Application of high-resolution
division tracking to determine
the role of regulators of haematopoietic cell development
Kap-Hyoun Ko
A Thesis Submitted for the Degree of Doctor of Philosophy
at
University of New South Wales
Faculty of Engineering
Graduate School of Biomedical Engineering
March, 2010
Originality Statement
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i
Abstract
The ex vivo expansion of haematopoietic stem cells (HSC) for clinical use is now recognized to be a safe procedure. Feasibility depends on developing cost- effective methods for growing HSC in vitro. The aims of this thesis were to characterise the kinetics of ex vivo expansion of HSC performed with different culture conditions and to characterise the role of Wnt signalling in regulation of haematopoietic cell cycle.
A flow cytometry-based method to characterize HSC growth and differentiation kinetics was developed by employing high resolution division tracking. The division tracking method identifies consecutive cell generations from the serial halving of carboxyfluoresceindiacetate succinidyl ester (CFDA-SE) fluorescence with cell division. Statistical methods for estimating precursor cell frequency, mean generation time, and cell renewal probability from CFDA-SE division tracking data were developed.
The methodology was used to characterise divisional kinetics of cultured umbilical cord blood (UCB) and mobilised peripheral blood (MPB)-derived
CD34+ cells. The influence of ex vivo culture conditions such as culture duration, addition of serum or co-culture with the bone marrow stroma cells was determined by division tracking analysis.
ii
In addition, this technique was successfully applied to access the role of Wingless
(Wnt) signalling in regulation of haematopoietic cell cycle. Wnt modulation was perturbed indirectly using the glycogen synthase kinase-3! (GSK-3!) inhibitor 6- bromoindirubin 3’-oxime (BIO). BIO delayed CD34+ cell expansion by increasing cell cycle time. Global gene expression identified cyclin D1 and cdk inhibitor p57 as the candidate mediators of BIO-induced cell cycle prolongation.
BIO-treated CD34+ cells demonstrated better engraftment in a xenogeneic transplantation model. The thesis contributes to a better understanding of the mechanisms regulating the balance between stem cell self renewal and quiescence and facilitates progress towards clinical application of ex vivo expanded stem cells.
iii
Acknowledgement
First of all I would like to thank my supervisor Dr. Robert Nordon for allowing me the opportunity to undertake my PhD in his laboratory and for his enthusiasm for the project. I would also like to thank my co-supervisor Dr. Alla Dolnikov for allowing me to work in her laboratory during my PhD. Thank you both for helping to develop my knowledge in the haematopoiesis stem cell field and writing skill. I gratefully acknowledge with thanks my co-supervisor Dr. Ross
Odell for helping me with statistic analysis for my thesis.
I would like to thank my boss Dr. Tracey O’Brian for giving me the opportunity to work at the cord and marrow transplant facility in Sydney Children’s Hospital.
Thanks also go to our group in the facility including Dr. Emma Song, Ms. Tiffany
Holmes, Ms. Patricia Palladinetti, and Mr. Guy Klamer for supporting me during my PhD. I would also like to thank the Sydney Cord Blood Bank for supply of cord blood and Dr David Haylock for kindly providing purified human CD34+ cell.
I gratefully acknowledge the University of New South Wales and Australian Stem
Cell Centre for UPA scholarship and post-graduate research bursary.
To my parents and parents-in-law, thank you very much for your love and unending support and encouragement. Thanks my daughter, Yuna for waiting for me to play. Now I can be with you all the time. Finally, to Akiko, thank you for being a lovely wife and supporting me. I love you from bottom of my heart. iv
Publications arising from this work
Journal articles
1. Ko, K.H., Holmes, T., Palladinetti, P., Song, E., Nordon, E. R., O'Brien,
T.A., Dolnikov, A. GSK-3! inhibition promotes engraftement of ex vivo
expanded hematopoietic stem cells. Submitted to Stem Cells. 2010.
2. Song, E., Ko, K.H., Klamer, G., O'Brien, T.A., Dolnikov, A., Glycogen
synthase Kinase-3! inhibitors suppress leukemia cell growth.
Experimental Hematology. June 2010.
3. Klamer, G., Song, E., Ko, K.H., O'Brien, T.A., Dolnikov, A. Using small
molecule GSK-3! inhibitors to treat inflammation. Current Medicinal
Chemistry. 2010
4. Dolnikov, A., Ko, K.H., Song, E., Holmes, T., O'Brien, T.A. GSK-3!
inhibition activates WNT, delays division and preserves the function of
hematopoietic stem cells. Blood (Society of Hematology Annual Meeting
abstracts). 2008;112:616.
5. Ko, K.H., Odell R., Nordon, E. R. Analysis of cell differentiation by
division tracking cytometry, Cytometry Part A. 2007;71A:773-782.
Conference papers
6. Ko, K.H., Holmes, T., Nordon, E. R., O'Brien, T.A., Dolnikov, A. Ex-vivo
expansion of haematopoietic stem cells results in stem cell 'aging' and
epigenetic dysregulation that can be delayed by co-culture with
mesenchymal stem cells or GSK-3! inhibition. The 2nd Australian Society v
for Stem Cell Research Annual Meeting, 22-24th Nov 2007 Canberra
Australia.
7. Ko, K.H., Dolnikov, A., Song, E., Holmes, T., O'Brien, T.A. GSK-3!
inhibition activates Wnt in the mesenchymal stem cell niche, delays
hematopoietic stem cell divisions and preserves stem cell function.
Australian Health and Medical Research Congress, 16-21st Nov. 2008,
Brisbane Australia.
8. Ko, K.H., Odell R., Nordon, E. R. Influence of cytokines and stroma on
cord blood CD34+ cell renewal probability in ex vivo expansion culture.
International Society for Stem Cell Research, 17-20th Jun. 2007, Cairns
Australia.
9. Ko, K. H., Odell R., Haylock, D., Nordon, E. R. Analysis of the regulation
of haematopoietic cell quiescence by division tracking. The Australian
Health and Medical Research Congress 2006, 26th Nov-1st. Dec. 2006,
Melbourne, Australia.
10. Nordon, E. R., Ko, K. H., Nakamura, M., Odell R. Mathematical
modelling of differentiation and proliferation from flow cytometric
division tracking of blood stem cells. Stem Cells in the Age of
Fluorescence Technology: the 2005 International Society for Analytical
Cytology Samuel A. Latt Conference, 6-9th Nov. 2005, Gold coast,
Australia.
11. Nordon, E. R., Ko, K.H., Nakamura, M., Odell R. Construction of
differentiation history pedigrees for multipotent stem cells from division
tracking data. The 7th International Conference on Cellular Engineering, 6-
9th Sep. 2005, Seoul, Korea. vi
Table of Contents
Abstract ...... i
Acknowledgement...... iii
Publications arising from this work ...... iv
Table of Contents ...... vi
List of Figures ...... x
List of Tables...... xiii
Abbreviations ...... xv
Chapter 1: Literature Review...... 1
1.1 Definition of haemopoietic stem cells...... 2
1.2 Haematopoietic stem cell transplantation...... 3
1.3 Ex vivo expansion of UCB stem cells...... 8
1.4 Characterisation of ex vivo expanded HSC ...... 12
1.4.1 In vivo assays...... 12
1.4.2 In vitro assays...... 13
1.4.3 Immunophenotypic analysis...... 14
1.4.4 Important considerations for assessment of ex vivo expansion dynamics .. 15
1.5 Analysis of cell cycle and division...... 17
1.6 Regulation of HSC growth and development...... 19
1.7 Thesis aims ...... 26
vii
Chapter 2: Development of high resolution cell division tracking methodology and data analysis ...... 28
2.1 Introduction ...... 29
2.2 General methods and materials ...... 31
2.2.1 Preparation of media components and solutions...... 31
2.2.2 Cell lines...... 33
2.2.3 Freeze and thawing cells ...... 34
2.2.4 Isolation of CD34+ cells from UCB ...... 34
2.2.5 Phenotype staining protocol...... 35
2.2.6 Viability staining protocol...... 36
2.2.7 CFDA-SE staining protocol ...... 36
2.2.8 Sorting protocol...... 37
2.2.9 Flow cytometry ...... 37
2.2.10 List mode data analysis software ...... 38
2.2.11 Estimation of division tracking artefacts: CFDA-SE degradation and auto- fluorescence...... 42
2.2.12 Derived quantities ...... 42
2.3 Results ...... 48
2.3.1 Optimisation of CFDA-SE and cell concentration...... 48
2.3.2 Sorting gate strategy...... 51
2.3.3 Clustering Algorithm ...... 52
2.3.4 Division tracking artefacts: CFDA-SE degradation and auto-fluorescence 54
2.3.5 Growth kinetics: Cell expansion and mean generation number...... 56
2.3.6 Differentiation kinetics: Precursor cell frequency and renewal...... 61
2.4 Discussion...... 63 viii
Chapter 3: Influence of culture components on divisional kinetics of ex vivo expanded haematopoietic stem cells ...... 69
3.1 Introduction ...... 70
3.2 Experimental design and statistical analysis ...... 72
3.3 Results ...... 75
1.1.1 Comparing ex vivo expansion of CD34+ cells derived from UCB and MPB
...... 78
1.1.2 The effect of culture time and stroma on the kinetics of stem cell expansion
...... 82
1.1.3 The effect of serum on the kinetics of stem cell expansion ...... 92
3.4 Discussion...... 101
Chapter 4: Application of division tracking to determine the effect of GSK-3! inhibition on ex vivo expansion of umbilical cord blood ...... 106
4.1 Introduction ...... 107
4.2 Materials and Methods ...... 109
4.2.1 Culture conditions ...... 109
4.2.2 Mesenchymal Stem Cells (MSC)...... 109
4.2.3 Colony-Forming Unit (CFU) assay...... 109
4.2.4 FITC- and Ki-67 staining ...... 110
4.2.5 Transplantation into NOD/SCID mice...... 110
4.3 Results ...... 112
4.3.1 Division tracking of UCB-derived CD34+ cells co-cultured with MSC .. 112
4.3.2 Functional analysis of ex vivo expanded CD34+ stem cells co-cultured with
MSC...... 116 ix
4.3.3 GSK-3! inhibition delays cell cycle progression and hematopoietic differentiation...... 121
4.3.4 GSK-3! inhibition may induce stem cell quiescence ...... 126
4.3.5 Influence of GSK-3! inhibition on transplantation of ex vivo expanded
CD34+ cells into NOD/SCID mice...... 129
4.4 Discussion...... 136
Chapter 5: Modulation of global gene expression during ex vivo expansion ..... 139
5.1 Introduction ...... 140
5.2 Method and materials ...... 140
5.2.1 Gene expression analysis ...... 140
5.2.2 RT-PCR...... 142
5.3 Results ...... 143
5.3.1 Modulation of gene expression during ex vivo expansion of UCB CD34+ cells ...... 143
5.3.2 GSK-3! inhibition modulates gene expression during ex vivo expansion of cord blood CD34+ cells...... 148
5.4 Discussion...... 157
Chapter 6: Conclusions ...... 160
References...... 169
Appendix – Statistical analysis ...... 185 x
List of Figures
Figure 1-1 . The development of different blood cells from HSC to mature cells. 2
Figure 1-2 Increase use of UCB and MPB for transplants...... 5
Figure 1-3 Higher cell dose resulted in greater probability of neutrophil
engraftment from UCB...... 8
Figure 1-4 Mechanism of Cellular labelling by CFDA-SE ...... 18
Figure 1-5 Niche signalling...... 22
Figure 1-6 Activation of canonical Wnt signalling by direct inhibition of GSK-3!
...... 25
Figure 2-1. Cluster analysis using linear regression CFDA-SE labelled and sorted
cord blood CD34+ cells ...... 46
Figure 2-2 Optimal CFDA-SE concentration vs. cell number...... 49
Figure 2-3 CFDA-SE concentration test with CD34+ cells derived from UCB... 49
Figure 2-4 The effect of BSA on CFDA-SE fluorescence by cell number...... 50
Figure 2-5 The effect of BSA on cell growth. Cell number was counted at day 3.
...... 51
Figure 2-6 Comparison of sorted and unsorted cell on CFDA-SE intensity...... 52
Figure 2-7. Assignment of generations and fitting of normal distributions to
clusters CFDA-SE histograms ...... 53
Figure 2-8 Mean fluorescence of clusters...... 55
Figure 2-9 Mean fluorescence of cell generations versus increase in mean
generation fluorescence per generation...... 56
Figure 2-10 Dot plot of CFDA-SE versus CD34 antigen expression for days 1-6.
...... 58 xi
Figure 2-11 Growth kinetics of cord blood CD34+ cells...... 59
Figure 2-12 Differentiation kinetics...... 62
Figure 3-1 Schematic presentation of experimental design...... 73
Figure 3-2 Effect of culture duration and stroma on expansion of UCB derived
CD34+ cells...... 76
Figure 3-3 Illustration of lag/cell cycle time, apoptosis and differentiation during
the 2nd week culture of CD34+ cell derived from MPB...... 77
Figure 3-4 Effect of culture conditions and cell source on expansion and precursor
expansion rates...... 80
Figure 3-5 Effect of culture conditions and cell source on division rate and CD34+
renewal probability...... 81
Figure 3-6 Effects of culture duration and stroma on cell expansion rate (a-d) and
precursor expansion rate...... 85
Figure 3-7 Effects of culture duration and stroma on cell division rate (a-d) and
renewal (e-f) of CD34+ cells derived from UCB and MPB...... 86
Figure 3-8 Differentiation increased in the time of culture...... 89
Figure 3-9 Effect of serum on expansion of MPB derived CD34+ cells...... 92
Figure 3-10 Effect of serum on cell expansion rate and precursor expansion rate.
...... 94
Figure 3-11 Effect of serum on cell division rate and CD34+ renewal probability.
...... 95
Figure 3-12 Illustration of lag/cell cycle time, apoptosis and differentiation of
CD34+ cell derived from MPB...... 96
Figure 3-13 Diagrammatic summary of results...... 100
Figure 4-1 Division tracking of CD34+ cells co-cultured with MSC...... 113 xii
Figure 4-2 Effect of MSC on UCB expansion as assessed by division tracking.
...... 114
Figure 4-3 Comparison of UCB derived stem cell kinetics with MSC and strom
MS5 cell...... 115
Figure 4-4 CFU produced by CD34+ cells co-cultured with MSC or in suspension
culture...... 117
Figure 4-5 UCB CD34+ cell engraftment in the NOD/SCID mouse model...... 120
Figure 4-6 BIO delays cell cycle progression...... 123
Figure 4-7 Effect of BIO on UCB expansion by division tracking analysis...... 125
Figure 4-8 Cell cycle analysis combined with FITC nuclear protein staining.... 127
Figure 4-9 Ex vivo expanded CD34+ cells treated with 0.5μM BIO for 5 days
before CFU assay produced more primary mixed CFU- granulocyte/
erythrocyte/monocyte/ macrophage (GEMM) and secondary CFU (10CFU
and 20CFU, respectively) in re-plating assay compared to un-treated cells.
...... 129
Figure 4-10 Ex vivo expanded cells reduce engraftment in the NOD/SCID model.
...... 132
Figure 4-11 BIO enhances engraftment of ex vivo expanded cells in the
NOD/SCID mouse...... 135
Figure 5-1 The top 5 functional categories of modulated genes during ex vivo
expansion...... 144
Figure 5-2 Reversal of cytokine-induced gene expression by BIO...... 150
Figure 5-3 Validation of p57 (top) and cyclin D1 (bottom) by RT-PCR...... 154
xiii
List of Tables
Table 2.1. Pseudo-code for finding clusters...... 40
Table 2.2. Pseudo-code for generational assignment...... 41
Table 2.3. The use of mean cluster fluorescence to assign generation number to
clusters using the “nearest neighbour” algorithm (Table 2.2)...... 47
Table 2.4 Average cell cycle time and quiescent lag for CD34+ and CD34- cells
...... 60
Table 3.1 Effect of culture duration and stroma on cell cycle and lag time (h) of
CD34+ cells derived from UCB and MPB...... 87
Table 3.2 Effect of culture duration and stroma on cell cycle and lag time (h) in
CD34- cells derived from UCB and MPB...... 87
Table 3.3 Effect of culture duration and stroma on apoptosis and differentiation.
...... 88
Table 3.4 Effect of serum on cell cycle and lag time (h) of CD34+ cells derived
from UCB and MPB...... 97
Table 3.5 Effect of serum on cell cycle and lag time (h) of CD34- cells derived
from UCB and MPB...... 97
Table 3.6 Effect of serum on apoptosis and differentiation on serum effect...... 98
Table 5.1 Commonly up-regulated genes from both cytokine-induced expansion
and BIO-treated cells...... 148
Table 5.2 Commonly down-regulated genes from both cytokine-induced
expansion and BIO-treated cells ...... 149
Table 5.3 List of up-regulated genes by cytokines and down-regulated by BIO.
...... 151 xiv
Table 5.4 List of down-regulated genes during expansion and up-regulated by
BIO...... 152
Table 5.5 Self-renewal and cell cycle related genes modulated by expansion and
BIO...... 153
xv
Abbreviations
ALDH Aldehyde dehydrogenase
ANG-1 Angiopoietin-1
ANOVA Analysis of variance
APC Allophycocyanin
BFU Burst Forming Units
BIO 6-bromoindirubin 3’-oxime
BM Bone marrow
BMP Bone morphogenic protein
BrdU Bromodeoxyuridine
BSA Bovine serum albumin
BSE Bovine spongiform encephalopathy
CAFC Cobblestone area forming cells
CD Cluster of differentiation cDNA Complementary deoxyribonucleic acid
CFC Colony-forming cell
CFDA-SE Carboxyfluorescein diacetate succinimidyl ester
CFU Colony Forming Unit cRNA Complementary ribonucleic acid
DMSO Dimethylsulfoxide
EDTA Ethylenediaminetetraacetic acid
FB(C)S Fetal bovine (calf) serum
FCS Forward-light-scatter
FITC Fluorescein isothiocyanate
FL Flt-3 ligand xvi
GEMM Granulocyte, Erythrocyte, Macrophage, Megakaryocyte
GM Granulocyte macrophage
GSK Glycogen synthase kinase
GVHD Graft-versus-host disease
HCL Hydrochloric acid
HLA Human leukocyte antigens
HPC Haematopoietic progenitor cells
HSC Haemopoietic stem cells
IL Interleukin
IMDM Isocove's modified dulbecco's media
IV Intravenous
LiCl Lithium chloride
LTC-IC Long-term culture-initiating cell
MCM Mini-Chromosome Maintenance
MEM Minimum Essential Medium
MPB Mobilized peripheral blood
MSC Mesenchymal stromal cells
NaCl Sodium chloride
NaHCO3 Sodium bicarbonate
NaOH Sodium hydroxide
NH4Cl Ammonium chloride
NMDP National marrow donor program
NOD/SCID Non-obese diabetic/severe combined immunodeficient
ORF Open Reading Frame
PBS Phosphate buffered saline
PE Phycoerythrin
PerCP Peridinin chlorophyll-alpha protein xvii
PI Propidium iodide rhG-CSF Recombinant human granulocyte colony-stimulating factor rhGM-CSF Recombinant human granulocyte-macrophage colony-stimulating
factor
RT-PCR Reverse transcription polymerase chain reaction
SCF Stem cell factor
SD Standard deviation
SEM Standard error of mean
SFM Serum free medium
SRC SCID repopulating cells
SSC Side-light-scatter
TCF T-cell factor
TGF Transforming growth factor
TNF Tumor necrosis factors
TOP Thrombopoietin
UCB Umbilical cord blood
Wnt Wingless
Chapter 1: Literature review 1
Chapter 1: Literature Review Chapter 1: Literature review 2
1.1 Definition of haemopoietic stem cells
Our body produces more than 1011 blood cells every day [1]. Haemopoiesis is the development of blood cellular components. In the developing embryo and fetus haemopoiesis occurs in the yolk sack, then liver, and finally the bone marrow. In normal adults it occurs in marrow and lymphatic tissues [1]. All blood cells are derived from a small common pool of multipotent cells, called haemopoietic stem cells (HSC) (Figure 1-1). They have the ability of self-renewal for long periods of time through cell division of at least one daughter cell, and of differentiation that gives rise to all lymphoid, myeloid and erythroid cells [2, 3].
Figure 1-1 . The development of different blood cells from HSC to mature cells.
Abbreviations: common lymphocyte progenitor (CLP); common myeloid progenitor (CMP); granulocyte monocyte progenitor (GMP); megakaryocyte erythrocyte progenitor (MEP); erythrocyte precursor (ErP); megakaryocyte precursor (MkP). [4] Chapter 1: Literature review 3
1.2 Haematopoietic stem cell transplantation
HSC transplantation is often used in patients undergoing high-dose chemotherapy and radiotherapy, but the mortality rate is high because of the cytotoxicity of conditioning, graft-versus-host disease (GVHD), and opportunistic infections [5-
8]. The limitation of transplantation is not just related to the availability of suitably matched donors but also related to the dose of cells transplanted to the patient [5, 9-11]. There are several different types of stem cell transplant - autologous, and allogeneic. Autologous stem cell transplantation is mainly used to treat lymphomas and multiple myeloma and solid tumours [12, 13]. In autologous stem cell transplantation, stem cells are removed from either bone marrow or peripheral blood of a patient and then they are frozen. These frozen cells are used after high doses of chemotherapy. While there is no risk of immune rejection, there is the possibility that cancer cells may be infused back into the patient [12,
13]. Allogeneic stem cell transplantation is commonly performed to treat leukaemia and related bone marrow disorders [14, 15]. The merits of allogeneic stem cell transplantation are to reconstitute host haematopoiesis destroyed by chemo or irradiation, and to produce donor-derived immune cells which may assist in destroying any cancer cells that remain after high dose treatment.
However, there are also side-effects such as graft rejection, slow engraftment and graft-versus-host disease (GVHD).
Sources of HSC
Bone marrow (BM), mobilised peripheral blood (MPB) and umbilical cord blood
(UCB) have been used for HSC transplantation. BM is known as a rich source of
HSC and bone marrow transplant was pioneered by E.D. Thomas in 1963 [16]. Chapter 1: Literature review 4
However, general or local anaesthesia is required for harvesting BM and restoration of haematopoiesis may be slow. MPB is another source of HSC for transplantation to facilitate haematologic recovery following high-dose chemotherapy. MPB stem cell transplantation was first performed in 1981 [17].
CD34+ progenitor cells circulate in very low numbers at a steady state in healthy donors but they can be mobilized to PB from BM by using cytokine treatment such as recombinant human granulocyte colony-stimulating factor (rhG-CSF) and recombinant human granulocyte-macrophage colony-stimulating factor (rhGM-
GSF) [18-20].
UCB is an alternative source of HSC for transplantation. The first successful UCB transplant in a child with Fanconi Anaemia – the recipient is alive and well 18 years later with full haematopoietic reconstitution – was reported in France in
1989 by Gluckman et al. [21]. According to the USA National Marrow Donor
Program (NMDP), there has been an increase in the use of MPB and UCB for allogeneic transplantation since 2001 (Figure 1-2A). The NMDP reported that
1,056 cord blood transplants were facilitated representing 22% of the total number of transplants in 2009 an 18% increase from 2008. In particular, UCB derived
HSC is used more frequently in paediatric than adult patients (Figure 1-2B) because of limited HSC numbers for adult patients. Chapter 1: Literature review 5
A
B
Figure 1-2 Increase use of UCB and MPB for transplants. Cell sources are used for adult (A) and paediatric recipients (B) [22].
Transplantation of UCB stem cells
Once considered a biological waste product, UCB has emerged as a viable source of stem cells for transplantation. Despite containing one-tenth of the number of Chapter 1: Literature review 6
HSC found in bone marrow, because of certain biological properties including a higher proliferative rate, greater colony forming and self-renewal capacity, transplanted UCB cells will result in reconstitution of haematopoietic and immune cells in the allogeneic setting [23]. After birth, blood from the umbilical cord, which ordinarily is discarded, can be collected at no risk to mother or infant, and cryogenically preserved. The first Australian UCB transplant was performed in
1992 at Sydney Children’s Hospital [21, 24]. Over 136 paediatric patients have since been transplanted in Australasia [25].
UCB as a source of stem cells has a number of advantages over bone marrow including no risk to the donor, reduced risk of viral contamination of the graft, greater ethnic representation, rapid donor identification and faster availability
[26]. Because cord units are pre-tested for human leukocyte antigens (HLA) compatibility and cryogenically stored, UCB is a stem cell source readily on hand reducing the delay in transplantation for patients with high-risk malignancies.
Compared with unrelated bone marrow, which takes on average 4-5 months from initiation of search to procurement of marrow from a volunteer donor, time from search to transplant using UCB can be as little as 3 weeks [27]. Furthermore, most patients who do not have a suitably matched bone marrow donor are likely to have a suitably matched UCB unit. This is because there is tolerance for HLA disparity, postulated to be due to the relative immunological naivety of UCB, which results in less GVHD, a major limitation of transplanting unrelated donor cells with high rates of morbidity and mortality.
Clinical results using UCB confirmed that one of the major drawbacks of cord blood is slow haematopoietic recovery and higher incidence of graft failure. This Chapter 1: Literature review 7
finding has been repeatedly demonstrated to relate to the infused cell dose with higher cell doses resulting in improved engraftment and that higher cell dose may compensate for deleterious effects of HLA disparity [28, 29]. Slow haematopoietic recovery in UCB graft recipients was shown to result from low stem cell numbers available. The minimal cell dose required to achieve good engraftment is variably reported to range between 2-3 x107/kg total nucleated cells per recipient weight (Figure 1-3) [30]. The average cell yield in a single cord blood collection provides sufficient cells for recipients less than 40 kg [31]. Thus insufficient stem cell numbers represent a major limitation of UCB as a stem cell source in older children and adult patients. For example, Laughlin et al compared results of 116 adults patients with leukaemia receiving an unrelated mismatched
UCB single unit graft with those receiving an unrelated matched bone marrow
(n=367) graft. Neutrophil recovery times were longer with UCB (medium 27 days) compared with matched bone marrow (n=18 days) with a resulting higher proportion of deaths due to infection in the first 100 days in recipients of UCB compared to marrow (45% vs. 21% respectively) [29]. In contrast to results in older children and adults patients, data from paediatric trials, where UCB cell doses are usually higher (based on the lower weights of recipients) suggest that a well-matched UCB is equivalent and in some circumstances superior to a unrelated bone marrow transplant [32]. More recently investigators have attempted to give two cord blood grafts simultaneously as a means of increasing cell dose and recovery times and whilst this has proven a safe strategy and resulted in faster neutrophil recovery, times compared to single cord unit transplants, recovery times still remain inferior to historical bone marrow data
[33]. Chapter 1: Literature review 8
Figure 1-3 Higher cell dose resulted in greater probability of neutrophil engraftment from
UCB.
1.3 Ex vivo expansion of UCB stem cells
Ex vivo expansion of hematopoietic stem cells was proposed as another means to increase stem cell numbers in the graft. Ex vivo expanded HSC must provide long- term durability and reconstitution of the haematopoietic and immune system. The infusion of more lineage-committed haematopoietic progenitors abrogates severe pancytopenia as a result of myeloablative conditioning regimens [34]. Several human clinical trials evaluating the use of ex vivo expanded UCB cells have been conducted: all demonstrated feasibility and safety [35-37]. Despite transplantation of increased numbers of HSC, no increase in speed of haematopoietic recovery was documented suggesting loss of function of expanded cells and highlighting a need for review of current methodology [38].
Chapter 1: Literature review 9
Two main methods to expand UCB cells were explored to date: static liquid and stromal co-culture systems [39-41]. The majority of static liquid culture expansion systems require the isolation of CD34+ or CD133+ cells from fresh or frozen haematopoietic tissue [42, 43]. Positively selected CD34+ or CD133+ cells enriched with HSC are incubated in culture medium supplemented with different combinations of cytokines including stem cell factor (SCF), thrombopoietin
(TPO), Flt-3 ligand (Flt-3), interleukin(IL)-6 etc (see [44, 45] for the detailed review of different cytokine combinations used for HSC expansion). Multiple different cytokine combinations and variable doses of the cytokines used were reported to produce impressive expansion [44]. Our group has conducted factorial analysis of different cytokine combinations [46, 47]. Although, this strategy is effective in increasing total cell numbers, it relies on addition of exogenous growth factors to prevent apoptosis and stimulate proliferation, and it has been suggested that as a consequence drives HSC differentiation at the expense of self- renewal [48]. The ex vivo expansion of short-term reconstituting progenitor cells at the expense of long-term reconstituting higher-quality HSC, may impact on the haematopoietic reserve of the graft. Evidence, primarily in animal models, suggests that this may occur under certain conditions with compromised long- term repopulating activity following ex vivo expansion reported in fetal sheep, non-human primate, feline and mouse models [49, 50] .
An alternative approach for ex vivo expansion is the co-culture of UCB cells with components of the haematopoietic microenvironment. The haematopoietic microenvironment contains the putative stem cell niche and is composed of haematopoietic and non-haematopoietic (cellular and extracellular) components, Chapter 1: Literature review 10
thought to provide the complex molecular cues that direct HSC self-renewal and proliferation and regulate the differentiation and maturation of haematopoietic progeny [51, 52]. Mesenchymal stromal cells (MSC) are a non-haematopoietic, well-characterised homogeneous population of adherent skeletal and connective tissue progenitor cells within bone marrow stroma that provide a rich environment of signals including cytokines, extracellular matrix proteins, adhesion molecules, and cell-cell interactions controlling the proliferation, survival and differentiation of HSC [53-55]. Studies have shown that MSC secrete interleukin(IL)-6, 7, 8, 11,
12, 14 and 15, M-CSF, Flt-3, SCF, similar to the cytokines and growth factors expressed by stroma cells [56]. In addition to the co-culture strategy for UCB ex vivo expansion, it has been shown that allogeneic MSC co-administration promoted engraftment of human CD34+ cells in the non-obese diabetic/severe combined immunodeficient (NOD/SCID) mice and fetal sheep [54, 57, 58]. MSC have been shown to have clinical immuno-modulatory activity that may impact favourable to decrease GVHD in human transplant patients [59, 60].
While static culture systems have been widely used for HSC expansion, these systems have several drawbacks. They have limited surfaces area for scale-up, require manual passaging (feeding), and are poorly mixed, resulting in concentration gradients in oxygen, pH and cytokines [61, 62]. An alternative approach to standard flask culture is a bioreactor. Ex vivo expansion using a bioreactor is ideal when a large number of cells are required, accessory cells are needed and high cell densities are desired [62]. There are several types of bioreactors currently in use including perfusion chambers [63, 64], stirred tank
[65, 66], and hollow fiber [67], and they have different purposes and advantages. Chapter 1: Literature review 11
Culture systems for large-scale ex vivo HSC expansion require design of simple, flexible and economic processes well-adapted for the clinical setting [62].
Growth factor combinations and co-culture with supportive cells play an important role in stem cell expansion conducted in static and bioreactor culture systems; however, other variables also influence the result of HSC expansion.
These include the duration of culture, the serum supplement or serum-free substitutes [68-70]
Establishing the appropriate culture time period to achieve sufficient cell number for treatment needs further systematic investigation. The prolonged duration of ex vivo expansion may also increase the cost of the procedure and the risk of contamination. The most common duration for cells cultured in a static system is
7 to 14 days with maximum expansion of progenitors occurring at 10 to 12 days
[70, 71]. There is a decrease in progenitor production and increase in senescence after 12-15 days of culture [69]. Therefore, we will investigate the kinetics of ex vivo expanded CD34+ cells over a 2 week culture period (see chapter 3).
Fetal calf (bovine) serum (FCS/FBS) or bovine serum albumin (BSA) has been used in ex vivo expansion culture systems traditionally for proliferation, differentiation and stroma culture [72, 73]. However, the concentration of inhibitors including transforming growth factors and other constituents vary between batches of sera. In order to minimize these variables, there has been a move towards the use of serum-free media. Additionally, from a regulatory perspective, non-animal sources of albumin and low density lipoproteins are Chapter 1: Literature review 12
required for human therapy. The use of commercially-produced, serum-free medium is now readily available for clinical evaluation [43, 74, 75].
1.4 Characterisation of ex vivo expanded HSC
The ex vivo expansion of HSC for clinical use is now recognized to be a safe, feasible approach [38]. Preclinical studies suggested that there are cellular defects during ex vivo expansion such as would promote apoptosis [76-78], prevent marrow homing [79-81], and impair proliferative capacity [82, 83]. If ex vivo expanded cells are used for treatment in a clinical situation, it is important to develop laboratory-based assessment methods to evaluate their capacity to regenerate haematopoiesis.
1.4.1 In vivo assays
The key of HSC is their capacity for steady long-term reconstitution of the haematopoietic system following transplantation into patients. Bone marrow transplantation assays have been established in experimental animal models and are an excellent way to explore the principles of stem cell engraftment biology [2,
84]. Evaluation of the engraftment of human HSC in an experimental setting has become possible due to the development of xenogeneic transplantation models in immunodeficient mouse strains such as NOD/SCID mouse and fetal sheep [85].
Several nonhuman primate species have also proven to be valuable models for human haematopoiesis, in particular for preclinical evaluation of conditioning regimens prior to transplantation and for cytokine therapy to accelerate haematopoietic recovery [2]. The NOD/SCID mouse bone marrow transplantation Chapter 1: Literature review 13
model, however, remains the most frequently used to evaluate the function of ex vivo expanded stem cells since it recapitulates human bone marrow transplantation in many aspects such as bone marrow homing, proliferation and multi-lineage reconstitution of human haematopoiesis [2, 84].
1.4.2 In vitro assays
Several surrogate in vitro assays are used to test the function of ex vivo expanded stem cells. The proliferation and differentiation ability of multi-potential and lineage-committed progenitors from ex vivo expanded culture are being tested
[70]. The ability of these progenitors to produce clonal progeny is the basis for their detection in the colony-forming cell (CFC) assay. Haematopoietic colony assays use semi-solid matrices, and culture supplements that promote the proliferation and differentiation of progenitors. The culture conditions provide a single progenitor cell to form clonal progeny which stay close together in culture.
A colony of more mature cells can be easily identified and counted using an inverted microscope. Methylcellulose and collagen are now widely used as gelling agents because they support the growth of multiple lineage progenitors, allowing them to be assayed simultaneously in a petri dish. (see [86] for review). CFC assay cannot detect more immature progenitors because the media does not support cells for more than 3 weeks. Secondary assays derived from primary colonies is required to measure the proliferative potential of more immature progenitors [84].
The long-term culture system was first established in vitro for murine marrow in the late 1970s [87] and then the system was adapted for human cells a few years Chapter 1: Literature review 14
later [88]. This long-term culture system was used to develop an assay for primitive haematopoietic cells, the long-term culture-initiating cell (LTC-IC) assay [89]. The assay identifies primitive haemopoietic cells that have properties such as capacity for extensive proliferation, differentiation and self-renewal in murine or human systems [90, 91]. LTC-IC can be set up in two stages: by growing the irradiated stromal cells including endothelial cells, fibroblasts, reticulocytes and adipocytes to confluence, then adding haematopoietic cell suspension [92, 93]. The stromal layer allows growth of stem cell in close association with supporting stroma [87]. CFC detected at weeks 5 and 8 represent the progeny of LTC-IC because CFC present in the input cell suspension have undergone terminal differentiation by this time. Limiting dilution analysis of
LTC-IC is a quantitative method of estimating the frequency of LTC-IC using
Poisson’s statistics (see StemCell Technologies (www.stemcell.com) for detailed protocol). The simplified version of LTC-IC is the Cobblestone Area-Forming
Cell (CAFC) assay that measures the formation of cell patches with cobblestone morphology on bone marrow stromal feeder layers. [94, 95]. The loss of erythroid capacity, the lack of lymphoid differentiation and the heterogeneity of these progenitors should be considered because the long-tem culture assay is performed at limiting dilutions which gives no information on the heterogeneity or multi- potency of individual colonies [84].
1.4.3 Immunophenotypic analysis
It is obvious that phenotypic assessment of stem cells by flow cytometry would have advantages over functional assays with respect to the required time and costs. In contrast to murine HSC, the specific surface markers for human HSC are Chapter 1: Literature review 15
not fully defined [96]. The CD34 antigen is most commonly used to enrich human
HSC although CD34 antigen function in stem cell biology is not fully determined
[97, 98]. There is increasing evidence to suggest that the stem cell compartment may be heterogeneous for CD34+ [99, 100]. CD34+CD38- and AC133+HLA-
DR- or AC133+ cell phenotype is currently considered as the best to define a cell population with the ability to restore long-term haematopoiesis in recipients receiving myeloablative doses of chemo-radiotherapy [101, 102] . In addition, the expression of CXCR4, and integrins VLA4 and 5 shown to be important for stem cell homing can be examined [103, 104]. Aldehyde dehydrogenase (ALDH) has been reported recently as a key regulator of HSC differentiation. The inhibition of
ALDH promotes HSC self-renewal. [105-107].
1.4.4 Important considerations for assessment of ex vivo expansion dynamics
There is consensus that none of these assays accurately predict the long-term reconstitution ability of stem cells in a clinical setting [108]. As HSC divide their phenotype changes rapidly as they progress from stem to differentiated cell [84].
Thus, transplantation potential can be lost over a few generations. Most studies ignore the complex kinetics of haematopoietic cell differentiation [109, 110].
At the establishment of HSC culture, most primitive cells will be in a quiescent state. Each cell will respond to the culture environment in different ways including an obligate period of time required for a cell to undergo the first round of division and enter the cell cycle followed by major lineage fate decisions or Chapter 1: Literature review 16
apoptosis. The timing of these events should be analysed in order to obtain a clearer picture of the rate of cell differentiation in ex vivo culture systems.
The production of multilineage progeny from HSC is a transient phenomena
[109]. One HSC will give rise to thousands of differentiated progeny. At steady state, the bone marrow cell compartment is maintained by the sequential recruitment of HSC [111]. Thus at steady state, marrow is composed of a mixture of stem cells, multipotent and committed progenitors at various stages of development, generated for sequentially recruited HSC. In contrast, in vitro expansion systems are initiated with HSC and progenitor cells that tend to increase and then decline after 1-2 weeks of culture, a transient culture system.
Thus in order to understand the kinetics of haematopoiesis in vivo and in vitro it is necessary to study the transient cellular response of HSC to haematopoietic growth factors [109].
Most studies thus far have focused on the ‘input/output’ properties of haematopoietic expansion systems [112]. The cell population that is produced by a culture system is analysed i.e., the output, given a variety of combinations and concentrations or proportion of media, serum and growth factors, the input. Such
‘black-box’ approaches are useful to optimise culture conditions by factorial analysis [47]. Haematopoietic responses are approximated by a linear model which fails to take into account the dynamics of the system. Therefore, experimental and analytical methods to determine the transient response of HSC to growth factors are required for a deeper understanding of haematopoietic cell differentiation dynamics. [109]. Chapter 1: Literature review 17
1.5 Analysis of cell cycle and division
For decades biologists have considered it a big issue to uncover the relationship between proliferation and differentiation in vitro and in vivo. Cell division tracking provides a powerful method for estimating the number of cells that transit the cell cycle and progress to the next cellular division [113]. This technique allows characterization of cell cycling and the relationship between cell proliferation and differentiation [113]. Cell division can possibly be tracked in vitro and in vivo by labelling cells using radioactive tracers or fluorescent dyes
(see [110, 114, 115] for review). The first study of cell division was carried out by
Takahashi (1966) and later by Lebowitz (1969) using tritiated thymidine (3H-Tdr) that is incorporated into the DNA of dividing cells [116, 117]. This method cannot provide individual cell division history and 3H-Tdr has been known to induce cell apoptosis [118].
Hoechst 33342 or 33258, a double stranded DNA fluorescent stain, is quenched by the incorporation of Bromodeoxyuridine (BrdU) in DNA. This method has been used to quantify 1-2 divisions in vitro and in vivo [119-121] and requires dual laser flow cytometry, cell permeabilization with technical difficulty in gating dead cells and immunophenotyping [115, 122].
PHK26 is a fluorescent probe that binds irreversibly to the lipid bilayer of cytoplasmic membranes, emitting red light [110, 114, 115]. This probe has the advantages of stable labelling and lower toxicity compared to fluorescein isothiocyanate or rhodamine isothiocyanate. However, the heterogeneity in staining intensity with the probe does not allow cell divisions to be resolved. Chapter 1: Literature review 18
The cell division tracking method using carboxyfluorescein diacetate succinimidyl ester (CFDA-SE) was first discovered by Lyons and Parish to achieve long term tracking of intravenously transferred lymphocytes [123]. It is a non-polar molecule that immediately penetrates cell membranes and is converted to anionic
CFSE [124] by intracellular esterases (Figure 1-4). Amine-reactive coupling of
CFSE to proteins results in stable long-term intracellular retention (Figure 1-4).
Cell generation number is estimated from the fluorescence intensity of cells; fluorescence decreases by half with each subsequent cellular division. One of the main benefits of employing the CFDA-SE division technique is that dividing cells can be identified in complex mixtures by immunophenotyping with appropriately conjugated monoclonal antibodies [114]. As CFDA-SE fluorescence can be analysed by flow cytometers equipped with a 488nm argon laser in the Fl-1 detection channel, the dye can be combined with fluorochromes including phycoerythrin (PE), peridinin chlorophyll-alpha protein (PerCP) and allophycocyanin(APC). Using this approach it is possible to track up to 6-8 division in vitro experiment and for several months in vivo [114, 125]
Figure 1-4 Mechanism of Cellular labelling by CFDA-SE (www.probes.com)
Chapter 1: Literature review 19
1.6 Regulation of HSC growth and development
The balance between self-renewal and differentiation of HSC is regulated by the bone marrow microenvironment. Multiple signalling pathways regulate stem cell fate decision in steady state haematopoiesis including Wingless (Wnt) pathway,
Notch, the Angiopoietin-1(Ang-1) / Tie2 and Bone morphogenic protein (BMP)
(Figure 1-5, [126-128]). In addition, haematopoietic cytokines play a crucial role in the maintenance of homeostasis by regulating hematopoietic cell growth, survival, and differentiation through the activation of a number of intracellular signalling pathways [129]. Regulation of HSC fate requires the cooperative action of several cytokines that bind to receptors on these cells [130]. Major advances in the identification of HSC supportive cytokines and the mechanisms by which they control hematopoietic stem cell fate decisions have been recently made (see [126-
128] for review). The study of HSC supportive cytokines facilitates HSC-based cell and gene therapies. Although identification and functional study of HSC cytokines have been made toward resolving the mechanisms of regulation of HSC in the past decade, they are still in a primitive stage [130, 131].
Stem cell factor (SCF), Flt-3/Flt-2 ligand (FL), granulocyte colony-stimulating factor (G-CSF) and thrombopoietin (TPO) are currently the cytokines most frequently being used in ex vivo culture. The combination of these four cytokines was used in our project, therefore they will be characterised here in more details.
SCF also known as mast cell growth factor, steel factor and c-kit ligand, was shown to promote the survival and growth of primitive haematopoietic progenitor Chapter 1: Literature review 20
cells when used with other cytokines [132]. SCF has also been shown to increase
HSC mobilization. Membrane-bound SCF may interact with the bone marrow niche, and may increase the number of osteoclasts associated with HSC mobilization [133]. FL was shown to lead to short-term expansion of early myeloid progenitor cells [134-136]. FL is secreted by stromal cells and regulates the expression of very late antigen (VLA4 and VLA5) which regulate stem cell proliferation and differentiation either directly or through the modulation of cytokine induced signals [126, 137]. A number of cytokines have little or no proliferative activity when used alone, but improve growth activities of other cytokines. SCF and FL are members of early acting cytokines which act predominantly to enhance ex vivo expansion [138]. Receptors for both SCF and
FL belong to the same family and share similar molecular structures [138]. G-CSF stimulates the proliferation and differentiation of committed precursors in the bone marrow to form granulocyte colonies and has been successfully used in the clinic to treat neutropaenia [139]. TPO promotes the proliferation and differentiation of immature megakaryocytes [140] and support long-term maintenance of primitive haematopoietic progenitors when used with other cytokines such as SCF and IL3 [141]. However, when TPO was used alone it produced only a marginal effect on HSC expansion [142]. A combination of SCF,
G-CSF and TPO is sufficient to produce a mixture of neutrophil precursors and megakaryocytic cells from ex vivo culture of MPB CD34+ cells that support haemopoietic recovery [38]. Haylock et. al. also reported that a combination of
SCF, FL and TPO is effective stimulus for UCB derived HSC survival and proliferation [38].
Chapter 1: Literature review 21
The doses of cytokines needed for the most efficient stem cell expansion is a matter of dispute. It is relevant that the doses of cytokines used for ex vivo cultures (10-300 ng/ml) are significantly higher than physiological cytokine concentrations (pg/ml) [137, 143, 144]. Zandstra et. al. examined the cytokine concentration dependence of human marrow CD34+ CD38- cells on LTC-IC amplification, CFC production, and cloning efficiency in liquid or semi-solid medium [145]. Increasing cytokine dose improves cell proliferation but also promotes stem cell differentiation [146]. Apparently, high doses of cytokines are needed to replace the normal microenvironment (stem cell niche). In addition, high doses of cytokines make ex vivo expansion very expensive. It is also relevant that not all cytokines promoting stem cell expansion are available in clinical grade that limits their use for transplantation.
It has been reported that the Notch pathway plays a role in control, of stem cell function [130, 147, 148]. Notch ligands Delta-1/3/4 and Jagged-2/2 were shown to regulate stem cell differentiation, proliferation, apoptosis and adhesion [126].
Activation of Notch signalling by Notch fragment was shown to promote ex vivo stem cell expansion [149]. The recent study by Delaney et al. clearly demonstrated that Notch-mediated expansion of CD34+ cells can achieve more rapid myeloid reconstitution in the clinic [144].
Chapter 1: Literature review 22
Figure 1-5 Niche signalling [127]. Stem cell factor (SCF), bone morphogenic protein (BMP),
Angiopoietin-1 (Ang-1) and Jagged are expressed by osteoblastic cells of the endosteal bone niche, while fibroblast growth factors (FGFs) are expressed by endothelial cells of the vascular niche. The origin of other signalling proteins including Wnt, hedgehog (hh), thrombopoietin
(TPO) and various cytokines remains to be determined.
BMP that belongs to the transforming growth factor-! family were also shown to regulate stem cell function [127]. BMP is known as an important factor of haematopoietic development in many species including mouse and zebrafish [128,
150]. In the human, BMP4 induces HSC proliferation and differentiation [151]. In addition, BMP4 promotes HSC expansion and mediates the influence of sonic hedgehog on human HSC in the culture [130, 152]. When used at high concentration BMP4 suppressed stem cell differentiation [126]. BMP4 was also shown to control the regulation of HSC indirectly by modulating the stem cell niche [126]. Chapter 1: Literature review 23
The Ang family of growth factors - composed of four members binding to the receptor tyrosine kinase Tie2 and known as important regulators of angiogenesis - were also shown to regulate stem cell function [130]. Ang-1 interacting with Tie2 was shown to maintain progenitors in vitro [153]. Administration of recombinant
Ang-1 to mice protected HSC from myelosuppressive stress [126]. The role of
Ang-1/Tie2 in the maintenance of HSC appears to be mediated through the maintenance of stem cell quiescence [130, 154].
Wnt proteins are secreted molecules that regulate multiple aspects of embryonic development, governing cell fates and proliferation [155]. Abnormalities in Wnt expression are associated with embryonic lethality and cancer [155, 156]. Wnt signalling was shown to be an important regulator of HSC proliferation (for review see [157]). Wnt associates with a number of proteins including Frizzled, lipoprotein receptor-related proteins, and extracellular Wnt-modulating proteins such as Dickkopf, Wnt-inhibitory factor, secreted Fzds and Norrin [155]. While more than ten Wnt signalling pathways have been identified, the canonical pathway is the most studied [158, 159]. Activation of the Wnt pathway by over expression of activated ß-catenin expands the pool of HSC in long term cultures
[160]. Furthermore, HSC in their normal microenvironment respond to Wnt signalling [157]. Reya et. al. showed that activation of Wnt signalling in HSC induces increased expression of HoxB4 and Notch 1 genes previously implicated in self-renewal of HSC [161]. Thus, the Wnt pathway is critical for normal HSC homeostasis in vivo and in vitro. UCB-derived HSC have been shown to express
Wnt-5A, one of the most important members of the Wnt gene family [162]. The ex vivo biological activity of Wnt genes on human fetal haematopoiesis using Chapter 1: Literature review 24
feeder cell co-culture systems have been previously demonstrated [157].
Haematopoietic progenitor cell numbers were markedly enhanced following exposure to stromal cells expressing Wnt genes [157]. The frequency of the most primitive CD34+ cells was also higher in Wnt-expressing co-cultures [157]. This data indicates that the gene products of the Wnt family function as haematopoietic growth factors, and that they may exhibit higher specificity for earlier progenitor/stem cells. Wnt proteins were later tested as reagents for ex vivo expansion of HSC. In contrast to other developmental signalling molecules, such as Hh proteins and BMP-4, it is difficult to isolate Wnt proteins in an active form
[163, 164]. Although Wnt proteins are secreted by stroma cells, their secretion is usually inefficient and previous attempts to characterize Wnt proteins have been hampered by their high degree of insolubility [165].
An alternate novel strategy, developed by Holemes et. al. [166] and others is activation of Wnt signalling by direct inhibition of glycogen synthase kinase 3!
(GSK-3!) (Figure 1-6). GSK3β phosphorylates !-catenin, the major effector of canonical Wnt signalling, and exposes !-catenin to ubiquitin dependent proteosomal degradation (Figure 1-6). GSK-3! inhibition results in the accumulation of !-catenin in the cytoplasm and further re-location to the cell nucleus where !-catenin binds to the T-cell factor(TCF)4/DNA complex and activates target gene transcription (Figure 1-6). Several inhibitors of GSK-3! such as 6-bromoindirubin 3’-oxime (BIO) and lithium chloride (LiCl) were shown to induce nuclear accumulation of !-catenin in ex vivo expanded CD34+ cells
(Figure 1-6), however, the effect of GSK-3! inhibition on the kinetics of stem cell division has not been characterised. The effect of GSK-3! inhibition on specific Chapter 1: Literature review 25
parameters of cytokine-induced stem cell proliferation will be examined in this thesis. In addition, the candidate genes modulated by GSK-! inhibitor BIO in conjunction with its effect on the kinetics of stem cell proliferation will be identified using global gene expression analysis.
a
b
Figure 1-6 Activation of canonical Wnt signalling by direct inhibition of GSK-3! a. Schematic presentation of Wnt signalling activation by BIO in cord blood HSC cells. b. Relocation of !-catenin from the cytoplasm (shown in green) to cell nucleus (arrow) where !-catenin can activates target gene transcription [160].
Chapter 1: Literature review 26
1.7 Thesis aims
We hypothesise that it is necessary to study the kinetics of stem cell division to understand the mechanisms regulating stem cell self-renewal and differentiation.
Aim 1 of the project is to characterise the kinetics of ex vivo expansion of CD34+ cells performed with different culture conditions. For aim 1 there are three specific of objectives.
Specific objective 1 is to optimise high-resolution division tracking of human haematopoietic stem / progenitor cells
Specific objective 2 is to develop statistical methods to analysis division tracking data.
Specific objective 3 is to characterise divisional kinetics of cultured UCB and
MPB-derived stem cells and to study the influence of culture duration, serum and co-culture with the specific cellular component of bone marrow (stroma cells or
MSC).
Aim 2 is to apply division tracking methodology to explore the role of Wnt signalling in CD34+ cells. Wnt has been previously shown to play an important role in stem cell biology. There are two specific objectives here.
Specific objective 1 is to examine the effect of GSK-3! inhibition during ex vivo expansion of haematopoietic stem and progenitor cells using high-resolution division tracking.
Chapter 1: Literature review 27
Specific objective 2 is to identify the genes and molecular pathways modulated by
GSK-3! inhibition in ex vivo expanded stem cells using genome-wide microarray gene expression analysis.
Optimisation of high-resolution division tracking and statistical methods for division tracking data analysis is presented in Chapter 2. Divisional kinetics of cultured UCB and MPB-derived stem cells with different culture conditions has been characterised in Chapter 3. Chapter 4 applies this methodology to determine the influence of MSC and the GSK-3! inhibitor BIO on CD34+ cell proliferation and differentiation. Chapter 5 characterises the genes and molecular pathways modulated by GSK-3! inhibition in ex vivo expanded stem cells using genome- wide microarray gene expression analysis. Chapter 6 discuses the significance of the methodology and experimental findings for basic and applied haematology research, and suggests future application for this methodology.
Chapter 2: Development of cell division tracking methodology and data analysis 28
Chapter 2: Development of high
resolution cell division tracking
methodology and data analysis
Chapter 2: Development of cell division tracking methodology and data analysis 29
2.1 Introduction
Lyons and Parish published the first study using CFDA-SE to evaluate lymphocyte division and used this dye for its homogeneous staining properties
[123]. The intensity of CFDA-SE is halved as a cell divides and flow cytometry allows us to track cell division for up to eight consecutive generations. This division tracking technique is useful to determine division history of cells and can be combined with other cell analysis methods such as immunophenotyping. High- resolution division tracking was first discovered by Nordon et al. for non- homogeneous staining cell types by employing a cell sorting method [46]. Since then, it was possible to apply this technique to investigate the regulation of quiescence and blood stem cell renewal [110, 167-173].
It is very important to assign the correct cell generation number to CFDA-SE clusters as this process depends on gating criteria. Division tracking artefacts such as CFDA-SE degradation and cell auto-fluorescence should be quantified to obtain correct generation number by identification of generational clusters. High- resolution division tracking using CFDA-SE was developed to clearly identify generational clusters [46]. A clustering algorithm was developed by Nordon to automate identification of generational clusters from list mode flow cytometry data files, and provide a comprehensive phenotypic profile for gated generations.
Whilst DNA analysis can be used to estimate the proliferating fraction of cells
[114], multi-type division tracking provides additional information to characterise cell proliferation and differentiation. Division tracking data is used to measure precursor cell frequency, mean generation time, and rate of phenotypic renewal. Chapter 2: Development of cell division tracking methodology and data analysis 30
Statistical methods to estimate these quantities will be presented. The present chapter will also describe the optimisation of the CFDA-SE staining method and the quantification of division tracking artefacts of CFDA-SE and illustrate the application of this methodology to characterise the divisional recruitment of UCB
CD34+ cell by haematopoietic growth factors. Chapter 2: Development of cell division tracking methodology and data analysis 31
2.2 General methods and materials
2.2.1 Preparation of media components and solutions
2.2.1.1 Culture Medium
Iscove’s Modified Dulbecco’s Media (IMDM) (Cat. No. 12200-036, Gibco,
Invitrogen, Australia) was dissolved in 900ml of Baxter water (Cat. No.
AHF7113, Baxter, Old Toongabbie, Australia) for irrigation and 2 grams of sodium bicarbonate (NaHCO3, Cat. No. S5761, Sigma-Aldrich, Castle Hill,
Australia) added for cells grown in a 5% carbon dioxide (CO2) incubator environment. The pH was adjusted to 7.2 using sodium hydroxide (NaOH) or hydrochloric acid (HCL). 100ml of Baxter water was added to make up a final volume of 1 litre. Media was sterilized by membrane filtration (0.22)m). Media was stored for up to 3 months at 4oC. Serum free medium (Stemline II
Haematopoietic stem cell expansion medium, Cat. No. S0192, Sigma-Aldrich,
Castle Hill, Australia) was used for UCB and MPB derived CD34+ cell.
2.2.1.2 Dulbecco’s Phosphate buffered saline (PBS)
9.55g of DPBS (Cat. No. D 5652, Sigma-Aldrich, Castle Hill, Australia) was dissolved in 1000ml of Baxter water for irrigation and a pH was adjusted to 7.2 using sodium hydroxide (NaOH) or hydrochloric acid (HCL) since pH may rise during filtration. Media was sterilized by membrane filtration (0.22)m). The solution was stored for up to 3 months at 4oC. Chapter 2: Development of cell division tracking methodology and data analysis 32
2.2.1.3 Bovine Serum Albumin (BSA)
BSA (Cat. No. A7906-500g, Sigma-Aldrich, Castle Hill, Australia) was made at concentration of 200mg/ml as a stock solution. 20g of BSA was dissolved in
100ml of PBS and pH was adjusted to 7.2 using sodium hydroxide (NaOH) or hydrochloric acid (HCL). BSA concentrate was sterilized by membrane filtration
(0.22)m). BSA was stored for up to 6 months at 4oC.
2.2.1.4 Beads stock for culture
Non-fluorescent polystyrene uniform microspheres (Cat.No.PS06N, 9.62 )m diameter, Bangs Laboratories, Fishers, IN) were washed and stored in 140 mM sodium chloride (NaCl) containing 1% of BSA to disperse hydrophobic beads.
200,000 beads / ml were added into cultures or into FACS tubes before flow cytometry analysis. The bead count was determined by gating on their distinctive low forward versus high side-light-scatter (FSC vs. SSC) properties.
2.2.1.5 Growth factors
Stem cell factor (SCF, Cat. No. 2550SC-050), granulocyte colony stimulation factor (GCSF, Cat. No. 214-CS-025), thrombopoietin (TPO, Cat. No. 288-TPN) and flt-3 ligand (FL, Cat. No. 308-FKN-025) are from R&D systems (Bioscience,
Gymea, Australia). Growth factors were dissolved in PBS containing 1% BSA to a final concentration of 100)g/ml. Dissolved growth factors were aliquoted into microtubes and stored at -20oC. Chapter 2: Development of cell division tracking methodology and data analysis 33
2.2.1.6 5-(and-6)-Carboxyfluorescein diacetate succinimidyl
ester (CFDA-SE)
CFDA-SE (Cat.No. C1157, Bioscientific, Gymea, Australia) was dissolved in dimethylsulfoxide (DMSO) at 2.8mg/ml. 50)l of CFDA-SE stock was stored in a sterile microtube at -20oC overnight and transferred to -70oC. CFDA-SE working stock was made up just before staining cells.
2.2.1.7 Ammonium chloride (NH4Cl) red cell lysing solution
A stock solution of NH4Cl (Cat.No. 10017, BDH Analar, USA) was made at concentration of 10 times that used to lyse red blood cells. 80.2g of NH4Cl, 8.4g of NaHCO3, and 3.7g of disodium EDTA were dissolved in 900ml of Baxter water. A pH was adjusted to 7.2 using sodium hydroxide (NaOH) or hydrochloric acid (HCL) and Baxter water was added to a final volume of 1 litre. The solution was sterilised by membrane filtration (0.22)m) and stored for up to 6 months at
o 4 C. The 10 times stock of NH4Cl was diluted in cold Baxter water before use.
2.2.2 Cell lines
The acute myelogenous leukemia cell line, KG1a [174], was used in a strategy to develop antibodies such as CD34 cell surface marker [175, 176]. In this study,
KG1a was used to optimise CFDA-SE staining. Cells were cultured in IMDM containing 10% FBS. The murine MS5 stromal cell line [177] was used to support
HSC ex-vivo expansion. MS5 cells were cultured in alpha-Minimum Essential
Medium (MEM, Cat. No. M0644 Sigma-aldrich, Castle Hill, Australia) containing
10% FBS. Only less than 13 passage MS5 cells were used in the experiment. Chapter 2: Development of cell division tracking methodology and data analysis 34
2.2.3 Freeze and thawing cells
The procedure for cryopreservation of cells was as follows. Non-adherent suspension culture cells were washed with PBS containing 10% FBS. For adherent cell such as MS5 [39, 52], cells were washed with PBS before trypsinization and then suspended in media containing 10% FBS. Cells were resuspended in 700)l of media, transferred to a cryovial, followed by addition of
200)l FBS and 100)l DMSO. Cells were kept in a -70oC freezer overnight and then transferred into liquid nitrogen.
Cells were thawed in a 37oC water bath. Cells were transferred into a sterile centrifuge tube. PBS or appropriate media containing 50% FBS was added slowly up to 10 ml. Care was taken to slowly dilute DMSO out of cells without lysing the cell pellet. Cells were washed twice and resuspended in appropriate media for culture. For adherent cells, culture media was changed after 24 hours of culture to remove dead cells.
2.2.4 Isolation of CD34+ cells from UCB
UBC was collected by Sydney Cord Blood Bank (Sydney Children’s Hospital,
Sydney, Australia). Informed consent was obtained from all mothers, and both
University of New South Wales and Eastern Sydney Area Health Service Human
Ethics committees approved the use of cord blood for this research project.
CD34+ cell isolation was performed within 24hr after UCB was collected. Cord blood was transferred from blood bag into 50ml Falcon tubes. Falcon tubes containing 30ml of aliquots cord blood were underlayed with Histopaque (Cat. Chapter 2: Development of cell division tracking methodology and data analysis 35
No.10771, Sigma-Aldrich, Castle Hill, Australia). The gradient was centrifuged at
400g for 35 minutes with collection of the buffy coat layer. The buffy coat was washed once with MACS washing buffer, PBS containing 2mM EDTA, resuspended in 50ml of cold NH4Cl lysing solution followed by 15 minutes incubation in the dark at 4oC. Cells were then washed twice with MACS washing buffer. Washed cells were resuspended at a final concentration of up to 108 cells in 300)l of MACS buffer. Up to 108 total cells were magnetically labelled with
100)l of MACS CD34 multisort microbeads and FCR blocking reagent (Cat. No.
130-046-702, Miltenyi Biotech, North Ryde, Australia). Labelled cells were incubated for 30 minutes at 4oC followed by two washes with MACS buffer.
Depending on cell number, cells were separated using a single MS or LS MACS columns (Cat.No. 130-042-201, 130-042-401, Miltenyi Biotech, North Ryde,
Australia). For example, LS MACS columns were used for 1st week UCB division tracking and MS MACS columns used for 2nd week UCB and MPB division tracking. A pre-separation filter (Cat. No. 130-041-407, Miltenyi Biotech, North
Ryde, Australia) was also used with MACS columns to prevent blockage of columns by cell aggregates. Isolated CD34+ cells were incubated in 37oC incubator before cell sorting with IMDM or serum free medium (SFM) with SCF,
G-CSF, TPO and FL at 100ng/ml to prevent overnight apoptosis.
2.2.5 Phenotype staining protocol
Less than 106 cells were transferred to FACS tube (Cat. No.352008, Becton
Dickinson Bioscience (BD), North Ryde, Australia) and centrifuged at 1000 rpm for 5min. The supernatant was removed with care, not to disturb the cell pellet and Chapter 2: Development of cell division tracking methodology and data analysis 36
5)l -20)l of antibody was added into the tube. Stained cells were incubated on ice for a half an hour after mixing of the suspension by gentle vortexing. The cell suspension was washed with PBS containing 10% FBS and analyzed by flow cytometry.
2.2.6 Viability staining protocol
Propidium iodide (PI, Cat. No. P4170, Sigma-aldrich, Castle Hill, Australia) used for cell viability penetrates the membrane of dead cells where it intercalates with the cellular DNA. 10ul of PI stock (100)g/ml PI in PBS) was added to 0.5 – 1 ml of cell suspension just before analysis by flow cytometry in FL2 channel. The PI stock was stored at 4oC in the dark.
2.2.7 CFDA-SE staining protocol
Cells were resuspended in 1ml of PBS containing 0.1% BSA after discarding supernatant and incubated in a 37oC water bath for 10 minutes. 5)M CFDA-SE solution that was made from the 5mM stock was prepared while cells were incubating. 1ml of 5)M CFDA-SE was added to an equal volume of cells to give final concentration of 2.5)M CFDA-SE and vortexed before incubation at 37oC in a water bath for 10 minutes. Staining was quenched by cold FBS; three times the original volume of the cell suspension was added. Cells were centrifuged for 10 minutes at 1000 rpm followed by two washes with PBS containing 10% FBS.
Stained cells were cultured with IMDM with 10% FBS.
Chapter 2: Development of cell division tracking methodology and data analysis 37
2.2.8 Sorting protocol
Overnight-cultured, CFDA-SE stained cells were centrifuged, resuspended and passed through a 40)m cell mesh strainer (Cat. No. 352340, Becton Dickinson
Bioscience, USA) to remove cell aggregates. Cells were kept on ice until sorting was performed. Four sterilised FACS tubes (Cat. No.352063, Becton Dickinson
Bioscience, USA) for collecting cells were prepared by adding 1ml of culture media with 1% of Penicillin-Streptomycin respectively and kept on ice. A Moflo sorter (Cytomation, Fort Collins) or FACSVantage (BD, North Ryde, NSW,
Australia) were used for sorting. A viable sort gate was established on the forward versus side scatter bivariate histogram, and left and right sort gates for CFDA-SE fluorescence established by bisecting the CFDA-SE peak at the mode. Sorting gates were set to less than 20 channels each side of the mode (see 2.3.2). Right and left sorted cell were cultured separately with IMDM or serum free medium
(SFM) containing SCF, G-CSF, TPO and FL at 100ng/ml in 37oC incubator for 6 days. Initial cell concentration was 2x104 cells/ml and final cell concentration was not exceeding more than 1x106 cells/ml.
2.2.9 Flow cytometry
Cell were analysed on the day of sorting and for five consecutive days using a BD
FACSort analyser (BD, North Ryde, NSW, Australia) equipped with a 15-mW argon laser tuned 488nm and standard sets of green (FL1) and red (FL2) filters.
The spectral overlap between CFDA-SE (FL1) and antiCD34 conjugated monoclonal antibody (HPCA2-PE -FL2) was compensated in hardware.
Instrument settings (PMT voltages and gains) were not changed for all of the Chapter 2: Development of cell division tracking methodology and data analysis 38
analyses. Cell counts were performed by adding beads to the culture, and gating live cells and beads. The cell expansion Et() was calculated by dividing the cell number at timet , Nt(), by the number of cells that were inoculated, N(0) . This ratio was estimated by dividing the ratio of cell to bead events at time t ,
Ct()/ Cb () t , by the ratio at the start of culture CC (0) /b (0) .
Nt() Ct()/ C () t Et()=≈ b NCC(0) (0) /b (0)
Equation 2-1
2.2.10 List mode data analysis software
The clustering algorithm was programmed in MatlabTM (Version 7, release 14,
Mathwork) and is summarized as pseudo-code in Table 2.1 and Table 2.2. We adopted a bivariated cluster algorithm (CFDA-SE versus FSC) since it was noted that FSC was correlated with CFDA-SE staining intensity (see Figure 2-1, dashed lines). The principle axis of a generational cluster is defined as the linear regression line for that cluster with independent and dependent variables FSC and
CFDA-SE, respectively. Cluster boundaries were set by cubic spline interpolation between the principle axes for consecutive generations. If CFDA-SE fluorescence does not decay by more that one generation per day then it would be possible to keep track of cell generation numbers by daily analysis. This assumption was generally correct (see Table 2.3).
The principle axis of generational clusters was found using an iterative algorithm Chapter 2: Development of cell division tracking methodology and data analysis 39
(see Table 2.1). A first approximation of a cluster region was set using reasonable assumptions (CFDA-SE range less than twofold difference in fluorescence) and regression analysis approximated the clusters’ principle axis. With the next iteration, the cluster region was adjusted so that it was centered with respect to the last regression line. This process was repeated until convergence criteria were met.
The algorithm for assignment of cell generations to segmented clusters is summarized as pseudo-code in Table 2.2. Table 2.3 illustrates application of the nearest neighbor algorithm, showing the mean fluorescence of segmented clusters, and the criteria that was used to align cell generations. The Matlab user interface also included tools for gating generation number with respect to phenotype gates
(e.g., viable CD34+ or CD34- cells). Chapter 2: Development of cell division tracking methodology and data analysis 40
Table 2.1. Pseudo-code for finding clusters.
STEP TASK
1 Import event data into Matlab from List mode file.
2 Display 2D histogram (CFDA-SE vs. FSC) and obtain gated region for analysis
from the user(R1).
3 Find the model CFDA-SE fluorescence within R1.
4 Define a region of interest within R1, which is the CFDA-SE mode +/- 39
CFDA-SE channels. Call this region ROI 1.
5 Find line of best fit for CFDA-SE vs. FSC data within ROI 1 by linear
regression.
6 Define ROI 2 within R1 using regression line from step 5(+/- 39 CFDA-SE
channels).
7 Repeat steps 5 and 6 to update the ROI recursively until convergence criteria are
met (sum of the regression coefficients change by less than 0.1%).
8 Construct a new ROI 1 for the next generation by displacing the last ROI from
the prior generation by + or – 60 CFDA-SE channels [200 X log10 (2)].
9 Repeat steps 5-8 recursively until all generation have been analysed.
10 Calculate generation gates by cubic spline interpolation between cluster
regression lines (Figure 2-1)
11 Proceed to generational assignment algorithm (Table 2.2)
The Matlab program presented in abbreviate form with Pseudocode. Linear regression analysis was used to find a line segment that is the principle axis of each cluster on CFDA-SE versus FSC plots (Figure 2-1). Developed by R. Nordon. Chapter 2: Development of cell division tracking methodology and data analysis 41
Table 2.2. Pseudo-code for generational assignment.
STEP TASK
1 Calculate mean CFDA-SE fluorescence for each cluster sij where i indexes the
acquisition time and j is the cluster number (Table 2.3). Note that cluster
number j does not necessarily equal generation number g.
2 Find the model CFDA-SE cluster (M) for the first acquisition i.e., cluster
number with the largest number of cell.
3 Reassign indexing so that cluster M for the first acquisition is generation 1.
Cluster number (j) are offset by 1-M to calculate generation number, i.e., g=j+1-
M.
4 Set the acquisition number, i=1.
5 Find the model CFDA-SE cluster (M) for acquisition i.
6 Find the cluster in the next acquisition i+1 (denoted K), which is the “nearest
neighbour” to the modal CFDA-SE cluster (denoted M) in the current
acquisition I with the constraint that mean fluorescence can only decrease over
time. Reassign indexing so that cluster numbers (j) are offset by 1-M to
calculate generation number, i.e., g=j+K-M.
7 Set i=i+1 and go to step 5.
8 Repeat steps 5-7 for all acquisitions.
9 Display generational clusters for checking by user (Figure 2-7).
The Matlab program is presented in abbreviated form using Pseudocode. The algorithm aligns generations from successive days using the assumption that mean cluster fluorescence decreases by less than 1/2 over 24hr. Table 2.3 provides a worked example of this algorithm. Developed by
R. Nordon.
Chapter 2: Development of cell division tracking methodology and data analysis 42
2.2.11 Estimation of division tracking artefacts: CFDA-SE degradation and auto-fluorescence
The model that was used to estimate the level of cellular auto-fluorescence is
sbs' =+
Equation 2-2 where s is the CFDA-SE signal, b is the average level of cellular auto- fluorescence and s′ is the measured signal. The CFDA-SE signal should decrease geometrically with mitosis i.e.
=× smsii+1
Equation 2-3
where si and si+1 are the mean fluorescence of consecutive cell generations and m is the dilution factor which should be close to one half. The expression relating
CFDA-SE fluorescence to the measured signal incorporating auto-fluorescence
Equation 2-2 is substituted into the CFDA-SE dilution model Equation 2-3
=×+ − smsbm''(1)ii+1
Equation 2-4
Auto-fluorescence and dilution factor were estimated by linear regression analysis.
2.2.12 Derived quantities
2.2.12.1 Precursor cell frequency
A perfect tracking label is defined as a label that is equally distributed between Chapter 2: Development of cell division tracking methodology and data analysis 43
daughter cells, halves in intensity, but does not degrade (e.g., bleaching or leakage out of cells) and affect cell function (e.g., cell growth and apoptosis). It will only disappear from the culture system if the cell dies. Cell generation is the number of divisions plus one, so generation one cells have not divided, generation two have divided once and so on. The label concentration in generation g is diluted by a factor of 12g−1 because label halves in intensity with each division. Therefore, the quantity of label that is in generation g cells with phenotype x is
Nt() Ft()= gx, gx, 2g−1
Equation 2-5 where () is the number of cells in generation cells with phenotype x . Ntgx, g
This is called the precursor cell frequency because it approximates the number of precursor cells that give rise to progeny in generation g with phenotype x .
2.2.12.2 Estimation of cell death in culture using precursor cell
frequency
The law of mass balance states that the total amount of perfect label or equivalently, precursor cell frequency, is conserved
()= () NFtt0B x for all xA∈
Equation 2-6 where the set A denotes all possible mutually exclusive phenotypes x including () dead cells. Ftx is the precursor cell frequency for phenotype x and is found by summing precursor cell frequencies for that phenotype across all generations Chapter 2: Development of cell division tracking methodology and data analysis 44
CS ()= () DTB FtFtgx, x . In words, the sum of all types of precursor cells (including EUg dead cells) should remain constant for all t . The precursor cell balance equation is used to estimate the number of cells that have died.
()+= () ( ) FtFtNviable dead 0
Equation 2-7 () where the viable precursor cell frequency, Ftviable , is found by gating and counting viable cells and transforming their number according to Equation 2-5
2.2.12.3 Mean generation number
() The mean generation number, Gtx , is calculated by averaging generation number with respect to precursor cell frequency.
FV() B HXFtggx, Gt()= g x () Ftx
Equation 2-8
The rate of change of mean generation number with respect to time is used to approximate cell generation times (Equation 2-11).
2.2.12.4 Renewal
A proportion of cells will renew their phenotype ()x with each cell division.
Denoting this proportion (or percentage) by px the number of cells with that
()g−1 phenotype in generation g will be 2 px because 2 px daughter cells are Chapter 2: Development of cell division tracking methodology and data analysis 45
formed at mitoses. Thus, cells in generation g will be direct descendents of
Nt() gx, cells in generation one at the start of culture. Summing over all ()g−1 2 px generations
Nt() N ()0 = B gx, x ()g−1 g 2 px
Equation 2-9 () where Nx 0 is the number of cells with phenotype x at the start of the experiment.
This equation is solved for px given the inoculum cell number and a CFDA-SE histogram data at time t . Rearranging Equation 2-9 gives the polynomial
=−()g−1 ( ) 00B NtzNgx, x g
Equation 2-10 which is solved by finding its roots. This equation was solved numerically with
FV= () Matlab. The only positive root will give the value of px HXzp12x .
Chapter 2: Development of cell division tracking methodology and data analysis 46
CFDA-SE
Figure 2-1. Cluster analysis using linear regression CFDA-SE labelled and sorted cord blood
CD34+ cells were cultured for 6days in media containing SCF, FL, G-CSF, and TPO. The bivariate dot plot (CFDA-SE versus FSC) shows cell events as well as the principle axis and polygonal boundaries of gated generations. CFDA-SE green fluorescence (x-axis) and FSC (y- axis) are plotted. Polygonal regions were generated by the clustering algorithm described in Table
2.1 and Table 2.2. Linear regression was used to find the principle axis for each cluster.
Chapter 2: Development of cell division tracking methodology and data analysis 47
Table 2.3. The use of mean cluster fluorescence to assign generation number to clusters using the “nearest neighbour” algorithm (Table 2.2).
Before alignment
Day\Cluster ID 1 2 3 4 5 6 7 8 9 10
1 834 778 728 ------
2 795 752 689 641 ------
3 799 738 665 597 532 484 429 - - -
4 803 761 719 639 567 492 423 359 299 -
5 771 730 689 623 547 470 403 336 266 203
6 660 606 528 451 384 316 250 195 146 111
After alignment
Day\Generation ID 1 2 3 4 5 6 7 8 9 10
1 778 728 ------
2 752 689 641 ------
3 738 665 597 532 484 429 - - - -
4 719 639 567 492 423 359 299 - - -
5 689 623 547 470 403 336 266 203 - -
6 660 606 528 451 384 316 250 195 146 111
The table shows an array containing the mean fluorescence of clusters indexed by their day (rows) and cluster ID (columns). The “nearest neighbour” algorithm reassigns cluster IDs with their correct generation number (generation 1 = undivided cells). The mean values shown in bold italics are the clusters with the greatest number of events referred to as the modal cluster for that day. On day 1 the modal cluster is assigned to generation 1. The generation number of the days modal cluster is assigned to a cluster on the next day that is its nearest neighbour with lower mean fluorescence (underlined).
Chapter 2: Development of cell division tracking methodology and data analysis 48
2.3 Results
2.3.1 Optimisation of CFDA-SE and cell concentration
The number of successive divisions that can be tracked depends on the fluorescence intensity. It is necessary that a titration experiment be carried out as at high concentrations, fluorescent tracking dyes are toxic. The cell number is another consideration for CFDA-SE staining protocols; at low cell numbers the adsorption of dye per cell is higher. Commercial protocols suggest that 1x107 cells/ml should be used for CFDA-SE staining. However, when working with rare cells such as HSC, cell number is a significant consideration. Here we examined the optimal CFDA-SE concentration for staining CD34+ cells. Figure 2-2 shows that CFDA-SE concentration and the cell concentration at the time of CFDA-SE staining affected KG1a cell growth. Higher cell numbers at the time of staining were less influenced by increases in CFDA-SE concentration compared to low cell numbers. For CD34+ cells isolated from UCB, cell expansion with 2.5uM of
CFDA-SE was close to that seen in unstained cells (14.8±0.5 vs. 15.5±0.08)
(Figure 2-3). Therefore, we used this concentration for CD34+ cell staining with
CFDA-SE. Chapter 2: Development of cell division tracking methodology and data analysis 49
5
4 e
s 3
a
e
r
c
n
i
2
d
l
o F 1
0 1X10e7cells l) 5uM 5X10e6cells m (/ CF 10uM 2.5X10e6cells er DA b -SE 15uM um c 1.25X10e6cells l n on el cen 20uM C tra tion
Figure 2-2 Optimal CFDA-SE concentration vs. cell number. KG1a cells were cultured in
IMDM with 10% FBS and initial cell number was 2x104 cells/ml. Cell number was counted at day
3.
18
16
14
12
10
8 Fold increase Fold 6
4
2
0 5uM 2.5uM Unstained CFDA-SE concentration
Figure 2-3 CFDA-SE concentration test with CD34+ cells derived from UCB. Initial cell number was 2x104 cells/ml and cultured for 5days in IMDM with 10% FBS containing SCF, TPO,
G-CSF and Flt-3 at 100ng/ml. Data is presented with mean±SD (n=3). Chapter 2: Development of cell division tracking methodology and data analysis 50
Next we investigated the effect of BSA solution by series dilution experiment. We hypothesised that BSA may ‘buffer’ CFDA-SE cell adsorption, making cell number at the time of staining less of a critical process parameter. When cells were stained with CFDA-SE in the absence of BSA, there were huge variations of
CFDA-SE mean fluorescence with different cell concentrations (Figure 2-4).
Above 1.25 mg BSA/ml there was very little effect of cell concentration on mean
CFDA-SE fluorescence (Figure 2-4). Cell growth was also examined with different BSA and cell concentrations at the time of CFDA-SE staining (Figure
2-5). Cell expansion was similar when cells were suspended with BSA at the time of CFDA-SE staining (Figure 2-5). Therefore, we used 1mg/ml BSA for CD34+ cell staining with CFDA-SE as 2.5)M of CFDA-SE was used for CD34+ cells.
0.08
e
c
n e
c 0.06
s
e
r
o
u
l
f
n
a 0.04
e
m
E S
- 0.02 A
D 1.25X10e6cells l) F m C 2.5X10e6cells (/ r e 0.00 5X10e6cells b m 0mg u 1.25mg n 2.5mg 1X10e7cells ll BSA 5mg e conc C entra 10mg tion (/ml)
Figure 2-4 The effect of BSA on CFDA-SE fluorescence by cell number. CFDA-SE mean fluorescence on y axis is the ratio of day 1 to day 3. KG1a cells with 5)M of CFDA-SE were used for this experiment.
Chapter 2: Development of cell division tracking methodology and data analysis 51
6
5 e
s 4
a
e
r c
n 3
i
d
l o
F 2
1
0 10mg 5mg ) 1.25X10e6cells l (/m 2.5mg n 2.5X10e6cells tio 1.25mg ra 5X10e6cells nt Ce e ll n 0mg nc um 1X10e7cells co be A r (/ BS ml)
Figure 2-5 The effect of BSA on cell growth. Cell number was counted at day 3. KG1a cells with
5)M of CFDA-SE were used for this experiment.
2.3.2 Sorting gate strategy
To obtain high resolution cell division tracking, a sorting strategy can be employed [46, 125]. We assume that cellular fluorescence halves with symmetric divisions. Using flow cytometry, 4 decade log amplifier corresponds to the 256 channel on the log scale which means 64 channels per decade, so we calculate that the fluorescence decrease 19.27 channels per division (Figure 2-6). Therefore, the sorting gate should be less than 20 channels.
Chapter 2: Development of cell division tracking methodology and data analysis 52
Unsorted Sorted
Day 2
Day 4
Figure 2-6 Comparison of sorted and unsorted cell on CFDA-SE intensity. KG1a cells were used to demonstrate the effect of sorting on CFDA-SE. Flow cytometry analysis at day 2 and day 4 was presented.
2.3.3 Clustering Algorithm
High resolution tracking of cell division using the sorting procedure outlined in the methods was a prerequisite for convergence of the clustering algorithm. The performance of the clustering algorithm was evaluated by checking a plot displaying gating regions and the medial axis of each cluster (see Figure 2-1).
The assignment of generation number to clusters was checked by colour-coding each generation on histograms showing CFDA-SE fluorescence (Figure 2-7).
CFDA-SE fluorescence for a gated cluster was approximately normally distributed. There was generally poor cluster resolution at fluorescence levels that were near autofluorescence (generations 9 and 10, day 6). Chapter 2: Development of cell division tracking methodology and data analysis 53
Figure 2-7. Assignment of generations and fitting of normal distributions to clusters CFDA-
SE histograms for consecutive observations (Day1-6) show the assignment of generational clusters to data. CFDA-SE green fluorescence (x-axis) is plotted on a logarithmic scale. The plots are used to check that cell generations have been correctly assigned to CFDA-SE peaks. For each cluster, a normal distribution has been fitted to the one-dimensional log transformed CFDA-SE data (A/D value) and superimposed over the histogram. The cluster separation can be estimated from the overlap between neighbouring normal distributions. There was generally poor cluster resolution at low fluorescence (generations 9 and 10, Day 6) Chapter 2: Development of cell division tracking methodology and data analysis 54
2.3.4 Division tracking artefacts: CFDA-SE degradation and auto-fluorescence
The degradation rate of CFDA-SE was estimated from a six day cord blood division tracking experiment by plotting the mean CFDA-SE fluorescence for each generation with respect to time (Figure 2-8). The log2 transformed mean fluorescence decreased linearly with respect to time and was approximated by an exponential decay with a half-life of 72 ± 4 hours. The horizontal gridlines in
Figure 2-8 correspond to halving of fluorescence. For each sample time, the mean fluorescence of consecutive generations was spaced at intervals corresponding to approximate halving of fluorescence, though the decrease per division was less than half in the first decade of the fluorescence scale because of the contribution of autofluorescence to the CFDA-SE signal.
From data pooled from five cord blood experiments (30 CFDA-SE histograms),
118 mitotic divisions with at least 200 cells per CFDA-SE cluster were analyzed.
Figure 2-9 shows a bivariate dot plot presenting the correlation between the mean fluorescence of two consecutive CFDA-SE clusters (x-axis) and the fold-increases in mean fluorescence (y-axis). There was a non-linear correlation between mean fluorescence and the fold-decrease per mitosis. The auto-fluorescence model
(Equation 2-4) was fit to the data by non-linear least squares (continuous line).
The level of auto-fluorescence (b, Equation 2-4, page 42) was estimated to be
1.31±0.12 (SE) fluorescent units.
Chapter 2: Development of cell division tracking methodology and data analysis 55
1250.00
625.00
312.50 GEN 1 GEN 2 156.25 GEN 3 78.13 GEN 4
39.06 GEN 5 GEN 6 19.53 GEN 7 9.77
Mean fluorescence of cluster of fluorescence Mean GEN 8 4.88 GEN 9 GEN 10 2.44
0 20406080100120140160 Hours after labeling
Figure 2-8 Mean fluorescence of clusters. The plot shows the time course of mean fluorescence for generations 1-10. The y-axis shows log2 transformed and the x-axis is time in hours after
CFDA-SE labelling. CFDA-SE fluorescence has an exponential decay with a half-life of 72 ± 4 hours (±SE). For fixed time, the CFDA-SE fluorescence decreases geometrically with generation number as expected, though the fold-decrease was less than or equal to two-fold with each division.
Chapter 2: Development of cell division tracking methodology and data analysis 56
2.0
regression 1.8
1.6
1.4 Fold increasein mean fluorescence per generation 1 10 100 1000 Mean fluorescence
Figure 2-9 Mean fluorescence of cell generations versus increase in mean generation fluorescence per generation. The dot plot shows the correlation between mean fluorescence for consecutive generations and the fold increases in mean fluorescence. The data was pooled from all five experiments (118 mitoses with at least 200 events). At lower fluorescence signals, the fold increases were well below two. The autofluorescence model (continuous line) was fitted by non- linear regression. Estimated autofluorescence was 1.31±0.12 (SE).
2.3.5 Growth kinetics: Cell expansion and mean generation number
Cord blood CD34+ cells were grown for six days with a cytokine cocktail containing SCF, TPO, FL and G-CSF. Cells were analyzed for CD34 antigen expression on consecutive days (Figure 2-10). The growth curves for CD34+ and Chapter 2: Development of cell division tracking methodology and data analysis 57
CD34- cells are shown in Figure 2-11. Data points represent the mean and standard deviation of five separate experiments (1-2 pooled units of cord blood per experiment). Cell numbers increase exponentially (Figure 2-11A). The doubling times for CD34- and CD34+ cells were respectively 15.9 ± 2.4 and 26 ±
0.7 hours. Even though CD34- cells were less than 5% of the inoculums, their rate of growth was greater than CD34+ cells so that by the end of the six-day culture period there were similar numbers of CD34+ and CD34- cells.
The mean generation number calculated using Equation 2-8 is shown in Figure
2-11B. There is a lag period of about two days before mean generation number increases. The lag is the time delay before the first cell division after cell culture commences. After a 24 hours, the generation number increases almost linearly with respect to time. A "lag/linear" model was fitted to the data.
t −τ Gt()=+1 for t >τ x δ =≤1 t τ
Equation 2-11 where δ is the generation time and τ is the time lag. The mean generation numbers from days 3 to 6 were used to estimate δ and τ by linear regression
(see Table 2.4). The CD34- cell cycle time (15.1 ± 0.9 hours) was approximately
10 hours shorter than the CD34+ cell cycle time (24.7 ± 0.8 hours). The quiescent lag time before the first division was about two days.
Chapter 2: Development of cell division tracking methodology and data analysis 58
Day 1 Day 2 Day 3
Day 4Day 5Day 6 FL-2 (AntiCD34PE)
Day 1Day 2Day 3
Day 4 Day 5 Day 6 FL-2(Isotype control)
FL-1 (CFSE)
Figure 2-10 Dot plot of CFDA-SE versus CD34 antigen expression for days 1-6. MACS purified CFDA-SE stained and sorted CD34+ cells were grown for 6 days in media containing
SCF, TPO, G-CSF and FL, each at 100 ng/ml. The bivariate histograms show CFDA-SE versus
CD34 antigen expression (HPCA-2 PE) on consecutive days. The lower bivariate histogram series shows corresponding isotype controls, which were used to define the CD34+ cell region. CD34+ and CD34- CFDA-SE positive events are shown as red and black dots, respectively.
Chapter 2: Development of cell division tracking methodology and data analysis 59
Figure 2-11 Growth kinetics of cord blood CD34+ cells. A. Fold expansion B. Mean generation number. A. The fold increase in cell number shown on the y-axis is the number of CD34+ or
CD34- cells divided by the input cell number at the start of culture. The x-axis is time in hours.
Data points shown as closed (CD34+) or open (CD34-) circles represent the mean and standard deviation of 5 separate experiments (1-2 pooled units of cord blood per experiment). Cell numbers increase in an exponentially fashion with doubling times for CD34- and CD34+ 15.9 ± 2.4 and 26 Chapter 2: Development of cell division tracking methodology and data analysis 60
± 0.7 hours (± standard error) respectively. B. The mean generation number (Equation 2-8) is the y-axis and the time in hours is the x-axis. Data points shown as open (CD34+) or closed circles
(CD34-) represent the mean and standard deviation of 5 separate experiments (1-2 pooled units of cord blood per experiment). The period of quiescence is clearly demonstrated with no divisions before 40 hours. A "lag/linear" model was fitted to the data (Equation 2-11) and the results of this model are shown in Table 2.4. Linear regression analysis was performed using Sigmaplot 2002 version 8 (SPSS Inc).
Table 2.4 Average cell cycle time and quiescent lag for CD34+ and CD34- cells
Parameter Duration in hours (± SE)
CD34+ 24.7 (0.8) Cycle time (δ) CD34- 15.1 (0.9)
CD34+ 38.0 (2.3) Lag (τ) CD34- 49.8 (3.8)
Cell doubling CD34+ 26 (0.7) time CD34- 15.9 (2.4)
The table shows the results for the linear/lag model (Equation 2-11) for mean generation number.
The best-fit parameters for the cell cycle time and lag were found by regression analysis excluding the first two data points at 24 and 48 hours. The cell doubling time was estimated suing an exponential model of cell growth. Chapter 2: Development of cell division tracking methodology and data analysis 61
2.3.6 Differentiation kinetics: Precursor cell frequency and renewal
The number of cells at the start of culture that gave rise to CD34 positive and negative cells at time t was estimated using the precursor cell frequency transformation (Equation 2-5). The precursor cell balance equation (Equation 2-7) was used to indirectly estimate the number of cells that had died.
Figure 2-12A shows the estimated proportion of precursor cells that gave rise to
CD34+, CD34- or dead cells (mean ± SD for 5 experiments). The steady decline in precursor cells that give rise to CD34+ cells, was balanced by a complimentary increase in CD34- and dead precursor cells. Figure 2-12B shows calculated renewal (Equation 2-10) for total and CD34+ cells. The renewal for all cells increases to a value of 0.96 ± 0.01 at day 6 (SD, n=4). The CD34+ cell renewal increases to a maximum of 0.91 ± 0.02 (SD, n=5) at days 4 to 5, and appears to be decreasing on day 6. The difference between the total and CD34+ cell renewal accounts for cells that have lost the CD34 antigen (0.06 ± 0.02 at day 6).
Chapter 2: Development of cell division tracking methodology and data analysis 62
1.0
0.8
0.6 CD34+ CD34- Apoptosis 0.4
0.2 Proportion of precursor cells
0.0 20 40 60 80 100 120 140 Hours 1.0
0.8
0.6 total CD34 total-CD34 0.4
Average renewal probability renewal Average 0.2
0.0 20 40 60 80 100 120 140 hours
Figure 2-12 Differentiation kinetics. A. Precursor cell frequency and B. Renewal The y-axis shows the proportion of precursor cells defined by Equation 2-5 and Equation 2-7 that become
CD34+, CD34- or dead cells. Data is displayed as closed (CD34+), open (CD34-) or grey filled circles (apoptosis) representing the mean and standard deviation of 5 separate experiments (1-2 pooled units of cord blood per experiment). There is a steady decline in precursor cells that give rise to CD34+ cells, and a complimentary increase in CD34- and apoptotic precursor cells. B.
Renewal is plotted with respect to days of culture and was undefined for day one because cells had not undergone any cell division. Data is displayed as open (total), closed (CD34+) or grey (total-
CD34+) filled circles. Total and CD34 antigen positive cell renewal increased to 96 ± 1% and 90 ±
1% (CD34+) by day 6. The difference between the total and CD34 renewal (differentiation from
CD34+ to CD34- cells) increased from day 3 (4.0 ± 1.6%) to day 6 (6.0 ± 2.0%). Chapter 2: Development of cell division tracking methodology and data analysis 63
2.4 Discussion
The purpose of developing list mode data analysis software using a clustering and nearest neighbor algorithm was to cluster and gate cell generations with phenotype. Commercially available software products for CFDA-SE histogram analysis (WEASEL™, Walter and Elisa Hall Institute, Melbourne, Australia and
MODFIT™, Verity Software House, Topsham, ME) do not have a tool to gate phenotype subsets with respect to generation numbers. The use of a two dimensional clustering algorithm (CFDA-SE versus FSC) revealed that there was a significant correlation between FSC and CFDA-SE fluorescence (Figure 2-1).
Larger cells (high FSC) tend to have slightly higher CFDA-SE fluorescence.
Inaccurate compensation between CFDA-SE and other colour channels – in this study we used the red channel to quantify CD34 (HPCA2-PE) – can result in distortion of the CFDA-SE histogram. We found that there was approximately
30% overlap between CFDA-SE and HPCA2-PE (Figure 2-10). Spectral overlap can be compensated using hardware or software. In principle, clustering using two or more dimensions can overcome such parameter dependencies.
CFDA-SE degradation and auto-fluorescence can occur during cell division tracking. We quantified CFDA-SE degradation as a first order exponential decay with a half-life of three days (Figure 2-8). We speculate that dye degradation is caused by intracellular proteolysis. We found that CFDA-SE fluorescence decrease by less the half with each division for generations with low fluorescence
(Figure 2-9). Cellular auto-fluorescence could explain this phenomenon. Chapter 2: Development of cell division tracking methodology and data analysis 64
Interpretation of data from time series division tracking is important to estimate phenotype specific growth parameters. Single cells can be tracked to characterize the complete division history using time-lap video microscopy and image analysis
[178] but it is time consuming and sometimes impractical. While multi-type time series division tracking analysis does not provide single cell differentiation histories, it provides accurate measurement of the distribution of cells across eight generations in a relatively short time. Here it was demonstrated that the quiescent lag and cell cycle time for CD34+ and CD34- cells can be estimated using linear regression model of mean generation number. One assumes that the cell growth rate is exponential, and doubling time is close to the mean generation time (Table
2.4). One also assumes that CD34+ and CD34- cells grow independently so the model ignores the effect of differentiation. More complicated models are required to describe cell cycle transit and differentiation of individual cell generations.
The estimation of cell generation number is the key to calculation of precursor cell frequency (Equation 2-5) and renewal (Equation 2-9). A precursor cell balance approach (Equation 2-7) is used to derive these equations; the number of precursors in a cell culture system is conserved, provided that their fate is tracked.
It was not possible to track the fate of precursors that became apoptotic, however it was possible to directly measure cells which retain CFDA-SE, and estimate apoptotic precursor cells indirectly. The use of precursor cell balance is far more accurate than total fluorescence because cells cannot be labeled perfectly with
CFDA-SE as the dye degrades, and cells exhibit significant auto-fluorescence.
The use of generation number to estimate precursor cell frequency is a novel Chapter 2: Development of cell division tracking methodology and data analysis 65
method to estimate the rate of differentiation, independent of division history. The renewal probability provides a quick ‘rule-of-thumb’ to estimate the number of
k-1 cells present after k generations i.e., (2px) cells. If px is less than 0.5, the cell subset will contract, or will expand px is greater than 0.5.
Loss of CD34+ precursor cells can be demonstrated through the increase in
CD34- precursor cells and/or apoptosis (Figure 2-12A). The transfer of label from the CD34+ to the CD34- cell compartment is a direct consequence of differentiation. If CD34+ and CD34- cells were independent cell lines without common ancestry, the CD34+ and CD34- precursor cell frequency would be constant. Therefore, it was concluded that CD34- cells were generated from two sources, differentiation from CD34+ cells and CD34- renewal divisions.
Zhang et al. investigated growth of primitive haematopoietic cells in vivo and in vitro using CFDA-SE data analysis [173]. They calculated the proportion of cells that remain quiescent, divide or undergo apoptosis by assuming a constant cell cycle time, and recursive expressions for generation of these subpopulations.
However, their model did not characterise self-renewal and differentiation of HSC.
Here one introduces the notion of phenotypic self-renewal probability ( px ) as another measure of differentiation. One assumes that the rate of renewal does not vary with respect to generation number, but can change with respect to time. This is an oversimplification since renewal probability is expected to vary over time and generation.
Chapter 2: Development of cell division tracking methodology and data analysis 66
Renewal rate is calculated by finding the roots of Equation 2-10. The polynomial terms with the greatest weights are those close to the mean generation
FV() number HXGtx . Thus, renewal will reflect those divisions close to the mean generation number. The low renewal rate at the start of culture may be attributed to a high rate of apoptosis in early generations. The increase over time can be attributed to higher renewal rate in later generations. At day 4-5, the CD34+ renewal rate was estimated to be 91% (see Figure 2-12). What happens to the remaining 9% of cells? They could either die or differentiate. The rate of cell death was estimated by subtracting the total renewal from 100% i.e., 4% of cells undergo apoptosis per division. Thus, it is concluded that the remainder, around
5% of CD34+ cells, form CD34- cells. In other words, the difference between the total and CD34+ renewal rates provided an estimate of the rate of differentiation from CD34+ to CD34- cells. The rate of differentiation (5% per generation) appears modest considering that CD34+ and CD34- cells numbers were almost equal by day 6. However, cell numbers increase geometrically with each division, and the cell cycle time for CD34- cells was short (16 hours).
Generation time and renewal rate are population statistics that attempt to estimate phenotypic cell cycle times and renewal probability. They are most likely correlated statistics, and bias is introduced by their dependence. For example cycling of CD34+ cells will tend to increase their mean generation number whereas differentiation to CD34- cells will decrease mean generation number.
Thus, the calculations of CD34+ mean generation time, which are calculated from the reciprocal of time derivative of mean generation number (see Equation 2-11, Chapter 2: Development of cell division tracking methodology and data analysis 67
Figure 2-11B) can overestimate generation times when renewal rates are low. The mean generation times for CD34+ and CD34- cells may be close to the actual cycling times of these cell types because of their high renewal rate (>90%).
The simple statistics presented here may provide a valid ‘first approximation’ for phenotype cycle times and renewal rate. Future work should quantify the level of bias introduced by the codependence of mean generation time and renewal more rigorously. Comprehensive models that couple differentiation and proliferation processes can estimate the level of bias as well as examine the sensitivity of these statistics to changes in generation time and renewal.
Significant progress has been made towards this goal. The Smith and Martin model of cell cycle transit which was based on study of the fraction of labeled mitoses, divides cell cycle into deterministic (fixed lag) and stochastic
(exponential random variable) phases [179]. Cells divide asynchronously because of the random duration of the cell cycle. The model predicts that a synchronized cell line with cell cycle time described by an exponential random variable would expand by 2.71 (e) -fold per division and become asynchronous over the first few divisions. A synchronized cell line with a negligible stochastic phase would double stepwise with each division and retain synchronization. The actual behavior of immortalized cell lines lies somewhere between these two extremes
[125]. Many subsequent models have built on the probabilistic framework introduced by Smith and Martin to describe the rates of mitosis and death in unipotent culture systems [125, 180-183]. Chapter 2: Development of cell division tracking methodology and data analysis 68
Phenotypic renewal is a concept of fundamental interest to stem cell biologists and technologists. The blood stem cell niche in marrow, which consists of stromal cells and their products, regulates the size of the blood stem cell compartment by control of stem cell renewal and quiescence [172, 184]. Renewal probability of at least 50% per division is required for expansion. This study has shown that cytokine exposure drives a 20-fold increase in CD34+ cell numbers over 6 days.
This expansion can be attributed to relatively few renewal divisions with a high rate of renewal (91%). The methodology can be combined with positive and negative regulators of stem cell cycling to define how the stem cell niche regulates stem cell quiescence and renewal. The following next two chapters we will apply the methodology to characterise divisional kinetics of cultured UCB and MPB-derived stem cells and to study the influence of culture duration, serum, and co-culture with the specific cellular component of bone marrow stroma cell. Chapter 3: Influence of culture components 69
Chapter 3: Influence of culture
components on divisional
kinetics of ex vivo expanded
haematopoietic stem cells
69 Chapter 3: Influence of culture components 70
3.1 Introduction
In the previous chapter the divisional kinetics of UCB CD34+ cell expansion was studied. In this chapter, these initial studies will be extended to determine the effect of some culture constituents on the kinetics of CD34+ cells expansion.
Many in vitro studies have shown that HSC culture systems driven by cytokines alone do not maintain HSC [126, 185]. We have recently shown that 5-day ex vivo culture of UCB CD34+ cells in the presence of the most commonly used three cytokines –SCF, Flt-3 and TPO, each at 100ng/ml, results in significant reduction in the number of SCID repopulating cells (see Chapter 4). Bone marrow stroma cells provide the environment of signals that control proliferation, survival and differentiation of hematopoietic progenitor and stem cells [51-53]. Schofield’s
‘niche hypothesis’ suggested the existence of a microenvironment containing cells that were postulated to enable HSC to indefinitely self-renew, while effectively inhibiting differentiation and maturation [126, 186]. A few murine and human stromal cell lines have been engineered to support maintenance and expansion of
HSC [164, 187, 188]. There is no data; however, comparing the kinetics of stem cell proliferation in suspension culture and in cells co-cultured with the stroma cells. In this study, the kinetics of ex vivo expansion of CD34+ cells in co-culture were examined using the murine bone marrow MS5 cell line. Stem cells from
UCB and MPB were expanded either in a cytokine-driven suspension culture or co-cultured with the murine bone marrow stroma cells. Serum has been used in the past for ex vivo expansion of HSC [75, 189]. FCS has been shown to promote overall expansion of haematopoietic progenitor cells, however, it also promoted
70 Chapter 3: Influence of culture components 71
their differentiation [189, 190]. In addition to xenogeneic stroma cells, FCS is another potential source of poorly characterised zoonotic infections such as BSE
(Bovine Spongiform Encephalopathy) [191] and xenogeneic proteins, making this culture system unsuitable for clinical application. The kinetics of ex vivo proliferation of CD34+ cells cultured in serum-free media developed for clinical applications and serum-containing culture system were also compared in this chapter.
These culture systems have been previously studied; however this is the first in- depth study of CD34+ cell division kinetics by high-resolution division tracking.
The study is original because it examines the effect of culture constituents on
CD34+ cell renewal and other novel kinetic parameters as described in chapter 2.
71 Chapter 3: Influence of culture components 72
3.2 Experimental design and statistical analysis
UCB-derived stem cells were provided by the Sydney Cord Blood Bank. CD34+ cells were purified and stained with CFDA-SE as described in Chapter 2. Dr
David Haylock kindly provided purified human CD34+ cell that were isolated from MPB. These cells were purified from multiple myeloma patients undergoing
MPB collections at the Peter McCallum Cancer Centre, Melbourne using a Baxter
Isolex system. MPB for this study was collected under informed consent with human ethics approvals from the Peter McCallum Cancer Centre and University of New South Wales. The ex vivo expansion properties of 20 UCB (2-3 pooled
UCB used for each experiment) and two MPB donors were examined.
The experimental protocol for analysis of three culture systems, namely i) suspension culture in serum-free media (SFM, see chapter 2 methods and materials), ii) co-culture with stroma cells (MS5 cell, see chapter 2 methods and materials) using SFM and iii) suspension culture using serum-containing media
(Figure 3-1), were examined. To span 10 days of continuous division tracking, expanded CD34+ cells (culture conditions A and B) were reisolated and stained with CFDA-SE at day 6. For the 1st week division tracking, CD34+ cell fractions were cultured with cytokines for 5 days and analysed every 24 h. To perform the
2nd week division tracking, suspension culture cells from the first 5 days of culture, were re-stained with CFDA-SE, CD34+ cells were purified by MACS, followed by daily cytometric analysis for the next 5 day (days 6-10).
72 Chapter 3: Influence of culture components 73
Alternatively, co-cultured cells from the first week were reisolated and tracked in suspension or co-culture for days 6-10.
Time 1 2 3 4 5 6 7 8 9 10 (Day)
CD34+ re-Isolation from A and B CD34+ Isolation 1st wk division tracking Suspension, SFM (A)
CD34+ Co-culture, SFM (B)
IMDM+20% FCS (C) 2nd wk division tracking Suspension, SFM (E)
Co-culture, SFM (F)
Suspension, SFM (G)
Figure 3-1 Schematic presentation of experimental design. CD34+ cells were isolated from
UCB and MPB. The 1st week division tracking was performed daily using flow cytometry using
CFDA-SE CD34+ cells cultured in suspension in serum free medium (condition A), co-cultured with the MS5 cells in serum-free medium (condition B) or cultured in suspension with the addition of serum (condition C). The 2nd week division tracking was conducted using cells cultured in suspension (conditions E and G) or co-cultured with stroma (condition F). Cells cultured with condition A were re-isolated CD34+ cells at day 6 for 2nd week division tracking and cultured in suspension (condition G). Cells cultured with condition B were re-isolated CD34+ cells at day 6 for 2nd week division tracking and cultured in suspension (condition E) and co-culture with the
MS5 cells (condition F). All culture conditions were performed in duplicate for both stem cell sources except culture condition C of UCB (n=5). The result of culture condition G was not discussed in the chapter as there was not a significant effect on kinetics of CD34+ cells ex vivo
73 Chapter 3: Influence of culture components 74
expansion. SFM: Serum free medium. Cytokines used in this study were SCF, GSCF, TPO and
Flt-3 each at 100ng/ml (see chapter 2 methods and materials).
The statistic significance of experimental effects on stem cell expansion, precursor cell frequency and mean generation number was estimated by linear regression and one-way or two-way analysis of variance (ANOVA) combining multiple comparisons. The paired two-tailed t-test was used to compare population means.
The details of all statistic analysis can be found in the appendix. Data are presented as the means ± standard error of mean (SEM) or standard deviation
(SD). Statistic analysis was carried out with Stata 6.0 (Texas, USA) and Microsoft
Excel 2003.
74 Chapter 3: Influence of culture components 75
3.3 Results
It is important to note that fold expansion and precursor cell frequency in these experiments were log-transformed prior to statistical analysis for two reasons.
These biological variables change exponentially with respect to time and are log transformed to ensure that the variance of these measures is uniform over time, a necessary requirement for ANOVA. Secondly log expansion or log precursor cell frequency change linearly with respect to time because these processes are exponential (Figure 3-2A and B). Therefore the rate of change in cell number or precursor cell frequency was found by linear regression. The rates of change of cell and precursor numbers are referred to as ‘cell expansion rate’ and ‘precursor expansion rate’, respectively. Likewise the gradient of a graph showing mean generation number versus time (not log transformed, see Figure 3-2 C) is linear, and the gradient is called the ‘cell division rate’. The lag before first mitosis and cell cycle times are therefore calculated from the reciprocal of cell division rate
(see equation 2-11, Chapter 2 and Figure 3-3 A). Renewal probability did not fit a well- recognised kinetic pattern, comparisons between culture conditions or cell source were performed on the mean taken over all time points (Figure 3-2 D).
Apoptosis and differentiation were estimated by subtracting the CD34+ cell renewal from the total renewal (see discussion in chapter 2 and Figure 3-3 B).
75 Chapter 3: Influence of culture components 76
A B 100 1st wk_suspension 2nd wk_suspension 10 1st wk_suspension 1st wk_co-culture 2nd wk_suspension 10 2nd wk_co-culture 1st wk_co-culture 2nd wk_co-culture
1 1
CD34+ fold expansion(log) 0.1 0 24 48 72 96 120 0.1 Culture duration (h) 0 24 48 72 96 120
CD34+ precursor cell frequency(log) Culture duration (h)
C D
1st wk_suspension 1.0 7 2nd wk_suspension 6 1st wk_co-culture 0.8 5 2nd wk_co-culture 0.6 4 1st wk_suspension 3 0.4 2nd wk_suspension 2 0.2 1st wk_co-culture 1 2nd wk_co-culture
0 CD34+ renewalprobability 0.0 0 24 48 72 96 120 0 24 48 72 96 120 CD34+ mean generationnumber Culture duration (h) Culture duration (h)
Figure 3-2 Effect of culture duration and stroma on expansion of UCB derived CD34+ cells.
Fold expansion (A) defined by equation 2-1 and precursor cell frequency (B) defined by equation
2-5 were presented with log-transformed data (y-axis) to stabilise variance. Mean generation number defined by equation 2-8 and renewal probability defined by equation 2-9 and equation 2-
10 were shown in C and D. The x-axis shows culture duration in hours. Each culture condition was performed in duplicate using 2-3 pooled UCB for each experiment. Error bars show the SEM.
76 Chapter 3: Influence of culture components 77
A
7
6 2wk suspension 2wk co-culture 2wk suspension lag/linear model 5 2wk co-culture lag/linear model
4
3
2 Mean generation number generation Mean
1
0 0 20 40 60 80 100 120 Culture duration (h)
B
1.0
0.9
0.8
0.7 Suspension total renewal Suspension CD34+ renewal 0.6 Differentiation Apoptosis 0.5 Co-culture total renewal Co-culture CD34+ renewal Co-culture differentiation 0.4 Co-culture apoptosis 0.2 Apoptosis / Differntiation
0.0 20 40 60 80 100 120 Culture duration (h)
Figure 3-3 Illustration of lag/cell cycle time, apoptosis and differentiation during the 2nd
week culture of CD34+ cell derived from MPB. (A) Mean generation number was fitted
“lag and linear” model (equation 2-11). (B) Apoptosis was estimated from total renewal rate
and differentiation is the difference between the total and CD34+ renewal rates. All
experiment was performed in duplicate using 2 MPB each experiment and SEM is presented.
77 Chapter 3: Influence of culture components 78
1.1.1Comparing ex vivo expansion of CD34+ cells derived from UCB and MPB
Two-way ANOVA was used to analyse the effects of culture conditions A, B, C,
E and F (see Figure 3-1) and cell source (MPB and UCB). The results for expansion rate, precursor cell expansion rate are shown in Figure 3-4. Cell division rate and CD34+ renewal probability are shown in Figure 3-5.
The effect of culture conditions A, B, C, E and F was significant for cell expansion rate (Figure 3-4 a, c), precursor expansion rate (Figure 3-4 e, g), division rate (Figure 3-5 a, c), and renewal probability (Figure 3-5 e). There was also a significant effect of cell source on cell expansion rate (Figure 3-4b and d).
Only CD34- precursor expansion rate (Figure 3-4h), CD34+ cell division rate and renewal probability (Figure 3-5b and f) were affected by cell source. The details of source comparisons on culture components such as culture time, stroma and serum will be discussed below.
UCB-derived CD34+ cells consistently demonstrated significantly higher cell expansion rate compared to MPB-derived cells in all culture conditions examined
(Figure 3-4 a and b). In contrast, UCB-derived CD34- cell expansion rate was lower compared to MPB (Figure 3-4 c and d). The increased UCB-derived CD34+ cell expansion rate is associated with an increased cell division rate (Figure 3-5 a and b). This means that cell cycle times are shorter. The cell cycle time in UCB- derived CD34+ cells was shorter for both suspension and co-culture with stroma during the 1st week (p<0.05 n=4) and the 2nd week (p<0.05 n=6) with little or no
78 Chapter 3: Influence of culture components 79
contribution from a reduced rate of differentiation (Table 3.1 and Table 3.3). The extent of apoptosis was higher in suspension culture of MPB during the 2nd week culture- compared to UCB-derived cells (Table 3.3). UCB-derived CD34- cells had a lower precursor expansion rate with no change in cell division rate compared to MPB-derived CD34- cells (Figure 3-4 g and h and Figure 3-5 c and d) confirming that UCB differentiates less than MPB. CD34+ renewal probability of UCB was higher than in MPB (Figure 3-5 e and f).
Lag time for UCB-derived CD34+ cells was not significantly different compared to CD34+ cells derived from MPB (Table 3.1). UCB-derived CD34- cells, however, took much longer to start their proliferation compared to MPB-derived
CD34- cells in the 1st week of culture (Table 3.2). This suggests that the first few divisions of UCB CD34+ cells produce very few CD34- cells compared to MPB
CD34+ cells. While MPB-derived CD34+ cells exhibited longer cycle time in all culture conditions compared to UCB as described above (Table 3.1), cell cycle time for CD34- cells was not different between MPB and UCB (Table 3.2).
In the presence of serum, cell proliferation and CD34+ renewal in UCB-derived
CD34+ cells was higher compared to MPB (Figure 3-4a and Figure 3-5e, culture
ID “C”). Shortening of cell cycle time of CD34+ cell (p=0.001, n=6 Table 3.4) and a lowering apoptosis may account for this difference (Table 3.6). Collectively, the results here show that UCB-derived progenitor cells exhibit better numerical ex vivo expansion of CD34+ cells but not CD34- cells compared to MPB.
79 Chapter 3: Influence of culture components 80
a b
1.00 1.00 * UCB * MPB 0.75 0.75
0.50 0.50
0.25 0.25 CD34+ cell expansion rate 0.00 CD34+ cell expansion rate 0.00 A B C E F UCB MPB
c d
*
2.25 * UCB 2.00 MPB 1.65 1.75
1.50 1.40 1.25
1.00 CD34- cellexpansion rate CD34- cell expansion rate expansion cell CD34- 0.75 1.15 A B C E F UCB MPB
e f
-0.0 -0.0 * UCB MPB -0.1 -0.1
-0.2 -0.2
-0.3 -0.3 A B C E F UCB MPB CD34+precursor expansion rate CD34+ precursorexpansion rate
g h
1.00 *
1.5 * UCB 0.75 1.3 MPB 1.1 0.50 0.9 0.7 0.5 0.25 0.3 0.1 0.00 A B C E F UCB MPB CD34- precursor rate expansion CD34- precursor rate expansion
Figure 3-4 Effect of culture conditions and cell source on expansion and precursor expansion rates. A: 1st wk suspension; B: 1st wk co-culture; C: 1st wk serum; E: 2nd wk suspension; F: 2nd wk co-culture. For detail definition of cultures see Figure 3-1. Each culture condition was performed in duplicate except culture condition C which was replicated five times.. Cell expansion rate and
80 Chapter 3: Influence of culture components 81
precursor expansion rate was compared using two-way ANOVA. A negative precursor expansion rates indicates a decline in precursor cell frequency over time. Asterix (*) indicates p-value is less than 0.05. Bar indicate single factor effects (culture condition or cell source)
a b 1.5 2 * * UCB MPB 1.0
1 0.5 CD34+ cell division ratecell division CD34+ CD34+ cell division0 rate 0.0 A B C E F UCB MPB
c d
1.7 2.0 * UCB 1.6 MPB 1.5 1.5
1.4 1.0 1.3 0.5 1.2 CD34- cell division rate CD34- cell division rate 0.0 1.1 A B C E F UCB MPB
e f
0.95 * 1.00 * UCB 0.90 0.95 MPB 0.90 0.85
0.85 0.80 0.80 0.75 0.75 0.70 0.70 0.65 CD34+ renewal probability CD34+ renewal probability 0.60 0.65 A B C E F UCB MPB
Figure 3-5 Effect of culture conditions and cell source on division rate and CD34+ renewal probability. A: 1st wk suspension; B: 1st wk co-culture; C: 1st wk serum; E: 2nd wk suspension; F:
2nd wk co-culture. For detail definition of cultures see Figure 3-1. The cell division rate (y-axis) and CD34+ renewal was compared by two-way ANOVA. Asterix (*) indicates p-value is less than 0.05. Bar indicate single factor effects (culture condition or cell source)
81 Chapter 3: Influence of culture components 82
1.1.2The effect of culture time and stroma on the kinetics of stem cell expansion
Several studies have shown that increased duration of ex vivo culture of HSC results in the loss of stem cell function [70]. The mechanisms triggered by long- term culture that lead to loss of stem cell function remain un-known. Cell division tracking was used here to analyse the changes in the level of apoptosis, differentiation, renewal, and cell cycle time during 10-day ex vivo expansion of
CD34+ cells. The experiments investigating the effect of culture duration were performed by comparing division kinetics during the 1st and 2nd week in suspension culture or in co-culture with stroma (MS5 cells). Both UCB and MPB- derived CD34+ cells were examined. Comparisons were made between culture during the 1st and 2nd week for suspension culture (cultures A versus E) and co- culture (cultures B and F).
The effect of time in culture during the 1st and 2nd weeks on a) cell expansion rate b) precursor expansion rate, c) division rate and d) CD34+ cell renewal probability was determined by one-way ANOVA combining multiple comparisons between the effect of time and culture type (suspension versus co- culture). The rate of CD34+ cell expansion significantly decreased during the 2nd week of culture for both suspension and co-culture conditions; the reduced expansion rate of CD34+ cells was observed for both stem cell sources (UCB and
MPB) (Figure 3-6 a and c). The CD34- progenitor expansion rate was positive, indicating increase in CD34- progenitors with time. The rate of CD34- progenitor expansion derived from UCB during the 2nd week of culture was higher than
82 Chapter 3: Influence of culture components 83
within 1st week (Figure 3-6 b). This observation is consistent with an increased rate of CD34+ cell differentiation that occurs during the 2nd week (Table 3.3) associated with decreased CD34+ cell renewal (Figure 3-7 e and f). CD34+ precursor expansion rate was negative in the 1st week indicating a decline in
CD34+ cell precursors. There was a more rapid decline during the 2nd week of culture for both UCB and MPB (Figure 3-6 e and g). The lower (negative) CD34+ precursor expansion rate could be related to apoptosis or differentiation into
CD34- cells. There was a reciprocal increase CD34- precursor expansion rate derived from UCB but not MPB (Figure 3-6 f and h), confirming that the rate of
CD34+ cell differentiation increases during the 2nd week. Loss of CD34 expression was accompanied by the increased expression of differentiation markers such as CD38 and CD14 during the culture (Figure 3-8b and c).
The CD34+ cell division rate was reduced during the 2nd week of culture for both suspension and co-culture conditions (Figure 3-7 a and c) indicating that CD34+ cell generation times are lengthening during the 2nd week. There was also a reduced CD34- cell division rate during the 2nd week for MPB (Figure 3-7 d).
CD34- division rate was not significantly affected during the 2nd week of culture for UCB (Figure 3-7 b).
There was a shorter lag time before the first mitosis at the start of culture in the
2nd week (Table 3.1). An increased cell cycle time was seen in CD34+ cells derived from UCB and MPB for both suspension (p=0.03 n=3 for UCB, p=0.001 n=6 for MPB, paired T test) and co-culture with MS5 cells (p=0.02 n=3 for UCB,
83 Chapter 3: Influence of culture components 84
p=0.0003 n=6 for MPB, paired T-test) during the 2nd week of culture consistent with the reduced overall expansion and mean generation numbers (Table 3.1).
Cell cycle time for CD34- cells only marginally increased during the 2nd week of culture and was significantly shorter than for CD34+ cells (p=0.02 n=6 for UCB
2nd week, p<0.001 n=6 for MPB 1st and 2nd week, paired T-test) (Table 3.2).
The rate of apoptosis was only slightly higher (not significant) during the 2nd week for both of UCB and MPB (Table 3.3). A remarkable increase in differentiation was seen during the 2nd week of culture (Table 3.3). It is relevant that a lower renewal rate of CD34+ cell can be attributed to increased cell differentiation. In summary, the results show that increasing culture duration reduced CD34+ cell renewal, increased CD34+ cell cycle time and differentiation.
84 Chapter 3: Influence of culture components 85
a b
1.1 Suspension Suspension Ψ 2.00 1.0 Co-culture Co-culture 0.9 1.75 * * 0.8 1.50 * Ψ * 0.7 1.25 0.6 * 0.5 1.00
0.4 0.75 1st wk 2nd wk
UCB_CD34+ cell expansion rate cell expansion UCB_CD34+ 1st wk 2nd wk UCB_CD34- cell expansion rate
c d
Suspension Suspension 0.9 Co-culture 2.2 Ψ Co-culture 0.8 2.1 0.7 * 2.0 0.6 Ψ 1.9 0.5 * 0.4 1.8 0.3 1.7 0.2 0.1 1.6 0.0 1.5 1st wk 2nd wk 1st wk 2nd wk MPB_CD34- cell expansionrate MPB_CD34+ cellrate expansion e f
Suspension Co-culture Suspension 1.00 -0.0 Co-culture
* 0.75 -0.1 Ψ * 0.50 * *
-0.2 0.25
-0.3 0.00 1st wk 2nd wk 1st wk 2nd wk UCB_CD34- precursor expansion rate expansion precursor UCB_CD34- UCB_CD34+ precursor expansion rate g h
1.4 -0.0 Suspension Suspension 1.3 Co-culture Co-culture * 1.2 -0.1 1.1 1.0 0.9 * -0.2 0.8 0.7 0.6 -0.3 0.5 1st wk 2nd wk 1st wk 2nd wk MPB_CD34- precursor expansion rate expansion precursor MPB_CD34- MPB_CD34+ precursor expansion rate
Figure 3-6 Effects of culture duration and stroma on cell expansion rate (a-d) and precursor expansion rate (e-h). Both suspension and co-culture with stroma from the 1st week and the 2nd week were compared. The cell and precursor expansion rates were compared using one-way
ANOVA following by multiple comparisons. Negative precursor expansion rate indicates a
85 Chapter 3: Influence of culture components 86
decline of precursor cell frequency over time. * (p<0.05) indicates significance effect on culture
duration (1st week and the 2nd week). (p<0.05) indicates significance effect on stroma.
a b
Suspension Suspension 2.0 Co-culture 1.7 Co-culture 1.6 Ψ 1.5 1.5 1.4 * 1.3 Ψ 1.2 1.0 1.1 1.0 0.9 * 0.5 0.8 0.7 0.6 0.0 UCB_CD34- cell division ratedivision cell UCB_CD34- UCB_CD34+ cell division rate 1st wk 2nd wk 1st wk 2nd wk
c d
Suspension Suspension Co-culture 1.9 1.3 Ψ 1.8 Co-culture 1.2 1.7 * 1.1 1.6 Ψ 1.0 1.5 * 0.9 1.4 0.8 * 1.3 * 0.7 1.2 0.6 1.1 0.5 1.0 MPB_CD34-cell division rate MPB_CD34+cell division rate 1st wk 2nd wk 1st wk 2nd wk e f
Suspension 1.05 Co-culture 1.05 Suspension 1.00 Ψ 1.00 Co-culture 0.95 0.95 0.90 * Ψ 0.90 * 0.85 * Ψ 0.80 0.85 * 0.75 0.80 0.70 0.75 0.65 1st wk 2nd wk 1st wk 2nd wk MPB_CD34+ renewal probability UCB_CD34+ renewal probability renewal UCB_CD34+
Figure 3-7 Effects of culture duration and stroma on cell division rate (a-d) and renewal (e-f) of CD34+ cells derived from UCB and MPB. Both suspension and co-culture with stroma from
1st week and 2nd week were compared. The cell division rate and CD34+ renewal were compared using one-way ANOVA following by multiple comparisons or the paired two tail t test (renewal).
86 Chapter 3: Influence of culture components 87
* (p<0.05) indicates significance effect on culture duration (1st week and the 2nd week). (p<0.05) indicates significance effect on stroma.
Table 3.1 Effect of culture duration and stroma on cell cycle and lag time (h) of CD34+ cells derived from UCB and MPB.
UCB MPB
Culture ID Cell cycle Lag time Cell cycle time Lag time time
1st wk(suspension) 31.6(8.2) 21.3(2.2) 35.8(2.5) 27.9(0.3)
2nd wk(suspension) 14.8(3.3) 37.2(3.8) 19.4(4.3) 43.2(0.9)
1st wk(co-culture) 36.4(1.6) 16.3(0.6) 36.7(0.8) 19.7(0.1)
2nd wk(co-culture) 25.1(4.4) 21.8(1.6) 27.6(0.5) 25.6(0.8)
The best-fit parameters for the cell cycle time and lag time were found by linear regression analysis excluding the first time point at 24h. The SEM is shown in the bracket.
Table 3.2 Effect of culture duration and stroma on cell cycle and lag time (h) in CD34- cells derived from UCB and MPB.
UCB MPB
Culture ID Cell cycle Lag time Cell cycle time Lag time time
1st wk(suspension) 49.3(4.7) 17.2(3.6) 38.1(1.5) 15.1 (0.2)
2nd wk(suspension) 23.5(3.2) 19.5(1.5) 21.8(0.7) 20.1 (0.8)
1st wk(co-culture) 48(0.6) 18.1(6.3) 36.2(2.6) 14.6 (0.7)
2nd wk(co-culture) 20.6(5.3) 18.3(1.4) 21.1(6.3) 18.9 (2.3)
87 Chapter 3: Influence of culture components 88
Table 3.3 Effect of culture duration and stroma on apoptosis and differentiation.
Apoptosis (%) Differentiation (%)
1 1st wk UCB 1.89 1 3.74*1
1 2nd wk UCB 2.42 2 §1 13.79*1
1 1st wk UCB+MS5 3.67 1 1.88*2
1 2nd wk UCB+MS5 4.00 2 14.74*2
1 1st wk MPB 3.73 5.11*3 4
1 2nd wk MPB 6.41 3 §1 19.32*3
1 1st wk MPB+MS5 3.22 2.01*4 4
1 2nd wk MPB+MS5 3.38 3 14.39*4
Apoptosis was estimated from total renewal (Figure 3-3 B). The difference between the total and
CD34+ renewal probability is differentiation from CD34+ to CD34- cells (Figure 3-3 B). Data presented is at day 5 analysis. Paired two tail t test was performed using 3 time points (72, 96 and
120 h). The detailed data can be found in appendix. * (p<0.05) indicates significance of culture
duration effect. (p<0.05) indicates significance of stroma effect. § (p<0.05) is indicates
significance of source effect. The numbers with *, , §, indicate the cultures to be paired comparisons.
88 Chapter 3: Influence of culture components 89
B A C Suspension 100 35 60 Suspension Suspension MS5 30 MS5 MS5 50 75 25 40 20 50 30 15 20 25 10 10 5 CD34+ expression (%) CD14+ expression (%) 0 0 0 day 0 day 5 day 11 CD34+CD38- expression (%) day 0 day 5 day 11 day 0 day 5 day 11
Figure 3-8 Differentiation increased in the time of culture. A. CD34+ expression B.
CD34+CD38- expression C. CD14+ expression. Cells were re-isolated CD34+ cells after 5 days culture using flow cytometry.
The effect of co-culture with bone marrow stroma MS5 cells on ex vivo expansion kinetics of CD34+ cells was examined. Bone marrow stroma is thought to regulate haematopoietic stem cell growth through direct cell contact and indirectly through the factors secreted by stroma cells [192, 193]. Co-culture with stroma cells promoted CD34+ cell expansion rate during the 1st and 2nd weeks of culture compared to suspension culture (Figure 3-6a and c). The rate of expansion of
CD34- cells was not affected by co-culture with stroma (Figure 3-6b and d).
CD34+ precursor expansion rate was reduced by stroma co-culture during the 2nd week for UCB but not MPB (Figure 3-6e and g) compared to suspension culture.
Stroma cells did not affect significantly CD34-precursor expansion rate (Figure
3-6 f and h).
Stroma increases the rate of CD34+ cell division rate but has no effect on CD34- cell division rate (Figure 3-7a-d). Co-culture with stroma shortened cell cycle time of CD34+ cells from 21 h to 16 h (UCB, p=0.02, n=4) and from 28 h to 20 h
(MPB, p<0.001, n=8) (Table 3.1). The shortening of cell cycle time for CD34+
89 Chapter 3: Influence of culture components 90
cells was even more obvious during the 2nd week of culture with stroma cells from
37 h to 22 h (UCB, p<0.001, n=8) and 43 h to 26 h (MPB, p<0.001, n=8) (Table
3.1). Cycle time for CD34- cells was not affected by co-culture with stroma
(Table 3.2). Thus increased mean generation number correlates with the reduced cell cycle time in co-cultures with stroma. Surprisingly, both MPB and UCB- derived CD34+ cells co-cultured with stroma exhibited longer lag time during the
2nd week of culture compared to cells cultured in suspension (Figure 3-3 A and
Table 3.1). In contrast, stroma did not affect lag time of CD34- cells (Table 3.2).
The CD34+ cell renewal probability was higher in MPB-derived CD34+ cells co- cultured with the stroma during both 1st and 2nd weeks of culture (Figure 3-7f).
UCB-derived CD34+ cells co-cultured with the stroma did not exhibit increased renewal after 1st week of culture (Figure 3-7e). In fact, the renewal probability was lower with CD34+ cells derived from UCB co-cultured with stroma cells during the 2nd week (Figure 3-7 e)
Co-culture with stroma appears to delay CD34+ cell differentiation (Only MPB was significantly affected during the 1st week otherwise not statistically significant in other conditions) (Figure 3-3 B and Table 3.3). Phenotypic analysis of CD34+ cells co-cultured with MS5 cells also demonstrated somewhat delayed
CD34+ cells differentiation (Figure 3-8). CD34 expression was higher and CD38 and CD14 expression was lower in co-cultured with stroma (Figure 3-8b and c).
90 Chapter 3: Influence of culture components 91
Co-culture with stroma was previously shown to protect haematopoietic cells from apoptosis [166]. Cell adherence to the components of the extracellular matrix and cytokines secreted by stroma cells contributed to the protective effect of co-culture with stroma [192, 193]. Consistent with these observations, the rate of apoptosis estimated in MPB-derived CD34+ cells co-cultured with stroma was significantly reduced during the 2nd week (Figure 3-3B and Table 3.3).
Unexpectedly, apoptosis estimated in UCB-derived CD34+ cells co-cultured with
MS5 cells was higher than in suspension culture (Table 3.3). Collectively, these results demonstrate that despite some differences in certain parameters of stem cell expansion identified between MPB and UCB-derived CD34+ cells, overall
CD34+ cell expansion is increased in co-cultures with MS5 stroma cells.
91 Chapter 3: Influence of culture components 92
1.1.3The effect of serum on the kinetics of stem cell expansion
The effect of serum on stem cell expansion was tested during the 1st week culture only (Figure 3-9). 20% FCS was added in the IMDM culture medium containing cytokines (see methods and materials in chapter 2). UCB derived HSC division tracking was performed 5 times using 2-3 pooled UCB units each experiment and
2 experiments were performed using 2 MPB.
A 10 B - serum 10 - serum + serum + serum
1 1 (log)
CD34+ fold expansion(log) fold CD34+ 0.1 0.1 0 24 48 72 96 120 0 24 48 72 96 120 CD34+ precursorcell frequency Culture duration (h) Culture duration (h)
C D 5 - serum 1.0 + serum 4 0.8
3 0.6
2 0.4 - serum 1 0.2 + serum
0 CD34+ renewalprobability 0.0 0 24 48 72 96 120
CD34+ meangeneration number 0 24 48 72 96 120 Culture duration (h) Culture duration (h)
Figure 3-9 Effect of serum on expansion of MPB derived CD34+ cells. Fold expansion (A) defined by equation 2-1 and precursor cell frequency (B) defined by equation 2-5 were presented with log-transformed data (y-axis) to stabilise variance. Mean generation number defined by
92 Chapter 3: Influence of culture components 93
equation 2-8 and renewal probability defined by equation 2-9 and equation 2-10 were shown in C and D. The x-axis shows culture duration in hours. Each culture condition was performed in duplicate using two MPB. Error bars show SEM.
Addition of serum reduced CD34+ and CD34- cell expansion rate and precursor expansion rate in MPB-derived CD34+ cells (Figure 3-10 c, d, g and h).
Furthermore, the addition of serum resulted in the decline of cell division rate seen in MPB (Figure 3-11 c and d). Addition of serum reduced CD34+ cell expansion rate in UCB-derived but did not affect CD34+ and CD34- precursor expansion rate and cell division rate (Figure 3-10 a,e, f and Figure 3-11 a, b).
Renewal probability was significantly reduced by addition of serum in UCB and
MPB-derived CD34+ cells (Figure 3-11 e and f). The lag time was not different between cells cultured in the presence and absence of serum in both cell sources
(Figure 3-12A, Table 3.4 and Table 3.5). Culture with serum increased cell cycle time in MPB-derived CD34+ (58 h) that is 30 h longer than in serum free medium
(28 h) (p=0.002, n=6). Cell cycle time in MPB-derived CD34- cells was also longer in serum (p=0.03, n=6). Cell cycle time in UCB-derived CD34+ and
CD34-cells was not affected by the addition of serum (Table 3.4 and Table 3.5).
Addition of serum did not affect cell differentiation, but promoted apoptosis in both of UCB and MPB samples (Figure 3-12B and Table 3.6). These results suggest that the reduced overall CD34+ cell expansion rate seen in serum- containing medium is mediated through reduced CD34+ renewal and increased apoptosis in both MPB and UCB cells. In addition, the increase cell cycle time also contributes the reduced CD34+ cell expansion rate in MPB.
93 Chapter 3: Influence of culture components 94
a b
* 1.50 0.8
1.25
0.7 1.00
0.6 0.75 - Serum + Serum - Serum + Serum UCB_CD34- cell expansionrate UCB_CD34+ cell expansion rate
c d * 0.6 * 2.1 0.5 2.0 0.4 1.9 1.8 0.3 1.7 1.6 0.2 1.5 0.1 1.4 1.3 0.0 1.2 - Serum + Serum
MPB_CD34+ cell expansionrate - Serum + Serum MPB_CD34- cell expansion rate
e f
-0.01 0.45 -0.02 0.40 -0.03 0.35 -0.04 0.30 -0.05 0.25 0.20 -0.06 0.15 -0.07 0.10 -0.08 0.05 -0.09 0.00 -0.10 -0.05 - Serum + Serum - Serum + Serum UCB_CD34+ precursor expansion rate UCB_CD34- precursor expansion rate g h
* 1.5 * -0.025
-0.050 1.0 -0.075
-0.100 0.5 -0.125
-0.150 0.0 - Serum + Serum - Serum + Serum MPB_CD34- precursor expansion rate precursorMPB_CD34- expansion MPB_CD34+ precursor expansion rate
Figure 3-10 Effect of serum on cell expansion rate and precursor expansion rate. Cell cultured with 20% FCS in IMDM or serum free medium. The cell and precursor expansion rates were compared using one-way ANOVA following by multiple comparisons. Negative precursor expansion rate indicates a decline of precursor cell frequency over time. Asterix (*) indicates p- value is less than 0.05.
94 Chapter 3: Influence of culture components 95
a b
1.25 1.75 1.20 1.15 1.10 1.05 1.50 1.00 0.95 0.90 1.25 0.85 0.80 0.75 0.70 1.00 UCB_CD34- cell division rate UCB_CD34+ celldivision rate - Serum + Serum - Serum + Serum
c d
* * 0.9 1.65 0.8 1.60 1.55 0.7 1.50 0.6 1.45 0.5 1.40 1.35 0.4 1.30 0.3 MPB_CD34- cell division rate MPB_CD34+ cell division rate - Serum + Serum - Serum + Serum e f
* *
0.950 0.95
0.90
0.925 0.85
0.80 0.900 0.75
0.70 - Serum + Serum - Serum + Serum MPB_CD34+ renewalprobability UCB_CD34+ renewal probabilityUCB_CD34+
Figure 3-11 Effect of serum on cell division rate and CD34+ renewal probability. The cell division rate and CD34+ renewal were compared using one-way ANOVA following by multiple comparisons or the paired two tail t test (renewal). Asterix (*) indicates p-value is less than 0.05
95 Chapter 3: Influence of culture components 96
A
7
6 MPB -serum MPB +serum -serum lag/linear model 5 +serum lag/linear model
4
3
2 Mean generation number generation Mean
1
0 0 20406080100120 Culture duration (h)
B -serum total renewal -serum CD34+ renewal -serum differentiation -serum apoptosis +serum total renewal +serum CD34+ renewal 1.0 +serum differentiation +serum apoptosis
0.8
0.6
0.4
Apoptosis / Differntiation 0.2
0.0
20 40 60 80 100 120 Culture duration (h)
Figure 3-12 Illustration of lag/cell cycle time, apoptosis and differentiation of CD34+ cell
derived from MPB. (A) The linear model was fitted to mean generation number (equation 2-
11). Cell cycle time was longer in serum containing cultures; whilst lag time was not affected
by serum. (B) Apoptosis rate was estimated from the difference between the total and CD34+
renewal rates (equation 2-9). Serum increased apoptosis but did not affect differentiation (loss
of CD34 antigen). All experiments were performed in duplicate using two MPB sources.
96 Chapter 3: Influence of culture components 97
Table 3.4 Effect of serum on cell cycle and lag time (h) of CD34+ cells derived from UCB and
MPB.
UCB MPB
Culture ID Cell cycle Lag time Cell cycle time Lag time time
- Serum 31.6(8.2) 21.3(2.2) 35.8(2.5) 27.9(0.3)
+ Serum 40.7(2.8) 23(2.4) 34.9(5.7) 58.6(10.8)
The best-fit parameters for the cell cycle time and lag time were found by linear regression analysis excluding the first time point at 24h. The SEM is shown in the bracket.
Table 3.5 Effect of serum on cell cycle and lag time (h) of CD34- cells derived from UCB and
MPB.
UCB MPB
Culture ID Cell cycle Lag time Cell cycle time Lag time time
- Serum 49.3(4.7) 17.2(3.6) 38.1(1.5) 15.1(0.2)
+ Serum 45.4(4.2) 16.4(2.1) 38(2) 17.8(1)
The SEM is shown in the bracket
97 Chapter 3: Influence of culture components 98
Table 3.6 Effect of serum on apoptosis and differentiation.
Apoptosis (%) Differentiation (%)
1st wk CB (- Serum) 1.89*1 3.74
1st wk CB (+ Serum) 4.65*1 §1 4.65
1st wk PB (-Serum) 3.73*2 5.11
1st wk PB (+Serum) 22.03*2 §1 4.53
Apoptosis was estimated from total renewal (Figure 3-12B). The difference between the total and
CD34 renewal probability is differentiation from CD34+ to CD34- cells (Figure 3-12B). Data presented is at day 5 analysis. Paired two tail t test was performed using 3 time points (72, 96 and
120 h). The detailed data can be found in appendix. * (p<0.05) indicates significance of serum effect. § (p<0.05) is indicates significance of source effect. The number with *, §, indicates pair comparisons between two cultures.
98 Chapter 3: Influence of culture components 99
A Increased culture duration reduces CD34+ cell expansion.
Day 1-5 Day 6-10 Renewal Cycle time CD34+ selection CD34+ CD34+
Differentiation Apoptosis Apoptosis ND
Cycle time ND
CD34- CD34-
B Co-culture with stroma promotes CD34+ cell expansion.
Day 1-5 Day 6-10 Renewal (MPB) Cycle time Renewal (MPB) (UCB) Cycle time CD34+ selection CD34+ CD34+
Apoptosis (UCB) Differentiation ND Differentiation (MPB) Apoptosis (UCB) (MPB)
Cycle time ND Cycle time ND
CD34- CD34-
C Serum reduces CD34+ cell renewal and increases apoptosis
Day 1-5 Renewal Cycle time (MPB)
CD34+
Differentiation ND Apoptosis
Cycle time (MPB)
CD34-
99 Chapter 3: Influence of culture components 100
D Increased UCB-derived CD34+ cell expansion compared to MPB not CD34-
cell
Day 1-5 Day 6-10 Renewal ND Cycle time (UCB) Renewal (UCB) Cycle time (UCB) CD34+ selection CD34+ CD34+
Differentiation ND Apoptosis Differentiation ND Apoptosis ND (UCB_suspension)
Cycle time ND Cycle time ND
CD34- CD34-
Figure 3-13 Diagrammatic summary of results. The effect of culture duration (A), stroma (B), serum (C) and source (D) on CD34+ cells ex vivo expansion kinetics was demonstrated. Up-arrow indicates higher rate or longer cell cycle time. Down-arrow is vice verse. The source that was affected is shown in brackets. If there are no bracketed sources the effect was present for both
UCB and MPB. Arrows indicate the effect of UCB compared to MPB (D). ND: no difference.
100 Chapter 3: Influence of culture components 101
3.4 Discussion
The effect of culture components on the growth and differentiation of stem cell populations derived from UCB and MPB was examined by dissecting the contribution of cell division rate, differentiation and apoptosis on overall expansion kinetics. The first finding from this study was that expansion of CD34+ cells derived from UCB and MPB dramatically declined during the 2nd week of culture. Reduced CD34+ cell expansion during the 2nd week of culture was found to be associated with increased cell cycle time, differentiation and reduced
CD34+cell renewal (Figure 3-13A). Each of these factors was previously shown to affect stem cell expansion during ex vivo culture [166, 194], however, this is the first report that accurately quantifies the contribution of these factors by division tracking. In contrast to CD34+ cells, CD34- cells derived from UCB expanded better during the 2nd week of culture. CD34- cells are enriched with myeloid precursors as characterised by CD38 and CD14 expression. Thus, prolonged ex vivo expansion of CD34+ cells results in preferential expansion of myeloid precursor cell at the expense of immature HPC/HSC.
The contact between hematopoietic cells and bone marrow stroma cells and the cytokines secreted by stroma cells were shown to modulate stem cell expansion
[192, 193]. Here it is shown that co-culture with stroma promotes stem cell expansion and proliferation mostly through shortening of CD34+ cell cycle time
(Figure 3-13B). Other effects depended on cell source. Co-culture with stroma also increased CD34+ cell renewal in MPB for both the 1st and 2nd week but
101 Chapter 3: Influence of culture components 102
decreased UCB CD34+ cell renewal during the 2nd week (Figure 3-13B).
Apoptosis was increased for UCB during both 1st and 2nd weeks, but reduced for
MPB during the 2nd week. Thus it appears that MPB and UCB have subtle differences in the biological response to stroma. It appears that MPB may have better survival on stroma.
It is obvious that bone marrow stroma cells are not very practical to use for ex vivo expansion in clinical settings. Human Mesenchymal Stem cells (MSCs), derived from bone marrow or placenta, are currently being used for ex vivo expansion of UCB-derived stem cells for allogeneic transplantation in Phase 1 clinical trial conducted by Mesoblast Pty Ltd (Mesenchymal Cell News
September 2009). It is claimed that MSCs promote stem cell expansion and can be easily isolated and produced in large amounts, though more precise experimental details are not available at this time. It is relevant that the critical factor/s affecting ex vivo expansion of UCB-derived HSCs in co-cultures with MSCs are still not identified. In the next chapter, the effect of MSC on CD34+ cells expansion will be examined in more depth by division tracking.
The effect of serum on ex vivo expansion of CD34+ cells derived from UCB and
MPB was investigated in the present study. In early studies FCS was always present as the critical component of culture medium supplying trophic and survival factors for the cultured cells [195-197]. In addition, FCS is often used in gene therapy targeting HSC as the important component in virus production [198].
Since FCS represents an animal product with poorly defined components, efforts
102 Chapter 3: Influence of culture components 103
were taken to replace serum with well characterised human serum components.
Thus media containing FCS is not being commonly used for ex vivo culture of
HSC.
Addition of FCS to stem cell culture reduced expansion and increased apoptosis
(Figure 3-13C). Increased cell cycle time and reduced CD34+ cell renewal were also seen in serum-containing cultures while the differentiation was not affected
(Figure 3-13C). Thus, induction of apoptosis, increased cell cycle time and reduced CD34+ cell renewal can be considered as another strong reason to exclude FCS from stem cell ex vivo culture. A number of positive and negative regulators of stem cell biology were identified in serum [199]. The negative effect of FCS on expansion observed in this study could be mediated by tumor necrosis factors (TNF-) and transforming growth factor (TGF-!) found in serum, both shown to inhibit haematopoiesis by inducing apoptosis and differentiation [68,
200, 201]. Neutralisation of TGF-! using specific antibody or antisense was shown to promote stem cell expansion [202, 203]. TGF-! signalling was recently shown to promote HSC quiescence ex vivo and maintain stem cell renewal [200,
204, 205]. TGF-! signalling was also shown to regulate stem cell renewal in steady state haematopoiesis in vivo [206]. Thus negative regulators in serum may be responsible for reduced CD34+ renewal rate, increased apoptosis and cell cycle times.
The ex vivo expansion of HSC derived from UCB and MPB was previously studied [199, 207, 208]. Our study has demonstrated reduced expansion of MPB-
103 Chapter 3: Influence of culture components 104
derived CD34+ cells compared to UCB (Figure 3-13D). UCB-derived progenitor cells exhibit better numerical ex vivo expansion of CD34+ cells but not CD34- cells compared to MPB. The former correlates with somewhat increased CD34+ cell renewal and, more importantly, increased lag time needed for CD34+ cells to produce CD34- myeloid precursor cells (Figure 3-13D and Table 3.2). Increased
CD34+ cell renewal at the expense of differentiation is likely the result of the more primitive stage of development of UCB-derived stem cells compared to
MPB stem cells.
Earlier clinical trials have demonstrated that ex vivo expanded MPB myeloid progenitor cells produce mature neutrophils and monocytes/macrophages sufficient to regenerate the innate immunity of the patient following myeloablation [209], though similar approaches with UCB have been disappointing [210, 211]. The UCB transplant was split with co-infusion of unmanipulated and expanded cells. Novel strategies recently developed to expand
UCB-derived stem/progenitor cells have resulted in improved haematopietic recovery in clinical trials. The study by Delaney et al. has demonstrated that ex vivo expansion of UCB-derived CD34+ cells using Notch ligand immobilised by fibronectin, in combination with unexpanded second UCB unit can significantly shorten myeloid engraftment[144]. In addition, Phase 3 clinical trials to evaluate ex vivo expanded UCB CD34+ cells using small copper-chelating agents (TEPA) was recently approved by Food and drug administration (FDA) [212]. It is relevant that the duration of ex vivo expansion in all these strategies lasts for 2 or more weeks with co-infusion of un-manipulated UCB.
104 Chapter 3: Influence of culture components 105
The expansion of myeloid progenitor cells has the potential to improve myeloid engraftment, but may not shorten T-cell immune recovery [213]. T-cell progenitor cells derived from HSC do not appear to be expanded by the conditions used in these trials [213]. Moreover, relatively short-term contribution of ex vivo expanded CD34+ cells was registered in patients transplanted with ex vivo expanded UCB-derived stem cells. Thus, ex vivo expansion or at least maintenance of HSC during ex vivo culture of UCB cells still remains an important consideration. Furthermore the scale of expansion required to rapidly reconstitute haemopoiesis may limit applicability of this technology because of the cost of large culture systems. More efficient progenitor engraftment will reduce the number of cells required for haemopoietic reconstitution. Recent findings have identified a number of important regulators of stem cell renewal operating in steady state haematopoiesis such as Rho, Tie2, Wnt, Notch etc [126,
185]. The results of the study investigating the role of GSK-3! regulating Wnt pathway will be presented in the next two chapters. High resolution division tracking as well as several molecular and functional approaches were used to characterise the effect of small molecule inhibitor of GSK-3! on the kinetics of ex vivo expansion, in vivo engraftment and gene expression in UCB-derived HSC.
105 Chapter 4: Regulation of HSC function by ex vivo expanded cells 106
Chapter 4: Application of
division tracking to determine
the effect of GSK-3! inhibition
on ex vivo expansion of
umbilical cord blood
106 Chapter 4: Regulation of HSC function by ex vivo expanded cells 107
4.1 Introduction
Blood stem cell renewal is a concept of fundamental interest to stem cell biologists and technologists. The blood stem cell niche in marrow, which consists of stromal cells and their products, regulates the size of the blood stem cell compartment by control of stem cell renewal and quiescence [172, 184]. The mechanisms that regulate stem cell function during extensive ex vivo proliferation have not been identified yet. The ex vivo production of haematopoietic progenitors to hasten haematological recovery following cord blood transplant is an urgent clinical need. However, extensive ex vivo proliferation of UCB is thought to impair engraftment of these cells because of dysregulation of the balance between self-renewal and lineage commitment: actively proliferating stem cells, rapidly differentiate and loose their primitive status [36].
Mesenchymal Stem Cells (MSC) represent an important component of the bone marrow niche that maintains the balance between stem cell renewal and lineage commitment (see chapter 1.6 for review, [57, 214, 215]). It was recently shown that MSC promote ex vivo HSC expansion compared to cytokines alone [54, 216].
Both studies used either CD34+ (or CD133+ cells) selection or mononuclear cells cultured with MSC. The mechanisms triggered by MSC to promote stem cell expansion have not been identified. It remains unknown whether co-culture with
MSC modulates self-renewal, proliferation, apoptosis and/or differentiation of
HSCs. In the present chapter, we have analysed the division kinetics of UCB- derived CD34+ cells co-cultured with placenta-derived MSC to define the effect
107 Chapter 4: Regulation of HSC function by ex vivo expanded cells 108
of MSC on measurements that characterise division kinetics: precursor cell frequency, self-renewal and mean generation number allowing us to estimate the quiescence lag period before entry into cell cycle, cell cycle time, apoptosis and differentiation rate.
MSC were shown to regulate Wnt signalling in HSC previously identified as a major regulator of haemopoietic progenitor or stem cell function [217, 218].
Addition of Wnt ligands was shown to promote haemopoietic progenitors/stem cell expansion [217-219]. Glycogen synthase kinase-3! (GSK-3!) is a negative regulator of Wnt signalling (see chapter 1.6 for review). Administration of small molecule inhibitors of GSK-3! activates !-catenin in ex vivo cultured cord blood
CD34+ cells and preserves their regenerative function during the ex vivo expansion process [160, 166]. In the present chapter, we are exploring the effect of GSK-3! inhibition on CD34+ cell cycle time and renewal by the division tracking methodology developed in chapter 2. The division kinetics of UCB- derived CD34+ cells treated with small molecule inhibitor of GSK-3! BIO (6- bromoindirubin 3’-oxime) was investigated to define the effect of Wnt activation on CD34+ cells proliferation and differentiation. Importantly, the engraftment potential of expanded cells was assessed in the non-obese diabetic, severe- combined immunodeficient (NOD/SCID) mouse transplantation model.
108 Chapter 4: Regulation of HSC function by ex vivo expanded cells 109
4.2 Materials and Methods
4.2.1 Culture conditions
CD34+ cells were isolated from UCB as described before (see chapter 2). CD34+ cells were cultured in IMDM with 10% FCS. SCF, TPO and Flt-3 were used at
100ng/ml concentration. Cells were exposed to 0.1 – 0.5μM of 6-bromoindirubin
3’-oxime (BIO, Sigma-Aldrich, Castle Hill, Australia) reconstituted in dimethyl sulfoxide (DMSO, Sigma-Aldrich, Castle Hill, Australia). Drug vehicle (DMSO) was added to control cultures.
4.2.2 Mesenchymal Stem Cells (MSC)
Placenta derived MSC were provided by Professor Kerry Atkinson (Mater
Medical Research Institute, Brisbane, QLD, Australia). MSC were cultured in
Dulbecco’s Modified Eagles Medium (DMEM, Invitrogen) containing 20% FCS.
Media was changed twice per week until cells reached 90% confluence. For further passages, cells were detached from a tissue flask by trypsinisation. MSC passaged 5-9 times were used in experiments.
4.2.3 Colony-Forming Unit (CFU) assay
Colony forming unit (CFU) assay was performed using MethoCult GF H4434
(Stem Cell Technologies, Vancouver, BC, Canada) and the manufacturers protocol. Briefly, 103 cells re-suspended in 1.1mL were plated in a 35-mm dishes in triplicate and cultured for 10 - 14 days. Colonies were scored into three
109 Chapter 4: Regulation of HSC function by ex vivo expanded cells 110
categories: pure erythroid Burst Forming Units (BFU), myelomonocytic Colony
Forming Unit-Granulocyte macrophage (CFU-GM) and mixed Colony Forming
Unit-Granulocyte, Erythrocyte, Macrophage, Megakaryocyte (CFU-GEMM). For serial re-plating CFU assay, 104 cells from bulk primary CFUs were re-suspended in 1.1mL of methylcellulose and again plated into 35-mm dish in triplicate to form secondary CFUs. Secondary CFUs were scored 10-14 days following plating.
Secondary colonies were subdivided into clusters (less than 50 cells) and CFUs
(more than 50 cells). The paired t-test was used to compare group means. A p value less than 0.05 was considered statistically significant.
4.2.4 FITC- and Ki-67 staining
Cell cycle analysis combined with nuclear protein analysis was conducted using propidium iodide (PI) / FITC (Sigma-Aldrich, Castle Hill, Australia) staining
[220, 221]. Cell membranes were rendered permeable with Tween 20 detergent
(MP Biomedicals, Ohio, USA). Samples were then stained with 50ug/ml of PI, and incubated at room temperature for 45 minutes in the dark prior to analysis with a FACS Canto (Becton Dickinson North Ryde, Australia). Ki67/PI staining was performed using antibody according the manufacturer’s protocol (Becton
Dickinson, North Ryde, Australia).
4.2.5 Transplantation into NOD/SCID mice
All the experiments in this study were conducted with approval from the South
Eastern Service Area Hospitals (SESAHS) human and animal ethics committees.
NOD/SCID mice at 6 to 8 weeks of age were irradiated with a sub-lethal dose of
110 Chapter 4: Regulation of HSC function by ex vivo expanded cells 111
2.5 Gy from a 60Co source 10-12 hours before being transplanted with UCB cells via intravenous (IV) injection. The UCB CD34+ cells were cultured with growth factors alone (control) or co-cultured with MSC for 5 days. To test the effect of
BIO on engraftment, UCB CD34+ cells were cultured for 5 days with growth factors and 0.5)M BIO overnight treatment (or drug vehicle) prior to injection.
The cell dose administered to all mice was equivalent to 1.25 x 105 (for MSC in vivo) and 1 x 105 (for BIO in vivo) unexpanded CD34+ cells i.e., the total output of cultures initiated with 1.25 (or 1) x 105 CD34+ cells were injected into individual mice (i.e., 1.25 (or 1) x 105 CD34+ input cells per mouse) . Mice were sacrificed at 6 weeks post transplantation and the bone marrow was harvested and assessed for human cell engraftment. The cell number of transplanted mice bone marrow was counted using a Sysmex cell counter (Kobe, Japan) to determine bone marrow cellularity. Human and murine cells were distinguished by the species-specific expression of CD45. The proportion of cells labelled with hCD45
(human) was taken to be the level of engraftment. Cells from bone marrow were analysed by flow cytometry to assess the contribution of different human hematopoietic cell lineages. Antibodies specific to human hCD33and hCD19 were used to examine multi-lineage reconstitution. Absolute cell number of each phenotype was calculated by the proportion of expression of antibody multiplied by bone marrow cellularity.
111 Chapter 4: Regulation of HSC function by ex vivo expanded cells 112
4.3 Results
4.3.1 Division tracking of UCB-derived CD34+ cells co- cultured with MSC
Cell division kinetics of CD34+ cells cultured in suspension or co-cultured with
MSC was examined by employing high-resolution division tracking method. Two experiments were performed using 2 pooled cord blood units in each experiment.
MSC isolated from the same donor were used in both experiments. Passage 5 and
8-cells were used in experiment 1 and 2, respectively. Flow cytometric tracking of
CFDA-SE fluorescence was performed daily for 5-days or every second day (at day1, 3 and 5) in experiment 1 and 2, respectively. Differences between groups were examined for statistical significance using a paired one tail t test. Day 4
CFDA-SE histograms and CD34 antigen expression is shown from experiment 1 in Figure 4-1.
256 104 R4
R3 1254 192 103 D4D5 940
Suspension 128 102 627 D6 D3 313 D2 64 101 0 100 101 102 103 104
0 100 100 101 102 103 104 100 101 102 103 104 FSC
256 104 R4 CD34PE
192 103 R3 1254 D5 MSC 940 D6 128 102 D4 627
1 313 D3 64 10 D2 0 100 101 102 103 104 0 100 100 101 102 103 104 100 101 102 103 104 CFDA-SECFSE
112 Chapter 4: Regulation of HSC function by ex vivo expanded cells 113
Figure 4-1 Division tracking of CD34+ cells co-cultured with MSC. Viable cells and beads
(arrow) were identified by FSC vs. SSC (plot no shown). Cells co-cultured with MSC were trypsinised and both suspension and adherent cells were analysed. The R3 gate was used to distinguish haematopoietic cells from MSC (left panel). FSClowCFDA-SEdim cells were identified as apoptotic cells or debris (left panel). FSChigh cells represent MSC contaminating the sample following trypsinisation (left panel). CD34+ cell gate (R4) was identified using an isotype control.
The CFDA-SE histogram shows cell division of CD34+ cells after 4 days of culture (middle panel). The mark indicates first generation (Division 0) that was analysed at day 1. Each cluster
(D2-D6) in the histogram represents one cell division (right panel). The small arrow in the upper and lower left bivariate plots points to the bead cluster.
MSC enhanced the production of CD34- cells but not CD34+ cells compared to suspension cultures (Figure 4-2 first row). Mean generation number was significantly higher in co-cultures with MSC (Figure 4-2 third row). The mean generation numbers (day 2 – 4) were used to estimate lag and cell cycle time by linear regression. Cell cycle time for CD34+ cells co-cultured with MSC was approximately 4hr shorter than in suspension (19±0.8 h versus 23 ± 1.2 h, SEM, p<0.05, n=3, paired T-test, average of 2 experiments). Cell cycle time for CD34- cells, however, was only marginally shorter in co-culture with MSC than suspension (15±1 h vs. 17±1 h, SEM, p=0.051). It is relevant that cycle time in
CD34-cells was significantly shorter than in CD34+ cells in both culture conditions. Precursor cell frequency of CD34+ cells was lower (p<0.05, n=4) and that of CD34- cells was higher (p<0.05, n=4) in MSC co-culture conditions compared to suspension (Figure 4-2, second row). There was no significant difference in lag time before the first division (37±5 h in cells co-cultured with
MSC vs. 36±4h in suspension for both CD34+ and CD34- cell). CD34+ cells co-
113 Chapter 4: Regulation of HSC function by ex vivo expanded cells 114
cultured with MSC or cultured in suspension exhibited similar renewal probability
(0.78±0.07 vs. 0.8±0.1). While apoptosis was not different, differentiation marginally increased in cells co-cultured with MSC (7±4% in suspension vs. 9
±6% in cells co-cultured with MSC at day 4).
Total CD34+ CD34- 15 6 8 Control Control 5 7 MSC MSC Control 6 MSC 10 4 5 3 4 5 2 3
Fold expansion 2 1 1 0 0 0 0 24 48 72 96 0 24 48 72 96 0 24 48 72 96 Time (hr) Time (hr) Time (hr)
Total CD34+ CD34- 1.25 1.2 Control 0.4 Control Control 1.20 1.1 MSC MSC MSC * 1.15 1.0 0.3 1.10 0.9 1.05 0.2 0.8 1.00 0.95 0.7 0.1 0.90 0.6 *
Precursor cell frequency 0.85 0.5 0.0 0 24 48 72 96 0 24 48 72 96 0 24 48 72 96 Time (hr) Time (hr) Time (hr)
Total CD34+ CD34- 5 4.5 6 Control 4.0 Control * 5 Control 4 MSC * 3.5 MSC MSC * 3.0 4 3 2.5 3 2 2.0 1.5 2 1 1.0 1 0.5 Mean generation number 0 0.0 0 0 24 48 72 96 0 24 48 72 96 0 24 48 72 96 Time (hr) Time (hr) Time (hr)
Figure 4-2 Effect of MSC on UCB expansion as assessed by division tracking. Fold expansion
(first row), precursor cell frequency (second row) mean generation number (third row); Total nuclear cell (first column), CD34+ cells (second column), CD34- cells (third column). MSC division tracking was performed twice. One representative experiment is shown here. Paired one tail t test was performed and p value less than 0.05 was presented with astrix (*).
114 Chapter 4: Regulation of HSC function by ex vivo expanded cells 115
Collectively, these results show that MSC promote total cell expansion by reducing cell cycle time. Increased CD34- cell expansion seen in co-culture with
MSC correlates with the increased CD34- and reduced CD34+ cell precursor cell frequency. It is relevant that the increased total cell expansion and shortened cell cycle time was also observed in CD34+ cells co-cultured with bone marrow stroma MS5 cells (Figure 4-3). Both MSC and stroma MS5 cells did not affect
CD34+ cell renewal. However, in contrast to MS5 cells that did not promote
CD34- cell proliferation, MSC increased CD34- cell precursor cell frequency and differentiation (Figure 4-3). Apoptosis was higher in co-cultures with stroma MS5 cells but not with MSC (Figure 4-3). Collectively, these results show that MSC appear to out perform MS5 cells in supporting CD34+ cell expansion. Apparently,
MSC derived from other donors should be investigated, and the effect of the passage of MSC on the supporting activity of MSC should be analysed.
Co-culture with MSCs Co-culture with stroma MS5 cells
Renewal ND Renewal ND Cycle time Cycle time
CD34+ CD34+
Differentiation Differentiation ND Apoptosis ND Apoptosis
Cycle time Cycle time ND
CD34- CD34-
Figure 4-3 Comparison of UCB derived stem cell kinetics with MSC and strom MS5 cell.
115 Chapter 4: Regulation of HSC function by ex vivo expanded cells 116
4.3.2 Functional analysis of ex vivo expanded CD34+ stem cells co-cultured with MSC.
The clonogenic function and transplantation potential of ex vivo expanded CD34+ stem cells co-cultured with MSC were examined [96, 222]. Total CFU numbers per culture were calculated multiplying fold expansion during ex vivo culture by
CFU plating frequency in clonogenic assay. CD34+ cells cultured in suspension or co-cultured with MSC exhibited similar clonogenic activity in primary CFU assay with somewhat higher CFU number in co-cultures with MSC (3933±83 in suspension vs. 4860±118 in co-culture with MSC (Figure 4-4). Serial CFU replating assay identifies cells retaining the capacity for the sustained proliferation and are more primitive than primary CFU-producing cells [223]. These assays showed that CD34+ cells co-cultured with MSC produced no colonies, only clusters in secondary CFU, whereas suspension culture cells produced a significant number of colonies(Figure 4-4B and C). In addition, the number of cells derived from the pooled secondary CFUs per plate was significantly higher
(p<0.0001) in suspension culture compared to co-culture sample (Figure 4-4B and
C). Thus co-culture with MSC promotes sustained cell proliferation suggesting the expansion of committed progenitor cells at the expense of more primitive progenitors.
116 Chapter 4: Regulation of HSC function by ex vivo expanded cells 117
A CFU(BFU) GEMM 90 GM 80 70 60 50 40 30 20 10 0 CFU number of colonies / plate
0 Suspension MSC 1
B C colony *** cluster 8 70000 7 60000 6 50000 5 40000 4 30000 3 20000 2 10000 1
CFU total cell number / plate cell number total CFU 0
0 0
2 Suspension MSC CFU number ofcolonies /plate Suspension MSC 0 2
Figure 4-4 CFU produced by CD34+ cells co-cultured with MSC or in suspension culture.
The ratio between BFU, GM and GEMM in primary CFUs (10) is shown (A). Clusters (less than
50 cells) and bigger colonies were identified in secondary CFU assay (20) (B). The total number of cells per 20 CFU was higher for suspension culture (p<0.0001) (C). CFU assay was performed in triplicates. The experiment was done twice. One representative experiment is shown here.
The in vivo repopulating capacity of human CD34+ cells co-cultured with MSC was next compared to suspension cells using the NOD/SCID transplantation model [224]. Ex vivo expanded CD34+ cells were transplanted into sub-lethally
117 Chapter 4: Regulation of HSC function by ex vivo expanded cells 118
irradiated NOD/SCID mice. Mice were analysed 6 weeks following transplantation for human cell engraftment. CD34+ cells co-cultured with MSC demonstrated a significantly lower level of engraftment (both the % and total numbers of human CD45+ cells per 2 femurs) than cells expanded in suspension culture (Figure 4-5A and C). There was no difference in bone marrow cellularity between two groups (Figure 4-5B). Note that the cell dose administered was equivalent to an input of 1.25 x 105 unexpanded CD34+ cells taking into account the fold expansion that occurred during culture. It is relevant that mice from the
MSC-group produced more cells in culture and were therefore transplanted with considerably higher total and CD34+ cell numbers – 4x105 CD34+ expanded cells per mouse in MSC-group (5.4 fold expansion, 59% CD34+) and 1.6x105 in the suspension culture group (1.4 fold expansion, 90% CD34+) because more cells were produced by the MSC co-culture system. Thus the increased ex vivo proliferative activity of CD34+ cells in co-cultures with MSC produced adverse effect on CD34+ cell engraftment.
The CD34+ cell percentages were similar in MSC and suspension groups (Figure
4-5E), however, total CD34+ cell numbers recovered from 2 femurs of the mice from the MSC group were significantly lower compared to suspension group suggesting that the growth promoting effect of co-culture with MSC did not translate into in vivo proliferative activity (Figure 4-5D). Similarly, the myeloid
CD33+ cell percentages were similar in both groups of mice and the total CD33+ cell numbers were not affected (Figure 4-5E). In contrast, the proportion and the numbers of CD19+ B-cell lymphoid cells were significantly higher in the
118 Chapter 4: Regulation of HSC function by ex vivo expanded cells 119
suspension group compared to the MSC group (Figure 4-5E and F). The expression of human CD45, CD34, CD19 and CD33 cell markers in one representative mouse from the suspension culture group is shown in Figure 4-5G.
Collectively, our results show that although co-culture with MSC promotes overall cell expansion, it does not promote the expansion of engrafting stem cells.
A B * ns 9 40 8 7 30 6 5 20 4 3 10 2 1 0 0
hCD45+ cellengraftment (%) Suspension MSC Suspension MSC (x10^6) cellularity marrow Bone
C D * * 25 15
20
10 15
10 5 5
0 hCD45+ cell number (x10^5) 0 Suspension MSC Suspension MSC hCD45+CD34+ cell number (x10^4)
119 Chapter 4: Regulation of HSC function by ex vivo expanded cells 120
E Suspension F MSC ** 10.0 * 20
7.5
5.0 10
2.5 % of expression
0.0 0 hCD34 hCD19 hCD33 Suspension MSC hCD45+CD19+ cell number (x10^6) GD
Figure 4-5 UCB CD34+ cell engraftment in the NOD/SCID mouse model.
Human cell engraftment was measured as the proportion of hCD45+ cells in the recipient bone marrow (A). Bone marrow cellularity was presented as the number of nucleated cells isolated from
2 femurs (B). Absolute CD45+ cell numbers per two femurs are shown in C. Absolute hCD34+ and CD19+ cell number was measured by hCD45+ cell number multiplied by the proportion of hCD34+ and CD19+ cell (D and F). The proportion of hCD34+, hCD19+ and hCD33+ cell in hCD45+ gate is shown in E. The expression of human haematopoietic markers CD45, CD34,
CD19 and CD33 is shown in one representative mouse derived from the suspension group (G).
Graphs represent mean values with SD. *: p<0.05 and **: p<0.01. NS: no significant.
120 Chapter 4: Regulation of HSC function by ex vivo expanded cells 121
4.3.3 GSK-3! inhibition delays cell cycle progression and hematopoietic differentiation
Holmes et. al. have earlier shown that the activity of GSK-3! can be specifically inhibited in cord blood CD34+ HSCs by the cell permeable small molecule inhibitor BIO [166]. In this thesis a concentration of 0.5)M BIO (as opposed to
0.1-0.2)M BIO as previously published by Holmes et. al.) was used to produce the most consistent effect with minimal toxicity. For micro array experiments, two
BIO concentrations (0.1 and 0.5 )M) were used to investigate the effect of dose.
Here the effect of BIO on CD34+ cell proliferation was determined by high resolution division tracking. CD34+ cells treated with BIO and analysed 5 days following 5 days continuous treatment exhibited higher numbers of slowly dividing cells (cells in division 0-2, D0-2) compared to untreated control cells
(Figure 4-6A). The higher proportion of slowly dividing cells was also observed for CD34+ cells (Figure 4-6A and B). The delayed expansion of CD34- cells which were derived from CD34+ cells following their differentiation was seen in
BIO-treated cultures (Figure 4-6A). Thus BIO delays CD34+ and possibly CD34- cell cycle progression. It is relevant that overall CD34+ and CD34- cell expansion was delayed by the addition of BIO (Figure 4-6 C).
121 Chapter 4: Regulation of HSC function by ex vivo expanded cells 122
A CD34- CD34+
9% 39%
Control
D0:59 D0:276 D1:489 D1:2170 D2:1974 D2:22049
62% 93%
BIO
D0:168 D0:874 D1:649 D1:9298 D2:7206 D2:48767
CFDA-SE
B Control BIO
64% 76%
5% 38% CD34+ CD34+
CFDA-SE
122 Chapter 4: Regulation of HSC function by ex vivo expanded cells 123
C 7 CD34+_control 6 CD34+BIO * 5 CD34-_control 4 CD34-_BIO 3 2 Fold expansion 1 0 0 25 50 75 100 125 150 Culture duration (h)
Figure 4-6 BIO delays cell cycle progression. A. Analysis of CD34+ cell proliferation using high resolution division tracking. CD34+ cells were labelled with CFSE and cultured in the presence of cytokines only (Control) or cytokines plus BIO (0.5μM) for 5 days. CFSE analysis was performed on day 5. Accumulation of slowly dividing cells (division 0-2, D0-D2) is seen in BIO treated cells.
Absolute numbers of cells in D0-1 and D2 are shown. The accumulation of slowly dividing cells was seen in CD34- and CD34+ samples. CFSE fluorescence is shown on the X axis; counts are shown on the y axis. B. Flow cytometry dot plot shows that BIO promotes a higher frequency of slowly dividing CD34+ cells in divisions 0-2 (gate P3). C. Fold expansion of CD34+ and CD34- cells was reduced in BIO-treated cells compared to untreated cells (control). Cells were treated with BIO for 5days at the concentration of 0.5)M. Experiments were performed at least 3 times.
Comparisons with control cultures (no BIO) were made using the paired two tail t test (*: p<0.05).
Division tracking was performed to further characterise the effect of BIO on important parameters of cell proliferation such as lag period needed for the initiation of cell division, cell cycle time, self-renewal, apoptosis and differentiation (see chapter 2, [113]). BIO was added to resting CD34+ cells (on day 1) together with cytokines to stimulate their proliferation, and CFDA-SE analysis was performed daily during 5-day treatment with BIO. Lag and cell cycle
123 Chapter 4: Regulation of HSC function by ex vivo expanded cells 124
time were determined as described in chapter 2 (Figure 4-7A). The lag time was not affected but cell cycle time (27 ± 1 hr vs. 29 ± 1 hr, SEM, p<0.05, n=4, paired t-test) was increased by BIO during the 1st week of culture. The effect of BIO during the second week of culture was investigated by expanding CD34+ cells for
5 days with cytokines alone, followed by MACs purification of CD34+ cells,
CFSE staining and sorting. The effect of BIO on cell generation time was more marked during the second week of culture (49 ± 3 h versus 35 ± 2 h, p<0.01) and similar findings were noted for CD34+ and CD34- cells (Figure 4-7A-C). BIO did not significantly decrease precursor cell frequency (Figure 4-7D). Collectively, these results revealed again that GSK-3! inhibition acts to lengthen cell cycle duration.
124 Chapter 4: Regulation of HSC function by ex vivo expanded cells 125
The first week The second week
5 5 A Control Control 4 BIO 4 BIO
3 3
2 2
1 1 Mean generation number Mean generationMean number 0 0 0 25 50 75 100 125 150 0 25 50 75 100 125 150 Time (hr) Time (hr)
B 5 5 Control Control 4 BIO 4 BIO
3 3
2 2
1 1
0 0 0 25 50 75 100 125 150 0 25 50 75 100 125 150 CD34+ Mean generation number generation Mean CD34+ CD34+ Mean generation number generation Mean CD34+ Time (hr) Time (hr)
C 5 5 Control Control 4 BIO 4 BIO
3 3
2 2
1 1
0 0 0 25 50 75 100 125 150 0 25 50 75 100 125 150 CD34- generation Mean number CD34- generation Mean number Time (hr) Time (hr)
1.2 1.1 Control D Control BIO 1.1 BIO 1.0 1.0
0.9 0.9
0.8 0.8 0.7
Precursor cell frequency 0.6 Precursor cell frequency 0.7 24 48 76 96 120 24 48 76 96 120 Time (hr) Time (hr)
Figure 4-7 Effect of BIO on UCB expansion by division tracking analysis. Lag time and cell cycle time were found using the lag linear model (equation 2-11, chapter 2). Mean generation
125 Chapter 4: Regulation of HSC function by ex vivo expanded cells 126
number of total (A), CD34+ (B) and CD34- (C) cell is shown for both of 1st week (left panel) and
2nd week (right panel). Precursor cell frequency is shown in D.
4.3.4 GSK-3! inhibition may induce stem cell quiescence
DNA analysis revealed only marginal differences between control and BIO- treated cells (Figure 4-8A), however, when combined with FITC nuclear protein staining [220, 221] it revealed the increased proportion of FITCdim cells in G1- phase of BIO-treated cells (Figure 4-8B). It is pertinent that actively cycling
CD34+ cells express high FITC staining in G1 phase of the cell cycle while the resting, mostly quiescent cells analysed before cytokine stimulation are FITCdim
(Figure 4-8B). In addition, antibody staining for Ki67, the nuclear protein specifically expressed in actively dividing cells and weakly expressed in quiescent cells [225, 226], revealed more Ki67dim cells in G1- phase in BIO-treated cells
(Figure 4-8C). Increased numbers of FITCdim and Ki67dim cells were also seen in the S+G2/M fractions of BIO-treated cells (Figure 4-8B and C). These results suggest that GSK-3! inhibition may induce a small subset of actively cycling cells to exit from the cell cycle (FITCdim Ki67dim G1 cells).
126 Chapter 4: Regulation of HSC function by ex vivo expanded cells 127
A UnexpandedBIO 0.2uM Expanded 1135 150
R5 99% R5 61% 851 1% 112 39% R6 R6 567 75
283 37
0 0 0 64 128 192 256 0 64 128 192 256
BIO 0.2uM BIO 0.5uM 199 199
R5 63% R5 59% 149 37% 149 41% R6 R6 99 99
49 49
0 0 0 64 128 192 256 0 64 128 192 256
PI
B Unexpanded Expanded BIO 0.2uM BIO 0.5uM 256 256 256 256
192 192 192 192
128 128 128 128 FITC
R4 R4 R4 R4 64 64 64 64 93% 0.5%(114) 5.4%(108) 14%(100)
0 0 0 0 0 64 128 192 256 0 64 128 192 256 0 64 128 192 256 0 64 128 192 256
PI
C Expanded Bio 0.2uM Bio 0.5uM 104 104 104
103 103 103
102 102 102 Ki- 67 R2 R4 R2 R4 R2 R4
101 39%(65) 101 52%(59) 101 57%(55)
100 100 100 0 64 128 192 256 0 64 128 192 256 0 64 128 192 256
PI
Figure 4-8 Cell cycle analysis combined with FITC nuclear protein staining. A. BIO did not appear to affect DNA analysis. Cells were treated with BIO for 72h.
127 Chapter 4: Regulation of HSC function by ex vivo expanded cells 128
B. Resting, mostly quiescent, un-expanded CD34+ cells analysed before cytokine stimulation are
FITCdim (left panel) while the cycling CD34+ cells express strong FITC staining in G1 phase of cell cycle (second upper left panel). The increased proportion of FITCdim cells was seen in G1- phase of BIO-treated cells (right upper panels).
C. Antibody staining for Ki67, the nuclear protein specifically expressed in actively dividing cells and weakly expressed in quiescent cells. BIO-treated cells exhibit higher proportion of Ki67dim cells in G1- phase of cell cycle. Increased numbers of Ki67dim cells were also seen in S+G2/M fraction of BIO-treated cell. (): Mean fluorescence intensity
128 Chapter 4: Regulation of HSC function by ex vivo expanded cells 129
4.3.5 Influence of GSK-3! inhibition on transplantation of ex vivo expanded CD34+ cells into NOD/SCID mice
Ex vivo expanded CD34+ cells pre-treated with BIO increased marginally the total
CFU number per 1000 cells and more obviously increased the total secondary
CFU number per 10000 cells (Figure 4-9). It is possible that delaying cell division with BIO increases the proportion of multipotent progenitors and secondary CFU.
The retention of stem cell activity inversely correlates with the number of divisions a cell has undergone during ex vivo expansion [52].
CFU(BFU) Big GEMM Small
GM (p=0.02) 2 125 * 20 0 CFU number of colonies of number CFU
100 / 10000cells /
75 10 50 / 1000 cells 25 CFU number of colonies 0
1 0 0 - BIO + BIO - BIO + BIO 10 CFU 20CFU
Figure 4-9 Ex vivo expanded CD34+ cells treated with 0.5μM BIO for 5 days before CFU assay produced more primary mixed CFU- granulocyte/ erythrocyte/monocyte/ macrophage
(GEMM) and secondary CFU (10CFU and 20CFU, respectively) in re-plating assay compared to un-treated cells. Secondary colonies were subdivided into small (less than 50 cells) and big colony (more than 50 cells). Results of one representative experiment are shown. Colony number was presented with mean ± SEM (n=2 for primary and n=4 for secondary CFU).
129 Chapter 4: Regulation of HSC function by ex vivo expanded cells 130
There is no consensus about the effect of ex vivo expansion on regenerative function of HSCs. Ex vivo expansion was reported to improve the overall engraftment in NOD/SCID mouse models [227, 228]. However, earlier studies showed that the extensive proliferation of HSCs results in a loss of regenerative function [166]. In the present study, the engraftment of 5-day ex vivo expanded
CD34+ cells was significantly lower compared to un-expanded cells derived from the same cord (Figure 4-10A and C-E). These experiments were performed in collaboration with Ms. Tiffany Holmes (Dr Alla Dolnikov’s laboratory, Sydney
Children’s Hospital). The proportion and absolute numbers of CD34+ cells was significantly higher in mice transplanted with un-expanded CD34+ cells (Figure
4-10E). Multi-lineage reconstitution was also affected by ex vivo expansion: the proportion and the numbers of B-cell lymphoid CD19+ cells were significantly lower in mice transplanted with ex vivo expanded stem cells compared to un- expanded cells (Figure 4-10F and G). The proportion of myelomonocytic CD33+ cells was not affected by expansion however, the absolute numbers of CD33+ cells was significantly lower in mice transplanted with ex vivo expanded CD34+ cells (Figure 4-10H and I). Thus ex vivo expansion of stem cells results in attenuation of B-cell lymphoid lineage development; the latter characteristic of stem cells with delayed immune reconstitution [229, 230].
130 Chapter 4: Regulation of HSC function by ex vivo expanded cells 131
A B ) 7 80 ** 3.5
3.0 60
2.5 40
2.0 20
1.5 0 cellularity(x10 marrow Bone Exp. Unexp. hCD45+ cell engraftment (%) cell engraftment hCD45+ Exp. Unexp.
C D 25 6) ** ** 30 20
15 20
10 10 5
hCD45+ cell number(x10 hCD45+ 0 0 Exp. Unexp. Exp. Unexp. hCD45+CD34+ cell engraftment (%) cell engraftment hCD45+CD34+
E F 6) ** ** 6 90 5 80 70 4 60 3 50 40 2 30 1 20 10 0 0 Exp. Unexp. Exp. Unexp. hCD45+CD34+ cell number(x10 hCD45+CD34+
(%) cell engraftment hCD45+CD19+
131 Chapter 4: Regulation of HSC function by ex vivo expanded cells 132
G H 6) 20 ** 15
10 10
5
0 Exp. Unexp. 0 hCD45+CD19+ cell number(x10 cell hCD45+CD19+ Exp. Unexp.
(%) engraftment cell hCD45+CD33+
I
6) * 2.5
2.0
1.5
1.0
0.5
0.0 Exp. Unexp. hCD45+CD33+ cell number (x10 cell number hCD45+CD33+
Figure 4-10 Ex vivo expanded cells reduce engraftment in the NOD/SCID model. A. Human percentage engraftment (CD45). Dot indicates the level of engraftment of each individual mouse and line indicates the mean of the group. B. Bone marrow cellularity was carried out from 2 femurs. C, E, G and I are cell number of each phenotype. D, F and H: human phenotype analysis and results are all in the hCD45 gate. *p<0.05 and **p<0.01
To examine the effect of GSK-3! inhibition on the regenerative potential of stem cells independently of the in vitro effect on cell proliferation, ex vivo expanded stem cells were treated with BIO, overnight before transplantation. Mice were injected with the similar numbers of CD34+ cells, expanded with or without BIO-
132 Chapter 4: Regulation of HSC function by ex vivo expanded cells 133
treatment (105 cells per mouse). BIO significantly increased the percent human engraftment compared to drug vehicle controls (DMSO) (Figure 4-11 A). The absolute numbers of hCD45+ cells was significantly higher in the BIO- group while there was no difference in total bone marrow cellularity between the two groups (Figure 4-11 B and C). BIO affected the proportion and the numbers of human CD34+ cells in the bone marrow (Figure 4-11 D and E). Pre-treatment with BIO increased the proportion and the absolute numbers of human
CD19+CD45+ B-cells significantly (Figure 4-11 F and G). There was no difference in the proportion of hCD45+CD33+ cells between two groups but the absolute numbers of myeloid hCD45+CD33+ cells was higher in mice from the
BIO-group (Figure 4-11 H and I). Thus the pattern of stem cell repopulation observed in mice from the BIO-group was very similar to that of seen in mice transplanted with un-expanded stem cells (Figure 4-10). GSK-3! inhibition applied prior to transplantation might be useful to enhance the engraftment efficiency of ex vivo expanded HSC.
) * 7 3.0 A 60 B
50 2.5 40
30 2.0 20
10 1.5 hCD45 engraftment(%)
0 Bone marrowcellularity(x10 Control BIO Control BIO
133 Chapter 4: Regulation of HSC function by ex vivo expanded cells 134
** * 20 C 6) D 25
20
15 10
10
5
hCD45+ cellnumber(x10 0 Control BIO 0
hCD45+CD34+ engraftment(%) Control BIO
** ** E 6) F 4 90 80
3 70 60
2 50 40
1 30 20
0 10 Control BIO 0 hCD45+CD34+ cell number(x10 Control BIO hCD45+CD19+ engraftment (%) engraftment hCD45+CD19+
** G H 10 6) 12.5 9
10.0 8
7.5 7
5.0 6
2.5 5 Control BIO 0.0 hCD45+CD33+ engraftment (%) Control BIO hCD45+CD19+ cell number(x10
134 Chapter 4: Regulation of HSC function by ex vivo expanded cells 135
6) ** I 1.5
1.0
0.5
0.0 Control BIO hCD45+CD33+ cell number(x10 cell hCD45+CD33+
Figure 4-11 BIO enhances engraftment of ex vivo expanded cells in the NOD/SCID mouse. A.
Human percentage engraftment (CD45). Dot indicates the level of engraftment of each individual mouse and line indicates the mean of the group. B. Bone marrow cellularity was carried out from 2 femurs. C, E, G and I are cell number of each phenotype. D, F and H: human phenotype analysis and results are all in the hCD45 gate. *p<0.05, **p<0.01
135 Chapter 4: Regulation of HSC function by ex vivo expanded cells 136
4.4 Discussion
One of the problems limiting UCB transplant is insufficient haematopoietic cell numbers. This is particularly relevant for transplantation in adult patients. Ex vivo expansion of stem cells has been explored to provide sufficient numbers of haematopoietic cells for more rapid reconstitution [35, 231]. Clinical and experimental data have shown that ex vivo expansion impairs stem cell function
[35, 37]. Here it is shown that transplantation of ex vivo cell proliferation UCB
CD34+ cells does not result in better engraftment compared to unexpanded cell equivalents suggesting the loss of regenerative function during the expansion process.
MSC have been used in the clinical setting to support haematopoietic recovery and treat acute GVHD [60] and many investigators are claiming that MSC promote expansion of CD34+ cells and HSC [54, 216, 232]. There are a number of clinical trials underway to examine the efficacy of this approach 1 . High- resolution division tracking shows that MSC enhance proliferation of CD34+ cells by shortening cell cycle times, and increasing the rate of differentiation into
1 1.Unmatched MSC for the treatment of steroid refractory acute GVHD in recipients of allogeneic HSC transplants (Phase I) conducted by professor Kerry Atkinson (Mater hospital,
Brisbane, Australia)
2. Unmatched placenta-derived MSC in recipients of unrelated UCB-derived HSC transplants
(Phase I) conducted by professor Kerry Atkinson (Mater hospital, Brisbane, Australia)
3. Bone marrow transplant using UCB expanded by MSC (Phase I/II) conducted by Mesoblast Pty
Ltd and Angioblast systems, Inc.
136 Chapter 4: Regulation of HSC function by ex vivo expanded cells 137
CD34- cells. CFU production was not significantly altered though there were reduced numbers of colonies in secondary CFU. Most significantly, co-culture with MSC profoundly reduced the percent of human chimerism in NOD/SCID mouse transplantation model. The mechanism that is triggered by co-culture with
MSC resulting in inferior engraftment was not elucidated in this study. Co-culture with MSC could affect homing of human SCID repopulating cells (SRC)
(Mechanism 1). The reduced numbers of SRC may also explain why the cells derived from co-culture with MSC exhibit reduced in vivo engraftment
(Mechanism 2). Apparently, LDA to define SRC frequency is needed.
Alternatively, co-culture with MSC could reduce the in vivo proliferative potential of each SRC (Mechanism 3). In vivo division tracking analysis will be performed in future to examine the proliferative activity of human CD34+ cells co-cultured with MSC or in suspension. These experimental findings argue against the efficacy of MSC co-culture in the clinical transplantation setting.
Division tracking experiments were performed to determine the effect of BIO on cell division rate and renewal showing that BIO increases cell cycle time, but has little or no effect on CD34+ cell renewal. Cell cycle analysis combined with FITC or Ki67 nuclear protein staining revealed that GSK-3! inhibition delays the progression of cells through S+G2/M and may induce a small subset of G1 cells to enter quiescence (FITCdim Ki67dim) [220, 221, 225, 226]. Cells derived from BIO- treated cultures had increased numbers of secondary CFU. Overnight treatment with BIO following 5-days of ex vivo culture resulted in improved human repopulation of the NOD-SCID mouse. Thus it appears that BIO treatment can
137 Chapter 4: Regulation of HSC function by ex vivo expanded cells 138
partly reverse the engraftment defect associated with expansion, and this or similar approaches deserve closer examination.
Stem cell quiescence is the major mechanism that maintains stem cell function in steady state haematopoiesis [233, 234]. The hypothesis that restoration of quiescence following sustained stem cell proliferation is needed for rapid and durable human engraftment is controversial. The main evidence for this hypothesis is circumstantial; mobilised peripheral blood stem cells which are predominantly quiescent engraft more rapidly than other HSC sources [14, 235].
The engraftment defect associated with short term expansion has been overcome by long-term, large-scale ex vivo expansion [71, 144]. The cost-benefit of ex vivo expansion will depend on the scale of expansion, and any treatment that improves engraftment efficiency of expanded cells will be of clinical value.
In summary, our results underline a potential clinical role for manipulation of cell cycle by small molecule inhibitors of GSK-3!. Future studies should examine the molecular mechanism of how BIO perturbs cell cycle progression, and what molecules mediate better engraftment. An initial step towards this goal would be to determine the genes that are modulated by BIO during cytokine-mediated expansion. The next chapter will address this problem by global gene expression analysis.
138 Chapter 5: Modulation of global gene expression during ex vivo expansion 139
Chapter 5: Modulation of global
gene expression during ex vivo
expansion
139 Chapter 5: Modulation of global gene expression during ex vivo expansion 140
5.1 Introduction
In the previous chapters we have demonstrated that ex vivo expansion of UCB
CD34+ cells significantly alters phenotype, cell cycle status and reduces engraftment in the NOD/SCID mouse transplantation model. Multi-lineage analysis of the progeny derived from the transplanted ex vivo expanded CD34+ cells revealed a qualitative impairment of repopulating function: tissue cultured
HSC exhibited developmental bias to myeloid development in expense of lymphoid. To gain insight into the molecular mechanisms that underlie functional defects in ex vivo expanded HSC, we examined gene expression in ex vivo expanded CD34+ cells on a genome-wide scale and compared it with gene expression in un-expanded CD34+ HSCs. Additionally the molecular signature of
GSK-3! inhibition will be investigated using comprehensive expression analysis of BIO-treated UCB derived CD34+ cell.
5.2 Method and materials
5.2.1 Gene expression analysis
Pooled CD34+ cells from four cord blood samples were used in these experiments. CD34+ cells were expanded for 5 days with cytokines (see chapter 4 method and materials). For the last 24 hours of culture, 0.5μM of BIO or drug vehicle control (0.1% DMSO) was added to cultures. RNA was isolated from un- manipulated CD34+ cells. Each condition was examined twice. The majority of cells (70%) retained CD34 expression after the five day expansion period. RNeasy
140 Chapter 5: Modulation of global gene expression during ex vivo expansion 141
Mini-kits (Qiagen) were used to extract total RNA from cells in log phase growth according to the manufacturer's instructions, with on-column DNase digestion
(Qiagen RNase-Free DNase Set). All RNA samples were run on an Agilent
Bioanalyzer (Agilent, CA) using an RNA 6000 Nano LabChip kit to check for
RNA integrity, purity and concentration. Only samples with an RNA integrity number (RIN) of >8.0 were used for microarray analysis. Biotinylated cRNA were prepared from 500ng of total RNA using an Illumina TotalPrep RNA
Amplification Kit (Ambion, TX) and cRNA yields were quantified using an ND-
1000 spectrophotometer (Nanodrop Technologies). cRNA (1500ng) were hybridized to Sentrix Human-6 Expression version 2 BeadChips (Illumina, USA) containing 45,000 human genes using the hybridization solution supplied by the manufacturer. All reagents and procedures for washing, detection, and scanning were performed according to the BeadStation 500X system protocols.
Hybridization was detected with 1)g/mL Cyanine3-streptavidine (Amersham
Biosciences), and the chips were scanned with Illumina BeadArray Reader. Data were analysed using beads studio software (Illumina, USA). Raw data were normalized to the baseline, and genes with detection precision value (DPV) >99% in all samples were filtered. Genes with differential score (DS) ≥ 13 were filtered.
A differential score ≥ 13 is equivalent to a p-value of 0.05 (Illumina software package). Genes with 11-fold ratio change (FR) were filtered for validation. For gene annotation, David online software was used to identify the molecular pathway modulated by the treatment. Annotation clustering was applied to the significant genes using standard correlation analysis as implemented in bead studio software, resulting in groupings based on a general pattern of expression
141 Chapter 5: Modulation of global gene expression during ex vivo expansion 142
changes with time following BIO treatment. Validation of differential expression was conducted using real-time RT-PCR
5.2.2 RT-PCR
Total RNA was prepared using the RNeasy Micro Kit (Qiagen). PCR was performed using standard techniques (Applied Bio systems). Template cDNA was synthesized using SuperScriptIII First-Strand Synthesis System for reverse transcription-PCR (RT-PCR) (Invitrogen). PCR mixtures (25 μL per reaction) contained cDNA, 0.2 μmol/L each of forward and reverse primers, and 12.5 μL iQ
SYBR Green Supermix (Bio-Rad, Hercules, CA). The reactions were done in triplicate in 96-well plates using the GeneAmp 5700 Sequence Detection System
(Applied Biosystems, Foster City, CA) and analyzed with the software from the manufacturer. The amount of transcript was determined based on a standard curve specific for each gene and normalized to the amount of !2-microglobulin transcript in the same sample.
142 Chapter 5: Modulation of global gene expression during ex vivo expansion 143
5.3 Results
5.3.1 Modulation of gene expression during ex vivo expansion of UCB CD34+ cells
Gene expression was analysed in un-expanded CD34+ cells and CD34+ cells expanded in suspension culture for 5 days in the presence of cytokines. Illumina cDNA array technology using 45000 gene probes was used for these experiments.
40% of the genes (10000 genes) presented on the array were expressed in both conditions. Raw gene expression data are available in Gene Expression Omnibus
(GEO) website [236] (accession number GSE21073). Cytokine-induced expansion of CD34+ cells was associated with the modulated expression of 4720 genes (40 % of expressed genes) - 2518 genes were up-regulated and 2202 down regulated.
The top five functional categories of differentially expressed genes defined by
David software [237] are shown in Figure 5-1.
143 Chapter 5: Modulation of global gene expression during ex vivo expansion 144
Cell division/mitosis Membrane-bound organelle Chromosome Nonmembrane-bound organelle ATP binding
Cell communication UP Stress/immune response Apoptosis DOWN Developmental process Negative regulation 0 10 20 30 Fold enrichment
Figure 5-1 The top 5 functional categories of modulated genes during ex vivo expansion.
David software was used to identify functional categories.
Modulation of significant numbers of cell cycle-regulated genes was registered.
Up-regulation of genes encoding positive regulators of cell proliferation and cell cycling such cyclins A1, A2, B1, B2, D1, E1 and F, cell division cycle molecules
CDC 20, 25A, B, C, 2L2, 45L, 6 and 7, cell division associated molecules CDCA
2-8, cyclin dependent kinases CDK4, 5, L3 and CDK2-associated protein 2, 5 centromere proteins CENA, B, E, F, H, J, M and O, centrosomal proteins
CEP250-, 55 and 72, four transcription factors belonging to E2F family, E2F2, 4,
7 and 8. Down-regulation of negative regulators of cell cycling such as cyclin D2,
G2, L1, T2 and Y was also registered in 5 day ex vivo expanded CD34+ cells
[238]. Extensive CD34+ cell proliferation was also accompanied by up-regulation of six translation initiating factors 1A, 2, 4E/1 and 2, 4 gamma1 and 5B [238]. In addition, the ex vivo expansion of CD34+ cells was accompanied by the altered expression of the genes belonging to the family of open reading frame (ORF) and
Mini-Chromosome Maintenance (MCM) complex genes both known to regulate
144 Chapter 5: Modulation of global gene expression during ex vivo expansion 145
chromatin activity [238]. While heterochromatic foci are a hallmark of quiescent cells, increased chromatin activity normally correlates with active transcription and translation in dividing cells [239].
Gene expression analysis also revealed that several members of TGF-! signalling pathways are modulated during ex vivo expansion of CD34+ cells. TGF-! is one of the important negative regulators of stem cell proliferation [240]. The reduced expression of two members of TGF-! family, TGF-! and 3, TGF-! 3 receptor 111 and associated protein1 (TGF-! RAP1) was observed in ex vivo expanded CD34+ cells [238]. In addition, SMAD3 and SMAD7, down-stream effectors of TGF-! - signalling, were down-regulated following extensive proliferation of CD34+ cells
[238]. In addition, down-regulation of two cyclin-dependent inhibitors - CDKI1A
(p21) and CDKI C (p57) both regulated by TGF-! in HSCs [241, 242] was observed in ex vivo expanded CD34+ cells [238]. Thus cytokine-induced ex vivo proliferation is associated with the down-regulation of TGF-! signalling, an important negative regulator of stem cell proliferation [200]. Down-regulation of
BMP8B, another member of the TGF-! family, was also observed in ex vivo expanded CD34+ cells [238]. BMP8B is another important regulator of stem cell quiescence [243].
The increased expression of positive regulators and the reduced expression of negative regulators of stem cell proliferation appear to be a hallmark of ex vivo proliferation of CD34+ cells. Both positive and negative regulators of stem cell proliferation were recently shown to control the maintenance of the primitive
145 Chapter 5: Modulation of global gene expression during ex vivo expansion 146
status of these cells: high expression of negative regulators of stem cell proliferation keeps stem cells in quiescence and acts to preserve stem cell long- term self-renewal capacity [244]. We hypothesise that modulation of some of these genes accounts for loss of stem cell self-renewal capacity in the process of ex vivo expansion.
Notch signalling was recently shown to promote ex vivo proliferation of CD34+ cells [126, 245]. Cytokine-induced CD34+ cell proliferation, however, was associated with down-regulation of several components of Notch signalling including Delta, the specific ligand for Notch, and more importantly, down-stream target HES 1/5 [238]. HES1 over-expression was shown to inhibit HSC cycling through binding to DNA and up-regulation of p21 [246]. Notch signalling was also shown to preserve stem cell self-renewal [126, 245]. Thus, the loss of Delta and HES1/5 may suggest down-regulation of Notch signalling during ex vivo expansion of CD34+ cells and account for impaired of stem cell function.
Activation of genes regulating the DNA-damage response and preserving genome integrity in proliferating cells was registered in ex vivo proliferating CD34+ cells: genes encoding protein kinases CHK1 and 2, tumour suppressor p53 and its direct transcriptional target BAX, were up-regulated [238].
Down-regulation of Kruppel-like factor 4 (KLF4), a very important developmental regulator, was seen in ex vivo expanded UCB CD34+ cells [238].
Apparently, the expression of certain members of KLF family is restricted to the
146 Chapter 5: Modulation of global gene expression during ex vivo expansion 147
un-expanded most primitive subset of UCB progenitors and is modulated during ex vivo expansion.
Our division tracking experiments and phenotype analysis have demonstrated the induction of cell differentiation in cytokine-induced proliferating CD34+ cells.
The increased expression of genes regulating HSC differentiation-
CCAAT/enhancer binding proteins CEBP, and , CD14, CD33, CD19, CD38,
CD300A and C, all markers of HSC differentiation, was seen in ex vivo expanded
CD34+ cells. Two important transcription factors both involved in the regulation of HSC differentiation, ETV4 and 5, were up-regulated in ex vivo expanded
CD34+ cells [238]. In contrast, down-regulation of the gene encoding CD34, the major marker of human HSCs was seen in cytokine-expanded cells [238]. Loss of
CD34 expression was registered in ex vivo proliferating CD34+ cells (Chapter 3) and is consistent with the induction of differentiation during ex vivo culture of
HSC in the presence of cytokines [238].
147 Chapter 5: Modulation of global gene expression during ex vivo expansion 148
5.3.2 GSK-3! inhibition modulates gene expression during ex vivo expansion of cord blood CD34+ cells.
GSK-3! inhibition led to the rapid transcriptional suppression of 239 genes that were consistent with growth suppression in BIO-treated cells and 169 genes were up-regulated by BIO [238]. There were only 10 genes that were up-regulated by both cytokine-induced expansion and BIO. Only 55 genes were down-regulated by both conditions (Table 5.1 and Table 5.2).
Table 5.1 Commonly up-regulated genes from both cytokine-induced expansion and BIO- treated cells
TargetID Exp vs Unexp Bio vs Exp DEFINITION C1ORF150 2.7 1.2 Homo sapiens chromosome 1 open reading frame 150 (C1orf150), mRNA. CA8 2.8 1.0 Homo sapiens carbonic anhydrase VIII (CA8), mRNA. CLDN12 1.1 1.1 Homo sapiens claudin 12 (CLDN12), mRNA. FUCA1 3.6 1.1 Homo sapiens fucosidase, alpha-L- 1, tissue (FUCA1), mRNA. GM2A 2.1 1.6 Homo sapiens GM2 ganglioside activator (GM2A), mRNA. GYPB 2.0 1.3 Homo sapiens glycophorin B (MNS blood group) (GYPB), mRNA. GYPE 2.8 1.5 Homo sapiens glycophorin E (GYPE), transcript variant 2, mRNA. HS.224794 5.4 1.1 Homo sapiens cDNA FLJ33375 fis, clone BRACE2006137 LOC651745 1.1 1.0 PREDICTED: Homo sapiens hypothetical protein LOC651745 (LOC651745), mRNA. PRSS23 1.6 1.7 Homo sapiens protease, serine, 23 (PRSS23), mRNA.
148 Chapter 5: Modulation of global gene expression during ex vivo expansion 149
Table 5.2 Commonly down-regulated genes from both cytokine-induced expansion and BIO- treated cells
TargetID Exp vs Unexp Bio vs Exp DEFINITION BCAS4 -1.4 -1.2 Homo sapiens breast carcinoma amplified sequence 4 (BCAS4), transcript variant 1, mRNA. BEX2 -3.3 -1.4 Homo sapiens brain expressed X-linked 2 (BEX2), mRNA. C6ORF204 -3.1 -1.2 Homo sapiens chromosome 6 open reading frame 204 (C6orf204), transcript variant 1, mRNA. CCNG2 -1.0 -1.1 Homo sapiens cyclin G2 (CCNG2), mRNA. CD69 -4.0 -1.2 Homo sapiens CD69 molecule (CD69), mRNA. CTGF -3.6 -1.1 Homo sapiens connective tissue growth factor (CTGF), mRNA. CTSF -2.0 -1.2 Homo sapiens cathepsin F (CTSF), mRNA. CXCL10 -1.9 -2.2 Homo sapiens chemokine (C-X-C motif) ligand 10 (CXCL10), mRNA. DUSP6 -1.5 -2.4 Homo sapiens dual specificity phosphatase 6 (DUSP6), transcript variant 1, mRNA. EFNA1 -3.9 -1.3 Homo sapiens ephrin-A1 (EFNA1), transcript variant 1, mRNA. EGR1 -3.5 -1.3 Homo sapiens early growth response 1 (EGR1), mRNA. EIF4E3 -1.5 -1.0 Homo sapiens eukaryotic translation initiation factor 4E family member 3 (EIF4E3), mRNA. FAIM3 -1.3 -3.0 Homo sapiens Fas apoptotic inhibitory molecule 3 (FAIM3), mRNA. FAM100B -1.6 -2.0 Homo sapiens family with sequence similarity 100, member B (FAM100B), mRNA. FAM49A -1.6 -1.1 Homo sapiens family with sequence similarity 49, member A (FAM49A), mRNA. FLJ13910 -2.5 -1.1 Homo sapiens required for meiotic nuclear division 5 homolog A (S. cerevisiae) (RMND5A), mRNA. GBP1 -1.3 -1.1 Homo sapiens guanylate binding protein 1, interferon-inducible, 67kDa (GBP1), mRNA. GBP5 -1.8 -1.5 Homo sapiens guanylate binding protein 5 (GBP5), mRNA. GPR56 -2.5 -1.4 Homo sapiens G protein-coupled receptor 56 (GPR56), transcript variant 2, mRNA. HOP -2.2 -1.4 Homo sapiens homeodomain-only protein (HOP), transcript variant 3, mRNA. HS.191591 -1.5 -1.3 AV736391 CB Homo sapiens cDNA clone CBMAGD02 5, mRNA sequence HS.403972 -1.2 -1.9 Homo sapiens cDNA clone IMAGE:6254031 HS.543887 -3.2 -2.6 AGENCOURT_14535501 NIH_MGC_191 Homo sapiens cDNA clone IMAGE:30415823 5, mRNA sequence HS.554324 -1.2 -1.4 full-length cDNA clone CS0DI056YK21 of Placenta Cot 25-normalized of Homo sapiens (human) HS.583806 -2.6 -1.8 AGENCOURT_7908292 NIH_MGC_82 Homo sapiens cDNA clone IMAGE:6102595 5, mRNA sequence IFITM1 -1.8 -2.2 Homo sapiens interferon induced transmembrane protein 1 (9-27) (IFITM1), mRNA. IFITM3 -2.0 -1.5 Homo sapiens interferon induced transmembrane protein 3 (1-8U) (IFITM3), mRNA. IL6 -1.6 -1.2 Homo sapiens interleukin 6 (interferon, beta 2) (IL6), mRNA. IRS2 -2.1 -1.3 Homo sapiens insulin receptor substrate 2 (IRS2), mRNA. KCNK17 -1.8 -1.0 Homo sapiens potassium channel, subfamily K, member 17 (KCNK17), mRNA. KIF27 -1.4 -1.4 PREDICTED: Homo sapiens kinesin family member 27, transcript variant 4 (KIF27), mRNA. KLHL24 -3.3 -1.9 Homo sapiens kelch-like 24 (Drosophila) (KLHL24), mRNA. LOC153222 -3.8 -1.2 Homo sapiens adult retina protein (LOC153222), mRNA. LOC388886 -1.1 -1.3 Homo sapiens similar to hypothetical protein LOC192734 (LOC388886), mRNA. LOC646463 -2.4 -1.0 PREDICTED: Homo sapiens similar to Ubiquitin-conjugating enzyme E2 H NBL1 -3.1 -1.3 Homo sapiens neuroblastoma, suppression of tumorigenicity 1 (NBL1), transcript variant 1, mRNA. NPR3 -1.8 -1.1 Homo sapiens natriuretic peptide receptor C/guanylate cyclase C (atrionatriuretic peptide receptor C) (NPR3), mRNA. NR4A2 -5.7 -1.5 Homo sapiens nuclear receptor subfamily 4, group A, member 2 (NR4A2), transcript variant 1, mRNA. PIK3CG -1.2 -1.1 Homo sapiens phosphoinositide-3-kinase, catalytic, gamma polypeptide (PIK3CG), mRNA. PPP1R16B -4.2 -1.1 Homo sapiens protein phosphatase 1, regulatory (inhibitor) subunit 16B (PPP1R16B), mRNA. PRIC285 -2.1 -4.4 Homo sapiens peroxisomal proliferator-activated receptor A interacting complex 285 (PRIC285), transcript variant 2, mRNA. PRR7 -3.2 -1.0 Homo sapiens proline rich 7 (synaptic) (PRR7), mRNA. PRR8 -1.8 -1.1 Homo sapiens proline rich 8 (PRR8), mRNA. PVRL2 -2.6 -1.2 Homo sapiens poliovirus receptor-related 2 (herpesvirus entry mediator B) (PVRL2), mRNA. RBM39 -1.5 -1.2 Homo sapiens RNA binding motif protein 39 (RBM39), transcript variant 1, mRNA. SPINK2 -1.3 -2.3 Homo sapiens serine peptidase inhibitor, Kazal type 2 (acrosin-trypsin inhibitor) (SPINK2), mRNA. TEK -1.3 -1.2 Homo sapiens TEK tyrosine kinase, endothelial (venous malformations, multiple cutaneous and mucosal) (TEK), mRNA. TNF -1.7 -1.7 Homo sapiens tumor necrosis factor (TNF superfamily, member 2) (TNF), mRNA. TNFRSF12A -1.8 -2.0 Homo sapiens tumor necrosis factor receptor superfamily, member 12A (TNFRSF12A), mRNA. TNFRSF25 -1.1 -1.5 Homo sapiens tumor necrosis factor receptor superfamily, member 25 (TNFRSF25), transcript variant 10, mRNA. TPM2 -2.6 -1.4 Homo sapiens tropomyosin 2 (beta) (TPM2), transcript variant 2, mRNA. UBE2B -1.6 -1.1 Homo sapiens ubiquitin-conjugating enzyme E2B (RAD6 homolog) (UBE2B), mRNA. YPEL1 -1.4 -1.2 Homo sapiens yippee-like 1 (Drosophila) (YPEL1), mRNA. YPEL3 -1.8 -1.2 Homo sapiens yippee-like 3 (Drosophila) (YPEL3), mRNA. ZSWIM4 -1.4 -1.1 Homo sapiens zinc finger, SWIM-type containing 4 (ZSWIM4), mRNA.
Importantly, GSK-3! inhibition reversed some of the changes induced by cytokines: the expression of 79 genes up-regulated during extensive CD34+ cell proliferation was down-regulated by BIO (Figure 5-2, Table 5.3). In addition, the expression of 47 genes down-regulated in cycling CD34+ cells was up-regulated by BIO (Figure 5-2, Table 5.4). These genes comprise 30% (126 out 408) of genes modulated by BIO while representing only a small fraction (3% -126 genes out of 4720) of genes modulated by cytokines (Figure 5-2).
149 Chapter 5: Modulation of global gene expression during ex vivo expansion 150
2439 79 160 2155 47 122
Cytokine-inducedCytokine- expansion vs. unexpanded. Up-regulated (2518) Cytokine-induced expansion vs. unexpanded. Down -regulated(2202)
BIO vs. cytokine -induced expansion. Up -regulated(169) BIO vs. cytokine -induced expansion. Down -regulated (239)
Figure 5-2 Reversal of cytokine-induced gene expression by BIO. 79 genes that are up- regulated by cytokines are down-regulated by BIO. 47 genes that are down-regulated by cytokines and up-regulated by BIO.
Remarkably, only 2 out of 98 cell cycle-related genes (referred from Kyoto encyclopedia of genes and genomes, http://www.genome.jp/kegg/) affected by proliferation of CD34+ cells - genes encoding cyclin D1 and cdki p57 were reversed by BIO (Table 5.5). Cyclin D1 and p57 gene expression was validated by
RT-PCR (Figure 5-3). Since cdki-p57 gene expression is down-regulated and cyclin D1 is up-regulated in actively cycling CD34+ cells, we speculate that up- regulation of p57 and down-regulation of cyclin D1 by BIO provides a possible mechanism for the delay in cell cycle progression (Table 5.5).
150 Chapter 5: Modulation of global gene expression during ex vivo expansion 151
Table 5.3 List of up-regulated genes by cytokines and down-regulated by BIO
TargetID Fold DEFINITION ABHD11 -1.01 Homo sapiens abhydrolase domain containing 11 (ABHD11), transcript variant 3, mRNA. ADI1 -1.11 Homo sapiens acireductone dioxygenase 1 (ADI1), mRNA. ADRB2 -1.12 Homo sapiens adrenergic, beta-2-, receptor, surface (ADRB2), mRNA. AGPAT3 -1.03 Homo sapiens 1-acylglycerol-3-phosphate O-acyltransferase 3 (AGPAT3), transcript variant 1, mRNA. ANKRD22 -1.01 Homo sapiens ankyrin repeat domain 22 (ANKRD22), mRNA. C10ORF125 -1.57 Homo sapiens chromosome 10 open reading frame 125 (C10orf125), mRNA. C16ORF30 -1.52 Homo sapiens chromosome 16 open reading frame 30 (C16orf30), mRNA. C1QR1 -1.41 Homo sapiens complement component 1, q subcomponent, receptor 1 (C1QR1), mRNA. C20ORF127 -1.40 Homo sapiens chromosome 20 open reading frame 127 (C20orf127), mRNA. CATSPER1 -1.62 Homo sapiens cation channel, sperm associated 1 (CATSPER1), mRNA. CBR3 -1.47 Homo sapiens carbonyl reductase 3 (CBR3), mRNA. CCDC26 -1.20 Homo sapiens coiled-coil domain containing 26 (CCDC26), mRNA. CCL23 -1.17 Homo sapiens chemokine (C-C motif) ligand 23 (CCL23), transcript variant CKbeta8-1, mRNA. CCND1 -2.61 Homo sapiens cyclin D1 (PRAD1: parathyroid adenomatosis 1) (CCND1), mRNA. CD1C -1.67 Homo sapiens CD1C antigen, c polypeptide (CD1C), mRNA. CD1D -1.76 Homo sapiens CD1D antigen, d polypeptide (CD1D), mRNA. CD1E -1.09 Homo sapiens CD1e molecule (CD1E), transcript variant 5, mRNA. CD2 -1.84 Homo sapiens CD2 antigen (p50), sheep red blood cell receptor (CD2), mRNA. CD300A -1.27 Homo sapiens CD300a molecule (CD300A), mRNA. CD300C -2.05 Homo sapiens CD300c molecule (CD300C), mRNA. CD38 -1.17 Homo sapiens CD38 molecule (CD38), mRNA. CEACAM6 -2.64 Homo sapiens carcinoembryonic antigen-related cell adhesion molecule 6 (non-specific cross reacting antigen) CEBPD -1.34 Homo sapiens CCAAT/enhancer binding protein (C/EBP), delta (CEBPD), mRNA. CECR6 -3.59 Homo sapiens cat eye syndrome chromosome region, candidate 6 (CECR6), mRNA. CGB7 -1.39 Homo sapiens chorionic gonadotropin, beta polypeptide 7 (CGB7), mRNA. CKLF -1.27 Homo sapiens chemokine-like factor (CKLF), transcript variant 6, mRNA. CLEC10A -2.05 Homo sapiens C-type lectin domain family 10, member A (CLEC10A), transcript variant 1, mRNA. CLEC12A -1.55 Homo sapiens C-type lectin domain family 12, member A (CLEC12A), transcript variant 3, mRNA. CLEC5A -1.20 Homo sapiens C-type lectin domain family 5, member A (CLEC5A), mRNA. COP1 -1.29 Homo sapiens caspase-1 dominant-negative inhibitor pseudo-ICE (COP1), transcript variant 2, mRNA. CRIPT -1.09 Homo sapiens cysteine-rich PDZ-binding protein (CRIPT), mRNA. CX3CR1 -1.88 Homo sapiens chemokine (C-X3-C motif) receptor 1 (CX3CR1), mRNA. DTNA -1.05 Homo sapiens dystrobrevin, alpha (DTNA), transcript variant 8, mRNA. ELA1 -2.76 Homo sapiens elastase 1, pancreatic (ELA1), mRNA. ETV4 -2.79 Homo sapiens ets variant gene 4 (E1A enhancer binding protein, E1AF) (ETV4), mRNA. ETV5 -2.07 Homo sapiens ets variant gene 5 (ets-related molecule) (ETV5), mRNA. FCGR1B -1.83 Homo sapiens Fc fragment of IgG, high affinity Ib, receptor (CD64) (FCGR1B), transcript variant 2, mRNA. FER1L3 -1.19 Homo sapiens fer-1-like 3, myoferlin (C. elegans) (FER1L3), transcript variant 1, mRNA. FLJ20701 -1.78 Homo sapiens phosphotyrosine interaction domain containing 1 (PID1), mRNA. GAL -2.15 Homo sapiens galanin (GAL), mRNA. GAS1 -1.05 Homo sapiens growth arrest-specific 1 (GAS1), mRNA. GLIPR1 -1.56 Homo sapiens GLI pathogenesis-related 1 (glioma) (GLIPR1), mRNA. GSTT2 -1.84 Homo sapiens glutathione S-transferase theta 2 (GSTT2), mRNA. HES6 -1.04 Homo sapiens hairy and enhancer of split 6 (Drosophila) (HES6), mRNA. HIST1H2BH -1.39 Homo sapiens histone cluster 1, H2bh (HIST1H2BH), mRNA. HIST1H3C -1.17 Homo sapiens histone cluster 1, H3c (HIST1H3C), mRNA. HS.225083 -1.39 601122564F1 NIH_MGC_20 Homo sapiens cDNA clone IMAGE:3346815 5, mRNA sequence HS.402146 -1.04 AGENCOURT_8911223 NIH_MGC_141 Homo sapiens cDNA clone IMAGE:6386615 5, mRNA sequence HS.579631 -3.22 AGENCOURT_10229596 NIH_MGC_141 Homo sapiens cDNA clone IMAGE:6563923 5, mRNA sequence IGSF6 -1.20 Homo sapiens immunoglobulin superfamily, member 6 (IGSF6), mRNA. IL13RA1 -1.03 Homo sapiens interleukin 13 receptor, alpha 1 (IL13RA1), mRNA. KCNE1L -1.64 Homo sapiens KCNE1-like (KCNE1L), mRNA. KIAA1274 -1.52 Homo sapiens KIAA1274 (KIAA1274), mRNA. LAT -1.13 Homo sapiens linker for activation of T cells (LAT), transcript variant 2, mRNA. LOC387882 -1.19 Homo sapiens hypothetical protein (LOC387882), mRNA. LOC648995 -1.58 PREDICTED: Homo sapiens hypothetical protein LOC648995 (LOC648995), mRNA. MGC24665 -1.05 Homo sapiens chromosome 16 open reading frame 75 (C16orf75), mRNA. MS4A6A -1.19 Homo sapiens membrane-spanning 4-domains, subfamily A, member 6A (MS4A6A), transcript variant 2, mRNA. MS4A7 -1.05 Homo sapiens membrane-spanning 4-domains, subfamily A, member 7 (MS4A7), transcript variant 2, mRNA. MT1F -1.14 Homo sapiens metallothionein 1F (MT1F), mRNA. MT1G -1.60 Homo sapiens metallothionein 1G (MT1G), mRNA. NCF1 -1.55 Homo sapiens neutrophil cytosolic factor 1 (47kDa, chronic granulomatous disease, autosomal 1) (NCF1), mRNA. NMB -1.10 Homo sapiens neuromedin B (NMB), transcript variant 1, mRNA. NTRK1 -1.13 Homo sapiens neurotrophic tyrosine kinase, receptor, type 1 (NTRK1), transcript variant 2, mRNA. PLEK2 -2.15 Homo sapiens pleckstrin 2 (PLEK2), mRNA. PLXNA1 -1.90 Homo sapiens plexin A1 (PLXNA1), mRNA. PYCARD -1.13 Homo sapiens PYD and CARD domain containing (PYCARD), transcript variant 3, mRNA. RETN -2.30 Homo sapiens resistin (RETN), mRNA. RHEBL1 -2.29 Homo sapiens Ras homolog enriched in brain like 1 (RHEBL1), mRNA. ROPN1L -2.61 Homo sapiens ropporin 1-like (ROPN1L), mRNA. S100P -1.69 Homo sapiens S100 calcium binding protein P (S100P), mRNA.
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TargetID Fold DEFINITION SELENBP1 -2.2 Homo sapiens selenium binding protein 1 (SELENBP1), mRNA. SERPINB8 -2.1 Homo sapiens serpin peptidase inhibitor, clade B (ovalbumin), member 8 (SERPINB8), transcript variant 1, mRNA. SLC16A3 -1.6 Homo sapiens solute carrier family 16, member 3 (monocarboxylic acid transporter 4) (SLC16A3), transcript variant 2 SNFT -2.6 Homo sapiens Jun dimerization protein p21SNFT (SNFT), mRNA. SUSD3 -1.5 Homo sapiens sushi domain containing 3 (SUSD3), mRNA. TLR7 -1.7 Homo sapiens toll-like receptor 7 (TLR7), mRNA. TNNI2 -3.7 Homo sapiens troponin I type 2 (skeletal, fast) (TNNI2), mRNA. VGF -2.8 Homo sapiens VGF nerve growth factor inducible (VGF), mRNA.
Table 5.4 List of down-regulated genes during expansion and up-regulated by BIO
TargetID Fold DEFINITION BHLHB9 1.3 Homo sapiens basic helix-loop-helix domain containing, class B, 9 (BHLHB9), mRNA. BMP8B 1.8 Homo sapiens bone morphogenetic protein 8b (osteogenic protein 2) (BMP8B), mRNA. CCL3 1.2 Homo sapiens chemokine (C-C motif) ligand 3 (CCL3), mRNA. CCL3L1 1.0 Homo sapiens chemokine (C-C motif) ligand 3-like 1 (CCL3L1), mRNA. CCL3L3 1.2 Homo sapiens chemokine (C-C motif) ligand 3-like 3 (CCL3L3), mRNA. CD6 2.1 Homo sapiens CD6 molecule (CD6), mRNA. CDKN1C 2.1 Homo sapiens cyclin-dependent kinase inhibitor 1C (p57, Kip2) (CDKN1C), mRNA. CHN2 1.6 Homo sapiens chimerin (chimaerin) 2 (CHN2), mRNA. CPEB2 1.2 PREDICTED: Homo sapiens cytoplasmic polyadenylation element binding protein 2, transcript variant 2 (CPEB2), mRNA. CXADR 2.0 Homo sapiens coxsackie virus and adenovirus receptor (CXADR), mRNA. CYP27B1 1.3 Homo sapiens cytochrome P450, family 27, subfamily B, polypeptide 1 (CYP27B1), nuclear gene encoding mitochondrial protein DNAH1 1.1 Homo sapiens dynein, axonemal, heavy polypeptide 1 (DNAH1), mRNA. DNM1 1.4 Homo sapiens dynamin 1 (DNM1), transcript variant 1, mRNA. DPY19L2 1.3 Homo sapiens dpy-19-like 2 (C. elegans) (DPY19L2), mRNA. ENPP2 1.2 Homo sapiens ectonucleotide pyrophosphatase/phosphodiesterase 2 (autotaxin) (ENPP2), transcript variant 2, mRNA. FZD6 1.0 Homo sapiens frizzled homolog 6 (Drosophila) (FZD6), mRNA. GAD1 1.4 Homo sapiens glutamate decarboxylase 1 (brain, 67kDa) (GAD1), transcript variant GAD67, mRNA. GADD45B 1.0 Homo sapiens growth arrest and DNA-damage-inducible, beta (GADD45B), mRNA. GPR132 1.2 Homo sapiens G protein-coupled receptor 132 (GPR132), mRNA. GSTA4 1.1 Homo sapiens glutathione S-transferase A4 (GSTA4), mRNA. HS.31290 1.4 Homo sapiens clone 23832 mRNA sequence HS.529514 1.1 Homo sapiens cDNA FLJ31093 fis, clone IMR321000161 HS.559604 1.5 603089645F1 NIH_MGC_120 Homo sapiens cDNA clone IMAGE:5228627 5, mRNA sequence HS.571946 1.1 CT000682 RZPD no.9017 Homo sapiens cDNA clone RZPDp9017N065 5, mRNA sequence IGFBP5 1.4 Homo sapiens insulin-like growth factor binding protein 5 (IGFBP5), mRNA. IL8 1.2 Homo sapiens interleukin 8 (IL8), mRNA. IQSEC3 1.6 Homo sapiens IQ motif and Sec7 domain 3 (IQSEC3), mRNA. LOC402562 2.0 PREDICTED: Homo sapiens similar to Heterogeneous nuclear ribonucleoprotein A1 (Helix-destabilizing protein) LOC644591 1.3 PREDICTED: Homo sapiens similar to peptidylprolyl isomerase A (cyclophilin A)-like 4 (LOC644591), mRNA. LOC647949 1.8 PREDICTED: Homo sapiens similar to 60S ribosomal protein L7a (Surfeit locus protein 3) (LOC647949), mRNA. LOC652253 1.7 PREDICTED: Homo sapiens similar to ribosomal protein L10 (LOC647074), mRNA. MAOA 1.8 Homo sapiens monoamine oxidase A (MAOA), nuclear gene encoding mitochondrial protein, mRNA. PRH1 1.3 Homo sapiens proline-rich protein HaeIII subfamily 1 (PRH1), mRNA. RNU43 1.1 Homo sapiens small nucleolar RNA, C/D box 43 (SNORD43) on chromosome 22. RPL37 1.3 Homo sapiens ribosomal protein L37 (RPL37), mRNA. SNORA32 1.2 Homo sapiens small nucleolar RNA, H/ACA box 32 (SNORA32) on chromosome 11. STXBP6 1.0 Homo sapiens syntaxin binding protein 6 (amisyn) (STXBP6), mRNA. TBC1D3B 1.0 Homo sapiens TBC1 domain family, member 3B (TBC1D3B), mRNA. TIGA1 1.1 Homo sapiens TIGA1 (TIGA1), mRNA. TIPARP 1.5 Homo sapiens TCDD-inducible poly(ADP-ribose) polymerase (TIPARP), mRNA. TSPAN7 2.4 Homo sapiens tetraspanin 7 (TSPAN7), mRNA. TUBB2B 2.8 Homo sapiens tubulin, beta 2B (TUBB2B), mRNA. USP36 1.1 Homo sapiens ubiquitin specific peptidase 36 (USP36), mRNA. ZNF167 1.7 Homo sapiens zinc finger protein 167 (ZNF167), transcript variant 2, mRNA. ZNF264 1.3 Homo sapiens zinc finger protein 264 (ZNF264), mRNA. ZNF336 1.1 Homo sapiens zinc finger protein 336 (ZNF336), mRNA. ZNF96 1.2 Homo sapiens zinc finger protein 96 (ZNF96), mRNA.
The expression of genes shown to regulate stem cell self-renewal was also modulated in BIO-treated cells. Up-regulation of JAG1, the receptor for Notch ligand, shown to regulate stem cell function, was up-regulated by BIO (Table 5.5).
In addition, up-regulation of ADAMDEC1 encoding the protease that cleaves
152 Chapter 5: Modulation of global gene expression during ex vivo expansion 153
JAG1 to enable its binding to Notch ligand was seen in BIO-treated cells (Table
5.5). We speculate that inhibition of GSK-3! increase sensitivity to Notch signalling through up-regulation of Notch receptor JAG 1. This may partly explain why BIO-treated cells expanded more efficiently in vivo in the NOD-
SCID mouse model. It is relevant that the Notch signalling was activated by GSK-
3! inhibitor administered in vivo in Bhatia’s study [247].
Table 5.5 Self-renewal and cell cycle related genes modulated by expansion and BIO
Genes modulated Genes modulated By Category Gene Name during expansion (fold) BIO (fold) STAT4 -4.8 NM Tie2(TEK) -1.3 -1.2 ANGPT2 NM -1.7 P21(CDKN1A) -3.1 NM CD44 -1.1 NM Self-renewal MYC 3.4 NM related genes GFI1 1.5 NM HES -3.8 NM
JAG1 NM 1.4 BMP8B -1.8 1.6
NOG NM 1 RARRES2 NM 3.4 P21(CDKN1A) -3.1 NM P53(TP53) 1.5 NM Cell-cycle P57(CDKN1C) -2.8 2 related genes CCND1 1.8 -2.6 CCND2 -1.1 NM
Minus (-) indicates down-regulation of gene expression while other gene expression was up- regulated or not modulated (NM).
153 Chapter 5: Modulation of global gene expression during ex vivo expansion 154
p57
**
*
Unexp.+ Exp.+ Exp.+ Exp. + Control Control 0.1uM BIO 0.5uM BIO
cyclin D1
*
*
Unexp.+ Exp.+ Exp.+ Exp. + Control Control 0.1uM BIO 0.5uM BIO
Figure 5-3 Validation of p57 (top) and cyclin D1 (bottom) by RT-PCR. Analysis of p57 and cyclin D1 was performed using RNA isolated from un-expanded, 5-day expanded control and
BIO-treated (0.1 and 0.5)M, 24 hrs) CD34+ cells. The amount of transcript was determined based on a standard curve specific for each gene and normalized to the amount of beta-2 microglobulin transcript in the same sample (Y axis). Cells were treated with DMSO for expanded control sample. Results are expressed as mean ± SD. Differences between groups were examined for statistical significance using student t-test. One asterisk (*) indicates p<0.05 and two asterisk (**) indicate p<0.01.
154 Chapter 5: Modulation of global gene expression during ex vivo expansion 155
The expression of BMP8B, a member of the TGF family, was up-regulated by
BIO (1.6 fold) and down-regulated in proliferating CD34+ cells (-1.8 fold) (Table
5.5). BMP4, another member of the BMP family, was shown to regulate stem cell self-renewal, while the role for BMP8B was not reported. Since BMP8B is modulated by both conditions – ex vivo expansion and GSK-3! inhibition but in a reverse manner, we suggest that this gene may play a role in control of stem cell function.
The expression of RARRES2 and Noggin, two important developmental genes, was up-regulated by BIO and not modulated during expansion (Table 5.5). Gene expression analysis also revealed down-regulation of a number of T, B- and monocytic cell markers in BIO-treated cells that is consistent with the suppressed phenotypic differentiation of ex vivo expanded CD34+ cells (Table 5.3, [238]). In addition, down-regulation of the CCAAT/enhancer binding protein (CEBPD), the transcriptional factor positively regulating stem cell differentiation was registered in BIO-treated cells (Table 5.3, [238]). It is relevant that CEBPD was significantly up-regulated upon cytokine stimulation that promotes stem cell differentiation suggesting that the inhibiting effect of BIO may be mediated through the suppression of CEBPD (Table 5.3, [238]).
Modulation of genes regulating chromatin remodelling including 4 members of histone cluster 1 - HIST1H2BH, -3C and -4K as well as 7 members of Open
Reading Frame (ORF) genes (all down-regulated) was also seen in BIO-treated cells [238]. It is relevant that cytokine-stimulated ex vivo expansion of CD34+
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cells was accompanied by the altered expression of several ORF genes as well as
MCM complex genes both known to regulate chromatin activity [238].
In summary, gene expression analysis identified the genes and molecular pathways modulated by GSK-3! inhibition in actively dividing cord blood stem cells and defined p57 as a candidate gene responsible for the induction of quiescence and improved stem cell function in BIO-treated stem cells. In addition, our results show that although ex vivo expansion induces global changes in gene expression, manipulation of a relatively small subset of genes modulated by ex vivo expression in CD34+ cells is sufficient to improve their regenerative potential.
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5.4 Discussion
A first step in defining molecular mechanisms that attenuate the engraftment and regenerating capacity of ex vivo expanded stem cells is to identify genes that are modulated by cytokines. In the present chapter, comprehensive gene expression analysis demonstrated that cytokine-induced proliferation of CD34+ haematopoietic progenitor cells is associated with modulation of the expression of multiple genes. Genes regulating cell cycling, differentiation, stress response, genomic integrity and chromatin remodelling, inflammation and protein folding dominated the expression profile modulated following extensive ex vivo proliferation of CD34+ cells. Extensive proliferation correlates with the up- regulation of positive regulators of cell cycling and down-regulation of cell cycle inhibitors. Ex vivo expanded CD34+ cells exhibit down-regulation of a number of positive regulators of stem cell renewal and up-regulation of positive regulators stem cell differentiation.
Treatment with BIO modulates the expression of 408 genes in 5-day cultures of
CD34+ cells. Transcriptional suppression dominated the expression signature of
BIO-treated cells and is consistent with the delayed cell proliferation seen in chapter 4. In addition, a small subset of genes that were modulated by both BIO and cytokines were identified. Among the several cell cycle-related genes over- expressed in ex vivo expanded cells compared to resting cells, only cyclin D1 was down-regulated by BIO. Among the negative cell cycle regulators suppressed in cycling cells compared, only cdk inhibitor p57 was up-regulated by BIO. Thus
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down-regulation of cyclin D1 and up-regulation of p57 gene suppression may provide a possible mechanism for the delayed cell proliferation observed in BIO- treated cells. Cyclin D1 and p57 recently emerged as important regulators of stem cell quiescence in steady-state haematopoiesis [248, 249]. The role of cyclin D1 and p57 in the regulation of stem cell renewal during extensive ex vivo proliferation was not previously reported. Loss-of-function studies will be performed to establish whether each of these genes plays a role in the regulation of cell cycling and self renewal in BIO-treated stem cells.
Gene expression analysis identified several other genes and signalling pathways modulated by GSK-3! inhibition. BIO modulates the expression of a subset of genes involved in the regulation of stem cell self-renewal. GSK-3! inhibition up- regulated the expression of JAG1 gene encoding the protease that cleaves
JAGGED1 needed to activate Notch signalling [250, 251]. Activation of Notch signalling by GSK-3! inhibitors was previously reported [247]. Increased sensitivity of the Notch signalling pathway may explain why BIO treatment following 5 days of expansion increased levels of human engraftment in the
NOD-SCID mouse model. It is hypothesized that Notch sensitivity may mediate in vivo expansion.
GSK-3! inhibition was shown to activate Wnt/!-catenin signalling in different cell types including embryonal stem cells [252]. Remarkably, despite stabilisation of !-catenin and its relocation to the nucleus [166] !-catenin target gene transcription was not registered in CD34+ cells treated with BIO. This is in
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contrast to human embryonal kidney 293 cells where addition of BIO activates the transcriptional activity of !-catenin [238]. Apparently, GSK-3! inhibition is not sufficient to induce Wnt/!-catenin signalling in CD34+ cells. Similarly to normal
CD34+ cells, BIO did not induce !-catenin target gene expression in CD34+ leukaemia progenitor TF-1 cells despite significant up-regulation of !-catenin
[166]. Thus it appears that GSK-3! inhibition does not activate the ‘canonical’
Wnt pathway, but acts by inhibiting phosphorylation and ubiquitination of other target proteins.
It has been shown that the GSK-3! system is an important regulator of haematopoietic cell development. Application of high-resolution division tracking methodology clearly defined the effect of GSK-3! inhibition on the division time of cytokine-activated UCB CD34+ cells. These kinetic studies showed that cell cycle progression was delayed by the GSK-3! inhibitor, BIO. Global gene expression identified cyclin D1 and p57 as the most likely mediators of BIO effect on cell cycle.
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Chapter 6: Conclusions
160 Chapter 6: Conclusions 161
The aims of this thesis have been successfully addressed. They were to characterise the kinetics of ex vivo expansion of CD34+ cells performed with different culture conditions and to characterise the role of Wnt signalling in regulation of haematopoietic cell cycle using division tracking analysis.
The utility of division tracking analysis was demonstrated in a number of CD34+ cell culture systems. It was shown that the expansion of CD34+ cells declined during the 2nd week of culture compared to the 1st week due to the increased cell cycle time, differentiation and reduced CD34+ cell renewal. Co-culture with stroma promotes CD34+ cell expansion and proliferation mostly through the reduced cell cycle time. This is different to the widely appreciated view that co- culture with stroma acts to maintain self-renewal of CD34+ cells by inhibiting their differentiation. Addition of serum to stem cell culture reduced expansion and increased apoptosis by increasing cell cycle time and reducing CD34+ cell renewal. Comparison of UCB and MPB showed that UCB-derived progenitor cells exhibit better ex vivo expansion of CD34+ cells but not CD34- cells compared to MPB. This study attempted to expand HSC and progenitor cells in vitro using recombinant growth factors, cell selection technologies and micro- environment. The methodology developed in this study for the first tme examines the proliferative and transcriptional mechanisms responsible for cell expansion, namely cell generation time, rate of apoptosis and CD34+ renewal. This approach will have utility in the development of clinically appropriate expansion methods.
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The method is indispensable for the analysis of the specific parameters of stem cell expansion in conditions modulating the critical regulatory pathways previously shown to play an important role in the regulation of haematopoiesis in vivo. High-resolution division tracking revealed that MSC promoted proliferation of CD34+ cells by shortening cell cycle times, and increasing the rate of differentiation into CD34- cells. Clonogenic activity was not different between co-culture with MSC and suspension culture though a numbers of colonies in secondary CFU were reduced by MSC. Co-culture with MSC significantly reduced the percent of human engraftment in NOD/SCID mouse transplantation model. There is much interest in the use of MSC to promote engraftment of UCB
[253, 254] or as a novel therapy for steroid-refractory GVHD [255]. In a recent phase I-II clinical trial patients with high-risk acute leukemia were infused with
UCB in combination with expanded MSC with accelerated haematopoietic recovery [253]. Blance et al. used ex vivo expanded MSC from either HLA- identical siblings, haploidential donors, or third-party mismatched donors for treatment of acute steroid-refractory GVHD. The phase II study showed that transfer of a median 1.4x106 cells per kg body weight of the recipient induced complete responses in 37% and partial or complete responses in 71% of 55 patients with acute GVHD grade 2-4 [255]. However the mechanism of action of
MSC in these clinical settings is unknown, underlying the importance of studying the effect of MSC on HSC and graft-versus-host disease in vitro. Division tracking of CD34+ cell growth in MSC co-culture has helped to determine whether co-infusion of CB with MSC is likely to have beneficial effects in patients. Future work should examine the effect of MSC on lymphocyte clonal
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expansion and lymphocyte-mediated cytotoxicity by division tracking as this may elucidate a possible mechanism for the beneficial effects MSC in the treatment of steroid –refractory GVHD.
Division tracking analysis was applied to determine the role of Wnt, an important regulator of haematopoietic cell development, and in the regulation of ex vivo stem cell proliferation. Using small molecule inhibitors of GSK-3! previously shown to up-regulate !-catenin, the major effecter of Wnt in human UCB derived
CD34+ cells, it was demonstrated that cell cycle delays are associated with a higher regenerative potential as determined by in vitro and in vivo in functional assays. Brenner et al showed that a late dividing population of human mobilized peripheral blood progenitor cells contributes most to gene-marked engraftment in the NOD/SCID model of human engraftment [256]. Here also it was shown that ex vivo expanded CD34+ cells have impaired regenerative function in the
NOD/SCID model of human engraftment compared to equivalent numbers of unexpanded cells. We hypothesise that continuous GSK-3! inhibition enhanced the regenerative potential of ex vivo expanded cells by inducing a late dividing population of cells in the culture. The continuous treatment with BIO that increased SRC frequency in the ex vivo expanded product, did not increase overall engraftment since SRC numbers were not expanded [166]. This is in agreement with the present study showing that self renewal capacity of ex vivo expanded
CD34+ cells was not affected by BIO.
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The results of gene expression analysis, verified by quantitative real-time PCR, provide a plausible mechanism for slowing of cell cycle. The delayed stem cell division seen in cultures treated with small molecule GSK-3! inhibitor BIO using division tracking was associated with the cyclin D1 down-regulation and up- regulation of cdk inhibitor p57. Cyclin D1 has been shown to promote stem cell division so its down-regulation is consistent with delay in cell cycle or entry into a quiescent state. Up-regulation of cdk inhibitor p57 has previously been shown to mediate stem cell quiescence. Gene expression analysis revealed that treatment with BIO did not modulate the expression of the canonical Wnt target genes up- regulated during cytokine-induced cell proliferation, but rather increased the expression of several genes regulating Notch and Tie2 signalling both shown to be down-regulated during ex vivo expansion. BIO up-regulates the expression of
Jag1 and ADAMDEC1 genes encoding the protease that cleaves Jagged 1 needed to activate Notch signaling [250, 251]. The increased expression of Jag1 and
ADAMDEC1 may act to increase the sensitivity of stem cells to the reduced levels of secreted Delta ligand to activate Notch signalling. Activation of Notch signalling by Notch fragment immobilised on the surface of the tissue culture plate was shown to promote ex vivo stem cell expansion [149]. A recent study by
Delaney et al clearly demonstrated that Notch-mediated expansion of CD34+ cells can achieve more rapid myeloid reconstitution [144]. Here evidence is presented suggesting that the small molecule inhibitor of GSK-3!, BIO appears to activate
Notch and, probably, can be used to promote Notch signaling during ex vivo expansion of HSC. One also speculates that modulation of the Notch pathway by short-term treatment with BIO may contribute to improved engraftment. Thus, use
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of gene expression analysis and division tracking together facilitated the identification of novel mechanisms that regulate haematopoietic stem cell development. These results may suggest that GSK-3! inhibitor can be used in ex vivo expansion culture condition to improve the engraftment potential in clinical setting.
Division tracking analysis will provide a powerful tool to understand the effect of anti-leukaemic drugs on leukaemic stem cells. Division tracking could be combined with apoptotic assays such as the caspase 3 assay. Song et. al. has recently shown that slowly dividing CFDA-SEbright leukaemic stem cells are less susceptible to the cell death induced by BIO compared to rapidly dividing leukaemic blast [257]. Thus it will be possible to explore the dosimetry of chemotherapy on various leukaemic sub populations as defined by division kinetics.
A novel methodology for the detailed analysis of the kinetics of haematopoietic cell growth and development has been developed. This approach facilitates dissection of the mechanism for cell growth and differentiation by quantifying the proportion of cells that renew, differentiate or undergo apoptosis for each division. Division tracking can be applied at any stage of haematopoiesis, and compared to traditional methods such as DNA content analysis and cell counting, provides a more detailed and quantitative method for analysing cell fate decisions.
By combining CFDA-SE staining with the analysis of cell markers such as CD34 antigen, division tracking analysis provides insight into how the fundamental
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processes of cell division, differentiation and apoptosis contribute to haematopoietic growth dynamics.
In addition to the detailed information on distinct cellular subtypes within a heterogeneous population the CFDA-SE-based assay has other advantages compared with the functional assays for the analysis of stem cell proliferation.
CFDA-SE staining is not toxic to the cells in the dose that was titrated in human
CD34 cells from both UCB and MPB and allows for the isolation and further culture of target cell populations from the suspension or examine these cells in vivo in bone marrow transplantation models. This assay may provide an alternate method to determine the engraftment potential of various ex vivo expansion protocols.
Although the assay is easy to perform, the flow cytometry read-out requires acquisition and analysis of multiple samples at regular time intervals. This makes it laborious compared with some other cell proliferation assays such as DNA analysis and BrdU incorporation. Therefore, application of division tracking is reserved for applications where a high level of sensitivity and specificity is required to detect subtle effects on cell cycle and differentiation.
The major drawback for CFDA-SE division tracking is the number of cells required for analysis. A specific improvement for this technique would involve lowering the number of cells required to determine cell fate decisions. Recent developments in continuous single cell imaging and lineage mapping hold great
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promise for analysis of stem and progenitor cell fate decisions [258, 259]. The major advantage of single cell tracking is that the complete division history of a progenitor is obtained for lineage mapping. This approach will revolutionize our understanding of stem cell development. The main technical problems limiting application of single cell division tracking by time-lapse video microscopy are cell segmentation and software algorithms for tracking cell movement. A major problem is that a single culture experiment requires a dedicated automated fluorescence microscope, limiting the number of cultures that can be studied in parallel. Future work should address this logistic problem by development of economic division tracking imaging equipment to increase experimental throughput. Scanning microscope systems are now on the market for ‘high- content’ cellular analysis [260, 261]. Lab-on-a-chip technologies will also contribute to this field [262].
Mathematical modelling will play an important role in the interpretation of division tracking data. Comprehensive models will enable prediction of the effect of microenvironment on cellular differentiation programs. The development of cell expansion protocols and tissue engineering approaches will be facilitated by an understanding of stem cell differentiation dynamics. The improvement of modelling at single and multi-cellular scales will be required to predict the statistical properties of HSC growth and development by ex vivo culture conditions. Such information will guide future stem cell research in directions that would not have been contemplated without a mathematical understanding of cell growth and development.
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This study has illustrated the utility of divisional history tracking. In combination with classical cell and molecular biological methods, division tracking provides a powerful paradigm for understanding blood stem cell development. This form of analysis will help develop better methods for expansion of UCB to prevent slow engraftment after UCB stem cell transplant.
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Appendix – Statistical analysis
1. Two-way ANOVA
Two-way ANOVA was used to analyse the effects of culture conditions (CU) and cell sources (SO). The analysis was performed on CD34+ and CD34- cell individually for expansion, precursor cell frequency and mean generation number.
1.1 CD34+ cells
Expansion
Number of obs = 27 R-squared = 0.9664
Root MSE = .046473 Adj R-squared = 0.9418
Source | Partial SS df MS F Prob > F
------+------
Model | .931987306 11 .084726119 39.23 0.0000
|
SO | .292974479 1 .292974479 135.65 0.0000
CU | .590186864 5 .118037373 54.65 0.0000
SO*CU | .069778543 5 .013955709 6.46 0.0022
|
Residual | .032395969 15 .002159731
------+------
Total | .964383275 26 .037091664
------+------
Culture | Source
ID | CB PB Total
------+------
A | 0.753 0.543 0.648
B | 0.906 0.757 0.831
185 186
C | 0.654 0.244 0.537
E | 0.463 0.303 0.383
F | 0.648 0.524 0.586
G | 0.547 0.309 0.428
|
Total | 0.660 0.447 0.565
------+------
In addition to the significant effects of cell source (MPB or UCB) and culture conditions (CU), there was a significant interaction between culture conditions and cell source (SO*CU).
Precursor expansion rate
Number of obs = 27 R-squared = 0.8579
Root MSE = .032896 Adj R-squared = 0.7538
Source | Partial SS df MS F Prob > F
------+------
Model | .098031671 11 .00891197 8.24 0.0002
|
SO | .002928833 1 .002928833 2.71 0.1207
CU | .078990847 5 .015798169 14.60 0.0000
186 187
SO*CU | .00990064 5 .001980128 1.83 0.1674
|
Residual | .016232216 15 .001082148
------+------
Total | .114263887 26 .004394765
------+------
Culture | Source
ID | CB PB Total
------+------
A | -0.025 -0.065 -0.045
B | -0.048 -0.054 -0.051
C | -0.059 -0.127 -0.079
E | -0.129 -0.174 -0.152
F | -0.233 -0.179 -0.206
G | -0.123 -0.147 -0.135
|
Total | -0.094 -0.124 -0.108
------+------
There is no significant interaction between culture conditions and cell source. While there is no difference between sources, culture conditions significantly affect precursor expansion rate.
187 188
Mean generation number
Number of obs = 27 R-squared = 0.9450
Root MSE = .092855 Adj R-squared = 0.9047
Source | Partial SS df MS F Prob > F
------+------
Model | 2.22290774 11 .202082522 23.44 0.0000
|
SO | .425521177 1 .425521177 49.35 0.0000
CU | 1.57069252 5 .314138504 36.43 0.0000
SO*CU | .258854707 5 .051770941 6.00 0.0030
|
Residual | .129329907 15 .008621994
------+------
Total | 2.35223765 26 .090470679
------+------
Culture | Source
ID | CB PB Total
------+------
A | 0.970 0.862 0.916
B | 1.478 1.215 1.347
C | 1.054 0.418 0.872
188 189
E | 0.646 0.556 0.601
F | 1.106 0.938 1.022
G | 0.811 0.519 0.665
|
Total | 1.019 0.751 0.900
------+------
Cell source and culture conditions have significant effects. The interaction between cell source and culture conditions is significant.
1.2 CD34- cells
Expansion
Number of obs = 27 R-squared = 0.7811
Root MSE = .199777 Adj R-squared = 0.6205
Source | Partial SS df MS F Prob > F
------+------
Model | 2.13571237 11 .19415567 4.86 0.0028
|
SO | .199695143 1 .199695143 5.00 0.0409
CU | 1.04517087 5 .209034175 5.24 0.0056
SO*CU | .661350701 5 .13227014 3.31 0.0325
189 190
|
Residual | .598662563 15 .039910838
------+------
Total | 2.73437494 26 .105168267
------+------
Culture | Source
ID | CB PB Total
------+------
A | 1.188 1.846 1.517
B | 1.296 1.834 1.565
C | 1.161 1.253 1.187
E | 1.676 1.820 1.748
F | 1.762 1.700 1.731
G | 1.843 1.539 1.691
|
Total | 1.422 1.665 1.530
------+------
There is a significant interaction between sources and culture conditions. The difference between UCB and MPB depends on culture conditions.
CB - expansion PB - expansion
.345 .1095 Residuals Residuals
-.345 -.1095 A B C D E F G A B C D E F G Culture ID Culture ID
190 191
Precursor expansion rate
Number of obs = 27 R-squared = 0.7983
Root MSE = .195708 Adj R-squared = 0.6504
Source | Partial SS df MS F Prob > F
------+------
Model | 2.27437286 11 .206761169 5.40 0.0017
|
SO | .22510699 1 .22510699 5.88 0.0284
CU | 1.22869048 5 .245738097 6.42 0.0022
SO*CU | .521170938 5 .104234188 2.72 0.0607
|
Residual | .574524967 15 .038301664
------+------
Total | 2.84889783 26 .109572993
------+------
Culture | Source
ID | CB PB Total
------+------
A | 0.252 0.926 0.589
B | 0.305 0.708 0.506
C | 0.180 0.330 0.223
E | 0.767 0.906 0.837
F | 0.729 0.769 0.749
G | 0.960 0.687 0.824
|
Total | 0.462 0.721 0.577
------+------
No significant interaction was observed. There are differences between cell sources and among culture conditions.
191 192
CB - surv PB - surv
.345 .1095 Residuals Residuals
-.345 -.1095 A B C D E F G Culture ID A B C D E F G Culture ID
Mean generation number
Number of obs = 27 R-squared = 0.5607
Root MSE = .192958 Adj R-squared = 0.2385
Source | Partial SS df MS F Prob > F
------+------
Model | .712735376 11 .064794125 1.74 0.1575
|
SO | .000804087 1 .000804087 0.02 0.8851
CU | .546123785 5 .109224757 2.93 0.0483
SO*CU | .13143184 5 .026286368 0.71 0.6279
|
Residual | .558489335 15 .037232622
------+------
Total | 1.27122471 26 .048893258
------+------
Culture | Source
ID | CB PB Total
------+------
A | 1.422 1.587 1.505
192 193
B | 1.410 1.650 1.530
C | 1.486 1.352 1.448
E | 1.232 1.198 1.215
F | 1.312 1.278 1.295
G | 1.189 1.053 1.122
|
Total | 1.371 1.353 1.363
------+------
No significant interaction was observed. Only differences were seen among culture conditions but not source.
CB - mean gen PB - mean gen .345 .2 Residuals Residuals
-.345 -.2 A B C D E F G A B C D E F G Culture ID Culture ID
193 194
2. One-way ANOVA
One-way ANOVA was applied to the data to determine the effect of specific culture parameters. These were culture time, stroma, serum, and cell source. Here each cell source was analysed separately.
2.1 CD34+ cells
Expansion
UCB
Number of obs = 15 R-squared = 0.9105
Root MSE = .051475 Adj R-squared = 0.8609
Source | Partial SS df MS F Prob > F
------+------
Model | .242746339 5 .048549268 18.32 0.0002
|
CU | .242746339 5 .048549268 18.32 0.0002
|
Residual | .023846982 9 .002649665
------+------
Total | .266593321 14 .01904238
MPB
Number of obs = 12 R-squared = 0.9782
Root MSE = .037747 Adj R-squared = 0.9601
Source | Partial SS df MS F Prob > F
------+------
Model | .384339465 5 .076867893 53.95 0.0001
|
CU | .384339465 5 .076867893 53.95 0.0001
194 195
|
Residual | .008548986 6 .001424831
------+------
Total | .392888451 11 .035717132
Precursor cell frequency
UCB
Number of obs = 15 R-squared = 0.8401
Root MSE = .036425 Adj R-squared = 0.7513
Source | Partial SS df MS F Prob > F
------+------
Model | .062759144 5 .012551829 9.46 0.0022
|
CU | .062759144 5 .012551829 9.46 0.0022
|
Residual | .011941353 9 .001326817
------+------
Total | .074700497 14 .00533575
MPB
Number of obs = 12 R-squared = 0.8718
Root MSE = .026742 Adj R-squared = 0.7649
Source | Partial SS df MS F Prob > F
------+------
Model | .029169209 5 .005833842 8.16 0.0119
|
CU | .029169209 5 .005833842 8.16 0.0119
|
Residual | .004290863 6 .000715144
195 196
------+------
Total | .033460072 11 .003041825
Mean generation number
UCB
Number of obs = 15 R-squared = 0.8690
Root MSE = .116617 Adj R-squared = 0.7962
Source | Partial SS df MS F Prob > F
------+------
Model | .811746509 5 .162349302 11.94 0.0009
|
CU | .811746509 5 .162349302 11.94 0.0009
|
Residual | .122395513 9 .013599501
------+------
Total | .934142022 14 .06672443
MPB
Number of obs = 12 R-squared = 0.9926
Root MSE = .033996 Adj R-squared = 0.9865
Source | Partial SS df MS F Prob > F
------+------
Model | .93189951 5 .186379902 161.27 0.0000
|
CU | .93189951 5 .186379902 161.27 0.0000
|
Residual | .006934393 6 .001155732
------+------
196 197
Total | .938833903 11 .085348537
2.2 CD34- cells
Expansion
UCB
Number of obs = 15 R-squared = 0.7414
Root MSE = .215314 Adj R-squared = 0.5978
Source | Partial SS df MS F Prob > F
------+------
Model | 1.19646747 5 .239293495 5.16 0.0165
|
CU | 1.19646747 5 .239293495 5.16 0.0165
|
Residual | .417240998 9 .046360111
------+------
Total | 1.61370847 14 .115264891
MPB
Number of obs = 12 R-squared = 0.7502
Root MSE = .173888 Adj R-squared = 0.5421
Source | Partial SS df MS F Prob > F
------+------
Model | .544962014 5 .108992403 3.60 0.0750
|
CU | .544962014 5 .108992403 3.60 0.0750
|
Residual | .181421565 6 .030236927
------+------
Total | .726383579 11 .066034871
197 198
Precursor cell frequency
UCB
Number of obs = 15 R-squared = 0.8125
Root MSE = .186805 Adj R-squared = 0.7083
Source | Partial SS df MS F Prob > F
------+------
Model | 1.36084588 5 .272169176 7.80 0.0043
|
CU | 1.36084588 5 .272169176 7.80 0.0043
|
Residual | .314066502 9 .034896278
------+------
Total | 1.67491238 14 .119636599
MPB
Number of obs = 12 R-squared = 0.6412
Root MSE = .20835 Adj R-squared = 0.3422
Source | Partial SS df MS F Prob > F
------+------
Model | .465432208 5 .093086442 2.14 0.1901
|
CU | .465432208 5 .093086442 2.14 0.1901
|
Residual | .260458465 6 .043409744
------+------
Total | .725890673 11 .065990061
Mean generation number
UCB
Number of obs = 15 R-squared = 0.2633
Root MSE = .240201 Adj R-squared = -0.1459
198 199
Source | Partial SS df MS F Prob > F
------+------
Model | .185626063 5 .037125213 0.64 0.6735
|
CU | .185626063 5 .037125213 0.64 0.6735
|
Residual | .519267357 9 .057696373
------+------
Total | .70489342 14 .05034953
MPB
Number of obs = 12 R-squared = 0.9305
Root MSE = .080852 Adj R-squared = 0.8726
Source | Partial SS df MS F Prob > F
------+------
Model | .525067652 5 .10501353 16.06 0.0020
|
CU | .525067652 5 .10501353 16.06 0.0020
|
Residual | .039221978 6 .006536996
------+------
Total | .56428963 11 .051299057
199 200
3. Multiple comparisons
Multiple comparisons were performed to determine the effect of specific culture parameters on HSC ex vivo expansion. This is an example to compare culture condition A and B across both UCB and MPB.
The main effect of MS5 in 1st week =−+−FV Lyy1 HX()AC BC() yy AP BP 2 the variance 1 var()Lyyyy=+++HXFV var() var() var() var () 1 4 AC BC AP BP 1 FVσσσσ2222 =+++GW 4 HXGWnnnnAC BC AP BP σ 2 = 2 The interaction of MS5 in 1st week with cell source =−−−FV Lyy2 HX()AC BC() yy AP BP 2 1 var()Lyyyy=+++HXFV var() var() var() var () 2 4 AC BC AP BP 1 FVσσσσ2222 =+++GW 4 HXGWnnnnAC BC AP BP σ 2 = 2 where n is 2 for all cases except culture C and UCB, in which case n = 5. The error variance σ2 is estimated from the anova.(s2). L Calculate a t ratio t = and for a test at the 5% level, compare to se() L
2 tν,upper 0.025 where the degrees of freedom ν is the degrees of freedom of the estimate s (from the anova table). For the simple effects i.e., compare A vs. B for UCB only
200 201
=− Ly3 AC y BC σσ22 =+=+=σ 2 var()Ly3 var()AC var () yBC nnAC BC The following table shows the result of multiple comparisons for the significance of culture parameters.
Multiple comparisons for CD34+ cells using two-way ANOVA (p-value)
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Multiple comparisons for CD34- cells using two-way ANOVA (p-value)
Main effect Interaction Culture conditions (ID) Expansion Survival MGN Expansion Survival MGN 1st wk +-MS5 (AB) 0.7387 0.5600 0.8542 0.6771 0.3430 0.7872 Media(AC) 0.0310 0.0194 0.5071 0.0462 0.0579 0.2531 1st 2nd wk +MS5(BF) 0.2583 0.1001 0.1056 0.0507 0.2094 0.3313 2nd wk +-MS5(EF) 0.9058 0.5367 0.5664 0.4772 0.7256 1.0000 1st 2nd wk -MS5(AE) 0.1228 0.0939 0.0509 0.0889 0.0723 0.4771
Multiple comparisons for CD34+ cells using one-way ANOVA (p-value)
Expansion (p-value) Survival (p-value) MGN (p-value) Culture condition (ID) CB PB CB PB CB PB 1st wk +-MS5 (AB) 0.0095 0.0000 0.5373 0.6866 0.0006 0.0000 Media(AC) 0.0363 0.0000 0.3654 0.0350 0.4824 0.0000 1st 2nd wk +MS5(BF) 0.0002 0.0000 0.0001 0.0003 0.0061 0.0000 2nd wk +-MS5(EF) 0.0027 0.0000 0.0120 0.8542 0.0013 0.0000 1st 2nd wk -MS5(AE) 0.0000 0.0000 0.0120 0.0010 0.0141 0.0000
201 202
Multiple comparisons for CD34- cells using one-way ANOVA (p-value)
Expansion Survival MGN Culture condition (ID) CB PB CB PB CB PB 1st wk +-MS5 (AB) 0.6232 0.9459 0.7805 0.3120 0.9608 0.4480 Media(AC) 0.9019 0.0039 0.7053 0.0119 0.7935 0.0108 1st 2nd wk +MS5(BF) 0.0470 0.4529 0.0384 0.7737 0.6890 0.0003 2nd wk +-MS5(EF) 0.6952 0.5007 0.8415 0.5208 0.7437 0.3381 1st 2nd wk -MS5(AE) 0.0386 0.8831 0.0147 0.9248 0.4413 0.0002
4. Paired t-test
Renewal probability, apoptosis and differentiation comparisons between culture conditions or cell source were performed using paired t-test. Three time points (48, 72 and 96 h culture time point were used for the tests.
CD34+ renewal probability Condition p-value 1st CB vs.2nd CB 0.0028 1st CB+MS5 vs.2nd CB+MS5 0.0039 Time 1st PB vs.2nd PB 0.0000 1st PB+MS5 vs.2nd PB+MS5 0.0000 1st CB vs 1st CB+MS5 0.0845 2nd CB vs 2nd CB+MS5 0.0273 Stroma 1st PB vs 1st PB+MS5 0.0242 2nd PB vs 2nd PB+MS5 0.0022 1st CB(SFM) vs.1st CB(IMDM) 0.0174 Serum 1st PB(SFM) vs.1st PB(IMDM) 0.0004 1st CB vs 1st PB 0.1652 2nd CB vs 2nd PB 0.0007 Source 1st CB+MS5 vs 1st PB+MS5 0.6030 2nd CB+MS5 vs 2nd PB+MS5 0.3597 1st CB(IMDM) vs 1st PB(IMDM) 0.0000
202 203
Apoptosis and differentiation
Apoptosis Culture condition p-value 1st CB vs.2nd CB 0.6457 1st CB+MS5 vs.2nd CB+MS5 0.1531 Time 1st PB vs.2nd PB 0.0849 1st PB+MS5 vs.2nd PB+MS5 0.2488 1st CB vs 1st CB+MS5 0.0311 2nd CB vs 2nd CB+MS5 0.0031 Stroma 1st PB vs 1st PB+MS5 0.1860 2nd PB vs 2nd PB+MS5 0.0146 1st CB(SFM) vs.1st CB(IMDM) 0.0158 Serum 1st PB(SFM) vs.1st PB(IMDM) 0.0001 1st CB vs 1st PB 0.0767 2nd CB vs 2nd PB 0.0166 Source 1st CB+MS5 vs 1st PB+MS5 0.5029 2nd CB+MS5 vs 2nd PB+MS5 0.1070 1st CB(IMDM) vs 1st PB(IMDM) 0.0001
Differentiation Culture condition p-value 1st CB vs.2nd CB 0.0000 1st CB+MS5 vs.2nd CB+MS5 0.0009 Time 1st PB vs.2nd PB 0.0003 1st PB+MS5 vs.2nd PB+MS5 0.0000 1st CB vs 1st CB+MS5 0.0910 2nd CB vs 2nd CB+MS5 0.0748 Stroma 1st PB vs 1st PB+MS5 0.0184 2nd PB vs 2nd PB+MS5 0.2592 1st CB(SFM) vs.1st CB(IMDM) 0.1268 Serum 1st PB(SFM) vs.1st PB(IMDM) 0.3823 1st CB vs 1st PB 0.5691 2nd CB vs 2nd PB 0.1349 Source 1st CB+MS5 vs 1st PB+MS5 0.3561 2nd CB+MS5 vs 2nd PB+MS5 0.2171 1st CB(IMDM) vs 1st PB(IMDM) 0.0662
203 204
204