Next-generation, quantitative measurements to validate a model for
nitrogen catabolite repression in Saccharomyces cerevisiae
By
Chris C. Stowers
Erik M. Boczko
Technical Report
February 2007
Nashville, TN
1
TABLE OF CONTENTS
1. Specific Aims……………………………………………………………………4
2. Introduction and Motivation……………………………………………………..6
a. Cell Cycle and Synchronization…………………………………………10
b. Cell Disruption- RNA Extraction………………………………………..11
c. Cell Volume- Detection and Modeling………………………………….12
d. Expression Level Measurements………………………………………...12
3. Biological Background of NCR Related Physiology……………………………14
4. About the NCR Model…………………………………………………………..16
5. Cell Disruption…………………………………………………………………..18
6. Cell Volume Determination……………………………………………………..26
7. Leslie Model- Cell Synchronization Techniques………………………………..31
8. Reverse Transcription and Polymerase Chain Reaction (PCR)…………………35
9. Microphysiometer- Single Cell Measurements………………………………….41
10. Conclusions………………………………………………………………………45
11. References………………………………………………………………………..47
12. Appendix…………………………………………………………………………53
2
LIST OF FIGURES
Figure 1. DAL80/GLN3 RiboProtection Assay time course gel……………………......7
Figure 2. Nitrogen Catabolite Repression in yeast circuitry……….…………………...14
Figure 3. Central dogma of biology related to NCR model…………………………….17
Figure 4. Yeast cell disruption data for isolating mRNA……………………………….20
Figure 5. Yeast cell disruption scoring algorithm………………………………………21
Figure 6. Cell disruption variance as a function of disruption time…………………….22
Figure 7. Cell disruption focal plane analysis…………………………………………..23
Figure 8. Cell disruption as a function of cell cycle…………………………………....25
Figure 9. Cell cycle description……………………………………….………………..26
Figure 10. Transient yeast growth of mothers and daughters…………………………..27
Figure 11. Coulter counter data for a population of daughter yeast……………………28
Figure 12. Separation of yeast via micro-fluidics………………………………………29
Figure 13. Continuous vs. batch synchronous culture data……….……………………31
Figure 14. Leslie Model Predictions……………………………………...…………….33
Figure 15. PCR process description…………………………………….........................37
Figure 16. PCR variance in data………………………………………………………..39
Figure 17. TAQ Polymerase efficiency as a function of cycle number………………...40
Figure 18. Drawing of the improved microphysiometer…………………………….….41
Figure 19. Improved microphysiometer photomicrograph………………………….….42
Figure 20. Original microphysiometer photomicrograph………………........................44
3
Specific Aims
This thesis work is motivated by the desire to quantitatively measure biological
dynamical systems. Our agenda is to describe and understand emergent behavior and to
explain the observed super robustness of biological dynamics. The specific system that
provides the focus for our work is an ostensibly simple stress response circuit in baker’s
yeast, Saccharomyces cerevisiae, that regulates the organisms' genetic response to
nitrogen limitation called nitrogen catabolite repression (NCR). The circuitry of the
network has been well studied for the last 40 years and comparatively much is known
about its function, however, little is known about its dynamics. In order to study the
dynamics at the same level of sophistication at which we formulate and reason with
mathematical models, we require quantitative biophysical and biochemical techniques
that are accurate at molecular dimensions on physiological timescales. Such techniques
are currently in their infancy. The overall goal of this thesis is to further develop the
tools and techniques to measure the quantitative biological behavior of the NCR circuit
well enough to refine a current NCR model and understand how to apply the model to
other regulatory networks leading to advances in biology, control theory and beyond [11,
45]. Towards these goals I intend to:
Aim 1: Quantitatively measure the expression of GLN3 in synchronous culture. GLN3
expression is known to be very low and the current model predicts periodic oscillations.
These two factors pose a very demanding challenge that requires the development of
novel techniques.
Aim 2: Develop rigorously normalized, direct and accurate assay techniques utilizing
engineered micro and nano-devices to perform quantitative biophysical measurements on
synchronous and segregated populations of yeast.
Aim 3: Quantitatively measure the expression levels of the NCR-circuit genes under
various nitrogen conditions including high frequency periodic oscillations. These
experiments are targeted to elucidate the limits of adaptive design.
4
The broader impact of this effort reaches far beyond understanding the molecular
physiology of a simple fungal stress response towards a deeper understanding of the
underpinnings of why some circuits persist while others do not.
5
2. Introduction and Motivation
Through the next several pages we wish to describe the two or three intertwined threads
of thought that illustrate our perception and define our intent. First is the simple
observation that technological progress has always precipitated basic scientific progress.
The second is that biological organisms are at their essence dynamical systems. Third is
that many if not most traditional biophysical techniques were not developed with
dynamics in mind and do not have the power to effectively interrogate the dynamics of
control systems. Finally, we observe that while our interdisciplinary emphasis may
appear to be saying lets utilize every discipline possible to solve a problem of interest to a
biologist, we are increasingly aware that biological adaptation points the way toward
future engineering breakthroughs. For instance, it has been observed that the biological
process of embryo genesis is a very advanced form of nanofabrication and self-assembly
(32). Even a rudimentary understanding of the dynamics of the genetic regulatory
circuitry that patterns developing embryos will allow engineers to abstract and implement
key design rules to self assemble arbitrary nanodevices. Also, software and hardware
engineers who are interested in designing fault tolerant and fault mitigating computer
clusters have begun to understand that the stress response circuitry of biological
organisms like yeast can provide important inspiration. These examples help to cement
the notion that all our scientific fates are intertwined, providing even further need for
interdisciplinary research to solve global problems.
In the early 1900’s, little beyond the simple taxonomy of biological diversity was known.
Eventually this focus deepened as experiments began to uncover the myriad molecules
and metabolic pathways that comprise self replicating persistent systems subject to
natural selection. These early experiments, like those of Wallace Coulter who developed
volumetric impedance theory required considerable technical sophistication. In the
1930’s Arnold Beckman began developing spectrohotometric methods which lead to high
precision chemical analysis. Without these types of technological advances countless
biological discoveries would have been impossible. The crowning achievement of the
1950's was the discovery of the structure of DNA that required the chemistry of
crystallography, the physics of diffraction and the mathematics of group theory to
accomplish. Over the next decade the tenets of the central dogma, see Figure 2, were
6
established and gave biology its first real logical molecular underpinnings. Interestingly,
up until the mid1970's sophisticated studies of yeast physiology and biochemistry were
routine [8,4,21]. However, the focus on the gene as the fundamental unit of biological
explanation led to a roughly 30 year change of focus toward static gene discovery and a
near abandonment of individual and population physiology. Paradoxically perhaps, the
ascendancy of genomics has now reintroduced the desire to understand individual and
population physiology at the molecular level. Armed with molecular information and
powerful computers there has been a steady increase in literature involving the modeling
of pathways, circuits, organelles and even whole cells [24]. In fact it is THE
reductionists dream to build a computational model of a cell that would respond to
stimuli as real cells do. Systems biologists from all disciplines have fully embraced this
goal. This endeavor has exposed several obstructions. We and many others have been
forced to focus on the problem of measurement. Consider for example the basic biology
lab staple of Southern Blotting. This technique was developed in the 1980’s and has been
used countless times to provide evidence for the existence of a particular gene in a cell
line. However, the technique lacks the sensitivity to detect subtle changes in gene
expression. Figure 1, shown below, depicts a RPA (Ribo Protection Assay) which results
in a stained acrylamide gel which can be used to quantify gene expression using photointensity measurements.
Figure 1
7
Figure 1: The results of a DAL80/ GLN3 RPA analysis run over a 2-hour time-course.
The yellow arrow points to the results corresponding to GLN3 and the black arrow
corresponds to the results of DAL80. The progression of time is represented by moving
through the columns from left to right. The top row of data represents actin, an internal
control gene. The problem with this type of assay is clearly seen in the GLN3 data.
Expression level is so low that it is difficult to detect level changes with greater than 20%
accuracy.
Currently there does not exist a sensitive and quantitative technique to measure changes
in gene expression in an individual cell with temporal resolution of a hz and error bounds
less than 5%.
A common misperception is that biological organisms are static. It has been well
established that many biological processes are not even under thermodynamic control and
that the processes depend on the microscopic details of the molecular dynamics [37].
The dynamical character of genetic regulation is increasingly studied due in part to the
representational similarities drawn between gene networks and electrical circuits. The
existences of regulatory loops, time delays and complex transport phenomena have
brought mathematical modeling and analysis to the forefront of systems biology. To date
many signal transduction motifs have been identified in well studied regulatory networks,
and also by large scale screens [44]. Carefully considered and important results about
model identification for motifs and general reaction terms have appeared [15, 27, 42].
General results for pattern forming circuits have appeared [6]. Numerical screen have
been performed that suggest that biologically adapted circuits poses exceptional
robustness [13]. Numerical screens and rigorous analyses have been performed to
understand the mathematical origins of robustness [42]. Most all of these results come
from systems that are relatively data poor. Whether or not these models and the
information contained within them survive the test of time depends crucially on our
ability to make careful measurements at the molecular level and on physiologic
timescales.
8
The measurements required to complete the aims of this study require the development of
several new quantitative tools. Since we hope to detect subtle changes in patterns of gene
expression such as periodic oscillations in budding yeast, we need to develop tools and
techniques to deal with such fundamental processes as the cell cycle. The cell cycle
poses a significant problem for the following reason. As yeast age their volume grows.
There is a volume checkpoint required of newly budded daughter cells to enter the cell
cycle that immediately and completely dephases mothers and daughters. The quantitative
phase shift depends on the specific growth conditions but in rich media is approximately
11 minutes, 1/12th of the total cell cycle time. This small difference will within three cell
cycles drive the entire population to stationary asynchrony that will obscure any periodic
signal. In addition since all age classes are mixed in a stationary culture one cannot
discern age differences in gene expression – an entirely open and fascinating subject. We
have conceived of two strategies to combat the problems introduced by the cell cycle:
Synchronous segregation and asynchronous isolation. Technological advances in the
design and fabrication of micro and nano fluidic devices will allow us to successfully
implement these ideas, as we will describe in detail throughout this manuscript.
We have completed several cell cycle dependent experiments in the controlled
environment of a 3L bioreactor. We have discovered that this scale has several
unfortunate and inherent limitations that prevent us from utilizing this technology to
study important physiological processes. For instance it is of great interest to us to
observe the timescales over which the yeast nitrogen circuit is responsive to periodic
oscillations in nitrogen source. How the NCR-circuit responds is completely unstudied
and holds deep clues for the forces that shape adaptive design. Using a traditional
bioreactor we have found that these measurements are impossible. Despite attempts at
designing spin filters and flocculation techniques, a complete media change in a 3L
bioreactor takes several minutes. On the other hand, a microphysiometer contains only
picoliters of volume that can be flushed and completely replaced in only a few seconds.
With a microphysiometer we also have the capability of following the dynamics of a
large array of isolated yeast cells simultaneously. We have designed and fabricated a
custom microphysiometer that can house on the order of a hundred to a thousand cells.
This is just one illustration of how we are utilizing and designing cutting edge
9
engineering devices to measure biological dynamics that have to date remained
unexplored and largely inaccessible.
In addition to the hurdles described above, the studies proposed in the Aims above will
require the measurement of gene expression, cell lysis and RNA extraction, reverse
transcription, and cDNA amplification. Each one of these processes must be
quantitatively controlled so that the final measurement is quantitatively robust and
reliable. Each of these steps contains specific problems which will be outlined in the
subsequent three sections.
Cell Cycle and Synchronization
Several methods are available for synchronizing yeast cultures including metabolic
starvation, temperature shocking, elutriation, and the use of mating factors [36]. The
majority of these methods induce genetic stress responses that could modify the dynamics
of the NCR circuit. However, the use of a mating pheromone, alpha factor, does not
induce a known genetic stress response and has no known direct impact on NCR. Alpha
factor arrests mat a yeast cells in the G1 (growth phase) portion of the cell cycle. Once
all the cells are arrested within G1, protease E can be use to consume the alpha factor,
releasing the cells from arrest. Unfortunately, it is fundamentally impossible to maintain
synchronous culture indefinitely. A mixed culture of yeast cells contains a distribution of
cells of different age, size, and cell cycle location. For this reason, cells within a
population inherently precede through the cell cycle at different rates. As the cells age,
they progress through the cycle slower. Additionally, yeast cells that are born smaller
take longer to complete their first cell cycle than larger born yeast [8]. We have proven
that cell synchrony is impossible to achieve for more than three cell cycles by the
development of a Leslie Matrix model, which is further described later in this document.
We have also produced experimental data supportive of the model findings. The model
has also been used to validate several hypotheses that the filtration of cells based on
volume and/or age could produce synchronous cultures for longer times.
10
Since the Leslie Model predicts that synchronous culture could be extended by
segregating a population based on age or volume, several devices have been investigated.
The first method, developed by R. Davis (1990), is the inclined cell settler [5, 11]. This
device was built and tested and proven to be troublesome [11]. Davis experimentally
validated the proficiency of the inclined settler, but never developed any useful design
equations [5, 11]. Others have shown that yeast will settle at different rates based on
their volume, but no one has built a device capable of continuously segregating cells
based on volume [35,47]. Thus, it is intrinsically difficult to build a settler with a specific
purpose without timely and costly experimentation. Another idea was to employ the
“baby machine” technology [7]. This device immobilized yeast cells on the bottom of a
cellulose membrane [7, 23, 40]. Media is passed through the membrane so that when
babies are produced, they fall into the effluent. This device was also fabricated, but
proven to be too inefficient. To produce enough babies for our experiments, a baby
machine several feet in diameter would need to be fabricated. Lastly, and idea originated
from previous work of Happel and Brenner [17, 18]. Happel and Brenner developed
analytical results for the trajectories of spherical particles in laminar pipe flow. Their
work was used to generate higher order approximations for yeast cells in laminar pipe
flow. By controlling the flow field, I showed that in theory yeast cells could be
segregated by volume. However, the construction of a device that could detect single cell
trajectories in pipe flow has proven to be difficult. Currently, we have had very little
success in obtaining a synchronous culture beyond three cell cycles. Several theoretical
ideas have been investigated and shown it to be fundamentally possible to obtain a
synchronous culture for four to five cell cycles. However, we have been unable to
produce experimental results to support the theory.
Cell Disruption- RNA Extraction
Perhaps the most overlooked area in quantitative genomics is cell disruption. The vast
majority of biological laboratories entirely ignore incomplete cell breakage when
harvesting genomic material from cell culture. We have developed a novel technique for
assaying cell disruption via the use of a stain called Analine Blue. Analine Blue stains
the cell walls of yeast and allows for a quick and easy analysis of cell lysis via
11
microscopy. The data we have collected from a scaled down Ambion RiboPure kit
protocol has been fit to a time to failure mathematical model. We have also shown that
typical yeast lysis protocols only generate 50-60% cell breakage. The data from the
disruption analysis is also supported from Bioanalysis and Nanodrop data. This data has
also shown that yeast cells are more prone to lysis in certain portions of the cell cycle.
This data has been used to develop more quantitative assays for gene expression analysis.
Cell Volume- Detection and Modeling
Since gene expression levels are measured based on concentration, cell volume is also of
utmost importance. In the past, scientists have used simple exponential growth models to
“control” for volume growth of yeast. Some experimental work has been performed
using microscopy that explicitly shows difference in growth volume for mother and
daughter yeast cells [8, 10, 35]. I have also performed microscopy studies that show
yeast cells do not grow exponentially, but rather in near step functions that correlate with
the cell cycle. However, over long times and course data intervals, these step functions
appear to be weakly exponential.
Expression Level Measurements
The last step of the measurement process is to carry out the method of choice for gene
expression level detection. Prior work has shown that RPA (Riboprotection Assay)
provides inadequate sensitivity for the detection level needed for some of the weakly
expressed NCR genes (DAL80, URE2). Thus, we have begun to develop protocols for
the use of RT-PCR (Real Time Polymerase Chain Reaction). This detection scheme has
the advantage of amplifying the expression level signal to detectable ranges. However,
the most troublesome problem with RT-PCR involves the preparation of the mRNA
(genetic expression level) sample to be utilized by PCR. Since it is impossible to amplify
mRNA, the mRNA must first be reverse transcribed into cDNA (single stranded DNA).
Reverse transcriptase has several traits that could become a quantitative nightmare for our
study. First, the enzyme proceeds along the mRNA strand at a constant rate like RNA
polymerase. Thus, shorter RNA strands will be amplified more in the reverse
12
transcription process. The enzyme also could be slowed down by the secondary structure
of an mRNA molecule. So transcripts with secondary structure would be amplified less.
Once reverse transcription is complete, the PCR can be performed. We are currently
using an intercalating dye called Sybr Green, but eventually plan to use Taqman Probes.
Sybr Green is nonspecific and will therefore not permit multiplexing reactions in a single
tube. Taqman probes are specific DNA sequence that binds to a single target. The probe
has a flourophore on one end and a quencher on the other. During the extension portion
of the PCR reaction, the quencher on DNA bound probe is released and the flourophore
fluoresces. Data is collected in real-time and later analyzed with the attempt to back
calculate the initial cDNA concentration. Several models are available for the analysis of
RT-PCR data. The most popular of these methods are the Comparative Method and the
Standard Curve Method. Both methods are quantitatively troublesome. The Standard
Curve Method only allows for the detection of relative changes in mRNA expression and
does not control for variations of efficiency in reverse transcription. The Comparative
Method assumes that the efficiency for replication of a house-keeping gene and the target
gene are equal. Since both methods have their flaws, we hope to develop a model of our
own for data analysis. This model will utilize a control DNA, which will be spiked into
the process before reverse transcription. This will allow for much more robust
quantitation compared to the traditional method of using a house-keeping gene such as
actin.
13
3. Biological Background of NCR Related Physiology
The regulation of nitrogen is one of the most ancient biochemical pathways. NCR is the
metabolic circuit which Saccharomyces cerevisiae uses good nitrogen sources in
preference to poor ones. NCR sensitive gene expression is mediated by the circuit shown
in Figure 2. In the presence of an abundant good nitrogen source, transcription of the
genes encoding for proteins needed to transport and utilize poor nitrogen sources occurs
at very low levels. On the other hand, when only poor nitrogen sources are available, the
genes encoding for proteins associated with poor nitrogen source transport and
catabolism are transcribed at high levels. Therefore, the NCR circuit is most active in
poor nitrogen source. As you can see from Figure 1, the NCR circuit is a very
complicated switch whose dynamics, sensitivity, and range remain unexplored. Under
good nitrogen source, URE2 acts as a gating mechanism to turn off the circuit. These
interactions are represented as blunted blue arrows labeled 1 and 2 in Figure 2. It is also
hypothesized that GLN3 and GAT1 are sequestered in the cytoplasm with URE2 under
excess nitrogen [2, 3, 6, 16, 19, 22, 28, 31, 42, 43].
Figure 2
Figure 2: The NCR-circuit. Green arrows indicate up regulation, blunted red arrows
represent down regulation at the level of transcription. Dashed lines represent a weaker
response. Blue lines represent regulation that does not occur at the transcription level.
The NCR circuit was chosen as the model system for several reasons. The NCR system
is complex, yet tractable. It incorporates both signal transduction and gene regulation,
14
both transcriptional regulatory interactions and protein-protein regulatory interactions.
The system is complex enough to contain five of the six motifs identified by Lee et. al.
[46]. One of these six motifs is described as a single input motif where a single regulator
binds a set of genes under specific conditions. This behavior can be easily seen by GLN3
in Figure 2. The outside loop of the NCR circuit can also be thought of as a regulator
chain motif, where three or more regulators in which one regulator binds to the promoter
of a second regulator, the second binds to the promoter for a third regulator and so forth.
Results concerning the dynamics of these motifs will likely be applicable to other
regulatory networks in other organisms. There are also several biological questions that
could be answered with the production of an accurate structure theorem for NCR.
15
4. About the NCR Model
The NCR model has two compartments, each with three components. The two
compartments within the model represent the cytoplasm and the nucleus of the cell. The
three components within the model are mRNA, nuclear protein, and cytoplasmic protein.
Because URE2 is not a transcription factor, it is only represented by two variables (U, μ)
cytoplasmic mRNA and protein. GLN3, which is gated by URE2 in the cytoplasm, is
represented by variables (X,x, ξ). GLN3 is believed to be constitutively expressed.
Therefore, there exist to transcription function, H, is within the equations GLN3 protein
and mRNA. The other NCR genes are represented by the following variables listed in the
standard order of mRNA (capitol letters), nuclear protein (lower-case letters), and then
cytoplasmic protein (greek letters): DAL80 (Y,y,ψ), DEH1 (W,w,ω), GAT1 (Z,z,ζ). The
model presented below is a minimal model, but every known critical interaction has been
mathematically represented. For simplicity, transport to and from the nucleus is modeled
with time delays. The function C, represents the bimolecular complex formed by the
protein-protein interaction between URE2 and GLN3 with rate constants kf(N) and kr(N).
There are two removal mechanisms operative on both mRNA and protein, dilution due to
growth and degradation. Originally, volume growth of yeast cells was believed to be
exponential [4, 8, 38]. For this reason we chose to use a cell cycle dependent function,
S(t,t0) proposed by Mahaffy [20] which takes into account volume growth, the cell cycle,
and discrete increases in genes due to the replication of DNA at t0. In the
model,α,β,γ,θ,κ, etc. , represent nitrogen dependent rate constants for degradation. The
functions within the model are named such that H represents transcription functions, T
represents translation functions (translation of mRNA into protein), and K represents
functions that govern transport of protein in and out of the nucleus. These functions are
easily explained in Figure 3.
16
Figure 3
Figure 3: The central dogma of biology related to the transport functions described above
in the NCR model.
T(·) is a concentration dependent translation initiation rate function and δ is a delay that
takes into account the elongation and folding of the nascent protein. Time delays are also
incorporated into the nuclear protein variables to account for mRNA elongation and
transport. The mathematical model is listed below and segregated into the GLN3-URE2
subcircuit (A) which drives the remainder of the NCR circuit (B).
17
5. Cell Disruption
The first step in analyzing any biomolecule is to extract the molecule from a living cell
and then purify the molecule of interest from cellular debris. Cell disruption to harvest
genetic material such as mRNA has been performed for many years by various assays
including hot phenol extraction, bead bashing, and temperature shocking. We have
chosen to use the bead bashing method because it does not appear to affect the NCR
circuit. Temperature shocking induces stress responses that could impact NCR, while the
mechanism of the hot phenol extraction appears to still be unclear to researchers. Our
measurements rely on the ability to harvest high quality mRNA representative of an
entire yeast population. mRNA from subpopulations could lead to conclusions not valid
to the global population since many genes are age and cell cycle dependent. Preliminary
data suggests that yeast at different portions of the cell cycle disrupt at different rates.
Thus, cell cycle dependent genes could be inaccurately represented if the population is
not fully disrupted.
There has been a long standing tradition in biochemistry and molecular biology to
normalize measured quantities, such as mRNA, with constitutive internal controls. Actin,
for instance is thought to be a housekeeping gene and is often used to normalize the
loading and comparison of samples. A problem with this practice is that without a known
gold standard this leads to circular reasoning. For instance, if Actin levels are actually
oscillating then anything that appears as constant compared to it, is not actually constant,
but rather oscillating. This example is not fortuitous, rather it is our goal to resolve a
conjecture that certain yeast mRNA's are oscillating periodically with the cell cycle as
predicted by the NCR mathematical model. In order to convincingly demonstrate the
nature of the mRNA expression we believe it is important to demonstrate a logically
sound method to control for between sample variation. Be that as it may, we and other
laboratories before us, have recently undertaken to introduce external controls. In this
practice a known quantity of RNA is introduced into the sample during extraction. The
single assumption of this practice is that the introduced species is indistinguishable from
the other RNA from the point of view of the remainder of the extraction process. The
practice depends on a single parameter--how much external control does one add to each
sample.
18
Typically, in a time course, the samples will contain different numbers of cells. With the
use of a coulter counter it is possible to directly measure the total volume of cells in a
sample. It seems natural and reasonable that this quantity be used to normalize mRNA
expression. It has thus been our goal to spike our yeast samples with a quantity of
external RNA control that is proportional to total sample volume. If, however the volume
of cells disrupted by a method for RNA extraction is not a constant and reproducible
fraction of the total volume then this practice cannot work. Thus, the need for internal
controls poses a second need for quantitative cell disruption.
It is important to observe that disruption for the purpose of biochemical isolation is
related to cell wall integrity and not cell vitality. We show that the dye Aniline Blue,
which specifically binds β-1,3-Glucan,clearly distinguishes those cells with disrupted
membranes [12]. Cells can then be analyzed with microscopy to produce images as
shown in Figure 4, 5, and 6. In our protocol, we disrupt cells suspended in phenol
chloroform by beating them against glass micro-beads on a vortex mixer. The protocol
suggests beating for 10 minutes. Our data demonstrate that 10 minutes is sufficient to
disrupt roughly 60%, while greater than 90% requires 30 minutes (see Figure 4 and
Figure 6). Because the percentage of cells disrupted at time t is a monotonically
increasing function, and the damage sustained by a cell over time is cumulative, it is
reasonable to suspect that this process is modeled by a cumulative distribution function.
We show that the percentage of cells disrupted by the protocol is well modeled by the
Wiebull distribution, that is
 
D( t)
1e
t

a
b
With a=8.44 min-1 and b=.725 and t in minutes.
19
Figure 4
Yeast Cell Disruption
1
0.9
0.8
Percent Disrupted
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
5
10
15
20
25
30
35
Time (minutes)
Figure 4: Cell disruption data compiled from averaging 5 independent sets of disruption
data from LHY3865 at OD (Optical density and at a wavelength of 600 nm) 0.6. All
experiments were performed under identical conditions and analyzed independently.
Error bars are shown as +/- 1 standard deviation. The Weibull distribution, fitted with the
parameters listed above, used to model the cell breakage is shown in black
20
Figure 5
A
B
C
D
E
F
Figure 5: Saccharomyces cerevisiae, strain LHY3865, stained with 1 mg/mL of aniline
blue after Ambion RiboPure RNA extraction treatment. Image A represents an intact cell
with no apparent damage. Image B represents two intact cells. The budded cell is only
be counted as one cell in this case because the septum has yet to form within the bud.
The septum will appear as a membrane which segregates the two cells. Image C shows a
damaged yeast cell that would be scored as intact. This cell, although damaged, has still
maintained its’ intracellular contents. Image D shows a budded cell that would be
counted as two cells. In this case the bud would be scored as intact (despite the small
knick near the bud) and the mother cell would be scored as disrupted since a large
segment of its’ membrane is clearly missing. Image E shows two cells which are clearly
21
disrupted. The cell on the right has been completely ripped open and obviously spilled
its’ intracellular contents. Image F shows another example of a disrupted and an intact
cell.
Figure 6
A
B
C
D
Figure 6: Image A represents cells beaten for 2 minutes on glass beads. Notice only one
of the cells appears to be minimally damaged and is scored intact. Image B represents
cells beaten for two minutes. This image contains two cells out of the five that are scored
as disrupted. Image C represents cells beaten for 20 minutes. The majority of these cells
are scored as disrupted, but there are at least 2 cells within the image that appear intact.
Image D contains cells beaten for 30 minutes. All of the cells within this image are
disrupted.
22
Figure 7
A
B
C
D
E
F
Figure 7: The above figure contains two separate yeast cells viewed at different focal
planes. The first cell corresponds to Images A-D and the second corresponds to Images
E-F. Both cells initially appear intact (Image A,E). However, both cells appear to have
abnormal structure. Thus, both cells were further investigated by imaging at several
different focal planes. For these cells, as the focal plane was changed, the cells were
determined to be disrupted. For this reason, all cells should be analyzed from multiple
focal planes. Cells that appear to have abnormal structure and still appear intact should
be very closely analyzed from every focal plane available.
23
Table 1
Extraction Time
RNA Extracted
260/280
260/230
10 min
10 min
30 min
30 min
310.31 ng/uL
309.51 ng/uL
571.96 ng/uL
570.54 ng/uL
2.18
2.19
2.18
2.17
2.34
2.34
2.3
2.34
Table 1: Two sets of samples prepared by 10 minute and 30 minute extraction times
The 30 minute samples contained nearly twice the amount of RNA as the 10 minute
sample, validating our Analine Blue algorithm for quantifying RNA extraction. The
260/280 and 260/230 numbers show that the RNA from both treatments are of
comparable quality. A wavelength of 260 nm is the optimal absorbance wavelength for
RNA, 280 nm is the optimal absorbance wavelength for protein, and 230 nm is often the
absorbance of other contaminants such as carbohydrates and organic solvents.
We have also collected data to show that yeast cells (LHY3865) disruption correlates
with the cell cycle (see Figure 8). Cells were arrested during the G1 portion of the cell
cycle using alpha mating factor and then released using Pronase E and resuspended in
preconditioned media. Samples were then taken at times after release corresponding to
critical locations within the cell cycle. The data shown in Figure 8, clearly shows that
cells are most prone to disruption during the mitosis portion of the cell cycle. One would
expect these results since the cell wall is weakened and the structural integrity of the cell
is compromised by the rearrangement of the actin filaments during the division portion of
the cell cycle. The data also shows that a 30 minute disruption time creates statistically
uniform cell disruption independent of cell cycle position. This behavior is most likely
due to the fact that nearly all the cells are disrupted. The direct relevance of this data to
our study is the acknowledgement that non-uniform disruption across the cell cycle
would produce a population of disrupted cells not proportional to the original culture
population. Since thousands of genes are known to be cell cycle regulated, genetic
analysis could easily be skewed by sampling a non-representative population.
24
Figure 8
Cell Cycle Disruption
1
0.9
0.8
2 min extraction
10 minute extraction
20 minute extraction
Fraction Disrupted
0.7
30 minute extraction
0.6
0.5
0.4
0.3
0.2
0.1
0
G1 Release
G1
G1/S trans
S
S/G2 trans
M
M/G1 trans
Cell Cycle Location
Figure 8: Previous experimentation was performed to determine the time at which the
LHY3865 (URA, LEU, bar1Δ) strain transitioned between the different phases of the cell
cycle. These times were used to harvest synchronized cells grown in a New Brunswick
3L bioreactor during each phase of the cell cycle. These cells were then subjected to the
optical assay for cell disruption describes above.
25
6. Volume Determination
As previously mentioned, gene expression levels can be decreased at the cellular level via
two methods: degradation and dilution due to volume growth. We have speculated that
the NCR dynamics are driven by the cell cycle, implying that GLN3 expression must be
directly linked to cell cycle dynamics. To isolate the relationship between GLN3
expression and the cell cycle, volume growth must be quantitatively measured. In the
past, biologists have modeled cell volume growth as a purely exponential process [8, 34,
38]. However, I have taken preliminary microscopy data to show that this is not true for
yeast growth. As mentioned by Wooldringh, yeast growth does appear to be a strong
function of age, which causes older cells to exhibit a decreased growth rate leading to the
belief that there is a maximal volume the cell is programmed to reach [8, 34]. Once this
volume is achieved, volume growth occurs only to replenish volume that is lost due to
cell division. This growth appears to occur in nearly step function spurts during the G1
and G2 portions of the cell cycle. Minimal growth occurs during the S and M portions of
the cell cycle due possibly to the fact that the cell is investing resources in DNA
replication and cell division machinery. A diagram (Figure 9) is provided below to help
elucidate the phases of the cell cycle and the physiological traits of the cell at each step.
Figure 9
The measurements used to collect data for Figure 10 were determined from individual
cells with a conventional microscope. Unfortunately, microscopy techniques only permit
a 2-D analysis of cell volume. The data shown here (Figure 10) are technically area
measurements, which have been assumed to be a proxy for volume. However, since
yeast cells are approximated as spheres, the cell area computed is highly dependent on
the cross-section analyzed by the experimenter. Nevertheless, the data in Figure 10
26
clearly show the cell cycle dependent step-function like growth observed in S. cerevisiae.
The data also show that an optimal volume exists for mature cells, leading to growth only
to replenish lost due to cell division. We also have the ability to measure bulk yeast
volume distributions by the use of a coulter counter. A coulter counter measures the
resistance imposed by an individual cell passing between an aperture while immersed in
an electrolytic solution. Since volume is directly proportional to the resistance imposed,
one can use the resistance data as a direct proxy for cell volume (see Figure 11). This
technique will be used to determine the amount of positive control RNA to be spiked into
the gene expression analysis protocol.
Figure 10
Transient Growth
1400
1200
Mother
1000
Daughter 1
Daughter 2
Daughter of Daughter 1
Area
800
600
400
200
0
0
50
100
150
200
250
300
350
400
Time
Figure 10: The volume growth (shown as cross-sectional areas) is shown for three
generations of cells including two sister cells. The data shows that over short times,
exponential volume growth is a poor approximation. The data also shows that mature
cells (the original mother) appears to reach an optimal volume and only grows to
replenish volume lost from cell division.
27
Figure 11
Yeast Daughter Volume Distribution
300
250
Number
200
150
100
50
0
0
50
100
150
200
250
300
Volume( um^3)
Figure 11: A coulter counter volume distribution of daughter yeast cells collected from a
Helmstetter “baby machine”. The coulter counter can be used to quickly determine cell
volume distributions of either mixed or segregated cultures.
Despite the ability of our current techniques, there still exists a need to determine yeast
volume growth more accurately on an invidual cell level and to develop techniques for
segregating mixed yeast populations based on volume. As previously mentioned,
separating yeast cultures based on volume can be used to extend the synchrony of yeast
cultures. Since our study requires the measurement of synchronous cultures over the
longest time-period possible, volume segregation of yeast populations is of utmost
importance.
Perhaps the most promising idea for cell volume measurement and fractionation is the
use to microfluidics. In the 1950’s, Happel and Brenner showed that the volume of
spheres with a known density could be determined analytically [17, 18]. I have used their
Method of Reflection solution along with higher order approximations to show that yeast
cell volume could theoretically be determined through the use of micro fluidics.
28
Coincidentally, dilute solutions of yeast could also be separated using the same technique
as shown in Figure 12.
Figure 12
Relative Local Cell Concentration
20
Relative Concentration
15
na ( r)
ma ( r)
da ( r)
10
ha ( r)
5
0
0.04
0.02
0
0.02
0.04
r
Radius (m)
Figure 12: Relative concentration of perfectly spherical yeast cells in vertical laminar
parabolic pipe flow. Budded yeast cells are assumed to be 8.5 um spheres, whereas
daughter yeast cells are assumed to be 2 um spheres. The pipe radius is 0.05m and the
maximal fluid velocity is 5.8x10-5 cm/s.
The method of Reflection Solution assumes that the yeast cells are perfect spheres and
cell to cell interaction is neglected. The solution is generated from the following system
of partial differential equations and boundary conditions.
d
 
1 d
 P
 dX
d
1d
vz 
r vr
r dr
dr
1 d
 P
 dr
2-D Continuity
0
1 d  d
2

v   r v
2 z r d r  d r z


dz
1 d  d  vr
vr    r  vr  
2
r d r  d r  r2
dz
z-Direction Navier Stokes
2
d
r-Direction Navier Stokes
Boundary Conditions:
@ r=a
vz
vr
0
@ r=R0
vr
0
vz
U
29
The overall second order solution for yeast cell velocity as a function of radial position
can be represented by the following equation:
Eq (6.1)

2
  s   f  R 2
 3 Uof R02  3 Uof r2  2 Uof a2  3  a2g
0
1 

9


u2 ( r)  
2
3
R0
In the above equation, Uof is the maximal velocity of the parabolic pipe flow, a is the
radius of the yeast cell, g is the acceleration of gravity, ρs and ρf are the densities of the
cell and fluid, μ is the fluid viscosity, and R0 is the pipe radius. Faxen’s theorem can also
be used to generate solutions for this system [9]. The equation can be algebraically
solved for the special case that the yeast cell velocity is zero, called stagnation. This
solution is shown below:
Eq (6.2)
SS ( A) 
1
3  Uof
2
2
2
2
2
2
 Uof  2 A gR0  s  2 A gR0  f  9  Uof R0  6  Uof A 
1
2
where A is now the cell radius and all other parameters are as described above. Equation
6.2 is further known as the characteristic radius equation, which can be used to track cell
volume or design experiments to segregate yeast cells based on volume.
Despite the theoretical work completed on utilizing microfluidics to detect cell volume,
experimentation has proven to be practically difficult. Others have produced theoretical
work for segregating cells using laminar flow fields, but no one has produced any
promising experimental results [25, 30]. For this reason, we hope to explore additional
methods for yeast cell volume measurement and volume segregation in the future.
30
7. Leslie Model- Cell Synchronization Techniques
Several months of experimentation with alpha factor synchronized yeast cells has proven
the continuous synchronous culture to be unobtainable. Others have noticed this to be an
impossible task as well, but the vast majority of yeast labs still claim to obtain
synchronous culture over much longer of a time period than possible [11, 39, 40]. The
problem lies within the definition of a synchronized culture. A synchronous culture is a
population of cells traveling through the cell cycle at the same rate and at any given time,
all the cells are in the same portion of the cell cycle [11]. Unfortunately, some people do
not understand that yeast cells traverse through the cell cycle at different rates due to
differences in age and volume [8]. I have taken several data sets from mixed cultures in
batch and in a bioreactor with optimal conditions (See Figure 13). The use of a pH,
dissolve oxygen controlled bioreactor significantly increased the lifetime of synchrony,
but still failed to produce good synchrony beyond three cell cycles.
Figure 13
Bioreactor vs. Batch
1
0.9
Bioreactor Culture
Batch Culture
0.8
0.7
Bud Index
0.6
0.5
0.4
0.3
0.2
0.1
0
0
100
200
300
400
500
600
Time (min)
Figure 13: This figure is a batch to continuous culture comparison. The batch data is
shown in green and the continuous culture data is shown in blue. Bud index is shown on
the y-axis as a proxy for cell synchrony. Since yeast cells are only budded during
31
approximately half of the cell cycle, step function data with a period the length of the cell
cycle (2.2 hours) would be represent a perfectly synchronized culture. The data show
that after 4 cycles, both conditions have nearly approached the bud index equilibrium of
63%. However, it is clear that the continuous culture conditions help maintain synchrony
within the first three cell cycles.
Since we believe that the conditions of the bioreactor are optimal for maintaining
synchrony, our only chance to extend the synchrony would be to initially or continually
segregate the culture based on volume or age. Not knowing the best approach to the
problem at hand, we set out to build a mathematical model based on cell cycle data
previously taken. The core of a model would be a Leslie matrix, whose numerical values
depend on the relationship of cell volume to cell division. In the yeast cell cycle, volume
and time can be treated as equivalent variables allowing us to construct a Leslie Matrix
based on the critical volumes at which a cell of a given age will divide.
Leslie models are generally of the following form.
Equation (7.1)
N(t)=A*N(t-1)
Where N(t) is the current distribution in a population and N(t-1) is the distribution at the
previous time step and A is the Leslie matrix. The Leslie matrix can be thought of as the
kernel of the model and controls how the population changes over time. The rows in the
matrix N(t) can represent age classes and in my model are represented by 10 minute
classes. Simulations were run for 500 minutes, the time to complete three full cell cycles
and diphase the culture.
The important characteristics to notice about the model are that all cells within a certain
age class within the population have the same survival rate (100%) and will produce the
same number of offspring. Thus, bulk population (averages) must be used within the
model. Mathematically, it is important to note that the initial growth of the population is
controlled by the initial age and volume structure of the population. The accuracy of the
32
model is dependent on the volume/time data inputted to the model to describe yeast
growth.
Leslie Models are typically used to model populations because the structure of the model
currently has survival and fecundity rates built into the matrix model for each generation.
The top row of the Leslie Matrix (the A matrix in Eq. 7.1) contains the fecundity rate of
each cell class. In our model these rates are expressed in terms of a division rate per
minimum volume. In general, this can be thought of as the average number of offspring
each cell class will produce during each time step. The values below the diagonal of the
matrix include the survival rate of the particular cell class. Considering that there are not
many cells that die in yeast cultures, this can be thought of as the rate at which cells enter
and leave a particular cell class. The remainder of the elements within the matrix are
zero.
The Leslie matrix is typically dotted by a transition matrix which represents the
progression of time. This matrix, along with the initial conditions, controls the dynamics
of the model and is comprised of time/volume dependent data for yeast bud percentage,
cell division, and culture aging. At each time step, the matrix algebra is carried out with
the new matrices developed from the last time step. This process is repeated until the end
of the simulation.
We have demonstrated the accuracy of the model based on experimental data for mixed
yeast culture volume from Woldrigh and synchrony data obtained from a batch culture
(see Figure 14). Additionally, we have shown that the model produces the steady state
conditions of an initially synchronized culture within 2% accuracy.
33
Figure 14
Leslie Model Comparison to Woldringh Data
Figure 14: Data extracted from collected by Woldringh [8] was used to simulate cell
synchrony and aging. The simulation was shown to fit synchrony data obtained from a
batch culture and the equilibrium conditions of an initially synchronized yeast culture.
The cell aging characteristics and age class bud percentage were also analyzed with
hopes to better understand the mechanisms that cause cultures to diphase.
34
8. Reverse Transcription and Polymerase Chain Reaction (PCR)
Reverse transcription is the process of using an enzyme called reverse transcriptase to
copy mRNA into cDNA. cDNA stands for complimentary DNA and exist in a single
stranded nature unlike natural DNA which typically exist as dsDNA (double stranded
DNA). Reverse transcription is a requirement for our study since there is no known
technique for amplifying mRNA. Preliminary data has shown that some of the NCR
genes have very low expression levels, thus we have chosen to use a gene expression
analysis technique called rt-PCR (real time PCR) which requires mRNA samples to be
converted to cDNA followed by PCR [11,44]. The process of reverse transcription is
fairly straight-forward and involves a relatively short, room temperature reaction with
mRNA, primers, and enzyme. The mechanics of reverse transcription are similar to that
of PCR, involving primers annealing to a template (mRNA) that function as a binding
site for reverse transcriptase. Once reverse transcriptase has bound to the 5’ primer, it
traverses along the DNA making the cDNA compliment. There are several options
available for reverse transcription priming including gene specific primers, random
hexamers, and random decamers. Random priming generates a pool of cDNA from the
mRNA that includes every mRNA in the pool including every possible fragment of
mRNA that could theoretically be generated. The idea behind random primers is that by
introducing a pool of primers of every possible sequence, every mRNA will get
transcribed at an equivalent rate. However, this also means that cDNA fragments will be
generated with only pieces of a gene, which could lead to problems in detection
downstream in the analysis. Additionally, since a massive pool of cDNA is generated,
the PCR will be far less clean than if gene specific primers are used. For this reason, we
have initially chosen to pursue gene specific priming.
Besides the issues with priming in reverse transcription, the mechanism of the reverse
transcriptase enzyme has proven to be extremely troublesome. Our goal for the reverse
transcription process is to quantitatively convert the harvested mRNA population into a
representative cDNA population. Obviously, we wish to keep the same relative ratios of
each gene in the pool when it is converted from mRNA to cDNA. The problem arises
when one begins to think about the mechanics of the reverse transcriptase enzyme. It has
been shown that the rate the enzyme copies is dependent on the secondary structure of the
35
transcript. Thus, genes with less secondary structure will be replicated faster than those
with more secondary structure. Additionally, if one were to think of two transcripts of
drastically different lengths, but equivalent secondary structure, the process still fails to
theoretically produce accurate conversion from mRNA to cDNA. Longer transcripts will
take longer to replicate since the enzyme moves along the strand at a constant rate. Thus,
one could envision an example to where the relative ratio of a longer transcript to shorter
was 2:1 before reverse transcription, but after the process the relative ratios could be 1:2.
Another flaw of the reverse transcription reaction is caused by the ability for multiple
enzymes to act simultaneously on the same substrate. In other words, as one reverse
transcriptase moves along an mRNA strand making a cDNA transcript another enzyme
can start on the 3’ end and begin transcription. This process could occur indefinitely
leading to several transcription reactions occurring on the same substrate simultaneously.
This process has not been well studied and is therefore very difficult to model.
Despite all of the problems with reverse transcription, hundreds of labs around the worlds
perform reverse transcription on a daily basis with the hopes of performing quantitative
PCR. Most do not even acknowledge the drawbacks and the few that do fail to provide
evidence that their data is still reliable. Although, we are not capable of addressing all of
the issues with reverse transcription, we hope to develop methods to control the reaction
as best possible. Our positive control technique will help alleviate some concerns since
we know that the secondary structure and length of the NCR genes are similar to the
control gene. Regardless, our hopes are to make the most reliable and accurate
measurements possible in a reasonable amount of time.
Once reverse transcription is complete, a population of pool of cDNA exist which is
theoretically represented of the original mRNA population. From this pool, we hope to
perform quantitative PCR. PCR utilizes an enzyme called DNA polymerase, better know
as TAQ (thermophilus aquarius), which replicates DNA via the addition of DNA primers
and thermal cycling. The reason for performing PCR is to amplify weak signals so that
they can be detected and measured. PCR is typically performed in three steps: DNA
denaturation, DNA primer annealing, and Extension. Figure 15 below provides a
physical representation of the process of PCR.
36
Figure 15
Figure 15: The temperatures shown are listed in degrees Celsius. The denaturation
temperature (step B) varies from 90-96° Celsius. The annealing temperature (step C)
varies from 50-65° Celsius. These two temperatures typically vary depending on the
template and primer sequences and lengths. The extension temperature (step D) is almost
always set at 72° Celsius since TAQ polymerase is believed to be most active at this
temperature.
We plan to perform PCR in real time, meaning that the amount of DNA present will be
measured after each cycle. Several techniques are available for measuring DNA in realtime PCR including DNA Beacons, Taqman Probes, and SYBR Green [1]. We plan to
use SYBR green for the initial measurements and to eventually move onto more
sophisticated multiplex (measuring multiple genes in a single tube) with the use of
Taqman probes. Taqman probes bind to the gene of interest during the annealing step of
the thermal cycling. The probe contains a flourophore on one end and a quencher on the
opposite end with a complementary strand of DNA in the center which is specific to the
DNA target. When the Taqman probe is in solution or bound to the complementary DNA
strand, it does not fluoresce due to the close proximity of the flourophore quencher.
However, during the extension phase, TAQ traverses over the probe and releases the
flourophore and quencher from the probe, allowing the flourophore to fluoresce. The
37
overall fluorescence can then be measured and used a proxy for the amount of DNA
present. SYBR green is a cyanine based intercalating stain that binds to the backbone of
the minor groove of DNA. The fluorescence of SYBR Green is enhanced over 100-fold
upon binding to dsDNA (double-stranded DNA) and can therefore also be used to detect
the amount of DNA present.
The intent of real-time PCR is to use the amplification data to back calculate the initial
concentration. Several methods for back-calculation of initial concentrations from PCR
curves exist including the standard curve method and comparative Ct method [29, 41].
The standard curve method is quantitatively coarse since it is difficult to develop a wide
range of standard curves using rt-PCR. The comparative Ct method involves normalizing
data against an endogenous control gene, which can lead to inaccurate results for reasons
already discussed. Others have developed more quantitatively robust methods using
mass action kinetics to develop mathematical models [29, 41]. These models have proven
to be more accurate for specific data sets. However, separate data sets from different
machines or reactions produce different sets of parameters for the model. Thus, the
models have shown to be incomplete. Additionally, we have noticed that the models do
not account for enzyme deactivation throughout the reaction process. We have collected
data to show that the enzyme deactivation has a significant impact on the reaction
efficiency (see Figure 17). Most currently available methods utilize only the exponential
points which usually consist of only a few data points near the beginning of the transient.
This type of analysis can be very inaccurate since these data points have been shown to
be not robust (Figure 16). We hope to use the entire portion of the curve to model the
process and therefore increase the robustness of the model. This model should have the
capability of modeling PCR reactions from any laboratory unlike previous models
developed [29].
38
Figure 16
PCR Variation
1000
900
800
700
RFU
600
500
400
300
200
100
0
0
5
10
15
20
25
30
35
40
45
Cycle Number
Figure 16: Three identical samples were prepared by mixing enough supermix, TAQ
polymerase, primers, and SYBR Green within the same epindorf tube. The master mix
was then split three ways and then run under identical conditions.
39
Figure 17
Taq Efficiency
12
10
Relative Efficiency
8
6
4
2
0
0
5
10
15
20
25
30
35
40
45
Cycle Number
Figure 17: Experiment was performed from 27 indentically, independent samples
prepared using Qiagen HotStarTAQ polymerase. The efficiencies were computed from
the average first derivative over the entire PCR curve. Samples were pre-worn by the
number of cycles shown on the x-axis then immediately ran for an additional 40-cycles
under the normal conditions discussed above.
Most PCR experts claim that the reaction efficiency decreases due to the increases
molecular collisions causes by the increasing amount of template [33]. We believe that
the reaction efficiency decreases due to enzyme deactivation causes by thermal cycling.
Surprisingly, it is common knowledge that TAQ polymerase has a half-life of 40-60
minutes at 94 °C, so the effects of enzyme deactivation as an important role in PCR
efficiency has been an overlooked problem for years. We hope to build a more
quantitative and robust model for PCR. Currently, our model is in its’ infancy, but we
have developed PCR efficiency data (Figure 16) from samples of known concentrations
40
with the hopes of developing a mathematical hypothesis test to show that our PCR curves
can be used to accurately predict the initial concentration of PCR sample.
9. Microphysiometer- Single Cell Measurements
The microphysiometer is a nano-scale bioreactor capable of single cell measurement
studies. We have shown that yeast can be successfully grown in a picoliter volume scale
and periodically measured within the device over time. Our current studies utilize a mat
a/α HIS TRP LEU, DAL80-GFP (Green Fluorescent Protein) strain (GYC86) to
quantitatively measure the dynamics of the DAL80 promoter. By using this strain within
the microphysiometer, we can detect the promoter dynamics of individual cells.
Preliminary problems with these studies involved cell tracking issues. Since cells were
free to move about the device, single cells could move in and out of the field of view of
the microscope. Other cells moved back and forth within the same field of view making
them difficult to measure over long periods of time. For this reason, we have designed a
new device that contains wells that individual cells can rest within. Laminar flow
presides on the top surface of the cells ensuring that fresh nutrients are supplied. Figure
19 shows the original microphysiometer design. The device itself is fabricated in PDMS
and clamped to a standard microscope slide for imaging. This device led to the problems
listed above as well as imagining problems since low NA objectives could not be used for
imaging. The new device is chemically etched into a No. 1 1/2 coverslip, eliminating the
imaging problem. Below is a drawing showing the design of our new device:
Figure 18
41
Figure 18: The microphysiometer is shaped similar to a bow-tie with all the work at the
center of the bow-tie. The two ends simply serve the purpose of inlet and outlet
profusion. The center contains a 20X20 grid with 10 micron diameter pits spaced at a
distance of 5 microns apart. Cells are inoculated through the inlet using a syringe pump.
10-minutes is allowed for the cells to settle into the pits and then profusion is begun.
Cells that have not settled into the pits will be washed out of the device. Each pit may
contain up to three cells, but this will not produce any problems in data analysis.
Figure 19
Figure 19: The new microphysiometer described above and depicted in Figure 18 is
shown loaded with GYC86 cells in a production experiment. The cells were loaded then
profused with a dilution rate of 0.1 hrs-1.
Once the DAL80 promoter has been fully quantified with the GYC86 strains we hope to
use a LHY3865 strain with pCTS1-YFP (Yellow Fluorescent Protein) to prove the extent
of synchrony achievable within these devices. CTS1 is a protein known to oscillate with
the cell cycle. Once synchrony is obtained, our goal is to begin more sophisticated
measurements on the NCR circuit that involve rapid nitrogen source cycling. These
studies will truly exhibit the power of using the microphysiometer for gene expression
analysis. Within a lab scale bioreactor, these measurements would be infeasible due to
the time required to change media. By using a cell-immobilized microphysiometer, we
will be able to exchange the media within the device in only a few seconds.
42
Since the microphysiometer technology has not been fully validated we plan to
investigate the yeast growth conditions within the microphysiometer as compared to
those of a standard bioreactor. We have already shown that the growth rates of yeast
within these devices are similar to those measured within a 3L BioFlo 110 bioreactor.
However, some concern exists regarding the oxygenation, temperature control, and well
mixing. Thus, we plan to further investigate these areas using oxygen sensitive coatings
and objective heaters. Since these devices will be used on a standard inverted
conventional microscope, the objective acts as a heat sink to the device. Even if the air
surrounding the device is held at fixed temperature, the device itself could exist at a much
lower temperature because of heat flow through the objective out of the system. For this
reason, we will also use an objective heater to maintain constant temperature around the
perimeter of the device. By using oxygen sensitive coatings, we will also determine the
optimal profusion rate for maintain sufficient dissolved oxygen levels.
In the long term, the goal will be to use the device for single cell RNA extraction,
purification, and detection. However, the techniques available for RNA purification and
detection are currently incapable of using the quantities available from a single cell.
Newer techniques using viral capsids have shown promising results for dealing with this
scale of RNA, but such techniques are still in their infancy.
43
Figure 20
Figure 20: Two transmitted light images of the original microphysiometer with yeast
growing within the bioreactor region. The top imagine (63X) shows chains of budded
yeast near the fence post of the device. The bottom image (20X) shows the inoculation
channel (top left) and the inlet and outlet profusion ports (top right and bottom left).
44
10. Conclusion
The thoughts and ideas presented within this document describe the notion that biological
research adapting to adversities encountered through the need to perform quantitative
experiments. In the past, researches would have completed this study by the use of
blotting techniques and assumed that cell-cycle and growth factors were independent of
NCR. However, little information would have been garnished from the study. Our
methodologies present a unique combination of novel engineering, quantitative
mathematics, and robust biological methodologies that will lead to high quality data
unlike the majority biological experiments performed today. This data will allow for
stronger conclusions that could impact the scientific community widespread. It has been
shown that genetic systems are surprisingly similar between organisms permitting the
conclusions from this study to have global impacts on medicine and biology.
The message foreseen throughout this proposal is that we hope to utilize all the tools of
science, engineering, and mathematics to provide a novel, but more powerful approach to
a traditional biological problem. By using these sophisticated multi-disciplinary tools,
our goal is to demonstrate that subtle patters, such as cell-cylce dependent oscillations,
exist within the genes of NCR, particularly the GLN3 gene. The quantitative nature of
methods and techniques presented here will allow us to strengthen our mathematical
model and develop more detailed conclusions.
45
46
11. References
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Green 1 Reaction Mixture for Real-Time PCR Quantification of Gene Expression”,
Biotechniques-Short Technical Reports. (2002)
[2]. A. Kulkarni, A. Sbdul-Hamd, R. Rain, H. El Berry, and T. Cooper, “Gln2P nuclear
localization and interaction with the URE2P in Saccharomyces cerevisiae” Journal of
Biological Chemistry. (2001)
[3]. A. Shamji, F. Kuruvila, and S. Schreiber, “Partitioning the transcriptional program
induced by rapamycin among the effectors of TOR proteins” Cur. Biol. (2000)
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12. Appendix
[1] C. C. Stowers and E. M. Boczko, “Reliable Cell Disruption in Yeast. Journal of
Yeast. Submitted 12/18/06.
53