AALBORG UNIVERSITY
Potential biomarkers for tumor
initiating cells in glioblastoma
Cell growth and qPCR-analysis of Cripto-1 expression in
C16 cell culture and U87 cell line
Group 403, MedIS/Medicine
29-05-2013
This study is not to be copied unless permission is granted by the authors and supervisors.
Aalborg University
Department of School of Medicine and Health
The programs for Medicine og Medicine with Industrial Specialization
Fredrik Bajers vej 7E, 9220 Aalborg East
Title:
Potential biomarkers for tumor initiating cells in glioblastoma
Project period: 22nd april 2013 – 29th may 2013
Semester:
4. semester
Group:
403
Abstract
Glioblastoma multiforme (GBM) is the
most aggressive form of malignant intracranial brain tumor. GBM has a high mortality rate, multimodal therapy being only
Group members:
Daniel Andreas Vestergaard
life lengthening. GBM can currently not be
cured and can relapse as glioma stem cells
___________________________
are resistant to traditional therapy. Can-
Jonas Ellegaard Nielsen
___________________________
cer/tumor cell biomarkers are a new ap-
Line Bay Sørensen
proach in the medical research field which
___________________________
aims at finding an indicator of cancer
Lotti Eggers-Kaas
pathological processes. The possibility of
___________________________
CR-1 being a GBM biomarker is investigated in this study; along with C16 and U87
Maria Lukacs
similarities under normoxic and hypoxic
___________________________
conditions. Cell counting and qPCR are
Sanne Ørnfeldt Larsen
used as tools in order to investigate the
___________________________
abovementioned. The results of this study
Supervisor:
Linda Pilgaard
indicate that U87 and C16 are not quite
Laboratory Technician:
Ditte Bech Kristensen
similar, in regards to proliferation rate and
Number printed:
9
gene expression. Therefore, it is difficult to
Number of pages:
68
conclude if C16 can be used on the same
Annex:
3
terms as U87.
Words:
18.077
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
1.0 - READERS GUIDE
Intended readers:
This study is aimed at persons with interest in health science and oncology, who have basic knowledge
of medical science and PCR-related procedures. It is required that the reader has knowledge similar to
that of a 4th semester MedIS/Medicine student at Aalborg University.
Abbreviations:
The terms will be written in full length in their primary chapter, followed by the abbreviation in a parenthesis, and later as an abbreviation eg. Glioblastoma multiforme (GBM).
References:
For this study the Vancouver reference is used. References will be stated as the designated reference
number listed in the bibliography, eg. statement (21). In addition, references can be stated at the end of
a sentence if the reference is related to that specific sentence, or at the end of a paragraph if the paragraph is based on the reference.
In relation to chapters, appendixes, figures, tables and diagrams the following reference method will
be applied:

Chapter reference - (chp. XX)

Appendix - (apx. XX)

Figures - there will references for the figures in the text. The figure will be numbered in relation to its chapter, eg. fig. 4.2.1

Tables - there will references for the tables in the text. The table will be numbered in relation
to its chapter, eg. tab. 4.2.1

Diagrams - there will references for the diagrams in the text. The diagram will be numbered in
relation to its chapter, eg. dia. 4.2.1
Page 3 of 68
2.0 - PREFACE
This study represents the work of 6 MedIS and Medicine students from Aalborg University in connection to a 4th semester project regarding the discovery of potential biomarker for tumor initiating cells
in Glioblastoma.
The structure of the study is partly shaped by the Aalborg model as it includes the working methods, a
problem solution and a following discussion of the results and a final conclusion. However the structure of the study is also somewhat inspired by the structure of an article as the initial analysis of a
larger field, is excluded.
The aim of the project is to investigate the influence of hypoxia/normoxia on a primary Glioblastoma
multiforme (GBM) cell culture named C16. This is done through cell growth curve analysis and qPCR
analysis of the expression of a potential biomarker of GBM named cripto-1. In addition this study aims
to explore the possibility of using C16 as a model cell line in GBM on the basis of an existing GBM model cell line named U87 which is used in many studies of GBM.
A special thanks to our supervisor Linda Pilgaard and laboratory technician Ditte Bech Kristensen for
always helpful assistance.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
TABLE OF CONTENT
1.0 - Readers guide ............................................................................................................................................ 3
2.0 - Preface ....................................................................................................................................................... 4
3.0 - Introduction............................................................................................................................................... 7
4.0 - Glioblastoma Multiforme .......................................................................................................................... 8
5.0 - Cancer stem cells ..................................................................................................................................... 10
6.0 - Hypoxia .................................................................................................................................................... 11
7.0 - Cripto-1 - a possible biomarker for GBM ................................................................................................ 13
8.0 - Glioblastoma cell cultures U87 and C16.................................................................................................. 16
9.0 - Method of choice .............................................................................................................................. 17
9.1 - Aim of the study .................................................................................................................................. 17
9.2 - Reference gene - TBP .......................................................................................................................... 17
9.3 – qPCR .................................................................................................................................................... 18
9.4 - Hypoxia vs. normoxia .......................................................................................................................... 20
9.5 - Cell counting ........................................................................................................................................ 20
10.0 - Experimental Setup ............................................................................................................................... 22
11.0 - Results ................................................................................................................................................... 23
11.1 - Cell counting ...................................................................................................................................... 23
11.2 - Fold Changes ..................................................................................................................................... 24
11.3 – Growth Phases and Doubling Time .................................................................................................. 24
11.4 – Standard Deviation ........................................................................................................................... 26
12.0 - Results - qPCR ........................................................................................................................................ 27
12.1 - Start quantity..................................................................................................................................... 27
12.2 - Fold Change ....................................................................................................................................... 28
12.3 – Standard Deviation ........................................................................................................................... 29
12.4 - Standard Curves ................................................................................................................................ 29
12.5 - Dissociation Curves............................................................................................................................ 30
13.0 - Discussion .............................................................................................................................................. 31
13.1 - Methods of qPCR ............................................................................................................................... 31
13.1.1 - RNA quality ................................................................................................................................. 31
13.1.2 - Primer design.............................................................................................................................. 31
13.2 - Data ................................................................................................................................................... 32
13.2.1 - Cell counting per day .................................................................................................................. 32
13.2.2 - Fold changes ............................................................................................................................... 33
Page 5 of 68
13.2.3 - Exponential regression ............................................................................................................... 33
13.2.4 - Time of doubling......................................................................................................................... 34
13.2.5 -Exclusion criteria qPCR ................................................................................................................ 34
13.2.6 - Start quantity.............................................................................................................................. 34
13.2.7 - Analysis of Standard Curve ......................................................................................................... 35
13.2.8 Melting Curve ............................................................................................................................... 36
14.0 - Conclusion ............................................................................................................................................. 38
15.0 - Bibliography........................................................................................................................................... 39
16.0 - Appendix................................................................................................................................................ 43
16.1 – Protocols ........................................................................................................................................... 43
16.1.1 - FCS Medium F12 ......................................................................................................................... 43
16.1.2 - Changing Medium ...................................................................................................................... 44
16.1.3 - Cell counting and Trypane-blue coloring ................................................................................... 45
16.1.4 - Characterization of C16 cell growth characteristics ................................................................... 46
16.1.5 - mRNA isolation by Aurum total RNA mini kit ............................................................................. 47
16.1.6 - cDNA-synthesis by iScript cDNA synthesis Kit ............................................................................ 48
16.1.7 - qPCR by iQTM SYBR Green Supermix ........................................................................................... 49
16.2 – APV and APB ..................................................................................................................................... 50
16.2.1 – APV............................................................................................................................................. 50
16.2.2 - APV ............................................................................................................................................. 53
16.3.1 – Cell counting .............................................................................................................................. 58
16.3.2 – qPCR Analysis ............................................................................................................................. 61
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
3.0 - INTRODUCTION
Glioblastoma multiforme (GBM) is the most aggressive form of malignant intracranial brain tumor (1).
The mortality among patients with GBM is still high despite surgery, chemotherapy and radiotherapy
(2). GBM can currently not be cured, multimodal therapy being only life lengthening (3). Patients have
a survival rate of 10 to 12 months (4) and the recurrence time after standard therapy is 6,9 months
(5).
GBM can relapse as glioma stem cells are resistant to traditional therapy (6). There has been little progress in treatment options for GBM in order to decrease mortality, compared with the treatment procedures in other solid tumor forms (7). Therefore it is important to accumulate a better understanding
of the pathogenesis in order to improve the treatment horizon.
Cancer/tumor cell biomarkers are a new approach in the medical research field which aims at finding
an indicator of cancer pathological processes. In GBM, the discovery of an effective biomarker gives the
opportunity to develop a therapy concept that can eradicate not only the tumor, but also the tumor
initiating cells or glioma stem cells, which are responsible for recurrence. Several studies have demonstrated that Cripto-1 is oncogenic, as it is involved in tumor progression in colorectal, breast, ovarian,
pancreatic, gastric, nasopharynx, endometrial and lung cancer (8). For this reason, it is interesting to
investigate whether or not Cripto-1 is a possible biomarker in GBM. This study will focus on comparing an established cell line and a primary cell culture from human GBM tumors, namely C16 and U87,
by analyzing both the proliferation rate and Cripto-1 gene expression. In respect to this, cells from C16
and U87 will be grown under normoxic or hypoxic conditions. The reason for comparing the C16 and
U87 is to find out if C16 can be used in the same terms as cell model line U87.
Page 7 of 68
4.0 - GLIOBLASTOMA MULTIFORME
In the process of understanding Glioblastoma multiforme (GBM), it is relevant to describe its classification and pathophysiology in general. According to the WHO classification of tumors in the nervous
system, GBM is a subtype of astrocytic tumors (9). Astrocytic tumors, known also as glioma, arise from
astrocytes in the brain and are a primary (intracerebral) brain tumor type (10). Approximately 20-25
% of all intracranial tumors are comprised of GBM (11). There are several other subtypes of astrocytoma as shown in dia. 4.1, but these will not be investigated in the study.
GBM can be subdivided into a primary and secondary group. Primary GBM develops spontaneously,
with no clinical and histological precursor-sign, predominantly in adults with a mean age of 55 years.
Secondary GBM develops slower, from grade II diffuse astrocytoma or III anaplastic astrocytoma (9).
Diagram 4.1 The WHO classification of tumors of the nervous system, with specific focus on Glioblastoma (9).
As shown in dia. 4.1, GBM is classified as grade IV. This is due to several characteristics which make it
a very aggressive tumor type:








(12)
Highly infiltrative
Poorly differentiated
Higher intracerebral pressure
Unresponsive to normal apoptotic signals
Desensitized to growth-inhibition factors
Angiogenic/vascularizing
Increase in cellularity
Varying mitotic activity
Necrosis is an important characteristic of GBM. During tumor growth, regions of the tumor may experience hypoxic conditions (chp. 6.0), and compensate by initiating angiogenesis (6). The newly
formed, but relatively weaker blood vessels are at risk of rupturing, thus leading to areas of hemorrhage and necrosis inside the tumor (13).
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
These necrotic areas inside the tumor in connection with hypoxia, is believed to be essential in regulation of growth, tumor-maintenance and up-regulation of certain genes responsible for angiogenesis
(14,15).
Moreover, studies have investigated the existence of stem-like cell properties in some types of cancer,
responsible for tumor remission. (10,16,17).
Page 9 of 68
5.0 - CANCER STEM CELLS
The origin of GBM seems highly unclear and the prognosis still remains poor (9). The cause and high
prevalence of relapse after treatment is poorly understood and seems to be a desirable field in cancer
biology to clarify. The ability of the tumor cells to infiltrate normal brain tissue makes them almost
impractical to resect using conventional surgery including the more common types of treatment, radiation and chemotherapies. Thus new treatments techniques or strategies must be required. (6).
Most solid cancers are believed to consist of a heterogeneous cell mass which exhibit different genetic
and morphological characteristics (18,19). Therefore cancer is formed by aberrant cell differentiation
and accumulation of mutations caused by genetic or epigenetic changes (19). Several studies support
the hypothesis that a subpopulation within the cell mass exhibit stem-like properties such as selfrenewal, multi lineage differentiation, high motility, surface cell marker expression, and is essentially
responsible for tumor initiating and their maintenance. The subpopulation is called tumor initiating
stem cells (TIC) or cancer stem cells (CSC). With respect to GBM the subpopulation is termed Glioma
stem cells (GSCs) respectively and displays similar properties as normal neural stem cells (19). The
presence of GCSs after surgery may be the main reasons for the tumor regeneration and recurrence
(2).
It is argued whether the GCSs arise from normal neural stem cells or progenitor cells (20) or if normal
tissue stem cells are capable of converting into CSCs (19). Some even suggests that the terminology
"stem cells" only refers to their function and not their origin (21). Nevertheless there seems to be a
consensus on the formation of GCSs, which can be due to genetic modifications seen in different mutations in genes or/and gene expression (20).
Several studies have managed to isolate different CSC from different types of solid cancers such as
brain, breast, liver and colon cancer by discovering specific surface markers in the previously mentioned cancers (22). This shows the diversity among different CSCs in relation to the cancer type and
the surface marker they posses. Several studies consider different biomarkers to be essential to GBM
incl. the surface marker CD133 which is expressed by normal neural stem cells. But the great heterogeneity each individual GBM exhibits makes the tumor vary greatly among patients. Currently there is
no universally accepted biomarker collected from GCSs (21). Some evidence suggests that the GSCs are
located in separate microenvironments within the GBM. These regions, called niches, seem to be influenced by local reduced oxygen levels (hypoxia) which furthermore regulate the GSCs and their expression of biomarkers. Additionally it is believed that the GSCs survival in the niches relies on their ability
to express a hypoxia-inducible transcription factor (HIF) (6). Members of the HIF family are proved to
regulate different processes/transcriptional modifications in the cell in response to hypoxia, including
proliferation and angiogenesis (19).
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
6.0 - HYPOXIA
GBM is characterized by extensive hypoxic regions which partake in making this type of tumor so resistant to therapy. The term hypoxia refers to an environment with low oxygen concentration, which
in most studies is 5 %, even though the hypoxic tumor microenvironment is much lower (0,5-2,5).
Normoxia is defined as an environment with 20-21 % oxygen concentration (6,23). By the time tumor
size reaches 1-2 mm3 the normal tissue blood supply is adequate to fulfill the tumors need for nutrients and oxygen (7).
In order to sustain tumor growth additional blood vessels have to be formed (7). Angiogenesis is induced by hypoxia which can stimulate vascular endothelial growth-factor (VEGF) secretion in cells,
through activation of Hypoxia Inducible Factors (HIFs) (24). HIFs take part in cancer progression, metastasis, chemo-radiation resistance and maintenance of CSC properties. Tumor cells have the ability
to adapt and survive hypoxia with assistance from HIFs which regulate the expression of hypoxia responsible genes. These genes’ protein products are involved in angiogenesis, metabolism and cell survival- HIF-1α (7).
Additionally hypoxia leads to loss of genomic stability by increasing generation of reactive oxygen species (ROS) and down-regulation DNA repair pathways, which can induce carcinogenesis. The resistance-mechanisms to chemotherapy of cells subjected to hypoxic conditions include resistance to
cell cycle arrest in G1- or G2-phase, resistance to apoptosis and long distance from vascularity. The last
mentioned contributes to tumor cells resistance to chemotherapy, as the cytotoxic treatment has difficulty in reaching avascular tissue. (25)
Two types of HIFs are identified: HIF-1 (α and β) and HIF-2α, but HIF-1α over-expression has been
associated with resistance to therapy. Low oxygen concentration results in upregulation of HIF-1α,
which is mainly induced by the oxygen-dependent dioxygenases, prolyl hydroxylase domain (PHD)
enzymes, a primary oxygen sensor. In response to upregulation of HIF-1α other genes than VEGF are
transcripted and help tumor progression. A few examples in this matter are; insulin-like growth factor-1 (IGF-1) which participates in cell survival, and the enzyme carbonic anhydrase 9 (CA9) which
gives the cell tolerance to acidosis. (25)
In a normoxic environment, pVHL, the product of Von Hippel Lindau tumor suppressor gene (VHL/E3ubiquitin ligase), evokes degeneration of the HIF-1α subunit of the HIF-1. In a normal situation HIF-1α
is secreted and then degraded within 10 min, which causes inactivation of HIF-1. This process is
named "futile cycle". In a hypoxic environment this cycle is intruded and HIF-1α fails to degenerate.
This leads to an active HIF-1 transcription factor and the above mentioned genes become expressed
(fig. 6.1). pVHL resides in cells in a complex with other proteins. pVHL has the capability to bind to
HIF-1α only when one of the two critical proline residues of HIF-1α has been oxidized to hydroxyproPage 11 of 68
line. The oxidation HIF-1α proline to hydroxyproline is enabled by an oxygen dependent enzyme. Afterwards pVHL spots and binds HIF-1α, thereby gathering the other proteins in the complex. Together
the HIF-1a-pVHL-complex acquires a molecule of ubiquitin and covalently attaches it to specific substrate proteins. As a conclusion, in the absence of oxygen the binding of pVHL to HIF-1α is not possible,
and HIF-1α cannot be destructed, cell survival being induced. The figure below shows both the fate of
HIF-1α in hypoxia and normoxia including the pathway of HIF-1α degeneration (fig. 6.1). (26)
Figure 6.1 HIF-1α’s fate in normoxia and hypoxia (26)
Furthermore, it has been demonstrated that Cripto-1 plays an important role in angiogenesis, tumor
formation and embryogenesis, processes regulated by hypoxia. Moreover, Bianco proved in a study
from 2009 that Cripto-1 gene over-expression is higher in cells grown in hypoxia than those in
normoxia. Likewise the same study demonstrates that the increase in this gene expression leads to
equal amounts of Cripto-1 protein translation. The mechanisms by which hypoxia increases Cripto-1
mRNA and protein expression, is binding of the complex HIF-1α and HIF-1β to specific hypoxiaresponsive elements (HREs) on the promoter section of the gene. After HIF-1’s binding to the HRE on
the promoter section, there is a direct transcription of the Cripto-1 gene (Chp. 7.0). (27)
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
7.0 - CRIPTO-1 - A POSSIBLE BIOMARKER FOR GBM
In general a biomarker refers to a measurable objective value which provides information of the normal biological and pathological processes of the body. Furthermore, it can provide knowledge on the
effect of a pharmacological intervention. The concept of biomarkers can be subdivided as follows: biometric values (blood pressure, height etc.), proteins/peptides (e.g. detectable in serum), nucleic
DNA/RNA (e.g. detectable by PCR), cytological values (karyotyping) and metabolites. The biomarkers
can be used for risk assessment before a potential disease will emerge - screening, for diagnosis and
for prognostic evaluation. (28) Biomarkers in cancer also known as tumor-markers are specific molecules or genes, expressed by specific cancer cells often by TICs or CSCs. These markers may be detected in histological examinations of excised tissue, via PCR or even in serum if the the biomarker is soluble. The biomarkers are useful for tumor diagnosis, classification, typing and control of tumor remission. (8,29) Understanding the expression of the specific biomarkers involved in GBM might help to
create an early diagnosis program and a possible new target of cancer therapy in GBM. Dia. 7.1 illustrates the stages in the discovery of a potential biomarker.
Diagram 7.1: Stages in the discovery of a potential biomarker. (28)( freely edited)
Human Cripto-1 (CR-1) is a gene/protein (fig. 7.2) of the Epidermal Growth Factor/Cripto/FRL1/Cryptic family (EGF-CFC). CR-1 is also known as teratocarcinoma-derived growth factor-1. This series of genes encodes for proteins important for cell signaling especially during the embryogenesis as
they initiate formation of endoderm and mesoderm. CR-1 is expressed in embryonic stem cells and is
also important for cell movements along the anterior-posterior axis of the embryo (embryo organization) and cell migration. (30)
Page 13 of 68
Figure 7.2: CR-1 protein exists in 2 forms; a cell membrane anchored (amino acids 1-188) and a soluble form. The cell membrane anchored form consist of a NH2-terminal signal sequence, a EGF-like domain, a CFC domain and a cleaving and attaching
COOH-terminal - a Glycosylphosphatidylinositol sequence (GPI). GPI-phospholipase-D (GPI-PLD) cuts CR-1 from its GPI anchor
and hereby creates the soluble form of CR-1 which is detectable in serum. (8,31)
In normal conditions CR-1 is not expressed in large quantities in adult tissue. However studies on mice
demonstrate that mouse cripto-1 (Cr-1) is elevated during pregnancy, lactation and is over-expressed
in various kinds of cancer such as mammary cancer. Studies on human CR-1 show, that CR-1 is overexpressed in premalignant and malignant lesions such as breast, colon, gastric and pancreatic cancer.
This makes CR-1 a potential significant biomarker in these cancer forms. Furthermore it is indicated
that CR-1 acts as an autocrine growth factor on specific tumor cells. (8,30)
CR-1 protein is anchored to the cell membrane and acts as a co-receptor for Nodal, a member of the
transforming growth factor-β (TGF-β) family. Together they activate the Cripto-1/Nodal dependent
intracellular signaling pathway (fig. 7.3) of the cell through Activine-Serine-Threonine receptors
(Alk4, Alk7). As Alk4 is activated this receptor phosphorylates the transcription factors Smad2 and
Smad3. These binds to Smad4 which translocated to the nuclei of the cell, will increase gene transcription of specific genes and thereby induce cellular growth (tumorigenesis). (8,32). CR-1 is thereby responsible for inducing cell proliferation, cell motility, invasion-ability of the cells, epithelialmesenchymal transition (EMT) and tumor angiogenesis, all which are important cancer hallmarks.
(27,33) In addition CR-1 binds to Activine and TGF-β1 and inhibits the inhibitory cell growth actions of
these molecules. (8) Hence CR-1 has been associated to poorer cancer patient prognosis. The actions of
CR-1 and its over-expression in various cancers makes CR-1 a potential target for new cancer therapies. Inhibition of CR-1 might inhibit tumor growth as CR-1 acts like a growth factor in tumorigenesis
(30).
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
Figure 7.3: Studies reveal that several signaling pathways involving CR-1 in embryonic stem cells/CSCs communicate. Wnt signaling pathway sustains the stem cells in an undifferentiated state, initiate and maintain carcinomas of the brain and other
organs and is involved in CR-1 and Nodal regulation as activation of β-catenin might increase the level of CR-1. HIF-1α binds to
the hypoxia responsive element of CR-1’s promoter region and initiate transcription of CR-1 (cph. 6.0). CR-1/Nodal dependent
intracellular signaling pathway is explained in this chapter. MAPK/Akt signaling pathway, activated by the binding of CR-1 to
Glypican-1, regulates the motility, proliferation and survival of the cell. The Notch pathway might have a regulating effect on the
CR-1/Nodal pathway. Notch signaling up-regulates Nodal expression whilst CR-1 binding of a notch-receptor increase Notch
signaling. This might help to regulate embryogenesis and tumorigenesis (8,31)
New research on GBM and Nodal shows that when GBM cells are exposed to Nodal, it will initiate cell
growth in GBM. (32) CR-1 and Nodal are highly connected in cell signaling in embryonic stem cells and
CSCs. Therefore, it becomes relevant to investigate the role of CR-1 in GBM, as a biomarker for GBM,
under hypoxic conditions seeing that hypoxia initiate CR-1 transcription and hypoxia is known to
regulate the CSCs in GBM (cph. 6.0). (2,8,31)
Page 15 of 68
8.0 - GLIOBLASTOMA CELL CULTURES U87 AND C16
In this study a primary GBM culture, C16, will be compared to an established GBM cell line, U87. In the
following these GBM cell cultures will be described.
U87 is a cell line derived from a grade 4 GBM, which has been investigated in several studies. The original tumor tissue was resected in 1966 from a 44 year-old female Caucasian patient. The tissue shows
an epithelial morphology. The cell line is obtained from the American Type Collection (34,35). The
passage number for U87 used in this study: p# 10 + 4 which means that the cell line has been cultivated 10 times, then frozen and later thawed and cultivated 4 times again.
An established cell line is immortalized through either a spontaneous mutation or an artificial manipulation. The transition of the cell line occurs as the enzyme telomerase elongates the telomeres. Normally the telomeres will shorten after each cell division due to the absence of telomerase and thereby determine the life span of the cells. However, it is possible to control this process by adding artificial telomerase to create an immortalized cell line. (26)
Conversely, C16 is a household AAU cell culture derived from a GBM tumor. The tumor was resected in
December 2011 from a 43-year old male patient following a CT- and MR-scan of cerebrum. The pathological analysis showed a malignant tumor tissue with infiltrations throughout cortex, necrotic areas
surrounded by anaplastic cells and numerous vessels with abnormal epithelial proliferation. Unfortunately a MR-scan in March 2012 revealed a large relapse, perhaps indicating activity of the before
mentioned GSCs (chp. 5.0). [Linda Pilgaard, contact supervisor] The passage number for C16 used
in this study: p# 2 + 6.
A primary cell line, derived from a primary culture proliferates rapidly at the beginning. However,
when the cells reach approximately 50 cell divisions the proliferations rate decreases as a consequence of the initiating apoptosis. (36)
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
9.0 - Method of choice
9.1 - AIM OF THE STUDY
The aim of this study is to investigate and characterize a primary GBM cell line (C16) in relation to an
established GBM cell line (U87). This will be done through analysis of growth curves and of a potential
biomarker in GBM, namely CR-1, via qPCR.
It will be examined how the proliferation rate of C16 and U87 responds to hypoxia and normoxia. Furthermore, this study will explore whether C16 can be used on equal terms as the model cell line U87.
In this manner an understanding of experimental setup and experimental methods used in lab work
(e.g. qPCR) will be achieved.
For C16 and U87 it will be investigated how the proliferation rate changes from day 1 normoxia
throughout day 4, 5 and 6 normoxia and from day 1 normoxia throughout day 4, 5 and 6 hypoxia. Furthermore the changes in the proliferation rates of U87 and 16 will be compared.
For C16 and U87 it will be investigated how the start quantity of CR-1 will change from day 1 normoxia to day 4 normoxia and from day 1 normoxia to day 4 hypoxia.
In addition fold changes in start quantity of CR-1 from day 1 normoxia to day 4 normoxia for C16 will
be compared to fold changes in start quantity of CR-1 from day 1 normoxia to day 4 normoxia for U87.
Finally fold changes in start quantity of CR-1 from day 1 normoxia to day 4 hypoxia for C16 will be
compared to fold changes in start quantity of CR-1 from day 1 normoxia to day 4 hypoxia for U87.
9.2 - REFERENCE GENE - TBP
When performing relative qPCR erroneous results may be obtained because of the unknown start concentration of cDNA or experimentally induced variation (bias) in each sample. To prevent a low validity a reference gene is used. (37)
The reference gene is sometimes referred to as an internal control, normalizer, housekeeping gene or
endogenous control (38). Its function is to normalize the final data in relation to different input
amounts of template. Therefore it is important for the reduction of the experimental variation. Furthermore, the reference gene is also used to normalize the data relative to the quality of the starting
material and differences in RNA preparation and cDNA synthesis. A good reference gene is one whose
expression is unchanged across varying experimental conditions, and will therefore often be a metabolic gene that are necessary for the continuation of the basic cellular functions. If that is not the case it
will instead produce artifact-induced changes. (37)
Page 17 of 68
In order to obtain a satisfactory normalization of the gene of interest, it is important to select a proper
reference gene. In addition, it have been suggested that at least two reference genes should be used to
minimize the risk of bias, because variation of expression levels can occur in every housekeeping gene.
In former studies some widely used reference genes have been perceived as reliable normalizers. (39)
However, recent studies have demonstrated that those genes had varying expressions in different experimental conditions, which has made the importance of the selection on a stable reference gene under the experimental conditions more focused. (37)
In this study the cells will be exposed to hypoxia and normoxia. Hence it is important to find a reference gene that is stable in these two conditions. Further it has to be stable in GBM, because that is the
type of cell, which will be studied. Therefore TATA box-binding protein (TBP) has been chosen, because it complies with above mentioned circumstances (37,40). TBP is a transcription factor that helps
positioning the RNA polymerase II by binding to a DNA sequence called the TATA box (41).
9.3 – QPCR
Real Time Relative Quantitative Polymerase Chain Reaction also known as qPCR, is a gene analysis
technique used in pathogen detection, drug target validation, gene expression analysis etc. With help
of PCR a DNA sequence or a cDNA can be amplified many thousands- to a million fold. In qPCR compared to traditional PCR it is possible to measure the amount of PCR product after each cycle. Another
advantage of using qPCR is that PCR reaction can be followed in real time and not at the end of it.
Moreover qPCR is able to detect down to a twofold change.
A qPCR reaction is normally run for 40 cycles and can be divided into three major steps.
1. Denaturation - the process in which the double stranded DNA is disrupted into a single strand
by heating (usually 95 degrees)
2. Annealing - in this step the temperature is lowered 5 degrees under the melting temperature
of the primers, so that they can bind to the single stranded DNA
3. Elongation - at this point a temperature of 70-72 degrees it is appropriate for the activity of
DNA polymerase and primer extension takes place, at rates up to 100 bases/ sec
(42)
In order to reach reliable quantization of gene expression, a high quality RNA is required. High quality
RNA is characterized as a solution free of protein and genomic-DNA. RNases are very stable and persistent proteins, which can degrade RNA, therefore purification is very important. RNA absorbance
(A260/280) ≥ 2.0 indicates a nearly protein-free solution. Out of a high quality RNA, cDNA can be synthesized by Reverse Transcription (RT) procedure. RT is defined as the process where cDNA is designed.
Relative quantification of gene expression demands quantification of two different genes, a target gene
and a reference gene (chp. 9.2). (43)
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
The baseline of the qPCR indicates the signal level throughout the first cycles of PCR (3-15 cycles), in
which there hardly is any variation in fluorescent signal. The baseline is used in determining the cycle
threshold (CT), which is defined as the cycle number where the fluorescent signal overpasses the
threshold (fig. 9.3.1a). The threshold of a qPCR reaction is the level of signal that represents a statistically significant increase compared to the baseline signal. CT is relevant for calculating the initial DNA
copy number, as CT is inversely related to the amount of starting template. In other words the higher
the starting template the lower the CT value of the given reaction. (42)
Figure 9.3.1 a & b): a) The figure illustrates the relation between the CT and the threshold of a qPCR amplification curve. b) An
example of a standard curve with starting quantity along the x-axis and the CT along the y-axis.
A fundamental curve in the analysis of a PCR reaction is the standard curve (fig. 9.3.1b), as it helps in
determining the reaction efficiency, the slope, the amount of target template at start (Sq), and correlation coefficient (R2). PCR efficiency is a measure for reactions template doubling after each cycle along
exponential amplification. PCR efficiency of 100 % is characterized by a slope of -3,32. An acceptable
reaction has an efficiency between 90 % and 110 %, which correlates to a slope of between -3,58 and 3,10. R2 calculates how well the data fit the standard curve. In other words it shows the linearity of the
standard curve. In an ideal situation R2=1, but in practice 0,999 is the maximum value. (42)
The melting curve, or dissociation curve, (fig. 9.3.3a) shows the variations in the fluorescence when
double stranded DNA dissociates into a single stranded DNA. Melting curves are used as a tool in analyzing the presence of contamination, nonspecific primers or primer dimers. Contamination can refer
to genomic DNA and foreign DNA. Primer dimers are defined as a binding of forward and reverse primers. Nonspecific primers are primers that don’t solely bind to the targeted cDNA sequence. These
factors contribute to lower efficiency of the PCR reaction. (42)
Page 19 of 68
Figure 9.3.3 a & b: a) An example of dissociation curve, using temperature along the x-axis and fluorescence along the y-axis.
b) An example of an amplification curve with the number of cycles run, along the x-axis, and the fluorescence-level along the yaxis.
Another essential curve to look at is the amplification curve (fig. 9.3.3b). It quantifies the amount of
amplicons generated during each cycle. The curve has a sigmoid shape and can be divided into three
phases:
1. Exponential - in this phase the material is doubled at every cycle if 100 % efficiency is assumed
2. Linear- at this stage the reactants are being used in the amplification process. Therefore the
amount of reactants decreases over time and the reaction becomes slower. Furthermore PCR
products are no longer doubling with each cycle.
3. Plateau - in this phase no PCR products are being created and the existing ones start to degrade
(44)
9.4 - HYPOXIA VS. NORMOXIA
In this study the cell line U87 and the primary cell culture C16 are grown in a hypoxic and a normoxic
environment in order to investigate the proliferation rate of the cells and the gene expression of CR-1.
The hypoxic environment is generated in an incubator with an oxygen-level (O2-level) at 5 % and a
carbon dioxide (CO2) level at 5 %. A stable level of CO2 maintains a stable pH-value which is essential
to avoid a changed metabolism of the cells and reduced ATP production/potentially leading to intracellular acidosis. (45)
The normoxic environment is generated in an incubator with an O2 level at 21 % and a carbon CO2
level at 5 %. The environments are maintained at 37.5 oC in order to replicate the temperature of the
body, hence creating a favorable growth milieu.
9.5 - CELL COUNTING
In order to investigate the proliferation rate of the cell cultures a simple cell counting method will be
used. This method will be used initially to count the cells before splitting and preparing them for a new
passage, in which approximately 25.000 cells are seeded in each of the 42 T-25 flasks. Moreover, the
cells will be counted continuously throughout this study to examine how the proliferation rate responds to hypoxia and normoxia.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
In this study, trypane blue is used to colour the cell cultures, colouring the non viable cells dark blue
whilst the viable cells remains bright. The viable cells are then counted manually using a haemocytometer and a microscope. This technique is chosen as it is widely used and fairly easy to execute. (46)
Page 21 of 68
10.0 - EXPERIMENTAL SETUP
In this section, the experimental setup will be briefly described using a simple flow-diagram (dia.
10.1) to illustrate each step taken in order to complete the experiments. Furthermore tab. 10.1 shows
the primers used in this study and tab. 10.2 shows the instruments used for this study.
29 -04-13
Day -1
02-05-13
Day 0
03-05-13
Day 1
06-05-13
Day 4
07 -05-13
Day 5
08 -05-13
Day 6
13-05-13
Day 0
14-05-13
•Mixing fresh medium
•Change medium in T75 flasks
•Both cell lines seeded in 21xT25 flasks
•New medium added to all flasks
•For each cell line 3xT25 flasks are counted using Trypane blue
•9xT25 flasks for C16 & U87 are stored in a hypoxic environment
•Change medium in both normoxia and hypoxia flasks
•For each cell line in both normoxia and hypoxia 3xT25 flasks are counted using Trypane blue
•For each cell line in both normoxia and hypoxia 3xT25 flasks are counted using Trypane blue
•For each cell line in both normoxia and hypoxia 3xT25 flasks are counted using Trypane blue
•Preparation of qPCR wells
•Kept at 4oC over night
•Run qPCR analysis!
Day 1
Diagram 10.1 illustrates the steps through which the experiments of this study will be conducted. For a more detailed elaboration of the sentences see apx. 15.1 and 15.2
Gene
Sequence
Annealing
Temp. (oC)
TPB
5’-GAGCTGTGATGTGAAGTTTCC-3’
5’-TCTGGGTTTGATCATTCTGTAG-3’
60
CR-1
5’-ATGCTGGGGTCCTTTTGTGCCT-3’
5’-GGGCACAGACCCACAGTTCTCTTT-3’
60
Size
21
22
22
24
Product
117
-
Forward
Reverse
fw
rv
fw
rv
Table 10.1, representing the primers for each gene and reverse/forward-transcriptase.
Instruments
Company
Model No.
Incubator, O2 5 %
Biosherix xvivo system
G300C
Centrifuge
Eppendorf
5430R
qPCR
Agilent Technologies Stratagene Mx3005P
Table 10.2, representing the instruments used in the setup.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
11.0 - RESULTS
In this section the data produced from the experimental setup (chp. 10.0) will be presented in two
following subdivisions named cell counting and qPCR. To provide an overview of abbreviations used in
relation to the following chapters, consult the table below.
Condition
Abbreviation
U87 in normoxia day 1, 4, 5, 6
U87d1N, U87d4N, U87d5N, U87d6N
U87 in hypoxia day 4, 5, 6
U87d4H, U87d5H, U87d6H
C16 in normoxia day 1, 4, 5, 6
C16d1N, C16d4N, C16d5N, C16d6N
C16 in hypoxia day 4, 5, 6
C16d4H, C16d5H, C16d6H
Table 11.1 listing each condition and abbreviation used henceforth
11.1 - CELL COUNTING
In the following the data from the cell counting will be presented within 2 graphs for each cell line for
normoxia and hypoxia, in order to visualize the possible outliers. A table presenting the fold changing
between means of U87 and C16 follows. Hereafter, an illustration of the growth phases and a table
with the doubling time in the exponential phase of growth will be presented. Finally the standard deviation for each mean is graphically illustrated. All data used in this section can be found in (apx.
15.3.1).
Figure 11.1.1 a-d shows the cell counting for U87 in normoxia (U87N), represented by a blue bullet, U87 in hypoxia (U87H),
represented by a light blue bullet, C16 in normoxia (C16N), represented by a pink bullet and C16 in hypoxia (C16H), represented
by a light pink bullet. The counting was performed at day 1, 4, 5 and 6 respectively. Each day 3 samples were counted. The time,
measured in days, is presented horizontally whereas the number of cells is displayed vertically. Each bullet presents a mean of 4
cell counting for each sample.
Page 23 of 68
11.2 - FOLD CHANGES
In order to investigate the development of the proliferation-rate of the cells, fold changes are calculated between different conditions presented in the table (tab. 11.2.1).
Folding (mean cell counting):
Situation A: U87d6N/U87d1N
Situation B: U87d6H/U87d1N
Situation C: C16d6N/C16d1N
Situation D: C16 d6H/C16 d1N
Situation B/Situation A
Situation D/Situation C
Situation A/Situation C
Situation B/Situation D
Folding Value:
10,13
5,73
2,52
9,77
0,57
3,88
4,02
0,59
Table 11.2.1 presents the fold changes between the means of cell counting from day 6 and day 1. Furthermore the fold changes
from each cell line are compared in normoxia and hypoxia.
Situation A indicates that the mean of cell counting from U87d6N is 10,13 times larger than U87d1N.
Situation B indicates that the mean of cell counting from U87d6H is 5,73 times larger than U87d1N.
Situation C indicates that the mean of cell counting from C16d6N is 2,52 times larger than C16d1dN.
Situation D indicates that the mean of cell counting from C16d6H is 9,77 times larger than C16d1N.
The comparison of situation B and A indicates that the fold changes from U87 day 1 to day 6 in hypoxia
is 0,57 times larger than the fold changes from U87 day 1 to day 6 in normoxia. Or in other words the
fold changes from U87 day 1 to day 6 normoxia is 1,77 times larger than the fold changes from U87
day 1 to day 6 in hypoxia. The comparison of situation D and C indicates that the fold changes from
C16 day 1 to day 6 in hypoxia is 3,88 times larger than the fold changes from C16 day 1 to day 6 in
normoxia.
The comparison of situation A and C indicates that the fold changes from U87 day 1 to day 6 in
normoxia is 4,02 times larger than the fold changes from C16 day 1 to day 6 in normoxia. The comparison of situation B and D indicates that the fold changes from U87 day 1 to day 6 in hypoxia is 0,59
times larger than the fold changes from C16 day 1 to day 6 hypoxia. Or in other words the fold changes
from C16 day 1 to day 6 hypoxia is 1,71 times larger than the fold changes from U87 day 1 to day 6 in
hypoxia.
11.3 – GROWTH PHASES AND DOUBLING TIME
The growth phases are divided into three phases:
●
Lag phase: slowly progressing growth this might be due to that the cells needs time adjust a
new growth media and start to proliferate.
●
Exponential phase: The cells proliferate rapidly and the amount of cells is doubled repeatedly.
●
Plateau phase: the amount of cells is now stable as the doubling time of the cells are extended.
(47)
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
Figure 11.3.1 shows the mean of the cell counting, represented as an exponential regression for each cell line. The four graphs
represent the growth phases of each cell line. Exclusions criteria were taken into consideration and a difference between 2 cell
countings greater than 100000 cells, within the same day, was excluded. The time, measured in days is presented horizontally
whereas the mean of cell number is displayed vertically.
The graph for U87N indicates a lag phase from day 1 to day 4 and an exponential phase from day 4 to
day 6 with a R2=0,9132. No plateau phase is seen.
The graph for U87H indicates a lag phase from day 1 to day 4 and an exponential phase from day 4 to
day 6 with a R2=0,9338. No plateau phase is seen.
The graph for C16N indicates a lag phase from day 1 to day 5 and an exponential phase from day 5 to 6
with a R2=0,9538. No plateau phase is seen.
The graph for C16H indicates a lag phase from day 1 to day 4 and an exponential phase from day 4 to
day 6 with a R2=1. No plateau phase is seen.
U87 N
U87 H
C16 N
C16 H
Time of doubling(days)
0,877
1,202
2,681
0,658
Table 11.3.1 shows the doubling time for each cell line. The doubling time is given in days. T2 is calculated on basis of the slope of
the exponential part of graphs.
The cells of U87N are almost a day to double the amount of cell whilst the cells of U87H are almost
1 ¼ day to double the amount of cells. The cells of C16N are almost 2 ¾ day to double the amount
of cells, whilst C16H are almost ¾ day to double the amount of cells.
Page 25 of 68
11.4 – STANDARD DEVIATION
Standard deviations (SD) are calculated in order to investigate the foundation for statistical analysis.
Fig. 11.1.3 is an example of mentioned calculations, and as is readily apparent, the SD is fairly large for
each mean. This also applies to the other calculations, (apx. 15.3.1).
Fig. 11.1.3 illustrates the standard deviation for C16N.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
12.0 - RESULTS - QPCR
In this chapter, the plots illustrating C16 and U87 starting quantity will be presented followed by fold
change data, standard- and dissociation curve data.
12.1 - START QUANTITY
Using the relative starting quantity (rSq) it is possible to calculate fold changes of CR-1. Observations
can be made to analyze if, according to theory, proliferation and hypoxia have influence on the CR-1
gene expression.
No calculations were carried out in relation to the standard- and dissociation curves, as the PCR program “MxPro©” automatically quantifies data and displays it both graphically and numerically. Outliers
have been removed from the standard curves, exclusion criteria being the removal of 3-digit samples
from the data.
Figures 12.1.1a & b show the normalized data of the Sq-values of CR-1 and TBP in C16d1N vs. C16d4N and C16d1N vs. C16d4H.
Outliers have been removed according to the exclusion criteria.
Figures 12.1.2a & b show the normalized data of the Sq-values of CR-1 and TBP in U87d1N vs. U87d4N and U87d1N vs. U87d4H.
Outliers have been removed according to the exclusion criteria.
Figure 12.1.1 a & b indicates that, disregarding the possible outlier (Sq=84,09), the data for C16d1N is
distributed in relative proximity to each other. The data presented for C16d4N and C16d4H also indicates a close distribution near 0. In fig. 12.1.2 a & b it is clear that all samples are distributed close to
each other, and close to 0.
Page 27 of 68
The four figures show 4 Sq values of the technical and biological replicates. However, some of the S q
values have been removed as they were classified outliers. Consequently in figure 12.1.2a there are
represented only two Sq values in day one and in figure 12.1.2b two Sq values in day four.
12.2 - FOLD CHANGE
Fold changes are calculated using rSq, which are presented in tab. 12.2.1, along with the mean and SD
for each condition. In practice, there are several calculations which need to be accomplished before
fold changes can be determined, these can be seen in apx. 15.3.2.
Condition
Mean:
SD:
C16d1N
4,86
2380
652
7510
2636,72
3400,03
C16d4N
5,82
15,4
2,19
2,27
6,42
6,22
C16d4H
No data
No data
1,06
21,7
11,38
14,59
U87d1N
0,87
7,96
5,36
7,38
5,39
3,21
U87d4N
5,47
19,1
107
2110
560,39
1034,05
U87d4H
No data
No data
3,35
4,58
3,97
0,87
Table 12.2.1 shows the mean Sq-value for each cell-line, day and condition. Data is missing from C16d4H and U87d4H due to
exclusion from the dataset.
Folding (mean Sq):
Situation 1: C16d4N/C16d1N
Situation 2: C16d4H/C16d1N
Situation 3: U87d4N/U87d1N
Situation 4: U87d4H/U87d1N
Situation 3/Situation 1
Situation 4/Situation 2
Situation 2/Situation 1
Situation 4/Situation 3
Folding Value:
0,0024
0,0043
103,92
0,74
42680,58
170,36
1,79
0,0071
Table 12.2.2 represents the folding values of each situation. The first four situations are calculated on basis of the previously
presented mean Sq-values. The four situations were then compared as specified in the table, to classify the folding value of U87
versus C16. The last 2 calculations express the fold change between hypoxia and normoxia for both C16 and U87.
In tab. 12.2.2 the folding values of each situation is listed.
Situation 1: Indicates that the expression of CR-1 in C16d4N is 0,0024 times higher than C16d1N.
In other words CR-1 C16d1N is 410,70 times more expressed than in C16d4N.
Situation 2: Indicates that the expression of CR-1 in C16d4H is 0,0043 times higher than C16d1N.
In other words CR-1 C16d1N is 231,70 times more expressed than in C16d4H.
Situation 3: Indicates that the expression of CR-1 in U87d4N is 103,92 times higher than U87d1N.
Situation 4: Indicates that the expression of CR-1 in U87d4H is 0,74 times higher than U87d1N.
In other words CR-1 U87d1N is 1,36 times more expressed than in U87d4H.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
The comparison of situation 3 and 1 indicates that the fold changes from U87 day 1 to day 4 in
normoxia is 42680,58 times larger than the fold changes from C16 day 1 to day 4 in normoxia.
The comparison of situation 4 and 2 indicates that the fold changes from U87 day 1 to day 4 in hypoxia
is 170,36 times larger than the fold changes from C16 day 1 to day 4 hypoxia.
The comparison of situation 2 and 1 indicates that the fold changes from C16 day 1 to day 4 in hypoxia
is 1,79 times larger than the fold changes from C16 day 1 to day 4 in normoxia.
The comparison of situation 4 and 3 indicates that the fold changes from U87 day 1 to day 4 in hypoxia
is 0,0071 times larger than the fold changes from U87 day 1 to day 4 in normoxia. Or in other words
the fold changes from U87 day 1 to day 4 normoxia is 140,43 times larger than the fold changes from
U87 day 1 to day 4 in hypoxia.
12.3 – STANDARD DEVIATION
SD’s are calculated in order to investigate the necessity for statistical analysis. Fig. 12.2.1 is an example of mentioned calculations, and as is readily apparent, the SD is fairly large for each mean. This also
applies to the other calculations (apx. 15.3.2). This was done for:
●
C16d1N vs. C16d1N
●
C16d1N vs. C16d1H
●
U87d1N vs. U87d4N
●
U87d1N vs. U87d4H
Figure 12.2.1 illustrates the difference in mean Sq between U87d1N vs. U87d4H. Incorporated in the graph is the SD of both
means.
12.4 - STANDARD CURVES
The standard curve for CR-1 illustrates that R2 = 0,812, while E > 1000 %, and a slope of -0,0886. Some
of the standards and their technical replicates are closely related, while others lie further apart. In
general, the standards do not follow the trend-line, (apx. 15.3.2).
Page 29 of 68
The standard curve for TBP illustrates the performance and various parameters of the reaction. As
observed on the figure the standards follow the trend-line and technical replicates lie next to each other. For TBP an efficiency of E = 99,3 %, R2 = 0,991 and slope =-3,339 has been obtained (apx. 15.3.2).
12.5 - DISSOCIATION CURVES
In tab. 12.5.1 the data from the dissociation curves for all wells, along with positive and negative controls for both genes is presented. CR-1 dissociation curves display more spikes relative to TBP. Comments are included in order to clarify the graphs, apx. 15.3.2.
Dissociation Curve
CR-1 All wells
CR-1 Positive ctrl.
CR-1 Negative ctrl.
TBP All wells
Spike-temp. (oC)
78-80
83-84
85-86
86
78-79
91
93
Comments
Following the third cluster of spikes, the fluorescence of some of
the wells seems to elevate slightly.
A single peak in the positive dissociation curve for CR-1 is seen
in one of the two replicates.
Two peaks are seen in the dissociation curve for CR-1 can be
observed in both replicates.
Only one clear cluster of spikes can be seen. Following the one
visible cluster of spikes, the fluorescence of some of the wells
seems to elevate slightly at some point, up until 94 oC.
TBP Positive ctrl.
A single peak in the positive dissociation curve for TBP is observed.
81
NB! This dissociation curve is a mean of the two positive TBP
replicates.
TBP Negative ctrl.
87
The dissociation curve for TBP fluctuates around 0, but two
92
peaks are observed.
Table 12.5.1 illustrates the temperature at which each cluster of spikes occurs in relation to each dissociation curve.
80-81
94
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
13.0 - DISCUSSION
In this section the methods of the experimental setup are discussed in two main sections, namely a
method section and a data section. The methods section discusses RNA-quality and primer design. The
data section is subdivided into a discussion of the results obtained through cell counting and a discussion of the results obtained through qPCR.
13.1 - METHODS OF QPCR
In the following, the impact of the RNA-quality on the qPCR analysis will be discussed, followed by a
discussion on primer design.
13.1.1 - RNA QUALITY
Using low quality RNA can lead to non-genuine qPCR outcomes. For this study no test for contamination has been performed. In the table below (tab. 13.1.1.1) different situations with poor quality RNA
and their consequences on PCR result are presented.
Table 13.1.1.1 Effects of poor quality RNA sample on PCR results (43)
As seen in the table, PCR inhibition can be caused both by high protein levels and chemical contaminants. As presented in the qPCR chapter high quality RNA is marked by A260/280 value ≥ 2. On the other
hand if A260/280 value ≤ 2 may indicate high protein levels. The more A260/280 deviates below 2, the
greater the risk of not obtaining reliable results. (43)
One of the ways in which PCR inhibition can be identified, is by looking at the standard curve. A slope
that is shallower than -3.3 (e.g. -1.1) and qPCR efficiency greater than 100 % are indicators of PCR inhibition. In a situation with PCR inhibition the CT values are affected as well. The less diluted samples
have higher CT values than the next dilution and therefore the most impressive inhibition can be observed in the samples with the highest template concentration. However, if the efficiency of the PCR
reaction is above 100 % and the CT difference between each standard is reasonable then a pipetting
mistake is the cause for the faulty results. (43)
13.1.2 - PRIMER DESIGN
A good primer design is important for successful reactions. Some of the factors that contribute to a
good primer are: primer length, primer melting temperature, primer annealing temperature and specificity. For example, short primers (18- 22 base pairs) have a higher specificity and it is easier for them
Page 31 of 68
to bind to the template at the annealing temperature. In order to improve specificity of the primer it is
essential to avoid regions similar to other gene sequences. If the primer is not specific enough it can
amplify other genes in the samples. Primer specificity can be checked by BLASTing the templates.
BLAST shows how many different gene sequences, from different or same gene a primer can bind to.
Other disadvantages of an unspecific primer are primer-dimers and self-folding (48).
Primer-dimers are a result of interaction between forward and reverse primers, but can also be a consequence of forward-forward or reverse-reverse primer junction. The problem with primer-dimers is
that they compete with amplicon generation and this can have an influence on the efficiency. If this
study’s primer is unspecific it can bind to e.g. CR-1 truncated form, which is not the target gene. There
is evidence for CR-1 truncated form’s presence in different human colon carcinoma and hepatoma cell
lines. This may leave the possibility open for CR-1 sliced version being present in GBM (43).
13.2 - DATA
In this section tendencies seen in the data will be discussed. However, no statistical significant data
will be stated as the SD in general for the entire data set is fairly large. This could indicate that the data
is too spread to obtain a p-value smaller than 0,05 as the tests for means are inverse proportional to
SD. Normally a very large population/large difference in means would compensate for a large SD but
as the data set in this setup is rather small, it could be difficult to assume any significance in the data.
Therefore no statistical tests of significance level are performed.
13.2.1 - CELL COUNTING PER DAY
The cell counting represented by bullets indicates that some means are too far apart from the majority
of the other means from each sample. Therefore it has been necessary to discharge some outliers in
order to achieve a more collected dataset. Preferably a box plot analysis would have been performed
in order to identify the outliers. However, it has not been possible to identify the outliers in this manner as the data set is fairly small. The outliers are excluded in an alternative manner where outliers are
identified on the basis of one exclusions criteria, namely more than 100.000 cells between means on
each day. The outliers are identified as U87d4N: 421000, U87d5N: 50388, U87d6N: 436800 and
U87d4H: 210400, U87d6H: 61000.
It could be argued that other cell counting data could have been excluded due to researcher’s bias
which was noted in the laboratory. One cell count for C16d6N was affected by trypane sediments on
the haemocytometer. All cell counts on day 5 could have been affected as the cells were dropped and
perhaps this day should have been excluded from the data set. Furthermore, 2 cell counts of C16d1N
were affected by the overexposure to trypane and perhaps these data should have been excluded.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
13.2.2 - FOLD CHANGES
Studies have shown that U87 proliferates more in a hypoxic than a normoxic environment (32,35).
However, the results of this setup indicate that U87N proliferates more than U87H, from day 4 to day 6
whilst U87H proliferates more in the beginning from day 1 to day 4. The proliferation of day 1 to day 4
is similar to the theory whilst the proliferation from day 4 to 6 is opposite the theory of CSCs which are
set to proliferate more in hypoxic conditions (chp. 5.0, 6.0, 7.0). This could indicate researcher’s bias
with erroneous cell counting or maybe it could indicate that the cell culture does not consist of solely
CSCs. Only CSCs would benefit from a hypoxic environment whilst many other kinds of cells would
degenerate. Conversely C16 proliferates more in a hypoxic than a normoxic environment from day 5 to
day 6. This could indicate that the cell culture contains CSCs.
In this study the setup tries to compare the similarities between U87 and C16 in order to tests whether
C16 can be used as a cell line model in the same manner as U87. In a normoxic environment C16 does
not proliferate in the same manner as U87 since U87 has a fold change ca. 4 times larger than C16. This
could indicate that C16 is not comparable to U87. In a hypoxic environment C16 does not quite behave
in the same manner since C16 has a fold change almost 2 times larger than U87. This could indicate
that C16 is not comparable to U87.
13.2.3 - EXPONENTIAL REGRESSION
In order to investigate the growth phases for U87 and C16 in normoxia and hypoxia the lag-phase of
the graphs was identified. The lag-phases for U87N and U87H were identified as 4 days with a cell
count on ca. 60.000 to 85.000 in the transition towards the exponential phase. The lag-phase for C16N
was ca. 5 days while the lag-phase for C16H was ca. 4 days with a cell count on ca. 25.000 to 35.000 in
the transition. This could indicate that C16 is a more sensible cell culture than U87 as the lag phase
was slightly prolonged in normoxia and the cell counts of the transition towards the exponential phase
are fairly smaller than those of U87. Perhaps the C16 has been passaged too many times and therefore
made the cell culture more sensible in relation to their accommodation-abilities. According to the theory an established cell line as U87 is more robust than a primary culture as C16 and this could explain
the prolonged lag-phase and smaller cell count in the transition.
In order to establish whether the identification of the lag-phase and the exponential phase is accurate
an exponential regression was made for the exponential-phase of the graphs. U87N has a R2=0,9132,
U87H has a R2=0,9338, C16N has a R2=0,9538 which all are lower than the preferably R2=0,98. This
indicates that the exponential phase of U87N, U87H, and C16N is not accurately identified. Conversely
C16H has a R2=1 which indicates that the exponential phase is identified. However, the exponential
phase is identified between 2 days and therefore the R2 is of little value in order to identify the phase
correctly.
Page 33 of 68
In addition, the lag-phase of the U87 and C16 could have been prolonged due to lack of humidity in the
incubator from day 0 to day 1. Even though the cell counting is not registered before day 1, it could be
speculated if the accommodation-ability of the cells seeded on day 1 could have been affected by the
low humidity and therefore prolonged the lag-phase before entering cell cycle.
13.2.4 - TIME OF DOUBLING
According to theory it is expected that U87H has a smaller time of doubling (T 2) than U87N, as U87
proliferates more rapidly in a hypoxic than normoxic environment (32,35). However, the results indicate that U87 in this setup proliferates more rapidly in normoxia than hypoxia as the T 2 is smaller for
U87N than U87H. This could indicate researcher’s bias or perhaps a smaller amount of CSCs in the test
sample.
In order to test if C16 proliferates with the same rate as U87 the T2 for C16 was also investigated. According to the theory CSC from GBM would benefit from a hypoxic environment and therefore proliferate faster in hypoxia than normoxia. C16, being a cell culture of GBM, could be expected to have a
smaller T2 in hypoxia than normoxia. The results of this study indicates that C16H proliferates more
rapidly in hypoxia supporting the theory regarding GBM and CSCs which could indicate the presence
of CSCs in the C16 sample. Hence C16 does not proliferate with the same rate as U87 in this study
which indicates that C16 is not compatible with U87. However C16 proliferation rate match the theory
of U87 as C16 proliferates more in hypoxia than normoxia.
13.2.5 -EXCLUSION CRITERIA Q PCR
It has been necessary to discharge some outliers in order to achieve a more collected dataset. Preferably a box plot analysis would have been performed in order to identify the outliers. Although, by using
this method too many outliers should have been removed, this would affect the credibility of the results. Therefore it was attempted to only exclude outliers that were formed by three or more digits. It
is controversial how effective this method has been as it was mostly based on observations rather than
calculations. On the other hand when standards needed to be removed more concrete and concise criteria’s have been followed. The difference between technical replicates should not exceed 1.01 Ct and
the technical replicate that deviate the most compared to the biological replicate has been removed.
It could be argued that other data could have been excluded due to researcher’s bias which was noted
in the laboratory. Three of the wells had less volume than required, which might have affected the calculations. Surprisingly none of the faulty wells have expressed a significant deviation compared to the
other wells.
13.2.6 - START QUANTITY
CR-1 gene expression for C16 and U87 does not follow the theory. It was expected that the geneexpression would increase under hypoxic conditions. However, only U87d1N vs. U87d4N had a raised
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
CR-1-expression of 103,92 fold. A cause to why increased gene-expression was not observed in hypoxic conditions could be the O2 concentration of 5 %. As presented in the hypoxia chapter (chp 6.0) the
tumor microenvironment has a O2 concentration between 0,5-2,5 %. Maybe 5 % O2 concentration is
too high to activate C16 HIFs, as the tumor cells might have accommodated to lower concentration
circumstances.
It can be noticed that CR-1 fold changes in gene expression between U87d1N to U87d4N is 42680,58
fold larger than CR-1 fold changes in gene expression between C16d1N to C16d4N.
Looking at the tendency in the hypoxic environment it can be seen that CR-1 fold changes in gene expression between U87d1N to U87d4H is 170,36 fold larger than CR-1 fold changes in gene expression
between C16d1N to C16d4H. One of the reasons for this tendency could be that C16 represents GBM
tissue cells and not necessarily only stem cells.
13.2.7 - ANALYSIS OF STANDARD CURVE
13.2.7.1 Efficiency
A good efficiency lies between 90 % and 110 % (chp. 9.3). The presence of PCR inhibitors in the wells
would produce efficiency higher than 110 %. Another influence on the efficiency could be the dynamic
of the reaction, non-optimal concentrations, or the quality of the enzymes, which would all result in <
90 % efficiency (42). The CR-1 standard curve shows efficiency above 1000 %. This could be caused by
a contamination in some of the wells. As the Ct standard values are generally consistent and the efficiency is above 100% a pipetting error is suspected (43).The standard curve for TBP (apx. 15.3.2) is
an example of a good efficiency, where the value lies within the normal range of the desired estimate.
13.2.7.2 R2
The ideal value of R2 is 1, but to reach the hallmarks of an optimized qPCR, a R2 < 0,99 might indicate a
questionable accuracy of the data. The standard curve for CR-1 shows R2 below the estimated criteria.
This could be caused by poor pipetting technique of the standards. For TBP, R2 lies within the range of
the appropriate estimate. (43)
13.2.7.3 Slope
For CR-1 the slope is more positive than the ideal estimate, which points to a poor pipetting technique
in the PCR preparation. The TBP slope value lies within the desired estimate.
13.2.7.4 Ct
The ideal qPCR test will indicate that the starting amount will double after each cycle during the exponential phase, in other words the PCR will be operating at 100 % efficiency.
For CR-1 the Ct values do not follow these criteria, and thus there is not 100 % efficiency. On the other
hand TBP follows these estimated values approximately.
Page 35 of 68
13.2.8 MELTING CURVE
When analyzing the melting curve of CR-1 in relation to whether or not CR-1 amplicon is present in the
data, implementing the positive dissociation control for CR-1 is relevant. The positive control for CR-1
is embryonic stem cells which are known to express the CR-1 gene, (chp. 7.0). The positive control
dissociation curve for CR-1 peaks at 86 oC, and if a peak is present in the dissociation curve for the CR1 wells, it indicates the presence of CR-1 gene in the data. This seems to be the case in this study, as a
cluster of peaks is present in the dissociation curve for the wells of CR-1 for both U87 and C16 at approximately 86 oC.
When analyzing the melting curves (apx. 15.3.2) several clusters of fluorescence-peaks are present at
certain temperatures. Having more than one peak in a melting curve indicates that contaminating
products are present. Contaminating of qPCR analysis may be the result of unspecific primers.
BLASTing each of the nucleotide-sequences revealed that all four primers bind to a relatively large
amount of genomic scaffolds, which make the primers unspecific. But more interestingly, they each
bind specifically to certain variations of their specific gene as seen in tab. 13.2.8.1.
Primer
TBP, fw/rv
Bindings
Query-cover
TBP, transcript variant 1, mRNA
100 %
TBP, transcript variant 2, mRNA
CR-1, fw/rv CR-1, transcript variant 1, mRNA
100 %
CR-1, transcript variant 2, mRNA
CR-1, pseudogene 3, non-coding RNA
Table 13.2.8.1 representing the variants to which the primers have an affinity to bind to.
The binding of primers to these variants may result in up-formation of unwanted mRNA if they are
present, which creates the cluster of peaks on the dissociation curve (apx. 15.3.2). As seen on the dissociation curve for CR-1, clusters of spikes were clearly visible at other temperatures than the expected temperature of 84-85 oC. These spikes are speculated to erupt from the formation and breaking
of primer-dimers (42), genomic DNA or the gene-variants.
Conducting a gel-electrophoresis before analyzing the data through qPCR would help in indicating
possible contamination of the data, e.g. the presence of primer-dimers, isogenes or genomic DNA. Primer-dimers would represent themselves as a milky-white cloud with approximately 100 base-pair
(42). The negative dissociation curve for CR-1 indicates a peak in fluorescence at about 79 oC, interestingly; this corresponds to the peak at 79 oC on the dissociation curve for the CR-1 wells (app. 15.0).
This contributes to the idea that primer-dimers are formed and contaminating the data, since no peak
should be present on the negative dissociation curve.
At the end of the dissociation curve of the CR-1 wells there are a series of changes in fluorescence level. This occurs after the amplicon-spikes presenting themselves at 84-85 oC. These somewhat smaller
changes in fluorescence at a relatively high temperature (86-94 oC) could be due to the presence of
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
broken genomic DNA, since even broken genomic DNA are long strands of human DNA, requiring more
energy in the form of heat, to separate the DNA-strings. The amount of genomic DNA found in the data
is dependent on the nucleic acid purification-process carried out prior to analysis (42).
When BLASTing for primer design for CR-1 it can be observed that it can bind 100 % to two variants, a
long (variant 1) and a short (variant 2) (49). This could possibly explain why the two peak clusters
appear at 79 oC and 86 oC. The longer the sequence, the higher temperature needs to be in order to
melt the hydrogen-bonds, therefore the melting curve shows a peak that is skewed to the right.
GC-nucleotide content and GC-clamps in the DNA are excluded as factors for the primer non-specificity
for both CR-1 and TBP. The percentage of G’s and C’s of the total bases lie within the normal range,
which is 40-60 % (48). Moreover, what can be observed in CR-1 and TBP’s structure is that the primers d(48)o not have more than 3 G’s or C’s in the last 5 bases at the 3’ end of the primer, fact that
promotes specific binding at the 3’ end because of the stronger bonding of G and C bases.
In the CR-1 dissociation curves, a difference can be seen in regards to the fluorescence-level of the
wells-, standards- and control-samples. This indicates a difference in the amount of amplicon produced in each well, as the absorbance of light decreases as less amplicon is present and vice versa.
Page 37 of 68
14.0 - CONCLUSION
The aim of this study was to investigate and characterize C16 in relation to U87. It is difficult to conclude on data as the SD was so large that the data had no significance. For this reason this study has
mainly followed tendencies rather than validation of data.
In relation to cell counting there has been focused on investigating C16 and U87 proliferation rate
changes from day 1 normoxia throughout day 4, 5 and 6 normoxia and from day 1 normoxia throughout day 4, 5 and 6 hypoxia. Furthermore the changes in the proliferation rates of U87 and C16 were
compared. Cell counting has revealed that the proliferation rate for U87 and C16 is not similar. U87 did
not follow the theory as it proliferated more in normoxia than in hypoxia. Conversely, C16 proliferated
as expected, namely more in hypoxia than in normoxia, when referring to doubling time.
Considering CR-1 gene expression it has been investigated for C16 and U87 how the start quantity of
CR-1 will change from day 1 normoxia to day 4 normoxia and from day 1 normoxia to day 4 hypoxia. It
can be indicated on basis of data that C16 CR-1 gene expression is decreasing from day 1 to day 4 both
in the normoxic and hypoxic environment. On the other hand, U87N CR-1 gene expression increases
from day 1 to day 4, but decreases in hypoxia.
Moreover fold changes in start quantity of CR-1 from day 1 normoxia to day 4 normoxia for C16 have
been compared to fold changes in start quantity of CR-1 from day 1 normoxia to day 4 normoxia for
U87. C16d1N vs. C16d4N suggest that in C16d1N CR-1 is considerably more expressed than in
C16d4N. U87d1N vs. U87d4N points out that in U87d4N CR-1 is several times more expressed than in
U87d1N. In addition fold changes in start quantity of CR-1 from day 1 normoxia to day 4 hypoxia for
C16 have been compared to fold changes in start quantity of CR-1 from day 1 normoxia to day 4 hypoxia for U87. Both C16d1N vs. C16d4H and U87d1N vs. U87d4H indicate a higher CR-1 gene expression on day 1 than on day 4.
Finally fold changes in start quantity of CR-1 from day 1 normoxia to day 4 hypoxia for C16 were compared to fold changes in start quantity of CR-1 from day 1 normoxia to day 4 hypoxia for U87. The results show that the fold changes are markedly larger for U87d1N vs. U87d4N compared to C16d1N vs.
C16d4H.
The qPCR results indicate that C16 and U87 are not similar in CR-1 gene expression.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
15.0 - BIBLIOGRAPHY
(1) Ledingham JGG, Warrell DA. Concise Oxford textbook of medicine. Oxford: Oxford University Press; 2000.
(2) Seidel S, Garvalov BK, Wirta V, von Stechow L, Schanzer A, Meletis K, et al. A hypoxic niche
regulates glioblastoma stem cells through hypoxia inducible factor 2 alpha. Brain 2010 Apr;133(Pt
4):983-995.
(3) Preusser M, de Ribaupierre S, Wohrer A, Erridge SC, Hegi M, Weller M, et al. Current concepts
and management of glioblastoma. Ann Neurol 2011 Jul;70(1):9-21.
(4) Reddy PS, Umesh S, Thota B, Tandon A, Pandey P, Hegde AS, et al.
PBEF1/NAmPRTase/Visfatin: a potential malignant astrocytoma/glioblastoma serum marker with
prognostic value. Cancer Biol Ther 2008 May;7(5):663-668.
(5) Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, et al. Radiotherapy
plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005 Mar
10;352(10):987-996.
(6) Bar EE. Glioblastoma, cancer stem cells and hypoxia. Brain Pathol 2011 Mar;21(2):119-129.
(7) Hypoxia and hypoxia-inducible factors in gliobl... [Exp Cell Res. 2012] - PubMed - NCBI.
Available at: http://www.ncbi.nlm.nih.gov/pubmed/?term=Liugi+Yang+2012. Accessed 5/28/2013,
2013.
(8) Bianco C, Salomon DS. Targeting the embryonic gene Cripto-1 in cancer and beyond. Expert
Opin Ther Pat 2010 Dec;20(12):1739-1749.
(9) Kleihues P, Louis DN, Scheithauer BW, Rorke LB, Reifenberger G, Burger PC, et al. The WHO
classification of tumors of the nervous system. J Neuropathol Exp Neurol 2002 Mar;61(3):215-25;
discussion 226-9.
(10) Hemmati HD, Nakano I, Lazareff JA, Masterman-Smith M, Geschwind DH, Bronner-Fraser
M, et al. Cancerous stem cells can arise from pediatric brain tumors. Proc Natl Acad Sci U S A
2003 Dec 9;100(25):15178-15183.
(11) Available at: https://www.sundhed.dk/sundhedsfaglig/laegehaandbogen/neurologi/tilstande-ogsygdomme/neurokirurgi/hjernesvulster-hos-voksne/.
(12) Kleinsmith LJ. Principles of cancer biology. San Francisco: Pearson Benjamin Cummings;
2006.
(13) McCance KL, Huether SE. Pathophysiology: the biologic basis for disease in adults and children. 6. ed. ed. St. Louis, Mo.: Mosby Elsevier; 2010.
(14) Keith B, Simon MC. Hypoxia-inducible factors, stem cells, and cancer. Cell 2007 May
4;129(3):465-472.
Page 39 of 68
(15) Bertout JA, Patel SA, Simon MC. The impact of O2 availability on human cancer. Nat Rev
Cancer 2008 Dec;8(12):967-975.
(16) Galli R, Binda E, Orfanelli U, Cipelletti B, Gritti A, De Vitis S, et al. Isolation and characterization of tumorigenic, stem-like neural precursors from human glioblastoma. Cancer Res 2004 Oct
1;64(19):7011-7021.
(17) Singh SK, Hawkins C, Clarke ID, Squire JA, Bayani J, Hide T, et al. Identification of human
brain tumour initiating cells. Nature 2004 Nov 18;432(7015):396-401.
(18) Pecorino L. Molecular biology of cancer: mechanisms, targets, and therapeutics. 3. ed. ed. Oxford: Oxford University Press; 2012.
(19) Huang Z, Cheng L, Guryanova OA, Wu Q, Bao S. Cancer stem cells in glioblastoma-molecular signaling and therapeutic targeting. Protein Cell 2010 Jul;1(7):638-655.
(20) Gil J, Stembalska A, Pesz KA, Sasiadek MM. Cancer stem cells: the theory and perspectives in
cancer therapy. J Appl Genet 2008;49(2):193-199.
(21) Gilbert CA, Ross AH. Cancer stem cells: cell culture, markers, and targets for new therapies. J
Cell Biochem 2009 Dec 1;108(5):1031-1038.
(22) Zhou BB, Zhang H, Damelin M, Geles KG, Grindley JC, Dirks PB. Tumour-initiating cells:
challenges and opportunities for anticancer drug discovery. Nat Rev Drug Discov 2009
Oct;8(10):806-823.
(23) Ivanovic Z. Hypoxia or in situ normoxia: The stem cell paradigm. J Cell Physiol 2009
May;219(2):271-275.
(24) Xu C, Wu X, Zhu J. VEGF promotes proliferation of human glioblastoma multiforme stemlike cells through VEGF receptor 2. ScientificWorldJournal 2013;2013:417413.
(25) Wilson WR, Hay MP. Targeting hypoxia in cancer therapy. Nat Rev Cancer 2011
Jun;11(6):393-410.
(26) Weinberg RA. The biology of cancer. New York: Garland Science; 2007.
(27) Bianco C, Cotten C, Lonardo E, Strizzi L, Baraty C, Mancino M, et al. Cripto-1 is required for
hypoxia to induce cardiac differentiation of mouse embryonic stem cells. Am J Pathol 2009
Nov;175(5):2146-2158.
(28) Available at: http://www.ugeskriftet.dk/LF/UFL/2005/20/pdf/VP44974.pdf.
(29) Fenger C. Almen patologi: teori og praksis. 2. udgave ed. Kbh.: F.A.D.L.; 2005.
(30) Saloman DS, Bianco C, Ebert AD, Khan NI, De Santis M, Normanno N, et al. The EGF-CFC
family: novel epidermal growth factor-related proteins in development and cancer. Endocr Relat
Cancer 2000 Dec;7(4):199-226.
(31) Bianco C, Rangel MC, Castro NP, Nagaoka T, Rollman K, Gonzales M, et al. Role of Cripto-1
in stem cell maintenance and malignant progression. Am J Pathol 2010 Aug;177(2):532-540.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
(32) De Silva T, Ye G, Liang YY, Fu G, Xu G, Peng C. Nodal promotes glioblastoma cell growth.
Front Endocrinol (Lausanne) 2012;3:59.
(33) Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000 Jan 7;100(1):57-70.
(34) Available at: http://www.lgcstandards-atcc.org/Products/All/HTB-14.aspx.
(35) Clark MJ, Homer N, O'Connor BD, Chen Z, Eskin A, Lee H, et al. U87MG decoded: the genomic sequence of a cytogenetically aberrant human cancer cell line. PLoS Genet 2010 Jan
29;6(1):e1000832.
(36) Brüel A. Genesers histologi. 2012:761 sider, ill. (nogle i farver).
(37) Fink T, Lund P, Pilgaard L, Rasmussen JG, Duroux M, Zachar V. Instability of standard PCR
reference genes in adipose-derived stem cells during propagation, differentiation and hypoxic exposure. BMC Mol Biol 2008 Oct 31;9:98-2199-9-98.
(38) Radonic A, Thulke S, Mackay IM, Landt O, Siegert W, Nitsche A. Guideline to reference gene
selection for quantitative real-time PCR. Biochem Biophys Res Commun 2004 Jan 23;313(4):856862.
(39) Valente V, Teixeira SA, Neder L, Okamoto OK, Oba-Shinjo SM, Marie SK, et al. Selection of
suitable housekeeping genes for expression analysis in glioblastoma using quantitative RT-PCR.
BMC Mol Biol 2009 Mar 3;10:17-2199-10-17.
(40) Kreth S, Heyn J, Grau S, Kretzschmar HA, Egensperger R, Kreth FW. Identification of valid
endogenous control genes for determining gene expression in human glioma. Neuro Oncol 2010
Jun;12(6):570-579.
(41) Available at: http://www.ncbi.nlm.nih.gov/gene/6908.
(42) Available at:
http://corelabs.cgrb.oregonstate.edu/sites/default/files/Real%20Time%20PCR.From%20Theory%20
to%20Practice.pdf.
(43) Available at: http://www.gu.se/digitalAssets/1125/1125331_ABI__Guide_Relative_Quantification_using_realtime_PCR.pdf.
(44) Available at: http://www6.appliedbiosystems.com/support/tutorials/pdf/rtpcr_vs_tradpcr.pdf.
(45) ADLER S, ROY A, RELMAN AS. Intracellular Acid-Base Regulation. I. the Response of
Muscle Cells to Changes in Co2 Tension Or Extracellular Bicarbonate Concentration. J Clin Invest
1965 Jan;44:8-20.
(46) Louis KS, Siegel AC. Cell viability analysis using trypan blue: manual and automated methods. Methods Mol Biol 2011;740:7-12.
(47) Available at:
http://books.google.dk/books?id=6Nz_87OLrtcC&printsec=frontcover&hl=da#v=onepage&q&f=fa
lse.
Page 41 of 68
(48) Available at: http://www.premierbiosoft.com/tech_notes/PCR_Primer_Design.html.
(49) Available at: http://www.ncbi.nlm.nih.gov/nucleotide/292494881.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
16.0 - APPENDIX
16.1 – PROTOCOLS
16.1.1 - FCS MEDIUM F12
O BJECTIVE :

To mix together FCS Medium F12 for use in the analysis of cell growth of C16, according to protocol part 2.
M ATERIALS :




DMEM F12 (The medium itself)
FCS
Pen. 10 ku/ml / Strep. 10 mg/ml
Gentamicin, 10 mg/ml
(Lonza, Cat.no:12-719)
(10 %, Invitrogen)
(1%, Invitrogen, Cat.no: 15140-122)
(0,5%, Invitrogen, Cat.no: 15710-049)
M ETHOD :
Mix contents depending on desired volume and pass through a 0.22 mm filter for sterility. All volumes
are in ml.
Total volume
DMEM F12
FCS
Pen./ Strep.
Gentamicin
50.00
50
5
0.50
0.25
100.00
100
10
1,00
0,50
250,00
250
25
2,50
1,25
500,00
500
50
5,00
2,50
C OMMENTS :
The medium can be used for a month, if used later the medium should be filtered through a 0.22 mm
filter for sterility again.
Total volume is: 500+50+5+2,5 ml = 575,5 ml in total.
The mixing-process is carried out in the LAF-bench under sterile conditions.
Page 43 of 68
16.1.2 - CHANGING MEDIUM
O BJECTIVE :

To replace the old medium in which the cells lie, with fresh medium.
M ATERIALS :







FCS DMEM F12 (15 mL/flask)
Pipetboy + Stripette pipette
T75 Flask x 4
Plastic cup for medium waste
Sterile glass for the medium
Plastic bag + holder for pipettes and waste
70 % ethanol for disinfection
(25 mL)
(2 w. C16 & 2 w. U87)
M ETHOD :
1. Put on gloves (white ones) and disinfect the LAF-bench with 70 % ethanol
2. Retrieve the T75 flasks from the Incubator.
3. Place T75 flasks, medium, waste-containers, pipetboy (disinfect first!) and stripette-pipettes
systematically inside the LAF-bench.
4. Disinfect the gloves with 70 % ethanol inside the LAF-bench
5. Extract 60 mL of medium into the sterile glass-container
6. Gently tip the T75 flask you are working with, from one side to another, to shake off any dead
cells.
7. Unscrew the lid from the T75 flask and pour out the waste-medium into the plastic cup without touching the side of the cup (minimizes contamination risk).
8. Place the 25 mL Stripette-pipette in the Pipetboy and extract 15 mL of the fresh medium.
9. Tap the 15 mL of medium into the T75 flask, down the side opposing the one lined with cells.
10. Place the Stripette-pipette in its original packaging once more, and throw it into the plastic bag.
11. Tighten the lid and place the T75 flask in the incubator. Repeat the process.
C OMMENTS :




All unscrewed lids must be placed in the back of the LAF-bench as to not risking contaminating
them.
Avoid moving your hands above any of the components to minimize contamination-risk.
Remember to turn the Stripettes correctly while still keeping them in their original packaging.
Before and after changing medium, we check for dead cells in the microscope
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
16.1.3 - CELL COUNTING AND TRYPANE-BLUE COLORING
O BJECTIVE :

The objective of this protocol is to color the cells with trypane-blue, and enable cellcounting using a haemocytometer.
M ATERIALS :





S-PBS
Trypsin
EDTA
Medium
Trypan-blue
(Cambrex, Cat. no: BE17-515Q)
(Invitrogen, Cat. no: 15090-046)
(Bie og Berntsen, Cat. no: 1.08418.0250)
M ETHOD :
1.
2.
3.
4.
5.
6.
7.
Remove (and store) medium.
Wash cells in S-PBS twice to remove remaining medium.
Add Trypsin/EDTA according to table. Incubate 2-5 min in incubator; follow the detachment
on the microscope.
Add medium according to table.
If needed spin cells down at 300g for 5min to reach appropriate cell concentration for seeding/counting.
Count cells using Trypane blue and haemocytometer with a reaction volume of 20µl.
Seed cells in new wells/bottles in appropriate cell seeding density (dependent on cell type).
175cm2 Bottle (T175)
75cm2 Bottle (T75)
25cm2 Bottle (T25)
6 Well plate (pr. Well) (9.2)
12 Well plate (pr. Well) (3.8)
24 Well plate (pr. Well) (1.9)
48 Well plate (pr. Well) (0.8)
96 Well plate (pr. Well) (0.32)
1 Well slide (pr. Well) (9.4)
2 Well slide (pr. Well) (4.2)
4 Well slide (pr. Well) (1.8)
8 Well slide (pr. Well) (0.8)
100 mm dish (78)
S-PBS
Trypsin/EDTA
Medium
14 ml
10 ml
5 ml
1 ml
0.5 ml
0.5 ml
0.3 ml
0.3 ml
1 ml
0.5 ml
0.5 ml
0.3 ml
10 ml
3 ml
1.5 ml
1 ml
0.5 ml
0.3 ml
0.3 ml
0.1 ml
0.1 ml
0.5 ml
0.3 ml
0.3 ml
0.1 ml
3 ml
30 ml
15 ml
6 ml
2 ml
1 ml
0.5 ml
0.3 ml
0.2 ml
2 ml
1 ml
0.5 ml
0.3 ml
15 ml
Page 45 of 68
16.1.4 - CHARACTERIZATION OF C16 CELL GROWTH CHARACTERISTICS
O BJECTIVE :

To investigate the effect of hypoxic culture format on primary C16 cells, relating to the expression of CR-1, and answer the following questions:
1. Does C16 cells’ proliferation-rate change in this culture format compared to normoxia?
2. Can the in-house established glioma cell line of C16 be used on equal terms as the model cell line U87?
C ELL LINES :


U87
C16
M ATERIALS :



42 Tissue treated T25 flasks
Hypoxic chamber at 5% oxygen
Control media FCS Medium F12
S ETUP :
Day 0 (Thursday May 2nd) Culture initiation:
Each cell line is seeded at 1000 cells/cm2 (5000 cells/ml) in 21 T25
 9 for hypoxia and 9 for normoxia
 Monolayer cultures are kept in control media with FCS added
Day 1 (Friday May 3rd):
 For each cell line count the cells in 3 T25 by trypane blue
 Initiate hypoxic culture by moving ½ of remaining cultures of C16 and U87 (9) to 5 % oxygen
Day 4 (Monday May 6th):
 For each cell line and culture format count the cells in 3 T25 by trypane blue
Day 5 (Tuesday May 7th):
 For each cell line and culture format count the cells in 3 T25 by trypane blue
Day 6 (Wednesday May 8th):
 For each cell line and culture format count the cells in 3 T25 by trypane blue
C OMMENTS :
Day 0 correlates to setup day 1.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
16.1.5 - MRNA ISOLATION BY AURUM TOTAL RNA MINI KIT
O BJECTIVE :

Isolation of mRNA from C16 cell population for use in the creation of cDNA in protocol part 4.
M ATERIALS :





Aurum totalRNA mini kit
S-PBS
70% EtOH prepared with DEPC water
DEPC water
RNAzap
(BioRad, Cat. no: )
( , Cat. no:)
(Gibco, Cat. no: )
(Sigma-Aldrich, Cat. no: )
M ETHOD :
1. Prepare lysis buffer by addition of b-mercaptoethanol, in the ratio 100:1 (10 ml pr. 1 ml) Prepare 350 ml pr. sample and 5 % extra.
2. Remove media from well and wash twice in S-PBS
3. Add 350 ml lysis buffer and incubate for 10 sec followed by pipetting up and down 10 times.
a. Store at -80°C till isolation. Thaw on ice
b. Add 350 ml ice cold 70 % EtOH and mix well.
c. Insert RNA binding column into a 2 mL capless tube.
d. Transfer lysate, centrifuge for 30 sec at 12000 RPM and discard filtrate.
e. Add 700ml low stringency wash, centrifuge for 30 sec at 12000 rpm, and discard filtrate.
f. Pr. sample dilute 5 ml reconstituted DNase I with 75 ml DNAse dilution solution. Prepare for one sample extra.
g. Add 80 ml diluted DNase I and incubate for 15 min at RT.
h. Centrifuge for 30 sec at 12000 rpm, and discard filtrate.
i. Add 700 ml high stringency wash, centrifuge for 30 sec at 12000 rpm and discard filtrate.
j. Add 700 ml low stringency wash, centrifuge for 1 min at 12000 rpm and discard filtrate.
k. Centrifuge for additional 2 min at 12000 rpm.
l. Place RNA binding column into 1.5mL capped tube.
m. Add 80 ml 70 °C elution solution onto membrane stack and incubate 1 min.
n. Centrifuge for 2 min at 12000 rpm to elute.
o. Store at -80 °C until analysis.
C OMMENTS :





Before starting the isolation procedure at step 5 clean all surfaces with RNAzap to prevent RNA
degradation by RNAses.
Work in RNA lab only from step 5.
Wear gloves at all times.
Use RNAse free tips, BioPure
Except for the elution step all steps are carried out at RT.
Page 47 of 68
16.1.6 - CDNA-SYNTHESIS BY ISCRIPT CDNA SYNTHESIS KIT
O BJECTIVE :

To create cDNA of the original CR-1-coding parts of the C16 cell line DNA for use in PCRanalysis.
M ATERIALS :


iScript cDNA synthesis Kit
RNAzap
(BioRad, Cat. no: 170-8891)
(Sigma-Aldrich, Cat. no: )
M ETHOD :
1. Mix a mastermix of 4 ml 5x iScript Reaction Mix and 1ml iScript Reverse Transcriptase pr.
sample.
2. Aliquots of 5 ml mastermix are put into individual wells in a PCR plate.
3. Add 15 ml RNA sample to each well.
4. Incubate the reaction:
5 min at 25°C
30 min at 42°C
5min at 85°C
Hold at 4°C (optional)
a. Store at -20 °C till use. Thaw in fridge
b. Prepare for a standard curve by collecting 2 ml of each sample in a cDNA mix
c. Dilute 1:10 (30 ml to 270 ml water) in DEPC water before use.
C OMMENTS :
The reaction can be scaled to a total reaction volume of 40 ml instead of 20 ml. Remember to tell the
PCR machine.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
16.1.7 - QPCR BY IQTM SYBR GREEN SUPERMIX
O BJECTIVE :

To perform a qPCR on the previously obtained cDNA, to investigate the presence of CR-1coding DNA.
M ATERIALS :


iQTM SYBR Green Supermix
RNAzap
(BioRad, Cat. no: 170-8880)
(Sigma-Aldrich, Cat. no: R2020 )
M ETHOD :
Standard-curve;
1. Prepare a standard curve by first mixing 1 ml diluted cDNA (1:30) from each sample to be run
+ 1 µl diluted cDNA (1:30) from each positive control.
2. From the mix of cDNA prepare std. 1024: 675 ml H2O + 75 ml cDNA mix.
3. Prepare 4-fold dilutions: 450 ml H2O + 150 ml standard, vortex, spin, next (256, 64, 16, 4, and
1)
Analysis;
1. Prepare a mastermix of 25 ml SYBR green, 1 ml 10 pmol forward primer, 1 ml 10 pmol reverse
primer and 1 ml water pr. sample. Prepare mastermix for n+1 sample
2. In PCR-tubes, mix 28 ml mastermix with 25 ml diluted cDNA (1:30)
3. For each sample, aliquot 25 ml reaction mix into individual wells in a PCR plate for duplicate
analysis.
4. Run the plate within 24 hours as follows
1x
3 min 95°C
40x
30 sec 95°C
30 sec Annealing temp
1x
1 min 95°C
30 sec 55°C
30 sec 95°C
P LATE SETUP :
A
B
C
D
E
F
G
H
1
Std1
2
Std2
3
Std3
4
Std4
5
Std5
6
Std6
7
Neg
8
Pos
Std1
1
1
Std2
2
2
Std3
4
4
Std4
5
5
Std5
7
7
Std6
8
8
Neg
19
19
Pos
20
20
9
10
11
12
22
22
23
23
25
25
26
26
C OMMENTS :
The reaction can be stored overnight at 4°C and run the day after. Remember to spin down before analyzing.
Page 49 of 68
16.2 – APV AND APB
16.2.1 – APV
Chemical risk for mutagenic, teratogenic and carcinogenic substances (Extended APV)
Compound:
Aim:
Date:
Semester/group nr.:
Name:
Laboratory Technician:
Supervisor:
Trypane blue
Staining of cells for cell counting
22/04/2013
403
Daniel Andreas Vestergaard
Sanne Ørnfeldt Larsen
Maria Lukacs
Jonas Ellegaard Nielsen
Line Bay Sørensen
Lotti Eggers-Kaas
Ditte Bech Kristensen
Linda Pilgaard
P ROCES DESCRIPTION
1. In a centrifuge tube are mixed 0.2% Trypane blue and a single cell suspension in a ratio of 1:1.
Wait for 5 minutes.
2. Count the cells in the haemocytometer (counting quadrants are divided into 16 x 9 squares).
Dead cells absorb trypane blue and get stained. Therefore dead cells are dark, while viable cells
remain bright. After a while the viable cells also die as they incorporate trypan blue. Note that,
when staining a 2x dilution of the cell suspension is used when added trypan blue. Both the total cell number and the number of dead (stained) cells is determined.
The percentage of live (viable) cells is determined (viability):
% Viable cells = number of viable cells x 100 / total cells
C HEMICALS
Name, cas. nr. og concentration
1. TRYPAN BLUE ,
CAS. NR. 72-57-1,
CONCENTRATION 0,4 %
SIGNIFICANT RISKS OF CHEMICALS
H or R statements
H350:
R 45:
P201:
P308/313:
S53:
S45:
May cause cancer.
May cause cancer.
Obtain special instructions before use.
If exposed or concerned: get medical advice/attention.
Avoid exposure – obtain special instructions before use.
In case of accident or if you feel unwell seek medical advice immediately (show the label
where possible)
SIGNIFICANT RISKS FROM working process (eg. Laser, vacuum mixing of chemicals, heating
the reagents etc.)

Nothing to indicate.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
POTENTIAL IMPACT (Rate real risk in the process. The fact that the chemical are dangerous when
inhaled does not mean that the process has an inhaling risk. Consider when in the process the risk is
present - is it under the whole process or only in one part).
On contact with skin:
Suspected of:
Carcinogen
Damage to the reproductive organs, eggs, sperm, embryo
INHALATION
Acute effects:
• Possibly irritation of the upper respiratory tract
Long-term effects:
• Carcinogenic.
• Suspected of having negative effect on fertility.
• May cause damage to the thyroid gland.
SKIN
Acute effects:
• Possibly irritation
Long-term effects:
• No information is available.
EYES
• Possibly irritation.
INGESTION
• Possibly irritation of the gastro-intestinal mucous membranes. Symptoms of nausea, vomiting and
diarrhea.
SUBSTITUTION CONSIDERATIONS (Here is explained how there has been thought and tried to substitute the dangerous chemicals or processes. Remember that use of small amounts rather than large
quantities is also a substitution).

Trypane blue is the most frequently used coloring method for cell counting. The compound is
easy to work with and cheap. www.kemibrug.dk reports no substitute.
N ECESSARY P RECAUTIONS
___________________________________________________________________________
Ventilation:
Stinkskab: Yes
Punktsug: No
Laf-bænk: No
Other: Is/are the specified needed under the whole working process or only partially, describe: No
Chemical protective gloves:
Type: Nitrile gloves
Are the specified gloves needed in the whole working process or only partially: No
___________________________________________________________________________
Other personal protective equipment:
Coat: Yes
Safety glasses: Respiratory system protection(specify filter): Special shoes (specify type):Other:Page 51 of 68
Is/are the specified needed under the whole
process or only partially, describe:___________________________________________________________________________
Other precautions
Heat source v. risk for fire:
Other: Cannot burn. The substance decomposes when heated. It forms toxic gases /
vapors such as carbon monoxide, nitrogen
oxides and sulfur oxides.
___________________________________________________________________________
Special emergency equipment
Fire protection system: Antidote: Others:___________________________________________________________________________
Specific education or instructions:
Education required by law, which: Instruction in the use of certain dangerous equipment: No
WHAT TO DO IN CASE ACCIDENT OR WASTE? (This describes the actions in case of accident, recovery and disposal of waste, procedure for information on incidents, etc.)
INHALATION:
The person is transported out to fresh air, kept at rest and under observation. If there is a risk of unconsciousness mobilize and keep the person warm. If the person is not breathing, give artificial respiration.
SKIN:
Rinse for thoroughly with water, remove contaminated clothing and jewelry.
EYES:
Immediately rinse with water. Rinse eyelids thoroughly. Remove lenses. Continue flushing until medical attention is obtained.
INGESTION:
Immediately wash mouth and drink water or milk. Never give fluids to unconscious. Do not induce
vomiting. For persistent symptoms seek medical attention and bring these instructions.
WASTE AND DISPOSAL
• Evacuate the area if necessary. Avoid dispersal and ventilate the area.
• Avoid contact with the substance. If possible use gloves and respiratory equipment with combination filter (type ABEK-P).
• Waste is absorbed with vermiculite or sand and disposed with hazardous waste label: "Contains
a substance that is reckoned by the Danish health and safety regulation as having cancer risk" (official
yellow sticker).
• Clean the area after collection of waste. Inform the municipal authorities and the institution's
environmental managers if major waste has been released to the environment.
Waste group
B
Class:
-
Class. code:
-
PKG:
-
UN nr.
No risks by transporting on the road
PREGNANT AND LACTATING
(Is the process safe for pregnant and breastfeeding)
Suspected to cause damage to the reproductive organs, eggs, sperm, embryo.
16.2.2 - APV
Safety in production of reagents.
It is the concentrated substances or stock solutions that must be reported.
Name
Cas. nr.
56-81-5
-
Concentration
Glycerol 5-10 %
Unpublished Non Ionic Detergent 2,5-5 %
iQ™ SYBR Green
Supermix
-
67-68-5
RNaseZAP®
2893-78-9
H/R-statements
The producer estimates that the compound does not have to be classified
according to Miljøministeriets
Bekendtgørelse nr. 1075 af 24.11.2011
concerning classification and labeling
etc.
P/S- statements
-
H
Unpublished carbohydrat 20
%
Dimethylsulfoxid 9,5 %
0-1%
H410: Very toxic to aquatic life with
long lasting effects.
R34: Causes burns.
H225: Highly flammable liquid and
vapour .
64-17-5
Waste
group
The producer estimates that the compound does not have to be classified
according to Miljøministeriets
Bekendtgørelse nr. 1075 af
24.11.2011 concerning classification
and labeling etc.
P273: Avoid release to the environment R26: Very toxic by inhalation.
R36/37/39: Use special clothing
suitable gloves and glasses.
Ethanol 70% v/v in
water
Pictures
70%
P210: Keep away from
heat/sparks/open flames/hot surfaces
– No smoking.
P260: Do not breathe
dust/fume/gas/mist/vapours/spray
B
C
H350: May cause cancer.
R45: May cause cancer.
P201: Obtain special instructions
before use .
P308/313: IF exposed or concerned:
Get medical advice/attention.
Trypanblå
72-57-1
0,4 %
B
S53: Avoid exposure - obtain special
instructions before use .
S45: In case of accident or if you feel
unwell seek medical advice immediately (show the label where possible) .
Fetal Bovine Serum
-
≥ 99,0 %
There is no information/dokumentation,
arguing for labeling of the product.
"Caution - substance not yet tested
completely".
There is no information/dokumentation, arguing for
labeling of the product.
"Caution - substance not yet tested
completely".
H319: Causes serious eye irritation.
P280: Wear protective
gloves/protective clothing/eye protection/face protection.
H335: May cause respiratory irritation.
H315: Causes skin irritation.
Penicillin
10 KU/ml
-
Strep.
10 mg/ml
H317: May cause an allergic skin
reaction.
R43: May cause sensitisation by skin
contact.
R36/37/38 Irritating to eyes, respiratory system and skin.
H319: Causes serious eye irritation.
Gentamicin
1403-66-3
10 mg/ml
S26: In case of contact with eyes, rinse
immediately with plenty of water and
seek medical advice.
-
H
Z
S36/37: Wear suitable protective
clothing and suitable gloves.
S24/25: Avoid contact with skin and
eyes.
H315: Causes skin irritation.
P280: Wear protective
gloves/protective clothing/eye protection/face protection.
H317: May cause an allergic skin
P302/352: If on skin wash with soap
H
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
reaction.
and water.
H335: May cause respiratory irritation.
R36/37/38: Irritating to eyes, respiratory system and skin.
R43: May cause sensitisation by skin
contact.
P304/340: If inhaled remove victim
to fresh air and keep at rest in a position comfortable for breathing.
P305/351/338: If in eyes rinse cautiously with water for several minutes.
Remove contact lenses if present and
easy to do. continue rinsing.
S26: In case of contact with eyes,
rinse immediately with plenty of water and seek medical advice.
S36/37: Wear suitable protective
clothing and suitable gloves.
S24/25: Avoid contact with skin and
eyes.
H317: May cause an allergic skin
reaction.
Nuclease- free water
-
-
-
-
-
R 42/43: May cause sensitisation by
inhalation and skin contact..
Safety for the diluted substances.
It is the final concentration in the mixed reagents that must be reported and not-labeled chemicals.
Name
Fetal Bovine Serum
Cas. nr.
-
Concentration
10 %
H/R- statements
There is no information/dokumentation,
arguing for labeling of the product.
"Caution - substance not yet tested
completely".
P/S- statements
There is no information/dokumentation,
arguing for labeling of the product.
"Caution - substance not yet tested completely".
Picture
Waste group
-
H
Page 55 of 68
H350: May cause cancer.
R45: May cause cancer.
P201: Obtain special instructions before
use.
P308/313: IF exposed or concerned: get
medical advice/ attention.
Trypanblå
72-57-1
0,2 %
B
S53: Avoid exposure - obtain special instructions before use.
S45: In case of accident or if you feel unwell seek medical advice immediately
(show the label where possible).
Trypsin-EDTA x1
9002-07-7
0,1 %
The product is not classified according to
Miljøministeriets Bekendtgørelse nr. 1075
af 24.11.2011 concerning classification and
labeling etc.
The product is not classified according to
Miljøministeriets Bekendtgørelse nr. 1075
af 24.11.2011 concerning classification
and labeling etc.
H319: Causes serious eye irritation.
P280: Wear protective gloves/protective
clothing/eye protection/face protection.
H335: May cause respiratory irritation.
H315: Causes skin irritation.
Penicillin
Strep.
-
100 u/ml
0,1 mg/ml
H317: May cause an allergic skin reaction.
R43: May cause sensitisation by skin
contact.
-
H
S26: In case of contact with eyes, rinse
immediately with plenty of water and seek
medical advice.
S36/37: Wear suitable protective clothing
and gloves.
Z
S24/25: Avoid contact with skin and eyes.
R36/37/38: Irritating to eyes, respiratory system and skin.
H319: Causes serious eye irritation.
H315: Causes skin irritation.
Gentamicin
1403-66-3
0,05 mg/ml
H317: May cause an allergic skin reaction.
H335: May cause respiratory irritation.
P280: Wear protective gloves/protective
clothing/eye protection/face protection.
P302/352: If on skin wash with soap and
water.
P304/340: If inhaled remove victim to
fresh air and keep at rest in a position
H
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
R36/37/38: Irritating to eyes, respirato- comfortable for breathing.
ry system and skin.
P305/351/338: : If in eyes rinse cautiousR43: May cause sensitisation by skin
ly with water for several minutes. Remove
contact.
contact lenses if present and easy to do.
continue rinsing.
S26: In case of contact with eyes, rinse
immediately with plenty of water and seek
medical advice.
S36/37: Wear suitable protective clothing
and suitable gloves.
S24/25: Avoid contact with skin and eyes.
Use of personal protective equipment and precautions for use individual reagents.
Name
Protective equipment / gloves / Precautions
RNase ZAP
Coat, nitrile gloves, stink skab
Trypan blå
Coat, stink skab (safety factor 45), nitrile gloves
Trypsin-EDTA 1x
Coat, stink skab
Fetal bovine serum
Coat, stink skab, gloves
Semester/group nr.:403
Laboratory Technician: Ditte B. Kristensen
Date: 22-04-13
Supervisor: Linda Pilgaard
Name:
Line Bay Sørensen
Daniel Andreas Vestergaard
Sanne Ørnfeldt Larsen
Lotti Eggers-Kaas
Jonas Ellegaard Nielsen
Maria Lukacs
Source List:




(http://kiros.chem.au.dk)
(www.kemibrug.dk)
(http://www.kemikalieberedskab.dk)
( http://www.bio-rad.com/webroot/web/pdf/WWMSDS/LSGC/CHE/CHE_ENG_7326800.pdf)
Page 57 of 68
16.3 – RAW DATA
16.3.1 – C ELL COUNTING
D AY 0
U87:
Cell counting:
130, 104,117, 117 → mean = 117∙2 (∙2, as it is diluted 1:1 with trypane
blue) = 234 cells
Concentration:
234∙10^4 cells/mL = 2,34 ∙ 106 cells/mL
Desired amount of flasks:
23 (n+2) flasks of 25.000 cells, i.e. total 575.000 cells

How many mL should be removed from the solution to seed 25.000 cells pr. flask?
575.00/(2,34∙106) = 0,002457 mL = 246 µL - seeded in each flask.
C16:
Cell counting:
224, 201, 225, 216 → mean = 217∙2 = 434 cells
Concentration:
434∙104 cells/mL = 4,34∙106 cells/mL

How many mL should be removed from the solution to seed 25.000 cells pr. flask?
575.00/(4,34∙106) = 0,00132 mL = 132 µL - seeded in each flask.
Growth media calculations: 5 mL media pr. flask
Desired quantity:
n+2 = 115 mL media for each cell line
Day 1
Desired re-suspension: in ca. 50 µL pr. flask.
Today: There should be approximately 25000 cells a flask in 5 mL e.i. 5000 cells/mL.
1. Centrifugation.
2. Pour out media, and make sure that approximately 50 µL media remains in the centrifugation
tube.
Day 1, 4, 5 & 6
Calculation example of cell counting:
Cells counts in one square from one sample: 51, 67, 63, 72 cells
Cell suspension: 150 µL
Mean = (51+67+63+72 cells)/4= 63 cells
To calculate the total amount of cells in one sample/tube:
1. Initially the mean of the 4 cell counts are multiplied by 2, as the sample is diluted 1:1 with trypan blue.
2. Furthermore the mean (multiplied by 2) are then multiplied by 10^4, as the volume of one
square is 0,1 mm3 i.e. 1*10^-4 mL and we want the concentration of cells per 1 mL - cells/mL.
This is done in order to find the quantity of cells per mL in the sample transferred to the hemocytometer.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
3. Finally the concentration is multiplied by the suspension volume in order to find the total
amount of cells in the sample
Total cells = (63 ∗ 2) ∗ 104 cells/mL ∗ 150 mL = 189.000 cells
The following table lists the cell counting:
Day
1
1
1
1
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6
Environment
N
N
N
Mean
N
N
N
Mean
H
H
H
Mean
N
N
N
Mean
H
H
H
Mean
N
N
N
Mean
H
H
H
Mean
U87
41.000
33.300
27.900
34.067
101.600
421.000
21.700
181.433
210.400
56.600
109.000
125.333
229.505
184.800
50.388
154.898
132.000
92.160
114.800
112.987
313.200
285.600
436.800
345.200
290.400
61.000
234.520
195.307
C16
43200
5600
9620
19.473
30340
37100
28860
32.100
10800
21080
37760
23.213
36400
52800
24640
37.947
28000
32000
73500
44.500
73500
45200
28800
49.167
200000
182000
189000
190.333
The following table presents the calculated means without outliers.
Corrected means
Day
1
4
5
6
U87 N
34.067
61650
207153
299400
U87 H
34.067
82800
112987
262460
C16 N
19473
32100
37947
49167
C16 H
19473
23213
44500
190333
The following two tables present the mean and SD for U87 and C16.
Cell line
U87d1N
U87d4N
U87d5N
U87d6N
U87d4H
U87d5H
U87d6H
Mean
34067
181433
154898
345200
125333
112987
195307
SD
6584
211282
93227
80519
78190
19982
119622
Cell line
C16d1N
C16d4N
C16d5N
C16d6N
C16d4H
C16d5H
C16d6H
Mean
19473
32100
37947
49167
23213
44500
190333
SD
20646
4393
14144
22613
13606
25194
9074
Page 59 of 68
The figure represents the means of every cell counting per day for each cell line, all collected in a single
chart. The means of the cell counting from U87 is shown by a blue and light blue graph for normoxia
and hypoxia respectably. In addition the means of the cell counting from C16 is shown by a pink and
light pink graph. Exclusions criteria were taken into consideration and a difference between 2 cell
counting greater than 100000 cells, within the same day, was excluded. The time, measured in days is
presented horizontally whereas the mean of cell number is displayed vertically.
U87N (blue graph) increases 1,8 folds from day 1 to day 4, increases 3,4 folds from day 4 to 5 and increases once more 1,4 folds from day 5 to 6. U87H (light blue graph) increases 2,4 folds from day 1 to
day 4, increases 1,4 folds from day 4 to 5 and increases once more 2,3 folds from day 5 to 6. C16N
(pink graph) increases 1,6 folds from day 1 to day 4, increases 1,2 folds from day 4 to 5 and increases
once more 1,3 folds from day 5 to 6. C16H (light pink graph) increases 1,2 folds from day 1 to day 4,
incre
ase
s
1,9
fold
s
fro
m
day
4 to
5
and increases once more 4,3 folds from day 5 to 6.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
Figure a, b, c & d illustrates the standard deviation (SD) for each mean on the graphs for U87N, U87H, C16N and C16H, respectably. The SD is fairly large for each mean.
16.3.2 – QPCR ANALYSIS
The following four graphs represent the normalized Sq (with outliers) in relation to day 1 and day 4,
for C16 and U87 under normoxic and hypoxic conditions.
The table below presents the normalized Sq, including outliers, used to plot the four graphs above.
Note that the two samples marked red in the table have been excluded for visualization purposes.
Time (days)
1
1
1
1
4
C16: d1n-d4n
0,04
15,04
9,31
84,09
0,04
C16: d1n-d4h
0,04
15,04
9,31
84,09
116,5
U87: d1n-d4n
0,04
0,3
0,21
0,23
0,02
U87: d1n-d4h
0,04
0,3
0,21
0,23
121750158,5
Page 61 of 68
4
4
4
0,13
0,03
0,03
268,03
0,06
1,2
0,05
0,38
6,71
127687727,4
0,01
0,02
In the following table, all Sq-values obtained from qPCR-analysis are presented. No values have been
excluded.
Well Name
C16 CR1
C16 CR1
C16 CR2
C16 CR2
C16 CR4
C16 CR4
C16 CR5
C16 CR5
C16 CR7
C16 CR7
C16 CR8
C16 CR8
U87 CR19
U87 CR19
U87 CR20
U87 CR20
U87 CR22
U87 CR22
U87 CR23
U87 CR23
U87 CR25
U87 CR25
U87 CR26
U87 CR26
SD1
SD2
SD3
SD4
SD5
SD6
NEG
POS
SD1
SD2
SD3
SD4
SD5
SD6
NEG
POS
CR-1 (relative Sq)
4,86
2380
652
7510
5,82
15,4
2,19
2,27
14400
30100
1,06
21,7
0,87
7,96
5,36
7,38
5,47
19,1
107
2110
38400000000
38600000000
3,35
4,58
1020
256
64
16
4
1
0
10200000000
1020
256
64
16
4
1
0
No CT
TBP (relative Sq)
138,5
158,2
70,06
89,31
146,5
117,4
80,04
90,03
123,6
112,3
17,95
18,15
23,85
26,91
25,57
31,93
325,3
387,5
283,1
314,5
315,4
302,3
225,6
241,3
1020
256
64
16
4
1
No CT
479,3
0
256
64
16
4
1
No CT
465
Normalization
0,04
15,04
9,31
84,09
0,04
0,13
0,03
0,03
116,5
268,03
0,06
1,2
0,04
0,3
0,21
0,23
0,02
0,05
0,38
6,71
121750158,5
127687727,4
0,01
0,02
1
1
1
1
1
1
No value
21281034,84
No value
1
1
1
1
1
No value
No value
Calculation-example of normalization and the mean
Firstly, Sq values needs to be normalized. This is done by dividing the values of Sq CR-1 with the corresponding values for Sq TBP as shown below. Normalization is important as a higher amount of cells in
a well is not equal to high gene expression.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
Secondly, outliers must be eliminated in order to exclude errors in further calculation. Exclusion criterias will be presented in the oncoming paragraph.
Moreover, the mean of Sq needs to be calculated before evaluating fold change, in order to make use of
all replicates (biological and technical). This is executed by summing up the Sq of the technical and
biological replicate and divide by 4.
The SD is calculated for the same technical and biological replicates in order to observe how dispersed
the Sq values are compared to their mean. At this point the data is ready for fold change determination. Fold changes are calculated in the following manner:
● mean Sq C16-d4n / mean Sq C16-d1n
● mean Sq C16-d4h / mean Sq C16-d1n
● mean Sq U87-d4n / mean Sq U87-d1n
● mean Sq U87-d4h / mean Sq U87-d1n
● (mean Sq U87-d4n / mean Sq U87-d1n ) /(mean Sq C16-d4n / mean Sq C16-d1n)
● (mean Sq U87-d4h / mean Sq U87-d1n)/(mean Sq C16-d4h / mean Sq C16-d1n)
Mean Sq-values with SD
Graph a (left) and b (right) illustrates the difference in mean starting quantity from day 1 to day 4 for C16. Graph a relates day
1 to day 4 under normoxic conditions, and graph b relates day 1 under normoxic conditions to day 4 under hypoxic conditions.
Standard deviation is shown as the vertical bar, although it is too small to identify in both graphs for day 4.
Graph c (left) and d (right) illustrates the difference in mean starting quantity from day 1 to day 4 for U87. Graph c relates day
1 to day 4 under normoxic conditions, and graph d relates day 1 under normoxic conditions to day 4 under hypoxic conditions.
Standard deviation is shown as the vertical bar, although it is too small to identify at day 1 in graph c.
Page 63 of 68
Standard Curves
The figure shows the standard curve for CR-1, with outliers excluded. Correlation coefficient, efficiency and slope can be observed
in the upper right corner of the figure. On the x-axis the initial quantities are illustrated, while on the y-axis the Ct values are
represented.
The figure shows the standard curve for TBP, with outliers excluded. Correlation coefficient, efficiency and slope can be observed
in the upper right corner of the figure. On the x-axis the initial quantities are illustrated, while on the y-axis the Ct values can be
found.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
Dissociation Curves for CR-1
The dissociation curve for CR-1 and all related wells (standards, positive/negative control and replicate wells). Along the x-axis is
temperature measured in oC, and along the y-axis is the fluorescence-level.
The figure illustrates the positive dissociation curve for CR-1. Along the x-axis is temperature measured in oC, and along the y-axis
is the fluorescence-level.
Page 65 of 68
The figure illustrates the negative dissociation curve for CR-1. Along the x-axis is temperature measured in oC, and along the yaxis is the fluorescence-level.
Aalborg University – MedIS/Medicine
Supervisor: L. Pilgaard
4th Semester Project, group 403
Assistant: D. Kristensen
Dissociation Curves for TBP
The dissociation curve for TBP and all related wells (standards, positive/negative control and replicate wells). Along the x-axis is
temperature measured in oC, and along the y-axis is the fluorescence-level.
Figure XX illustrating the positive dissociation curve for TBP. Along the x-axis is temperature measured in oC, and along the yaxis is the fluorescence-level.
Page 67 of 68
Figure XX illustrating the negative dissociation curve for TBP. Along the x-axis is temperature measured in oC, and along the yaxis is the fluorescence-level.