student in the lab.
The use of the filter turret as well as being able to place the objective at the bypass channel of the
SMR (which has optical access through glass instead of silicon) proved to be effective in
simultaneously detecting single-cell fluorescence signal along with buoyant mass and passage time
characterizations. However, the PMT setup was not sensitive enough to detect some internal
fluorescence reporters, such as the YFP expressed by the Snail promoter in EpCAMhYFPIO
MMTV-PyMT cells ( 2.2).
A
B
10'
PMT Signal (V)
-Excitation
Iris
0.5
Filter
Dichroic
Filter
0A
a
(
E mission
Objective
e
10
fe
100
0
10
102
1'
Buoyant Mass (pg)
Fig. A-6. PMT addition for fluorescence read-out
A) Schematic diagram of PMT setup. B) Example of CMFDA-dyed pbl.3G (murine mammary
carcinoma line from the Weinberg lab) mixed into mouse blood. The black circles correspond to cells
that had no fluorescence signal detected by the PMT.
97
A.2.3 Variations of SMR constriction designs
In addition to varying on-chip imaging methods, the device design itself was also varied (Table A1). Further characterizations of Gen4-311 and Gen5-513 are in A.1.3, while an example peak
shape from Gen4-312 is shown in Fig. A-7.
Table A-1. SMR constriction device designs*
Device
Width of Length of I st
Width of
Name
I St
Constrict.
2 nd
Constrict.
(Im)
Constrict.
(Im)
(pm)
Gen4-311
6
50
N/A
Length of 2"d
Cantliever
Constrict.
(Im)
length
(Im)
N/A
315
Gen4-312
6
7
6
150
315
Gen4-313
6
50
8
50
315
Gen4-314
6
50
7
50
315
Gen4-315
8
150
N/A
N/A
315
Gen4-316
6
50
6 (optical
access)
50 (optical
access)
315
Gen5-510
6
50
N/A
N/A
315
Gen5-511
6
50
N/A
N/A
350
Gen5-512
6
100
N/A
N/A
350
Gen5-513
4
100
N/A
N/A
350
Gen5-5i4
3
100
N/A
N/A
350
Gen5-515
2
100
N/A
N/A
350
*All entrance angles are 45 degrees, and all buried channel depths are 15ptm.
98
Notes
Optical
access not in
cantilever
N
1970F
U
196C
I
a)
a)
U-
1940-
1MO-
0
100
2W
S
4
5M
6Wl
7M
am
900
Time (a.u.)
Fig. A-7. Example of peak shape from device Gen4-312
A screen capture of the SMR signal of a Tme cell passing through a double constriction device (Gen4-
312).
In addition, glass and silicon channels were fabricated such that the cells squeezing through the
constrictions could be better visualized. Parallel channels with 4pm and 6pm constrictions as well
as 3ptm and 2pm constrictions were made. An example layout is shown in Fig. A-8.
Fig. A-8. CAD layout of alternating 4pm and 6pm constrictions
A drawing showing the layout of a silicon device with a glass layer on top, such that there is optical
access to all channels. This device is composed of parallel channels, with alternating constriction
widths of 4ptm and 6pm. The branching of the channels is designed such that the pressure across each
constriction should be the same.
99
A.3 Preliminary study of small cell lung cancer*
Small cell lung cancer (SCLC) comprises 10-15% of newly diagnosed lung cancers. Although the
percentage appears to be small, SCLC is the deadliest and most aggressive type of lung cancer. It
has been found that SCLC tumors are often composed of phenotypically different cells with
neuroendocrine (NE) or mesenchymal (nonneuroendocrine, nonNE) markers, which, when
propagated in vitro, can grow in a suspended or adherent state, respectively [160]. A subcutaneous
injection of a mixture of NE and nonNE cells render the NE more highly metastatic than if either
population were injected into the mouse alone [160].
A
10
E
S984LN nonadherent cell line, N = 1500
-984L N adherent cell line, N =892
00
1,
0
Mouse SCLC CTCs, N=1466
1*
0.1
0
*0
I 0.01
M.001
0.01
1
*.
10
10
0.001.
10
B uoya nt ma ss (pg)
C
* 984LN nonadherent cell line, N = 1500
* 984L N adherent cell line, N = 892
101 * Mouse SCLC CTCs, N=1466
100
Buoyant mass (pg)
0
I'
0
0
~1,
L
00
0.1
a
r
a
0%
000
0.01.
**0o
A Afli
10
100
Buoyant mass (pg)
Fig. A-9. Passage time versus buoyant mass for SCLC cell lines and mouse CTCs
A) Passage time versus buoyant mass characteristics of 984LN nonadherent and adherent cell lines. B)
Tumor cells obtained from the blood of a mouse that developed SCLC autochonously, ~12 months
post infection, and had large tumors by micro-CT. C) An overlay of (A) and (B), demonstrating the
similarity in mechanical properties between the 984LN nonadherent cell line and the mouse CTCs.
* This study was done in collaboration with Thales Papagiannakopoulos and Sheng Rong Ng, who provided mouse
blood and cell lines from the Jacks laboratory.
100
Fig. A-9 shows the passage time and buoyant mass properties of the nonadherent and
adherent subpopulations ofthe 984LN SCLC cell line from the laboratory of Professor Tyler Jacks.
In comparison, I have also measured the circulating tumor cells (CTCs) from a SCLC mouse
model, which developed tumors autochthonously based on the deletion of p53 and Rb.
Interestingly, the SCLC tumor cells found in the mouse (expressing tdTomato, sorted by FACS
after red blood cell lysis) had similar mechanical properties to the nonadherent 984LN cell line.
These results provide more evidence that the nonadherent subpopulation in SCLC may be the more
invasive, malignant cell type, seeding new tumors at metastatic sites.
A.4
Further studies on tumor cells in circulation*
As a complement to the study in 3.3, we designed a study to determine whether tumor cells that
were filtered out of circulation were less deformable than cells that persisted in circulation. In 3.3,
the cells in circulation may have intravasated from tumors in the lungs, but it is not known how
long they have been in circulation. Thus, we injected tumor cells via the tail vein of an
immunocompromised mouse to see which cells remained in circulation after two-three minutes.
Fig. A-10 shows results from an experiment where we injected two doses of one million pBl.3G
cells (a murine tumor cell line, dyed with CMFDA) separated by five minutes. After two to three
minutes after the second dose of cells, blood was harvested from the left ventricle of the mouse.
Red blood cell (RBC) lysis was performed on the blood, and the remainder of the sample was
measured in the SMR with simultaneous PMT detection for the fluorescence signal of the cells.
A
10
- Control pbl.3G cells, N =676
A
Fluorescent cells in blood, N
I
=
10
118
- Non-fluorescent cells in blood, N = 396
Fluorescent cells in blood, N = 118
A
A
A
4
A
_l
A
C 0.1
0.1
A
0.1
.A
.
A.
A
A..
A
0.01
0.01
AA
1n
0.001-
--lb
A
10
'40;~*
i.&..
O~00A
1, 0
100
b
A&
16
0.001----
io"
Buoyant Mass (pg)
Buoyant Mass (pg)
1&
Fig. A-10. Passage time versus buoyant mass of tumor cells circulating in blood
A) Passage times of the tumor cells found in circulation (red triangles) two-three minutes after cell
injection are compared with those of an aliquot of the initially injected cells (gray circles). B) The same
tumor cells found in circulation as in (A) compared with some blood cells (non-fluorescent) from the
same mouse.
This study was done in collaboration with Dr. Xin Ye of the Weinberg laboratory.
101
B
A
101
4100
A
~A
4
~4A
A
s
st
F
10
A0
310-0
A
A
WA
F
A
A
A
C
C~u
A
AAA
I
A
A
A
AA
A
A
Control
Fluorescent cells in blood
16o
30
A
A
A
Control
Fluorescent cells in blood
30......10
B uoya nt ma ss (pg)
B uoya nt ma ss (pg)
Fig. A-li. Entry and transit velocities versus buoyant mass of tumor cells circulating in blood
A) Entry velocity versus buoyant mass of tumor cells found in the blood compared with the initially
injected tumor cell line. B) Transit velocity versus buoyant mass of the same cells as in (A). The data
for both panels are from the same data set as in Fig. A- 10.
Interestingly, the results demonstrate that even after two to three minutes after injection
through the tail vein, and passing through the lungs, cells that have long passage times are still
able to persist in circulation. It seems as though any cell from the original pBl.3G population,
based on its passage time alone, could have persisted in circulation without being filtered out by
capillaries. Also, the trends of the entry and transit velocities of the cells persisting in circulation
(Fig. A- 11) do not seem to differ from the originally injected cells.
However, a few times of repeating this experiment showed that the number of cells that
persist in circulation after a few minutes post-injection varies widely from mouse to mouse. A few
times, there were not enough tumor cells found in the blood to proceed with an SMR measurement.
Further studies would need to be carried out to determine whether a larger number of cells come
back into circulation after a longer amount of time. It would be ideal to be able to carry out
experiments at different time points post-injection to see if the stiffer cells preferentially get filtered
out by the circulatory system over time or whether the filtration is an entirely stochastic event.
102
A.5
Agarose
bead
standardization*
measurement
for
potential
stiffness
In search for a way to standardize SMR measurements and compare the passage time
characteristics to other known mechanical parameters, such as a Young's modulus, measurements
of agarose beads were performed. Beads of varying agarose (types IX and IXA) concentrations
were created in the Di Carlo laboratory at the University of California, Los Angeles. They were
first introduced in a glass/silicon parallel constriction device for initial imaging. Only the highest
concentration of agarose (2%) had enough contrast for bright-field imaging. The bead sizes were
relatively disperse, with some larger ones having diameters of~30 pm. After attempting to apply
pressure to cause the beads to pass through 6im wide constrictions, some beads mechanically tore
or broke (Fig. A-12).
Fig. A-12. Bright-field image of broken agarose bead
Measurements of beads containing lower concentrations of agarose were then measured in
an SMR, having a 6tm-wide constriction (Fig. A-13). Due to the low density of the porous
particles, their buoyant masses were lower than typical cells. However, the volume of the beads
were difficult to determine since the coulter counter also could not accurately assess their volume,
likely due to their porous nature. The 0.2% agarose beads, which the UCLA group deemed to be
most similar to typical cells by their deformability measurement device, have a similar range of
passage times as adherent cells (4T1). Their response to different shear rates also seems similar
for 0.9 and 1.5 psi applied pressure (Fig. A-13E, F). A volumetric comparison, however, would be
more informative than a buoyant mass comparison. Unfortunately, the study was never completed,
and the information regarding the Young's modulus of the beads (determined by AFM, for
example) was never obtained. However, this study demonstrates the potential and feasibility of
measuring low concentration (less than 2%) agarose as a stiffness standard in the future.
* This study was performed in collaboration with Mahdokht Masaeli from the Di Carlo laboratory at the University
of California, Los Angeles.
103
B
Agarose IX
0.2% Agarose IX (N=1 224)
0.5% Agarose IX (N=1 220)
2% Agarose IX (N=17)
L1210 (N=1 220)
4T1 (N=636)
100
10
100
10
*
I-
12.
Agarose IXA
0.2% Agarose IXA (N=720)
0.5% Agarose IXA (N=720)
1% Agarose IXA (N=8)
L1210 (N=458)
4T1 (N=636)
-
A
At
0.1
0.1
0.01
0.01
-A
1E-3
1E-3
10
160
Buoyant Mass (pg)
10
100
Buoyant Mass (pg)
C
0.2% Agarose IX & IXA
-
100, -
D
0.5% Agarose IX & IXA
0.2% Agarose IX (N=720)
0.2% Agarose IXA (N=720)
100.
-
0.5% Agarose IX (N=1100)
0.5% Agarose IXA (N=1106)
10.
101
I0.1.
0.1E
I-
l0
.
*
0.
0.01,
CL 01
1
1E-31
1E-3,
100
10
E
Agarose IX (0.9, 1.5psi)
100.
10.
*
*
*
*
*
*
100
10
Buoyant Mass (pg)
Buoyant Mass (pg)
F
Agarose IXA (0.9, 1.5psi)
*
*
*
100"
*
10.
0.2% Agarose IX, 0.9 psi (N=655)
0.2% Agarose IX, 1.5 psi (N=700)
L1210, 0.9 psi (N=700)
L1210, 1.5 psi (N=700)
4T1, 0.9 psi (N=573)
4T1, 1.5 psi (N=636)
0.2% Agarose IXA, 0.9 psi (N=664)
0.2% Agarose IXA, 1.5 psi (N=720)
L1210, 0.9 psi (N=440)
L1210, 1.5 psi (N=458)
4T1, 0.9 psi (V-=573)
4T1, 1.5;1i (N=636)
.5
I0.1
0.1
*
.l
&.
CL 0.01
0.01
1 E-3i
1E 3
1,0
0
100
10
Buoyant Mass (pg)
Buoyant Mass (pg)
Fig. A-13. SMR passage time versus buoyant mass measurements of agarose beads
A) Passage times of Agarose IX beads of varying agarose concentrations, plotted with L1210
(suspension) and 4T1 (adherent) cells for comparison. B) Passage times of Agarose IXA beads of
varying agarose concentrations, plotted with L1210 and 4T1 cells for comparison. C) 0.2% Agarose
IX and IXA beads in comparison with each other. D) 0.5% Agarose IX and IXA beads in comparison
with each other. E) 0.2% Agarose IX beads measured with different applied pressures, plotted with
LI 210 and 4T1 cells for comparison. F) 0.2% Agarose IXA beads measured with different applied
pressures, plotted with L1210 and 4T1 cells for comparison.
104
A.6
Further surface characterizations of cells*
The glycocalyx of cells were altered in order to characterize the effect of cell surface treatments
on passage time measurements in the SMR. A list of experiments is shown in Table A-2.
First, untreated and heparinase treated (30 min in PBS) TMet cells were compared in a PEGcoated SMR, with an applied pressure of 0.9 psi. The two conditions showed similar passage times
as well as entry and transit velocities, suggesting that heparinase did not induce a significant
change (Fig. A-14). Interestingly, a significant second population of cells or debris was detected.
The whole population can be divided into two distinct groups, following the green dotted line (Fig.
A-14B). This can be similarly observed after cells undergo freeze-thaw cycle, which creates
potential debris (lysed cells) with different passage time characteristics. After 30 min treatment
with PBS (with or without heparinase), spontaneous cell dissociation from the surface and ~ 1030% cell death were observed (Nathan Stebbins). However, it seems unlikely that the PBS induced
a large amount of cell death and debris. Moreover, the number of small cells is much greater than
10-30% of the total number of cells counted during the measurement. It is possible that this
population represents normal but small cells. One caveat is that the changes described above after
incubation in PBS may have induced a change in cell deformability and masked the effect of
heparinase.
As a follow-up experiment, TMet cells were treated with heparinase in DMEM culture
medium and measured in a PLL-coated SMR (Fig. A- 15). Cells were detached from the surface
using Non-enzymatic Cell Dissociation Solution in PBS (Sigma). The same applied pressure of
0.9 psi was used. Again, no significant change in passage time was observed. The protocol for
non-enzymatic dissociation is as follows. 1) All materials are warmed to 37'C before use. 2) Wash
cells once with PBS. 3) Incubate cells with a few mL of the dissociation solution for 5 min in the
37'C incubator. 4) Add an equal volume of PBS and dislodge the remaining cells with a pipette.
5) Pellet cells. 6) Re-suspend in media.
Next, to test the effect the duration oftrypLE (cell dissociation) treatment on passage time,
Lewis Lung Cancer (LLC) cells were treated with trypLE for 5 min (short) and 20 min (long) for
the detachment and measured in a PLL-coated SMR. Opti-MEM was used for culture medium.
The same applied pressure of 0.9 psi was used. As a result, longer incubation in trypLE resulted
in slightly shorter passage times (Fig. A-i 6A). The transit velocity, but not the entry velocity, was
affected by longer treatment (Fig. A-16B, C). The transit vs. entry velocities plot supports the fact
that the change was surface-dominant (Fig. A- 1 6D). Also, by the microscope, it was readily
observed that cells with the shorter treatment were much stickier than those with the longer
treatment.
* This study was mostly carried out by Dr. Sangwon Byun from the Manalis Laboratory in collaboration with
Nathan Stebbins from the Sasisekharan Laboratory. I also measured a subset of the cells in this study simultaneously
with Dr. Byun and performed some analysis on these replicate experiments, although they are not shown due to
redundancy. The results shown here were prepared and written by Dr. Byun, and edited by me for completeness of
the record of the Manalis Lab.
105
Table A-2. List of experiments
Cells
Surface
Applied Pressure
Conditions
Tmet
PEG
Control (30 min, PBS)
Heparinase treated (30 min, PBS)
0.9 psi
Tmet
PLL
0.9 psi
LLC
PLL
LLC
PLL
LLC
PEG
LLC
P'LL
Control (30 min, DMEM)
Heparinase treated (30 min, DMEM)
TrypLE treatment for 2 min
TrypLE treatment for 20 min
Control (30 min in PBS)
400 nM heparinase I+ 400 nM heparinase III (30 min,
PBS)
TrypLE treatment for 2 min
Incubation in PBS for 30 min
Incubation in OptiMEM for 30 min
Control (OptiMEM)
Sialidase treated
Tmet-Ctrl
* Tmet-Hep
-
:
*i
I*
* '%
1-1
...
*
*
AS7
a)
E
E
,
.
10-
0.9 psi
'
.;.*f
Tmet-Hep
-
0.9 psi
*
B
" Tmet-Ctrl
0.9 psi
*
10
*
A
0.9 psi
)
a)
cc
01
-
IE-3
U) 0.01
U)
M~
fu
1 E-3
10
100
10
CtrI: 1226
Hep: 753
100
Buoyant'mass (pg)
Buoyant mass (pg)
D
100-
I.p
-
4.
-
Ua
CLn
100-
10-
10..
-
1
U)
0
0.1
0.1
-
0
2ii
0.01
C
-
w
Tmet-Ctrl
* Tmet-Hep
0.01-
-
1E-3-
100
1
)
10
1E-3-
Buoyant mass (pg)
* Tmet-Ctrl
* Tmet-Hep
100
Buoyant mass (pg)
Fig A-14. Heparinase-treated and untreated TMet cells in PBS
A) Passage time vs. buoyant mass of heparinase-treated and untreated (control) TMet cells in PBS. B)
A second population of cells or debris is apparent after incubation in PBS. C) Entry velocity vs. buoyant
mass for the same data set as in (A). D) Transit velocity vs. buoyant mass for the same data set.
106
10-
-
Tmet-Ctr
Tmet-Hep
-- ',.
CD
E
.
0.1
%
.. 'A
0.01
-
'p
.
'
.:
1 E10
Ctri: 1051
Hep: 915
100
Buoyant mass (pg)
Fig A-15. Passage time of heparinase-treated and untreated
A
TMet
cells in DMEM medium
B
10-
-
1
C
C.,
E
WT
I
I10.1-
0
'
-
0.1
CO
#OE
*
-
Short TRP: 515
Long TRP: 584
0.01-
20
100
200
-
w
P
.:. .-. - LC-Short TRF
0.01-
300
LLC-Long TRP
100
200
300
Buoyant mass (pg)
Buoyant mass (pg)
C
D
U)
-
LLC-Short TRP
LLC-Long TRP
10-
10A=!
10
C.,
0
N..
e e
"
1
-
0
a)
U)
4-a
C
U)
H
Ha
0.1
-
Cu
-
Ac*
LLC-Short TRP
LLC-Long TRP
100
'6
200
'0.1
300
Buoyant mass (pg)
0.01
01
1
10
Entry Velocity
Fig A-16. LLC cells having been in trypLE for different amounts of time
A) Passage time vs. buoyant mass of LLC cells, having been in trypLE for various amounts of time
(short: 5 min, long: 20 min). B) Entry velocity vs. buoyant mass for the same data set. C) Transit
velocity vs. buoyant mass. D) Transit velocity vs. entry velocity
107
Then, the combined effect of heparinase I and III was tested on LLC cells. Untreated LLC
cells (30 min in PBS) were compared to heparinase-treated cells (30 min in 400 nM heparinase I
+ 400 nM heparinase III in PBS). No significant change in passage time was detected (Fig. A-17).
However, distinct subpopulations were observed with both conditions, which was not observed in
the previous experiments (Fig. A- 16) that compared short and long trypLE treatment in Opt i-MEM
(Opti-MEM is a medium with minimal amounts of protein). The subpopulation having a smaller
size showed longer passage times, which was similar to the result from TMet cells (Fig. A-14),
where it seemed that incubating cells in PBS might cause changes in cells that were distinguishable
by the SMR but not by trypan-blue.
In order to test whether the subpopulation described above was a result from harsh
treatment, i.e., 30 min incubation in PBS, LLC cells were treated under three different conditions:
incubated with trypsin for 2min, in PBS for 30 min, or in culture medium for 30 min before
measurement. As a note, LLC cells can be collected without trypsin, so incubating in trypsin was
considered as a surface treatment. Also, the SMR surface was coated with PEG for these
measurements. In all three conditions of treatment, a subpopulation was observed (Fig. A-18). The
relative count of cells or debris within the subpopulation was highest with PBS and lowest with
trypsin (Fig. A-18B). Interestingly, the 2 min trypsin treatment resulted in a slightly faster passage
time but it is not clear whether such a short treatment was enough to change the transit through the
PEG-coated surface (Fig. A-18D, E). Also, the SMR peaks from the subpopulation were compared
to those from the main population (Fig. A-19). The cells from the main population showed typical,
canonical peak shapes as seen in most cell lines (Fig. A-19B). However, some cells from the
subpopulation showed different shapes (Fig. A-19C, especially top row), which exhibited a more
dramatic transit through the constriction. Those cells were much smaller than main population,
suggesting that they might represent only a fragment of a cell.
Finally, the effect of sialidase on the passage time of LLC cells was examined. Untreated
(in culture medium) and sialidase-treated LLC cells were measure in a PLL-coated SMR (Fig. A20). Sialidase treatment reduced cell viability, so for future studies, the treatment needs to be
modified. Sialidase treatment increased the passage time, which was counter-intuitive, and this
result might be related to the cell death caused by the treatment. Separation between the main
population and subpopulation became unclear after sialidase treatment. Interestingly, the change
in velocity was surface-dominant sine the relative change in transit velocity was greater than that
in entry velocity (Figs. A-20B, C). SMR peak shapes were compared within the two populations
(Figs. A-21). The cells from main population showed canonical peak shapes even with sialidase
treatment (Fig. A-21B, left side). However, most of cells from the smaller, subpopulation did not
show canonical peak shapes. Those SMR peaks were also different from the peaks with dramatic
change observed with the previous experiment (Fig. A-19). Further study is needed to confirm
whether the cell passage changes seen with sialidase-treatment were induced by cell death.
108
A
B
-
10
10U)
-
1
.
0)
, -
. I.
Op*
1-
+. .*
-
0)
.
0
'I)
-
0.1
1I-
Ca
*.-
0.1-
Ca
a_
, ..
'.:.
. .*:
-
**
-
1E-3 1
CtrI: 415
Hep: 375
w
L LC-CtrI
LLC-Hep
0.01-
-
LLC-CtrI
LLC-Hep
100
..
100
Buoyant mass (pg)
Buoyant mass (pg)
D
.40
~10-
*
LLC-CtrI
*
LLC-Hep
10.
.'.".- -
0
-
.
-
CD,
Cl, 0.01
..
0
it'''*
*soo*0
I
1Cl)
C:
Ha 0.1-
c0.1LLC-CtrI
LLC-Hep
0.01
100
0.1
1
10
Entry Velocity
Buoyant mass (pg)
Fig. A-17. Untreated (control) and heparinase-treated LLC cells
A) Passage time vs. buoyant mass of untreated and heparinase-treated LLC cells. B) Entry velocity vs.
buoyant mass. C) Transit velocity vs. buoyant mass. D) Transit velocity vs. entry velocity.
109
A
10
B
STrypsin
- PBS
OpUIMEM
-
Trypsin
--
PBS
OptiMEM
18-
E
C,)
01
9.
U)
U) 0.01
.. +
..
01
Trypsin: 74 3
PBS: 878
OptiMEM: 822
C
150 200250
100
50
E
D
1
100-
Trypsin
PBS
OptiMEM
100:
100-
*
Co
10-
0
0.1.
0
C
Cu
Cu
-
I15 0 200250
0.1
*
Trypsin
PBS".
50
H
.
C
100
101
CO
Co
50
Trypsin
PBS
OptiMEM
13
U
0
13
0
150 200250
100
50
Buoyant mass (pg)
Buoyant mass (pg)
.
1 E-3
0.1 4.
0.01
150 200250
100
.
0.1
Buoyant mass (pg)
Buoyant mass (pg)
Y.
100
10
1
Entry Velocity
Fig. A-18. LLC cells treated with trypsin, PBS, or OptiMEM
LLC cells were incubated in trypsin for 2 min (red), or PBS for 30 min (blue), or OptiMEM for 30 min
(green) prior to SMR measurement. A) Passage time vs. buoyant mass. B) Counts of cells based on
buoyant mass. C) Entry velocity vs. buoyant mass. D) Transit velocity vs. buoyant mass. E) Transit
velocity vs. entry velocity.
C
B
140 < Mass < 150 pg
0.1 < Passage < 0.5 s
A
1-
Trypsin
PBS
OptiMEM
.
10-
0 < Mass < 60 pg
0.1 < Passage < 0.5 s
q...
Now
0.1,
U
a)
0)
-----------
Cr
W~
di
U.
-
aU 0.01
-
1 E-3-
-
50
100
150 200250
Time (s)
-
/
/
-
-
..
-I
..
.
-I,
II)
/
E
-
CD
Buoyant mass (pg)
Fig. A-19. Comparison of SMR peak shapes between cells in different subpopulations
A) For the same data in Fig. A-18, SMR measurements were compared for cells having passage times
between 0.1 s and 0.5 s. B) For cells having a buoyant mass between 140 pg and 150 pg, the frequency
vs. time SMR readout had canonical peak shapes, as found with most cell lines. C) For cells in the
smaller subpopulation, having a buoyant mass less than 60 pg, the peak shapes demonstrated varied
patterns both in the entry and transit regions as the cells passed through the constriction.
110
A
.
.
B
Control
Sialidase
100**
E
U)
(0
1
.-.
cc)
CO
**
0-
w
50
Control: 1070
Sialidase: 1421
C
-'
r* *A
*
Sialidase
. .. :
.
. . , . , , ....
1E-3 i-
''
-
0.01,-A) assge -im vs uyatms .o, unrae..nrl adsaiaetetd L el.B nr
CO
01
'
'
* 'gp
*
*
0.-
100
160
80
150 200250
240
Buoyant mass (pg)
Buoyant mass (pg)
D
100-
100
Ctrl
SSialidase
.,
--.
1-1
..
-S
10
100
77D
'.,''
0
- '. .2
C
U)
Ca
*
0 1-
Ctrl
...
* Sialidase
50
4.
100
0.1
150 200250
0.1
Entry Velocity
Buoyant mass (pg)
Fig. A-20. Untreated and sialidase-treated LLC cells
A) Passage time vs. buoyant mass of untreated (control) and sialidase-treated LLC cells. B) Entry
velocity vs. buoyant mass. C) Transit velocity vs. buoyant mass. D) Transit velocity vs. entry velocity.
ill
A
:ontrol
100 <Mass <125,
007<passage<0.1
Mass <70, 0.07<passage<0 1
Z
Cr
0)L
6-
Time (s)
Time (s)
Sialidase
B
Mass <70, 0.07<pas sage<0.1
100 <Mass <125, 0.07<passage<0.1
C:
Time (s)
Time Ws
Fig. A-21. SMR peak shapes for untreated and sialidase-treated LLC cells within different
subpopulations
A) Representative frequency vs. time traces of untreated (control) LLC cells as they pass through the
SMR. B) Representative frequency vs. time trances of salidase-treated LLC cells as they pass through
the SMR. Cells shown have a passage time between 0.07 s and 0.1 s. The left column for both panels
are cells from the main population of cells, having a buoyant mass between 100 pg and 125 pg; whereas
the right column for both panels are cells from the subpopulation of cells having a buoyant mass less
than 70 pg.
112
A.7
Characterizing heterogeneity
in single-cell
biophysical
properties using SMR passage time measurements*
Mechanical properties of cells are largely governed by cytoskeletal proteins, such as actin
filaments, microtubules, and nuclear lamina. Disrupting such cytoskeletal structures may lead to a
change in holistic cellular biophysical properties, such as the viscoelasticity of individual cells.
Although biophysical parameters of cells undergoing cytoskeletal changes have been studied in
great detail, how those parameters become more homogenous or heterogeneous are not well
understood. Here we use drug treatments and genetically altered cells to study the effects of
cytoskeletal changes on heterogeneity in biophysical properties by measuring passage time using
an SMR with a 6pm-wide constriction. Lamin A/C deficiency in mouse embryonic fibroblasts
(Lmna -/- MEF), which is known to reduce cell stiffness while the cells are in an adherent state,
significantly decreases the passage time through the SMR constriction. We also find that disrupting
actin filaments by latrunculin B (LatB) or microtubules by nocodazole (Noc) both induce a
decrease in passage time in Lmna +/+ and Lmna -/- MEFs. These results, however, are unexpected
since the mechanical properties of Lmna -/- MEFs measured in an adherent state (by ballistic
intracellular nanorheology) are not affected by the same cytoskeletal disruption. Despite these
significant effects on passage time, lack of lamin A/C and drug treatments do not affect variability
in passage time. In contrast, treating FL5.12 cells with LatB, which naturally grow in suspension,
induces a decrease in passage time and also a decrease in heterogeneity of passage times, whereas
treating with Noc affect neither. Changes in passage time induced by LatB and Noc together are
similar to those by LatB alone. High throughput cellular phenotyping (HTPC) on FL5.12 cells
shows that the heterogeneity in microtubules is decreased by all three treatments. However, the
heterogeneity in actin filaments is decreased by LatB and Noc separately but not by LatB and Noc
together, suggesting an interplay between actin and microtubules. These results suggest that
cytoskeletal disruption can attribute to the heterogeneity in passage time, but that its effect is
limited by anchorage dependence.
This study was mostly carried out by Dr. Sangwon Byun from the Manalis Laboratory in collaboration with the
Wirtz Laboratory (especially Dr. Shyam Khatau and Jorge Marchand) at Johns Hopkins University. I also measured
the FL5.12 cells in this study simultaneously with Dr. Byun and performed some analysis on these replicate
experiments, although they are not shown due to redundancy. The results were prepared and written by Dr. Byun,
and edited by me for completeness of the record of the Manalis Lab.
113
Suspended MEFs
10-
-
1
0.1
-
E
(D,
-
.
0.01-
a_
* *Lmna+/+
SLmna-/-
....
1 E-3
300
200
100
Buoyant mass (pg)
Transit Velocity
Entry Velocity
100-
100.
01
-
0.01
1E-1
200
100
300
1 -1 L 4100
200 300
Buoyant mass (pg)
Buoyant mass (pg)
Fig. A-22. SMR measurements of wild-type and lamin A/C knock-out MEFs
Passage time measurement shows that MEFs without lamin A/C (Lmna -/-) are more deformable than
the wild-type (Lmna +/+). The change in deformability (seen in entry velocity) is a more dominant
factor causing a decrease in passage time than friction (seen in transit velocity).
Imna+/+
Imna-/-
0
-0.1
(0
-0.2
-0.3
U
0
-0.4
2
-0.5
E
0
I
-0.6
LatB
* Noc
"
-0.7
-0.8
mean
sd (n=3-4)
Fig. A-23. Offset in passage time for MEF cells after treatment with LatB or Noc
Disrupting the actin filaments by latrunculin B (LatB) or the microtubules by nocodazole (Noc) both
induce a decrease in passage time in Lmna +/+ and Lmna -/- MEFs. These results are unexpected since
mechanical properties of Lmna -/- MEFs measured in adherent state are not affected by the same
cytoskeletal disruption.
114
10-
S..
E
T-ctrl
0.1-
T-latB
T-noc
LF ANA-ctrl
L MNA-IatB
L OiNA-noc
L
250 300
*
CO
0.01
CL
.
.*
-
'
-
100
150
200
Buoyant mass (pg)
Fig. A-24. Passage times of Lmna +/+ MEFs treated with LatB compared with those of Lmna-/- MEFs
The passage times of wild-type (Lmna +/+) MEFs that have been treated with LatB are similar to those
of Lmna-/- MEFs without any treatment. This is consistent with a previous study by nanorheology.
Unclear Transition
Bump Transition
0
Cr
Co
..
C
Co
.060 l0
0.15
60
01
oa 4o
..
1..
~dy/dt
-
N
0. 10
Time (s)
........
:0.2
0.3
0'4
0.5
0 .6
0.0
0.1
0.2
0.3
Time (s)
Time (s)
dy/dt
dy/dt
0.4
0.5
M
SN
0066
0.06
01 0
Time (s)
01 2
0150
020
0.30
Time (s)
0.40
0L20
025
0.30
0.35
040
Time (s)
Fig. A-25. Trajectories of SMR peaks reveals cells with a distinct ("bumpy") peak pattern
After LatB treatment, certain cells demonstrate a bump in the transition region of the SMR peak shape,
while entering and transiting through the constriction.
115
Imna-/-: control vs. latB
lmna+/+: control vs. IatB
*Cr-Smooth
4%)
( LW-Unclear
kptUnlo
LatB*Snooth (4%)
W5%
(41%)-
(80%)
a* Ctrl-Uncear
CtrI Smooth (20%)
MWaB-Bunp (12%)
10.
LatB-Unclear (94%)
0' Lall-Smooth (2%)
LalB-ump (4%)
10
I?.,-
E
E
0.1*
8)
0.1
0)
In
U)
0.01
0.01
1E-3
100
300
200
100
Buoyant mass (pg)
200
300
Buoyant mass (pg)
Fig. A-26. More "bumpy" peaks are observed with Lmna +/+ than Lmna -/- MEFs after LatB treatment
15
Cri
LatB
Noc
-
Lmna +/+
Ctri
LatB
Noc
Lmna -/-
-
*Driven by I outlier
0 04V 0.08V
L1210
Fig. A-27. Heterogeneity in passage times
The coefficient of variation (CV) in the passage times of MEFs is not affected by lamin A/C deficiency
or drug treatments. The CV of L121 Os (or FL5s), however, is significantly lower than that of MEFs.
Analysis was performed by Nathan Cermak
1
* Ctr
Ctr
Noc
LatB.
Noc+LatB
-
-
Noc
LatB
Noc+LatB
-
E
0.11
-
Ctrl
-
Noc+LatB
a)
CO)
-
LatB+Noc
Nocodazole
Latrunculin B
E
= 0.1
0)
..
.
.-
0.0
-f.
10
100
Buoyant mass (pg)
10
-
0.017
-
0)01
* .
C-
100
Buoyant mass (pg)
10
100
Buoyant mass (pg)
Fig. A-28. Treating FL5.12 cells with LatB induces a decrease in passage time and heterogeneity
LatB treatment of FL5.12 cells causes a decrease in both passage time and the heterogeneity of passage
times; whereas Noc treatment affects neither. Changes in passage time induced by LatB and Noc
together are similar to those by LatB alone.
116
Nucleus
Morphology
1.5-
1.
E
05
Cell
Morphology
*Bar graphs represent
population averages
normalized to control set
15
Ctr
LatB
Noc
LcN
1
0.5
EN
Ctr
LatB
Noc
L+N
Ctr
LatB
Noc
L+N
15
1
Ctr
LatB
Noc
L+N
1.5
Actin
0.5=0.5
E
U
0.1
0.111
1.5
Microtubule
Ctr
LatB
Noc
0-N
Ctr
LatB
Noc
L+N
15-
W
:3
>0
t4
0
C5
Ctr
5
N0+
LatB
Noc
IoN
Ctr
LatB
Noc
L-N
Fig. A-29. High throughput cellular phenotyping (HTPC) on FL5.12 cells
HTPC shows that heterogeneity in microtubules is decreased by all three treatments: LatB alone, Noc
alone, and LatB and Noc together. However, heterogeneity in actin filaments is decreased by LatB and
Noc separately, but not by LatB and Noc together, suggesting interplay between actin and microtubules
(Jorge Marchand, JHU).
117
A.8 Characterizing the effect of Mena and Mena" on the stiffness
of cancer cells*
All measurements were performed by Dr. Sangwon Byun of the Manalis Lab, and all cell lines
(MDA-MB-23 1) were provided by Dr. M. J. Oudin of the Gertler group. The applied pressure for
all measurements was 0.9 psi.
Ctri
Mena
Mena-INV
.-
a)*
E
120
60
Ctrl: 1260
Mena: 1053
Mena-INV: 1024
180
Buoyant mass (pg)
Fig. A-30. Passage time vs. buoyant mass of control, Mena, and MenaNV cells
the
For large cells, Mena" cells had the longest passage times. However, more large cells from
average
on
were
cells
MenaINV
since
cells,
control
or
Mena' cell line were counted than for Mena
due to
larger in volume. It is undetermined whether the longer passage times for Mena"N cells are
size.
similar
volume alone, or whether they are indeed stiffer than control cells of
300 --
-
Ctrl
Mena
Mena-INV
2502001500
100500~
2000
4000
6000
Vol (fL)
Fig. A-31. Volumes of control, Mena, and Mena' cells measured by a Coulter counter
with the
* This study was carried out entirely by Dr. Sangwon Byun from the Manalis Laboratory in collaboration
included
and
Byun,
Dr.
by
written
and
prepared
were
results
The
Laboratory.
Gertler
Dr. Madeleine Oudin from the
Lab.
Manalis
the
for
record
the
of
completeness
the
here for
118
100
-
011- .---
...
-
10
100-
Mena-INV..
.-
Z-
001
100
200
300
100
Buoyant mass (pg)
200
300
Buoyant mass (pg)
1
-
CtrI
Mena
Mena-INV
- --
'
.
Fig. A-32. Entry and transit velocities of control, Mena, and MenaINV cells
Entry and transit velocity trends showed similar results as the passage time.
a)
E
0.1
0C
2000
3000
4000
5000
Vol (fL)
Fig. A-33. Passage time vs. volume instead of buoyant mass.
The buoyant mass of each cell was converted to the volume using the density estimated from the
population average of buoyant mass (SMR) and the volume (Coulter counter) of each cell line (Control
= 1.0521 g/ml, Mena = 1.0547 g/ml, Mena"v = 1.0535 g/ml). Since densities were similar to each
other, conversion didn't cause a significant shift in passage time. However, with the volume, MenaINV
and control cells became slightly more separated.
119
- - --
1.
--
-
CtrI
Mena
Ctr
Mena
1)
Mena-INV
- -
Mena-INV
Mena-INV
1)
E
0)
I
.E o.j.
0.1.
0
0.01-
0.01,
0.
Standard deviation (lognormal)
2000
3000
4000
Standard error (lognormal)
5000
2000
Vol (fL)
3000
4000
5000
Vol (fL)
- 9 - CtrI
- -Mena
- e - Mena-INV
1-
Counts
E
0.1.
-
0)
0)
0.1
Standard deviation (lognormal)
100
150
Buoyant mass (pg)
200
Mena
Bin center (fL)
CtrI
1400
39
1800
245
222
2200
2600
3000
3400
327
229
241
166
104
57
185
116
60
250
'ILULL
Mena-INV
27
112
172
202
192
142
80
16
45
Fig. A-34. Passage time plotted vs. binned volumes and buoyant masses
120
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