Transcriptome and Glycome Profiling of Human Hematopoietic Cd133+ and Cd34+ Cells

FINNISH RED CROSS BLOOD SERVICE AND FACULTY OF BIOCIENCES, DEPARTMENT OF BIOLOGICAL AND ENVIRONMENTAL SCIENCES, DIVISION OF GENETICS, UNIVERSITY OF HELSINKI, FINLAND

TRANSCRIPTOME AND GLYCOME PROFILING OF HUMAN HEMATOPOIETIC CD133+ AND CD34+ CELLS

Heidi Anderson

ACADEMIC DISSERTATION

To be publicly discussed, with permission of the Faculty of Science of the University of Helsinki in the Nevanlinna Auditorium of the Finnish Red Cross Blood Service Kivihaantie 7, Helsinki, on February 15th, at 12 noon.

Helsinki 2008 ACADEMIC DISSERTATIONS FROM THE FINNISH RED CROSS BLOOD SERVICE NUMBER 52

SUPERVISORS

Docent Taina Jaatinen, PhD Finnish Red Cross Blood Service Helsinki, Finland

Docent Jukka Partanen PhD Finnish Red Cross Blood Service Helsinki, Finland

REVIEWERS

Docent Heli Skottman, PhD University of Tampere Regea Institute for Regenerative Medicine Tampere, Finland

Docent Timo Tuuri, PhD Infertility Clinic The Family Federation of Finland Helsinki, Finland

OPPONENT

Professor, Director Riitta Lahesmaa, MD, PhD Turku Centre for Biotechnology University of Turku, Turku, Finland

ISBN 978-952-5457-16-2 (print) ISBN 978-952-5457-17-9 (pdf) ISSN 1236-0341 http://ethesis.helsinki.ﬁ Helsinki 2008 Yliopistopaino To my family CONTENTS

PUBLICATIONS ...... 6

ABBREVIATIONS...... 7

ABSTRACT ...... 8

REVIEW OF THE LITERATURE ...... 10 Hematopoietic stem cells ...... 10 Phenotypic characterization ...... 11 Functional characterization ...... 14

Cord blood-derived hematopoietic stem cells ...... 15 Collection and banking ...... 15 Cord blood transplantation ...... 16 Ex vivo expansion ...... 21

Transcriptome profi ling ...... 22 Methods for transcriptome profi ling ...... 22 Gene expression profi ling of murine hematopoietic stem cells ...... 26 Gene expression profi les of human hematopoietic stem cells ...... 28

Glycosylation ...... 29 N-glycans and N-glycan synthesis ...... 29 Glycosylation in hematopoiesis ...... 33 Effect of glycosylation on homing...... 33

AIMS OF THE STUDY ...... 35

MATERIALS AND METHODS ...... 36 Ethics ...... 36 Cells ...... 36 Methods ...... 37 Gene expression reanalysis ...... 39 Annotation ...... 40 RESULTS AND DISCUSSION ...... 41

CD133+ and CD34+ cells have similar gene expression (studies I and II) ...... 41

Novel molecular markers for HSCs could not be found (study II) .... 42

Differential gene expression between CD133+ and CD34+ cells suggests that CD133+ cells are more primitive (studies I and II) .... 43

CD133+ and CD34+ cells demonstrate similar total colony forming potential (studies I and II) ...... 44

Reanalysis of the original gene expression data focuses on the similarities between CD133+ and CD34+ cells ...... 45

CD133+ and CD34+ cells have divergent roles in hematopoietic regeneration (studies I and II) ...... 49

N-glycosylation-related gene expression is similar in CD133+ and CD34+ cells and correlates with the N-glycome proﬁ le (study III) ... 52

Characteristic mannosylation pattern of CD133+ and CD34+ cells (study III) ...... 52

Differential expression of N-acetylglucosaminyl-transferase genes and the enrichment of biantennary complex type N-glycans in CD133+ cells (study III) ...... 54

Despite the differential expression of genes coding for β1,4-galactosyltransferases there are no differences in the β1,4-galactosylation of CD133+ and CD133- cells (study III) ...... 55

CD133+ and CD34+ cell speciﬁ c biosynthesis of N-glycan terminal epitopes (study III) ...... 55

CONCLUSIONS ...... 58

ACKNOWLEDGEMENTS ...... 60

REFERENCES ...... 62 6 PUBLICATIONS

PUBLICATIONS

This thesis is based on the following original publications, which have been reproduced with the permission of the copyright holders.

I. Jaatinen T, Hemmoranta H, Hautaniemi S, Niemi J, Nicorici D, Laine J, Yli-Harja O, Partanen J. Global gene expression proﬁ le of human cord blood-derived CD133+ cells. Stem Cells 2006;24:631-641.

II. Hemmoranta H, Hautaniemi S, Niemi J, Nicorici D, Laine J, Yli- Harja O, Partanen J, Jaatinen T. Transcriptional proﬁ ling reﬂ ects shared and unique characters for CD34+ and CD133+ cells. Stem Cells Dev 2006;15:839-851.

III. Hemmoranta H, Satomaa T, Blomqvist M, Heiskanen A, Aitio O, Saarinen J, Natunen J, Partanen J, Laine J, Jaatinen T. N-glycan structures and associated gene expression reﬂ ect the characteristic N-glycosylation pattern of human hematopoietic stem and progenitor cells. Exp Hematol 2007;35:1279-1292.

In addition, some unpublished data are presented. ABBREVIATIONS 7

ABBREVIATIONS

BFU burst-forming erythoid BM bone marrow CB cord blood CD cluster of differentiation cDNA complementary deoxyribonucleic acid CFU colony-forming unit DNA deoxyribonucleic acid E erythroid GEMM granulocyte-erythroid- macrophage-megakaryocyte GM granulocyte-macrophage HSC hematopoietic stem cell Lin lineage differentiation N-glycan asparagine-linked glycan NMR nuclear magnetic resonance mHSC murine hematopoietic stem cell mRNA messenger ribonucleic acid PB peripheral blood RNA ribonucleic acid SAGE serial analysis of gene expression qRT-PCR quantitative real-time reverse transcriptase- polymerase chain reaction 8 ABSTRACT

ABSTRACT

Human blood contains a large variety of differentiated cells with a limited half-life. New blood cells are continuously provided by self-renewing multipotent hematopoietic stem cells (HSC). The capacity of HSCs to regenerate the hematopoietic system is utilized in the treatment of patients with hematological malignancies. HSCs can be obtained from bone marrow (BM) or peripheral blood (PB) after cytokine-induced HSC mobilization. In addition, blood from both placenta and umbilical cord is also rich in HSCs. Cord blood (CB) can be collected after delivery, cryopreserved for the possible future need of an HSC graft for patients with no suitable BM donor available.

HSCs can be enriched using an antibody-based recognition of CD34 glycoprotein on the cell surface. Another glycoprotein, CD133, has also been used for identiﬁ cation of primitive cells. The CD133+ cell population has been shown to be more homogeneous and contain a higher number of potential HSCs than CD34+ cells. CD133 and CD34 molecules are often coexpressed, yet the CD133+ and CD34+ cells may have partly different roles in hematopoiesis.

Each cell has a glycome typical for that cell type. The glycome of HSCs is poorly known. Glycans at the outermost surface of the cell transmit various signals between the matrix environment and the surrounding cells. A CD34+ cell-speciﬁ c glycoform of CD44, cell adhesion molecule, has been shown to have a possible function in HSC homing.

The aim of present studies has been to characterize the global gene expression of CB CD133+ and CD34+ cells. Furthermore, the identically constituted gene expression proﬁ les of CD133+ and CD34+ cells were compared to unravel shared and unique gene expression in CD133+ and CD34+ cells. A prioritization algorithm was used to rank the genes best separating CD133+ and CD34+ cells from their counterparts, CD133- and CD34- cells. Thus, these genes were suggested to play a part in the regulation of primitive hematopoietic cells. CD133+ and CD34+ cells solely expressed several genes encoding transmembrane proteins that could putatively serve to identify HSCs. Moreover, cell type-speciﬁ c gene expression was demonstrated in both CD133+ and CD34+ cells. These differentially expressed genes were associated with divergent biological processes suggesting differences in the transcriptional regulation of ABSTRACT 9

CD133+ and CD34+ cells. Thus, they may represent differences in the self-renewal, differentiation or homing properties. Moreover, CD133+ cells expressed a higher number of genes associated with HSC maintenance and pluripotency indicating the more primitive nature of CD133+ cells. Furthermore, the gene expression data bank of CD133+ and CD34+ cells may be used to study the pathogenesis of hematological disorders and the development of malignancies.

Another aim of these studies was to identify the characteristic glycosylation pattern of primitive hematopoietic cells and the genes producing the key glycan entities. High mannose type N-glycans were the most prevalent glycan type in CD133+ cells. CD133+ cells had more biantennary complex type N-glycans than CD133- cells, whereas multiantennary complex type N-glycans were fewer in CD133+ cells than in CD133- cells. Moreover, the CD133+ cell N-glycans showed enrichment of terminal α2,3-sialylation. Combined gene expression and glycan proﬁ ling allowed the identiﬁ cation of the key genes regulating the N- glycosylation characteristic for CD133+ cells. The critical genes were similarly expressed in CD34+ cells, suggesting a similar glycosylation pattern in CD133+ and CD34+ cells. Knowledge of HSC glycobiology can be used to design therapeutic cells with improved cell proliferation or homing properties. Improved HSC homing would diminish the number of HSCs needed for the therapy of patients, and allow for greater use of CB grafts. 10 REVIEW OF THE LITERATURE

REVIEW OF THE LITERATURE

Hematopoietic stem cells

HSCs are small cell populations in adult BM. They have enormous capacity to self-renew and to generate progenitor cells that can differentiate into mature blood cells1. The life span of most white blood cells is from a few hours to a couple of days. The most common cell type in blood, red blood cells, has an average life expectancy of 120 days. Adult humans have tens of billions of red blood cells. Thus, an average of two million new red blood cells are required every second. HSCs produce new cells to balance the loss of cells each day throughout life.

The mammalian hematopoietic tissue derives from the mesoderm layer of the embryo. The HSCs ﬁ rst germinate in the dorsal aorta2. They are also produced in the major embryonic vessels, umbilical and vitelline arteries2. From the aorta, HSCs migrate to yolk sac, and thereafter to liver and BM. In addition, the placenta supports a large stem cell pool during development and is suggested to serve as source of pre-HSCs/ HSCs3. The number of HSCs in the placenta and cord veins drops during the trimester of the gestation. Yet, blood from the umbilical cord and placenta of full-term infant provides a rich source of HSCs.

BM is the primary location for HSCs in the adult. In addition, small amounts of HSCs are in both circulation and in the spleen. Most of the HSCs reside in their BM niche in a quiescent, noncycling state. However, to sustain hematopoiesis and to maintain the HSC pool, cell divisions are required. HSCs have been thought to divide both symmetrically and asymmetrically4,5. In symmetric cell division, two identical HSCs are formed. Asymmetric cell division leads to the formation of two different daughter cells, one that is similar to the mother cell and contains long- term repopulation capacity, and a daughter cell that has short-term repopulation capacity. Long-term HSCs have life-long hematopoietic reconstruction capacity. Short-term HSCs have reduced capacity to sustain hematopoiesis, but they give rise to lineage committed progenitors. According to estimates, only 1/10 000 cells in BM has long- term repopulation capacity6. Once HSC has been committed to lineage differentiation, it no longer has HSC capacity and properties.

The tremendous progeny of HSCs is able to repopulate the marrow. HSC transplantation for marrow repopulation after high doses of chemotherapy REVIEW OF THE LITERATURE 11 or total body irradiation is an established procedure for the treatment of leukemia. HSC transplantations have also been used in therapy for other hematological disorders, immunodeﬁ ciency syndromes, inborn errors of metabolism and autoimmune diseases7-9. It has been suggested that HSCs have the capacity to differentiate into cells of nonhematopoietic tissue as well10-12. Current studies are targeted to ﬁ nd out if HSCs can be utilized in the treatment of such diseases as diabetes, Parkinson’s disease and spinal cord injury.

Phenotypic characterization

HSCs are morphologically similar to leukocytes. They are found among small, rounded cells that have little cytoplasm13. HSCs are mainly noncycling. They stain poorly with vital dyes such as rhodamine123, which locates the mitochondria of living cell, or Hoechst stains 33342/33258, which recognizes DNA of live or ﬁ xed cells. Poorly stainable noncycling cells are called Rho/Hoechst 33342low, in contrast to Rho/Hoechst 33342high which indicate the actively cycling cell population. Thus, Rholow cells contain a higher number of HSCs than Rhohigh cells14. Moreover, HSCs have high aldehyde dehydrogenase activity that can be used to enrich primitive cell populations15.

Leukocytes carry various cluster of differentiation (CD) molecules on their cell surface. These have been used as markers to characterize cell type, maturity and activity of the cell. There is no single marker to identify HSCs, but the presence of the CD34 molecule marks a highly enriched HSC population. As a result, CD34-based isolation of HSCs is most widely used. CD34 antigen is also carried by hematopoietic progenitor cells and cells in the vascular endothelium. Determination of the cell type depends on which other CD molecules are present with CD34. HSCs are found among a population of CD34+ cells that do not express CD38 or other lineage differentiation (Lin) markers such as CD2, CD3, CD14, CD16, CD19 CD24, CD56, CD66b and Glycophorin A (Figure 1). 12 REVIEW OF THE LITERATURE

Figure 1. Schematic representation of the hematopoietic lineage differentiation markers during hematopoiesis. Modiﬁ ed after Kyoto Encyclopedia of Genes and Genomes Pathway Database16. REVIEW OF THE LITERATURE 13

However, other markers such as CD90 (Thy-1) and CD117 (c-Kit) are carried on HSCs. The expression of CD135 (FLT3) is typical for multipotent progenitors17.

Hematopoiesis has been studied mainly in the mouse model. Murine HSCs (mHSC) have phenotypic characteristics divergent of human HSCs. They are enriched by selecting Lin- cells that carry Sca-1 and c-Kit molecules on their cell surface18. Unlike in humans, mHSCs are CD34 negative18,19. CD90 is expressed only at low level in mHSCs20. The signaling lymphocyte activation molecule family receptors, the presence of CD150 molecule and the lack of CD48 molecule, can also be used to highly purify mHSCs21.

A small population of CD34-CD38- cells carrying the CD133 molecule on their surface seems to be able to give rise to CD34+ cells in humans22. However, in human HSCs, long-term marrow repopulating capacity is obtained only after the expression of CD3423. On average, 80% of the CD34+ cells carry CD133 on their surface. CD133+ cells are almost all CD34 positive as well24. Adult stem cells of non-hematopoietic origin have been found to express CD13325-27. Its expression is also typical in the embryoid bodies derived from embryonic stem cells28. The CD133 molecule is present in circulating cells with endothelial differentiation capacity, suggesting that the differentiation capacity of CD133+ cells might be greater than in CD34+ cells25.

HSC enrichment based on antibody-recognition of cell surface molecules is not an ideal method. Antibody binding on HSCs decreases HSC engraftment potential, which is an outcome that should be avoided29. HSC markers have been present on functionally limited cells30. HSCs can have altered cell surface marker expression in in vitro conditions31. Fur- thermore, both functionally competent HSCs and cells with less capacity may have the same phenotype. Many ongoing investigations are aimed toward HSC enrichment by methods that take use of such methods as density gradient difference between cell types or the divergent selectin binding affi nity of HSCs32-35. These methods may exceed up to a three- fold enrichment of HSCs when compared to a mononuclear cell population. However, no methods have been more effi cient in HSC identifi cation than antibody-based strategies. In the immunomagnetic selection of CD34+ or CD133+ cells purities of over 90% can be obtained36. Evalu- ation of HSC potential requires the study of their functional properties. 14 REVIEW OF THE LITERATURE

Functional characterization

Functional assays are useful in the measurement of the potential of the cell. However, they are complicated to use for cell selection purposes. Better understanding of HSC biology may help to develop novel methods for fast read-out of HSC potential.

The best model used to measure long-term marrow repopulating capacity is provided by severe combined immunodeﬁ ciency mice repopulating assay. Of the severe combined immunodeﬁ ciency mice transplanted human CD34+CD38- cells, one out of 617 cells is able to engraft37. The relatively low capacity of the CD34+CD38- cell population to repopulate mouse marrow is affected by the xenoenvironment. Engraftment capacity has increased with direct intra-BM transplantation, where one out of 44 CD34+CD38- cells has been able to populate the marrow38,39. Human CD34+ cells negative for CD133 have not been able to provide engraftment in mice model40. Moreover, the number of vacant HSC niches in the recipient’s BM regulate the engraftment of transplanted HSCs41.

In vitro assays, such as long-term culture initiating cells or cobble stone area forming cells, have also been used to estimate the number of long-term HSCs. In these methods, stromal cells are used to support HSC development and their colony forming capacity is tested after the long-term culture. However, differential kinetics between cells in in vivo and in vitro assays indicate that long-term culture conditions may not necessarily be able to represent the in vivo long-term repopulation capacity of HSCs40.

Colony forming unit assay (CFU) exploits a cell’s ability to produce progeny in vitro. The cells possessing multipotent progeny, the highest differentiation potential, form granulocyte erythrocyte macrophage megakaryocyte colonies. CD133+CD34+ cells’ progeny are mainly granulocyte macrophage and granulocyte erythrocyte macrophage megakaryocyte colonies40. Thus, CD133+ cells are suggested to be more naive than CD133-CD34+ cells, whose progeny also produce some unipotent burst-forming erythrocyte and erythrocyte colonies40. REVIEW OF THE LITERATURE 15

Cord blood-derived hematopoietic stem cells

CB CD34+ cells have greater proliferation potential and they produce signiﬁ cantly more progeny than BM CD34+ cells10,42,43. Thus, the greater number of more immature HSCs may be found in CB than in BM ori- gin44. Moreover, CB HSCs have been shown to be able to differentiate into endothelial and cardiomyocyte-like cells45. Unlike BM CD34+ cells, the CB CD34+ cell population contains very few B cell lineage committed CD34+CD19+ progenitors (Figure 1). In addition, late-erythroid CD34+CD71highCD64-, late-myeloid CD34+CD33+CD15+ and granulo- monocytic CD34+CD71+CD64+ progenitors are rarer in CB than BM46. On the other hand, early-myeloid CD34+CD33+CD15- and T cell lineage committed CD34+CD7+CD3- cells are more abundant in CB than in BM.

In cell culture, CB-derived CD34+ cells signiﬁ cantly increase their proliferation when exposed to external cytokines and growth factors and are easier to culture in vitro than BM CD34+ cells47,48. The environment of CB HSCs is different from that of BM HSCs. BM HSCs are attached to the osteoblastic cells via the engagement of very late antigen 4 and vascular cell adhesion molecule 149,50. The osteoblastic cells secrete growth factors and cytokines such as interleukin-6, stem cell factor and chemokine (C-X-C motif) ligand 12, and provide ligands such as ﬁ bronectin for the adhesion of HSC51. CB HSCs are, in contrast, devoid of osteoblastic cells. While the placenta is a supportive niche for HSCs in the fetal environment during midgestation3, the placenta and CB of a full-term infant is likely to contain low amounts of growth factors and cytokines. Moreover, CB HSCs may be naturally prepared for oxidative stress and survive better in the 20% oxygen of most cell culture. The oxygen concentration in the CB environment is higher than in BM endosteum, the niche for BM HSCs51. The greater success in culturing CB HSCs may also be aug- mented by the fact that CB HSCs have markedly longer telomeres than in BM or PB HSCs’52. This suggests that CB HSCs are able to go through more cell duplications than BM HSCs.

Collection and banking

After the ﬁ rst successful CB transplantation in 198853, CB has been stored and cryopreserved for potential future use. CB banks have been created to increase the availability of CB units. Today, there are tens of public CB banks worldwide storing more than 200,000 CB units54,55. In addition, an unknown number of CB units have been stored privately. 16 REVIEW OF THE LITERATURE

The CB units stored in public banks are available to practically everyone. The International NETCORD Foundation provides information regarding the CB banks jointly in Europe, the United States, Japan and Australia. An online virtual ofﬁ ce for real-time searching allows an even faster survey of available CB grafts and makes their use more efﬁ cient. This especially enhances the search of grafts for patients with a minority genetic background that have only a few suitable stem cell donors available55. The accredited CB banks of the Foundation for the Accreditation of Cel- lular Therapy, such as the Finnish Red Cross Blood Service Cord Blood Bank, ensure that minimum requirements for CB quality, handling and storage are met56. As a result, accreditation criteria maintain the quality of the banked CB.

CB is a byproduct of delivery, it is easy to obtain and ex utero collection carries no risk to the donor57. CB collection is similar to PB collection, i.e. the cord vein is punctured with a needle and the collection bag is ﬁ lled by gravity. Suitable CB donors have no known genetic or transmitted disease, have been informed of the situation and have provided written consent for the utilization of the donated CB. Further, CB is tested for human immunodeﬁ ciency virus, hepatitis C virus, hepatitis B virus, human T cell leukemia virus and syphilis. In addition, donor mothers have been analyzed for the presence of parvo and cytomegalovirus.

Yet it is not known whether CB cells, cryopreserved for long periods of time, are successful in transplantation. The results on mouse marrow populating assay and CFU suggest a rather long half-life for stored CB units. Thawed CB HSCs have been able to repopulate the marrow in a mouse model after 15 years of storage in liquid nitrogen58. The colony forming potential of HSCs has been shown to be 97% of that of fresh HSCs after 12 years of storage in liquid nitrogen59.

Cord blood transplantation

Because fast treatment of the patient may be crucial, CB is a noteworthy alternative to transplantation when no suitable related BM donor is readily available. CB as readily available and cryopreserved in CB banks is faster and easier to obtain than unrelated BM donor graft. To obtain BM graft, the donor candidates in the BM donor registry have to ﬁ rst be located. Their HLA-typing results are conﬁ rmed, they are informed of the collection process and their consent is requested. Moreover, donors need to pass a complete physical examination and and need to take REVIEW OF THE LITERATURE 17 additional blood tests to become a suitable BM donor. After all this, the date for transplantation can be scheduled. An average of 13.5 or 49 days are required to obtain CB or BM graft, respectively60.

CB transplantation has already been performed on approximately 4000 patients. CB has been used to treat a variety of diseases7-9,55,61. A full list of diseases treated by CB transplantation is shown in Table 1. Novel applications aim to improve the engraftment of CB grafts. Methods such as HSC expansion, supplement of CD34+ cells from PB, mesenchymal stem cells and platelet microparticles are under investigation62-67. Gene transfer into CB HSCs has been used to improve HSC capacity and their use in non-hematopoietic injured tissues. Initial transduced CD34+ cells have been successfully used in the treatment of children with severe combined immune deﬁ ciency caused by adenosine deaminase-deﬁ ciency68. These patients have been given autologous CB CD34+ cells transduced with an adenosine deaminase gene.

Human leukocyte antigen-matched adult donors are not available for all the patients. A patient receiving an unrelated CB graft has an equal survival prognosis than patients getting a BM graft from an unrelated donor69. However, patients receiving a human leukocyte antigen-mismatched CB graft from an unrelated donor show less severe graft versus host disease than patients treated with an human leukocyte antigen- mismatched BM or PB graft70-75. It has been suggested that greater tolerance of human leukocyte antigen-disparity in CB transplantation is due to the immature lymphocytes of newborn76. Also, CB dendritic cells are more immature and have diminished functionality when compared to PB73. Dendritic cells present antigens to T and B cells and are responsible for initiating and shaping the adaptive immune system.

The main inconvenience of CB transplantation is the limited number of cells available in a single CB unit. In CB transplantation, 2x107 cells per kilogram is typically considered adequate for transplantation77. The number of cells per kilogram is 10 times more in BM transplantation. Lower cell dose causes less graft versus host disease, whereas higher cell dose has been associated with faster myeloid and platelet engraft- ment71.

CB grafts have been mostly used in the treatment of pediatric patients. However, reduced intensity transplantation or double CB grafts have been incorporated to overcome the shortage of cells for the therapy of adult patients78-80. Smaller cell doses are suitable for reduced intensity 18 REVIEW OF THE LITERATURE transplantation which may be applied when patients are not strong enough to go through standard pre-transplant treatment to destroy disease cells81. In the reduced intensity transplantation, the donor immune cells are used to ﬁ ght the disease. Moreover, the double CB grafts, when compared to the single CB grafts, have shown better engraftment and improved graft versus leukemia response in patients82,83.

Despite the lower risk of graft versus host disease with a CB graft, a similar graft versus leukemia response has been seen after CB and BM transplantations. Immune reconstruction, recovery of early T cells and B cells after CB transplantation, is similar to that of BM85,86. However, some clinics have observed an increase in treatment-related mortality with CB grafts. This has been suggested to be due to slower engraftment of the graft, but could also be due to greater human leukocyte antigen- disparity or lower cell dose of CB grafts used for the treatment. Slower engraftment increases the risk of bacterial infections, which seem to be slightly more common after CB transplantation87. However, infection- related mortality is not increased after CB transplantation87. REVIEW OF THE LITERATURE 19

Table 1. List of diseases and conditions treated with CB transplantation. Adapted from Cord Blood Center84. 20 REVIEW OF THE LITERATURE REVIEW OF THE LITERATURE 21

Ex vivo expansion

The ex vivo expansion systems have succeeded in augmenting HSC proliferation and increasing the number of progenitor cells with the ability to generate T lymphocytes and Natural Killer cells88. The challenge remaining is the expansion of long-term HSCs. CD34+CD38- cells have been expanded in culture, but the marrow repopulating capacity has not been increased88. Moreover, expanded and cycling cells have demonstrated increased susceptibility to apoptosis when compared to quiescent HSCs89. This could cause the early death of transplanted HSCs and impaired initial engraftment.

In the ex vivo expansion, the CB-derived cells are cultured in a media supplemented with various growth factors and cytokines such as interleukin-1β, -3, -6, -11, stem cell factor, fms-tyrosine kinase 3 ligand, trombopoietin, granulocyte-macrophage colony stimulating factor, erythropoietin and chemokine ligand 3. Alternatively, HSC growth has been supported by co-culture with stromal cells or mesenchymal stem cells. A variation of this method uses conditioned media containing secreted factors from stroma or mesenchymal stem cells.

The engraftment potential of HSCs is limited mostly to cells in the 90 91 G0 state . HSCs induced to proliferate do not reenter the G0 state . These are the challenges that need to be overcome in order to expand HSCs. Fortunately, the quiescent state of the cell is not as critical to the engraftment potential of CB HSCs as it is to PB HSCs91.

A better understanding of molecular mechanisms controlling self- renewal would enhance the ability to expand CB cells. The protein level of transcription factor Homeobox B4 (HOXB4) has been shown to positively correlate with better in vitro proliferation92. HOXB4 transduced HSCs have been shown to maintain mHSC capacity93. Unfortunately, the same outcome has not been achieved in humans93. The potential risk with transduction-based method and cell culture is that they change HSC gene expression and the expression of cell surface molecules94. This may have an effect on HSC properties such as homing and therefore on the engraftment of the transplanted cells. 22 REVIEW OF THE LITERATURE

Transcriptome proﬁ ling

In recent years, transcriptome profi ling has been widely used to understand the genetic regulation of a particular cell type. Transcriptome encompass the global gene expression in a cell at a specifi c time. It can proffer valuable information on the signifi cant biological processes behind the maintenance of the functionality of the cell. Transcriptome profi ling is used to reveal the key genes involved in HSC self-renewal versus differentiation decisions.

The technology for the study of transcriptome is not dependent on any prior knowledge of the genes expressed in the cells of the study. However, challenges remain in regards to the administration and interpretation of the enormous data provided by transcriptome proﬁ ling. Transcriptomics provides fundaments for more deﬁ nitively designed studies and guidance to select the genes for functional studies. It should be kept in mind that the small change in the expression level of a gene may relate to a great difference in the total amount of the corresponding protein present in the cell. The presence of a molecule in a cell depends on the translation features of the mRNA sequence and the amount of antisense sequences degrading the corresponding sense sequences95,96. Results from the transcriptome and proteome analysis of a similar cell type can not usually be fully matched partly due to the heterogeneity or instability of the proteins97. Technical limitations may also explain some of the divergences found in trancriptome and proteome level98.

Methods for transcriptome proﬁ ling

Gene expression proﬁ ling of 45 Arabidopsis thaliana genes was reported in 1995 as the ﬁ rst microarray-based experiment99. Today there are many different kinds of microarray chips used, some of which are the laboratories own cDNA arrays and others which are commercially produced microarrays. The basic idea behind these is the same (Figure 2). Preanalysis and normalization of the data prior to the analysis of the results is one noticeable part of the data interpretation.

Microarray analysis is still the most widely employed technology for transcriptome proﬁ ling, but serial analysis of gene expression (SAGE) (Figure 3) and subtractive hybridization (Figure 4) have also been commonly used. Microarray is designed for the study of differential gene REVIEW OF THE LITERATURE 23

Figure 2. Schematic representation of microarray technology. Total RNA from the cell is isolated and converted into cDNA. cDNA is labeled and hybridized to an array containing oligonucleotide probes. After hybridization, the level of ﬂ uorescence of the attached sequences on the chip is measured with a laser scanner, and microarray analysis methods are used to calculate the abundance of each represented RNA. Adapted from Affymetrix GeneChip Expression Analysis Technical Manual100.

expression. The subtractive hybridization reveals solely the differentially expressed genes. On the other hand, SAGE technology is efﬁ cient in detection of the copy number of the transcripts expressed. 24 REVIEW OF THE LITERATURE

Figure 3. The overview of SAGE technology. RNA from the cells is converted into cDNA molecules in the presence of oligo(dT) magnetic beads. Immobilized cDNA is cleaved with an anchoring enzyme (usually NlaIII) and tagged (usually with BsmFI) to form SAGE tags. Tags are combined to di-tags, ampliﬁ ed and linked to concatamers of several SAGE tags. Concatamers are cloned in bacteria and then sequenced to reveal the identity and abundance of unique sequences. Modiﬁ ed after (Broxmeyer HE 2004) with data from Serial Analysis of Gene Expression101,102.

Several other methodologies such as differential display analysis, representational difference analysis, massive parallel signature sequencing and analysis of expressed sequence tags can also be utilized. The advantages and disadvantages of the three most commonly used REVIEW OF THE LITERATURE 25

Figure 4. The general concept of subtractive hybridization. RNA or cDNA from the cells of interest is hybridized to single-stranded cDNA-containing paramagnetic beads from control cells. Hybrids and excess driver cDNA are removed using a magnet. The remaining fragments present the RNA expressed speciﬁ cally in the cells of interest. Modiﬁ ed from (Lönneborg et al. 1995)103.

technologies; microarray analysis, SAGE and subtractive hybridization are summarized in Table 2. The high costs of using several high throughput methods often limit parallel use of these technologies by a single laboratory. Instead, quantitative real-time reverse transcriptase- 26 REVIEW OF THE LITERATURE polymerase chain reaction (qRT-PCR) is often used to verify global gene expression proﬁ ling results. Usually this means that expressions of a selected set of genes are reanalyzed.

Table 2. Comparison of three different methods for gene expression analysis.

It has been shown that results from qRT-PCR, microarray and SAGE are usually correlated, especially when the gene is expressed on the high or medium level104. As a sequence-based technology, the sensitivity of qRT- PCR and SAGE analysis is higher than the sensitivity of hybridization- based microarray analysis. Both qRT-PCR and SAGE are able to detect gene expression at a very low level. The level of differential gene expression is often detected lower in microarray analysis than in qRT- PCR105.

Gene expression proﬁ ling of murine hematopoietic stem cells

Previously, particular with the earliest studies, the focus was on discovering transcriptional regulation of mHSCs. Despite the differences in the genes related to hematopoiesis in mice and humans, murine gene expression proﬁ ling has proven to be valuable in understanding HSC biology. Genetic programming in stem cells has been underlined in the comparison of the transcriptional expression in HSCs, progenitors and mature hematopoietic cells. REVIEW OF THE LITERATURE 27

The ﬁ rst large scale analysis of HSCs identiﬁ ed over 2000 genes differentially expressed in fetal mHSCs (AA4.1+Sca+c-Kit+Lin-/low) and mature leukocytes (AA4.1-)106. The primitive cells showed enriched expression of genes associated with cell signalling, RNA synthesis, metabolism, protein synthesis, cell division, cell structure, and cell defence. The majority (53%) of the expressed transcripts were not previously known. Gene expression associated with cell signalling has been particularly prevalent in long-term HSC populations, whereas an increase in cell cycle initiation, DNA repair and protein synthesis has been shown to be characteristic of short-term HSCs. Genes typical to lineage committed progenitors are expressed in low levels in short-term mHSCs107.

In the study of gene expression in HSCs and various lineage committed progenitors, 43% of the differentially expressed genes were shown to be speciﬁ c to the HSC population108. Gene expression proﬁ les of fetal and adult mHSCs have been shown to have 70% overlap109. Fetal HSCs express a few embryonic stem cell-related genes such as Dnmt3b and microH2A1.2 that have not been found to be prevalent in adult BM HSCs. It has also been shown that fetal HSCs are in an active state of cell cycle more often than adult HSCs110,111.

HSCs and neural stem cells have shown common gene expression not present in mature hematopoietic and neuronal cells108. However, highly HSC enriched cells have shown more similar gene expression patterns with mature hematopoietic cells than with stem cells from neural or embryonic origin112. Different stem cell types possess more similarities in the representative biological processes than on the level of individual genes113. Genes associated with chromatin remodeling have been upregulated in various stem cell populations. It has been suggested that this process regulate the self-renewal and differentiation potential of stem cells114. Of the genes common to all stem cell populations, only 4 transcripts uridine phosphorylase, suppressor of Lec15 and two expressed sequence tags have been identiﬁ ed. This suggests that only a few genes have restricted expression in stem cells112. 28 REVIEW OF THE LITERATURE

Gene expression proﬁ les of human hematopoietic stem cells

The differential gene expression between BM, PB and CB-derived CD34+ cells may be related to the possible differing roles of CD34+ cells in different environments10,109,115. The HSC mobilization actuate the gene expression resulting in partly divergent gene expression in BM or granulocyte colony-stimulating factor mobilized CD34+ cells from humans116. Cell cycle promoting genes are more common in BM CD34+ cells than mobilized PB CD34+ cells. The number of genes encoding transcription factors or gene products associated with apoptosis is abundant in mobilized cells when compared to BM CD34+ cells116. The gene expression in CB and BM CD34+ cells is more similar than gene expression in mobilized PB CD34+ cells10,115. BM CD34+ cells have shown enriched expression of genes associated with support of myeloid lineage development and inhibition of apoptosis. Cell adhesion and mobilization- associated genes such as CXCR4, which is important in the homing and engraftment of CD34+ cells, are underexpressed in CB CD34+ cells when compared to BM CD34+ cells.

Typically, a high amount of expressed genes is common to HSCs. This may be due to their relatively open or transcription permissive chromatin structure117,118. CB CD34+ cells have the highest number of overexpressed genes when CB, BM and PB CD34+ cells are compared10,115. The number of overexpressed genes is higher in BM CD34+ cells than in PB CD34+ cells. The greater number of active genes suggests broader transcriptional options for HSCs. Many genes expressed by HSCs have been located in the pericentric heterochromatin, unlike the genes related to activation in mature lymphocytes located in centromeric heterochromatin118. This suggests at least a partly different pathway for the activation of genes in HSC and mature lymphocytes. Its possible role is to provide fast recruiting cells to generate the type of blood cells needed.

Low levels of embryonic stem cell marker gene Oct4 have been detected in CB CD133+ cells119. Other pluripotent markers such as Sox1, Sox2, FGF4 of Rex1 are also expressed in CB CD133+ cells119. These ﬁ ndings suggest that CD133+ cells, especially from CB, may have the potential to differentiate into nonhematopoietic tissue types. Moreover, the slowly dividing CB CD34+CD38- cells have been shown to express several genes differently when compared to fast dividing CD34+CD38- cells120. These slowly dividing, more primitive, hematopoietic cells were shown to express CD133 abundantly. REVIEW OF THE LITERATURE 29

Glycosylation

Glycosylation is speciﬁ c to each cell type. It involves common posttrans- lational modiﬁ cations that take place on the nascent protein. There are two kinds of glycosylation; N-glycosylation and O-glycosylation. Nearly all proteins traveling through endoplasmic reticulum become N-glycosylated. Glycosylated proteins mainly function in the extracellular compartments and in the secretory pathway. Due to their location on the cell surface, they serve a role as providers of information transfer in cell recognition and in interactions with other cells or in the extracellular matrix.

Revealing glycosylation features characteristic of HSCs broadens the knowledge of HSC biology. Two molecules may have identical amino acid composition, but differ due to differential glycosylation. Diverse glyco- forms of Thy-1, a stem cell marker, have been found in the thymus and brain121. Moreover, individual glycosylation leads to the formation of blood groups such as ABO(H), Secretor, P and Lewis on erythrocytes122. Changes in glycosylation are involved in cell differentiation, embryogenesis and tissue morphogenesis.

N-glycans and N-glycan synthesis

N-glycans support protein folding, stability and oligomerization, and participate in trafﬁ cking, recognition of receptor binding sites and protein localization. N-glycans are vital for organisms from yeast to human, and most of the known glycosylation disorders are caused by defective N- glycosylation123,124. The defects have been most dramatic in cells going through rapid alterations, such as leukocytes. For example, the inhibition of N-glycan backbone trimming mannosidases cause a blockage in B cell differentiation125 (Figure 5). In addition to N-glycan, other glycan types including O-glycans, glycosphingolipids, glycosaminoglycans and glyco- phospholipid anchors are common126. Due to the technical challenges of studying the structure of these other gycan types, they are less well known.

In the biosynthetic pathway of N-glycosylation, the preformed glycan is transferred to the amino acid asparagine (N) glycosylation site on the nascent protein in endoplasmic reticulum (Figure 6). N-glycans can be grouped into three classes; high mannose, hybrid and complex type structures, depending on the amount and type of glycan modiﬁ cations they have gone through127 (Figure 6). 30 REVIEW OF THE LITERATURE

Figure 5. Schematic representation of N-glycans. N-glycans consist of distinct regions of N-glycan core, backbone and terminal epitopes that are synthesized by different glycosyltransferase and glycosidase families. Monosaccharide symbols: = N-acetyl-D- glucosamine; = D-mannose; = D-galactose; = L-fucose; = N-acetylneuraminic acid.

High mannose N-glycans represent the unprocessed glycan structures with a maximum of 9 D-mannoses attached to the N-glycan core. The hybrid type or intermediately processed glycans possess characteristics of both high mannose and complex type N-glycans. Complex type glycans are so-called fully processed N-glycans and occur as bi-, tri-, tetra- or pentaantennary structures.

The α-mannosidase 1 enzymes encoded by MAN1 genes and β-N-acetyl glucosaminyltransferase 1 encoded by MGAT1 promotes the conversion of high mannose N-glycans into more processed glycan types (Figure 6). MAN1 gene products remove the terminal α-mannose residues, while β- N-acetylglucosaminyltransferase 1 transfer an N-acetyl-D-glucosamine residue to form the ﬁ rst branch towards processing more developed N-glycans. The synthesis of hybrid and complex type N-glycans is developmentally essential128.

MAN2 genes encode α-mannosidase 2 enzymes, which trim the hybrid type N-glycans prior to the action of MGAT2 coded β-N-acetylglucosaminyltransferase 2. Further, β-N-acetylglucosaminyltransferase 2 forms the complex type N-glycans. Complex type N-glycans have also been found in MAN2-deﬁ cient mice despite the absence of family 2 α-man- nosidases129. This suggests that there are other enzymes, possibly other α-mannosidases, involved in the demannosylation of hybrid type N-glycans. Accumulation of hybrid type N-glycans and the lack of complex N- REVIEW OF THE LITERATURE 31

Figure 6. N-glycan synthesis. The attachment of N-glycan precursor to the glycosylation site asparagine (N) of a nascent protein initiates the synthesis. Further, the three glucose and one mannose residues are enzymatically removed from the N-glycan. The processing of N-glycan into the hybrid type starts by the removal of the mannose monosaccharides by family 1 mannosidases and adding one N-acetyl- D-glucosamine by β-N-acetylglucosaminyltransferase 1. The rest of the removable mannoses are docked by mannosidase 2 family members. Different glycosyltransferases, modifying the glycan backbone and terminal epitopes, form the vast variety of N-glycans by differentially adding N-acetyl-D-glucosamine, D-galactose, L-fucose and N-acetylneuraminic acid residues. Monosaccharide symbols: = glucose; = N-acetyl-D-glucosamine; = D-mannose; = D-galactose; = L-fucose; = N-acetylneuraminic acid. Glycosidic linkages are indicated by lines connecting the monosaccharides.

glycans in organisms may affect cell signaling. A mutation in the human ortholog of the MGAT2 gene has been associated with a rare human disease known as congenital disorders of glycosylation type IIa130. This disease affects disfunction of multiple physiological systems.

The tri-mannosyl core on N-glycans of both hybrid and complex types can be modiﬁ ed by MGAT3 encoded β-N-acetylglucosaminyltransferase 3, which adds the so called bisecting N-acetylglucosamine. Two other branching moieties, the tri- and tetra-antennary structures, are gener- 32 REVIEW OF THE LITERATURE ated by MGAT4a, MGAT4b and MGAT5 encoded β-N-acetylglucosami- nyltransferases. Insufﬁ cient expression of MGAT4a has been shown to cause failure in pancreatic beta cells leading to type 2 diabetes131. In some cases, β-N-acetylglucosaminyltransferase 5 forms another branch in the α1,3-linked core mannose. MGAT5 does not seem to be essential for survival132. On the other hand, enhanced MGAT5 expression has been demonstrated to support tumor growth and metastasis133.

The variability of N-glycans is increased with terminal epitopes modifi ed by β1,4-, β1,3-galactosyltransferases, α2,3-, α2,6-sialyltransferases and α1,2-, α3/4-, α1,6-fucosyltransferases (Figure 5). Mutations in the terminal epitopes are not usually crucial for survival, but they often affect cells in the hematopoietic system. Mice with mutations in the B4GALT1 gene have reduced neutrophil traffi cking in the infl amed tissues134. In humans, the mutation in B4GALT1 cause congenital disorder of glycosylation type IId disease with severe neuronal disease and blood clotting defects135,136. The role of the 4 other β1,4-galactosyltransferases in N-glycans synthesis is poorly known. However, it has been suggested that these β1,4-galactosyltransferases have overlapping functions.

Immunodefi ciency and attenuated B cell formation has been observed in ST6Gal1-knockout mice137. ST3Gal4 is involved in the biosynthesis of sialyl-Lewisx and sialyl-Lewisa antigens, both known as selectin ligands138. ST3Gal4-defi ciency causes infl ammation response defi cit, formation of defi cient platelets and defects in blood coagulation139. The role of ST- 3Gal6, the fourth α2,3-sialyltransferase in the N-glycan synthesis is still obscure140.

Fucosyltransferases do not seem to be essential in mice141-144. However, FUT genes have an important role in hematopoiesis and the immune response. FUT4-null mice have partial neutrophil adhesion deﬁ cit and selectin ligand defect similar to the FUT7-knockout144,145. The selectin ligand activity is completely lost in FUT4 and FUT7 knockout mice144, suggesting that the gene products are pivotal glycosyltransferases responsible for the synthesis of active selectin ligands. In humans, the mutation in the FUT7 gene results in modest reductions of leukocyte selectin ligand activity146.

Considerable glycosylation differences are prevalent between organisms. All other mammals except the humans, apes and Old World monkeys, have functional α1,3-galactosyltransferase147. In humans, the Gal- α1,3-Gal glycoconjucates synthesized by α1,3-Galactosyltransferase REVIEW OF THE LITERATURE 33 are antigenic and induce serum anti-Gal antibodies148. Moreover, the defect in N-glycolylneuraminic acid synthesis is speciﬁ c to humans149. The glycosylation differences between organisms need to be taken into account when deﬁ ning the components of a cell culture media for stem cells to be transplanted into humans150,151.

Glycosylation in hematopoiesis

One of the most dramatic alterations of glycosylation occurs at the time of postnatal development, where fetal erythrocyte i type polylactosamine changes into branched I type polylactosamine152. Thus, glycans can be used as biomarkers for cellular maturity. Moreover, typical glycosylation features of hematopoietic lineages separate the different cell types. Lymphoid cells have a large amount of N-glycan α2,6-sialylation, whereas sialyl-LewisX antigens are enriched in myeloid cells153,154. In these cells, a localized increase in α2,6-sialylation of CD11b/CD18 molecules has been seen during myeloid cell maturation and liberation from BM155.

Glycosylation of HSCs is still poorly known. α2,6-sialylation is also dominant in CD34+ cell N-glycans156. α2,6-sialylation has been increased in granulocyte growth stimulating factor mobilized CD34+ cells when compared to naturally circulating CD34+ cells157. It has been suggested that sialylation events modify the adhesion between HSCs and osteoblasts158.

Effect of glycosylation on homing

Glycosylation plays an important role in the adhesion and homing of HSCs and leukocytes occurring in a selectin-dependent manner159. Leu- kocyte rolling on endothelial cells is supported by the interaction between the lymph node homing receptor L-selectin and its ligands. Leukocytes express the P-selectin glycoprotein ligand 1 which is able to bind to L-, P- and E-selectins160,161. The selectin binding of P-selectin glycoprotein ligand 1 is dependent on O-glycosylation162.

CB CD34+ cells have impaired α1,3-fucosylation not seen in BM HSCs163. CB CD34+ cells express a form of P-selectin glycoprotein ligand 1 that does not bind to P-selectin163. Enforced α1,3-fucosylation of CB CD34+ cells improved the binding to P-selectin possibly in a P-selectin glycopro- 34 REVIEW OF THE LITERATURE tein ligand 1 -dependent manner164. α1,3-fucosylation is important for the synthesis of the selectin ligand glycan structure sialyl-Lewisx144.

A selectin ligand, CD44, has a speciﬁ c role in CD34+ cells binding to both L and E-selectin165,166. A CD34+ cell speciﬁ c glycoform of CD44 contains N-glycosylation, which is crucial to its function165,167. CD44 has been suggested to play a major role in CD34+ cell adhesion and movement along endothelium and leukocytes168.

In human leukemia CD34+ KG1a cells, the CD34+ cell-speciﬁ c CD44 has been shown to have a 5-fold higher afﬁ nity to selectin binding than P-selectin glycoprotein ligand 1165,166,168. Both P-selectin glycoprotein ligand 1 and CD44 may participate in the homing of HSCs and the dysfunction- al P-selectin glycoprotein ligand 1 in CB-derived cells may conduce to reduced homing capacity of CB HSCs164,165,168. The enforced fucosylation of the glycans of CB CD34+ cells have shown contrasting results in their ability to improve their homing163,164.

The modifi cation of HSCs by glycan remodelling may provide new tools for HSC therapeutics. Engineering of cells through glycan modifi cations could be used to enhance HSC homing capacity. This would provide a greater use for CB HSCs, as a lower number of cells might be suffi cient for successful engraftment. AIMS OF THE STUDY 35

AIMS OF THE STUDY

The aims of this study were

1. To characterize the global gene expression and glycome of human hematopoietic CD133+ and CD34+ cell populations from CB (studies I-III) 2. To reveal the differential gene expression in CD133+ and CD34+ cells and to relate the fi ndings to the characteristic biological processes of these cells (study II) 3. To identify the genes responsible for cell type-specifi c N- glycosylation in CD133+ and CD34+ cells (study III) 4. To fi nd novel molecular markers specifi c to HSCs (study II) 36 MATERIALS AND METHODS

MATERIALS AND METHODS

Ethics

This study was approved by the ethical board of the Finnish Red Cross Blood Service and Helsinki University Hospital. The CB donors were informed and written consent was obtained. Only CB units unsuitable for therapeutic use were studied. A small total volume or the low number of total cells were the most common reasons for discharging CB units.

Cells

CB units were collected at the Helsinki Maternity Hospital and the Department of Obstetrics and Gynaecology at Helsinki University Hospital. The collections were performed during 2004-2007. In all the studies, fresh CB units were used.

In study I, CD133+ cells were selected from the 4 CB unit and CD133- cells were collected for control purposes. CD133+ data were compared to CD133- data from the same CB unit in the microarray analysis. The differential gene expression between CD133+ and CD133- cells was to be the same in all replicates. The fold-change cutoff value was set to 3. To verify the information obtained from the microarray data, selected genes were subjected to qRT-PCR analysis using pools of three samples in each. Analysis was performed on two biological replicates. In addition, CD133+ and CD133- cells were used to evaluate the purity of the cell fractions. The CFU assay was performed for CB CD133+, CD133- and mononuclear cells in duplicate.

In study II, CD34+ cells were selected from the 3 CB unit and CD34- cells were collected for control purposes. Similarly to study I, CD34+ data were compared to CD34- data from the same CB unit and the results verifi ed using qRT-PCR analysis. Moreover, the assessment of the purity of the cell fractions and the CFU assay were performed for CD34+ and CD34- cells as in study I. Further, similarly constructed CD133+ and CD34+ gene expression profi les were compared to reveal the specifi c expression patterns and differences of CD133+ and CD34+ cells. In addition, gene expression data of CD133+ and CD34+ cells were directly compared to the gene expression data of PB mononuclear cells. Thus, the PB mononuclear cell sample was provided as a common MATERIALS AND METHODS 37 benchmark to control the differential gene expression in CD133+ and CD34+ cells.

In addition, the gene expression proﬁ le results suggested several putative membrane proteins that could serve as an HSC marker in the isolation of HSCs. For the analysis of the presence of novel HSC marker molecules, selectively monoclonal antibody-labeled CB mononuclear cells were the subject of the ﬂ ow cytometric analysis.

In study III, CD133+ and CD133- cell N-glycans were isolated for mass spectrometric analysis. During the preparation process, ultrapure bovine serum albumin was used to avoid oligosaccharide contamination. To demonstrate the quantitative differences between N-glycosylation of CD133+ and CD133- cells, CD133+ and CD133- cell N-glycan profi les were compared to each other. In addition, CB CD133+ and CD8+ T cell N-glycan profi les were compared to each other. The CD8+ T cell N-glycan profi le served as an independent, immunoganetically selected mature hematopoietic cell control.

The mononuclear cells were analyzed in nuclear magnetic resonance (NMR) analysis to obtain detailed data about most abundant N-glycan structures. In addition, N-glycan fractions of CD133+ and CD133- cells were digested with speciﬁ c exoglycosidase enzymes to characterize the terminal epitopes on CD133+ and CD133- cell N-glycans. Further, the glycan proﬁ le and glycosylation-related gene expression were compared to unravel the transcriptional regulation of the glycosylation features characteristic of CD133+ cells. Also, the glycosylation-related gene expression of CD34+ cells was observed. The glycan determinants on the cell surface of mononuclear and CD34+ cells were demonstrated by a lectin binding assay. For this, Glycophorin A-depleted CB mononuclear cells from 3 CB units were used.

Methods

Methods used in the following studies (I-III) are described in detail in the original publications and are listed in Table 3. 38 MATERIALS AND METHODS

Table 3. The methods used in studies I-III36,100,169-180. MATERIALS AND METHODS 39

Gene expression reanalysis

The improved knowledge on gene and transcript sequences, as well as genome assembly, reveal a great number of previously deﬁ ned probe sets in Affymetrix microarray GeneChips that are not applicable in the detection of the gene. The unreliable representative gene identiﬁ er has been assigned for approximately 18% of the probe sets in the Affymetrix Human Genome U133 Plus 2.0 chip181. Moreover, about 12% of the probe sets contain probes that may hybridize to transcripts of more than one gene or to a non-coding sequence, while some probes no longer match any sequence in the current databases. Thus, reanalysis of all the gene expression data was performed to access more accurate annotation of the differentially expressed genes.

Raw data was normalized using the GC Robust Multi-array Average background adjustment for intensities, quantile normalization, and median-polish summarization182. Biostatistic tools were used in R software. The probes of each Affymetrix probe set, directed toward identifi cation of a transcript, were redesigned to sets of probes using GeneChip library fi les (http://brainarray.mbni.med.umich.edu/ CustomCDF). The probe sets of these library fi les were chosen to match a more recently updated Unigene cluster representing certain genes (http://arrayanalysis.mbni.med.umich.edu/ps/)181. To obtain CD133+ and CD34+ cell gene expression profi les, CD133+/CD34+ cells were compared to CD133-/CD34- cells. From the CD133+ and CD34+ profi les, 500 genes with the most differential expression pattern were selected according to their median fold change. That is, the ratio of the median gene expression level in CD133+/CD133- cells or CD34+/CD34- cells was calculated and transformed to an absolute value of log2. Differentially expressed genes could be overexpressed or underexpressed in CD133+/ CD34+ cells when compared to CD133-/CD34- cells. The statistical signifi cance of the differential expression was performed using a T-test. The differentially expressed genes with a q-value>0.05 refl ected the probability of a false positive result and were discharged. To directly compare differences in CD133+ and CD34+ cells, a pairwise analysis utilizing GeneSifter software was employed183. The signifi cant change in the expression between CD133+ and CD34+ cells was validated using Student’s T-test and a p-value that was ≥0.05. 40 MATERIALS AND METHODS

Annotation

Due to the ongoing changes and revisions in the annotations of Affyme- trix probe sets, the differentially expressed genes in studies I and II were reannotated using the GeneAnnot Microarray Gene Annotation software (Weizmann Institute of Science, http://bioinfo2.weizmann.ac.il/geneannot/)184,185. The Database for Annotation, Visualization and Integrated Discovery tools were employed to obtain molecular functions, biological processes, and cellular components for the transcripts common in the CD133+ and CD34+ expression proﬁ les in the original and reanalyzed data186. The molecular interactions for the protein products were as- sessed through GeneSifter software, which uses Kyoto Encyclopedia of Genes and Genomes Pathway database16. RESULTS AND DISCUSSION 41

RESULTS AND DISCUSSION

CD133+ and CD34+ cells have similar gene expression (studies I and II)

The comparison of CD133+ and CD133- data sets resulted in 690 transcripts that were differentially expressed at least three-fold in all the samples. Of these, 393 transcripts were upregulated and 297 were downregulated in CD133+ cells. Similarly, CD34+ data sets were compared to CD34- data sets and 620 differentially expressed transcripts with at least a three-fold difference were found. In CD34+ cells, 169 transcripts were overexpressed and 451 transcripts underexpressed. The integrated sets of differential gene expression between CD133+ and CD133- cells, and CD34+ and CD34- cells, showed that 85% of the transcripts overexpressed in both CD133+ and CD34+ cells and 36% of the transcripts underexpressed in both CD133+ and CD34+ cells were the same. Transcripts found in both CD133+ and CD34+ cells showed the highest overexpression when compared to CD133-/CD34- cells. Both cell types expressed many genes previously identifi ed to be present in HSCs (such as KIT, LAPTM4B, MEIS1, RBPMS, HLF, BAALC and C17)109,115,120,187-190. Also, many genes supporting the undifferentiated state of HSC (such as GATA2 and N-MYC and HOXA9)106,191,192. These genes were expressed at a very similar expression level across the CD133+ and CD34+ cell replicates indicating an important role for these genes in the regulation of CD133+ and CD34+ cells. Both cell types expressed a high number of transcripts. In addition, these transcripts were arranged similarly in SOM analysis suggesting similar regulation for the common genes in CD133+ and CD34+ cells. In the CD133+ profi le, the number of overexpressed transcripts was higher than in the CD34+ profi le. This could explain the additional cluster of overexpressed transcripts illustrated in U-matrix of CD133+ data.

CD133+ and CD34+ cells expressed 85 transcripts that were not present in CD133- or CD34- cells. These transcripts were either downregulated or absent in PB mononuclear cells. The 85 common transcripts represented 80 genes. The encoded molecules are involved in cell proliferation, metabolism and localization. Of these, the expression of 44 genes in HSCs has been conﬁ rmed in other studies109,115,120,188,193. The transmembrane protein encoding genes FLT3 (CD135), ALCAM (CD166) and DSG2 were expressed in CD133+ and CD34+ cells only. Moreover, expression of SV2A was found in CD133+ cells, but not in CD34+ cells. 42 RESULTS AND DISCUSSION

Novel molecular markers for HSCs could not be found (study II)

To determine whether transmembrane protein encoding genes expressed solely in CD133+ and CD34+ cells could provide novel molecular markers for HSCs, the presence of the protein products on the cell surface were observed. Total leukocyte population was labeled with speciﬁ c antibodies binding to CD135, CD166, DSG2 or SV2A. In addition, anti-CD34 or anti-CD133 antibody labeling was used. Coexpression of CD135 and CD34/CD133 molecules was demonstrated (Figure 7). Moreover, CD166 was expressed on some of the CD133+/CD34+ cells (Figure 6). CD135 was also present on 11% of the leukocytes and CD166 was carried by 20% of the leukocytes. Thus, neither CD135 nor CD166 alone can be used for selective puriﬁ cation of HSCs.

SV2A or DSG2 molecules could not be found on leukocytes, including CD133+/CD34+ cells. It is possible that the antibodies against SV2A and DSG2 molecules do not posses adequate speciﬁ city. Therefore, no single molecular marker to identify HSCs could be found using microarray analysis. In addition, known embryonic stem cell markers SSEA3, SSEA4, TRA-1-60 and TRA-1-81 were searched for on the leukocytes (data not shown). No hematopoietic cell populations carrying these markers could be demonstrated.

Figure 7. Presence of CD135 and CD166 molecules on CD34+ and mononuclear cells. RESULTS AND DISCUSSION 43

Differential gene expression between CD133+ and CD34+ cells suggests that CD133+ cells are more primitive (studies I and II)

Despite the highly similar gene expression in CD133+ and CD34+ cells, unique gene expression patterns related to divergent biological processes were distinguished in CD133+ and CD34+ cells. The number of overexpressed transcripts in CD133+ cells, when compared to CD133- cells, was higher than in CD34+ cells when they were compared to CD34- cells.

In the study I, a set of 257 transcripts were identiﬁ ed expressed in CD133+ cells, but not in CD133- cells. Of these, 125 genes were also expressed in CD34+ cells. However, the 172 transcripts overexpressed solely in CD133+ cells (when compared to CD133- cells) showed signiﬁ cant association with regulation of chromatin structure, cell cycle and DNA metabolism. CD133+ cells expressed SMARCA1, SMARCA2 and SMARCA4. SMARCA genes are related to chromatin remodeling pathway194,195. Only SMARCA1 expression was detected in CD34+ cells. The high number of expressed transcripts is typical for HSCs that have been suggested to have permissive or more open chromatin structure.

Embryonic stem cell-related transcripts including DNMT3A, DNMT3B and DPPA4 were shown only in CD133+ cells. The expression of transcripts related to pluripotency suggests a more primitive nature for CD133+ cells. The embryonic-type gene expression may also be due to the CB origin of the cells. The expression of MPDZ, encoding a protein associated with tight junctions and maintenance of cell polarity, suggests that some of the CD133+ cells may be going through asymmetric cell division.

The products of the expressed transcripts in CD34+ cells were related to regulation of development, response to stimulus and DNA metabolism. These biological processes were also typical of CD133+ cells. On the other hand, CD34+ cells expressed transcripts encoding markers for lineage committed cells.

Several studies have shown transcriptional as well as functional differences between CD34+ cells from CB, BM and PB115,196-198. These studies have suggested a more primitive nature for CB CD34+ cells. Moreover, differential gene expression has been found between CB CD133+ and PB CD133+ cells193. This study demonstrated divergent gene expression between CB CD133+ and CD34+ cells. The gene expression proﬁ les of 44 RESULTS AND DISCUSSION

CD133+ and CD34+ cells were identically constructed. The samples were prepared in the same laboratory, hybridized at the same time and analyzed using equal parameters. Therefore, many nonbiological error sources for differential gene expression between the two cell populations were avoided. The CD133+ and CD34+ gene expression profi les of the present study were also compared to CD133+ cell gene expression profi le in another study193. CD133+ gene expression profi les were more similar to each other than with CD34+ cell gene expression profi le of the present study. This confi rms that the different transcriptional expression in CD133+ and CD34+ cells is due to differences in these two cell populations.

The differentially expressed transcripts between CD133+ and CD34+ cells were related to divergent biological processes, suggesting different role for these cells in hematopoiesis. The divergences between CD133+ and CD34+ cell populations may be more obvious in those cells from BM or PB. It may also be that the divergences between CD133+ and CD34+ cells reﬂ ect speciﬁ c primitiveness of CB CD133+ cells.

CD133+ and CD34+ cells demonstrate similar total colony forming potential (studies I and II)

CFU assay was used to identify primitive hematopoietic cells from CD133+, CD34+ CD133-, CD34- and mononuclear cells. A similar number of total CFUs were formed by CD133+ and CD34+ cells. CD133+ cells formed almost only multipotent granulocyte-erythroid-macrophage- megakaryocyte (38%) and granulocyte-macrophage colonies (58%). Burst-forming colonies represented 4.2% of the progeny in CD133+ cells, whereas no erythroid colonies were observed. A vast majority of CD34+ cell progeny was also multipotential, 33% granulocyte- erythroid-macrophage-megakaryocyte colonies and 58% granulocyte- macrophage colonies. A little more of both the burst-forming (7.2%) and unipotent erythroid colonies (1.4%) were formed by CD34+ cells (Figure 8). Combining the CFU assay and gene expression proﬁ ling results it seems likely that CD133+ cells are more primitive in their nature when compared to CD34+ cells. RESULTS AND DISCUSSION 45

Figure 8. Clonogenic progenitor capacity of CD34+ and CD133+ cells by CFU assay. Abbreviations: BFU, burst-forming erythroid; CFU, colony-forming unit; GEMM, granulocyte-erythroid-macrophage-megakaryocyte; GM, granulogyce-macrophage.

Reanalysis of the original gene expression data focuses on the similarities between CD133+ and CD34+ cells

Due to the fast development of gene expression analysis and data normalization methods, the original data from microarray analysis (study I and II) were reanalyzed using a more modern method. In the reanalysis, the top 500 genes with differential expression in CD133+/ CD133- or CD34+/CD34- cells were chosen. After discharging putative false positive results, based on the q-value, 492 genes in the CD133+ gene expression proﬁ le and 425 genes in the CD34+ gene expression proﬁ le were considered valid (Figure 9). Of these, CD133+/CD133- and CD34+/CD34- cells had 283 differentially expressed genes in common. In comparison to CD133- and CD34- data, 118 genes were overexpressed and 165 genes were underexpressed in CD133+ and CD34+ cells.

Reanalysis of the microarray data verifi ed the overexpression of 67 genes in CD133+ and CD34+ cells when compared to CD133- and CD34- cells. These include KIT, FLT3, MEIS1, HOX9A, GATA2 and SOCS2 genes with a signifi cant role in HSC regulation. Similarly, 73 genes downregulated in CD133+ and CD34+ cells, when compared to CD133- and CD34- cells, were the same between the original and reanalyzed data. No genes that were defi ned as overexpressed in CD133+ and CD34+ cells by the original data analysis were shown to be underexpressed after reanalysis, or vice versa. However, the reanalysis of the data identifi ed 51 new genes with a higher expression level in CD133+ and CD34+ cells than in 46 RESULTS AND DISCUSSION

Figure 9. Schematic representation of the number of differentially expressed transcripts in CB CD133+ and CD34+ cells (when compared to CD133- and CD34- cells) and their intersections. A) In the original data, differentially expressed transcripts between CD133+/CD133- and CD34+/CD34- samples represented at least a three- fold change in all sample pairs (studies I and II). B) Reanalysis results represented the most differentially expressed genes between CD133+/CD133- and CD34+/CD34- samples based on the mean gene expression level of the replicates. No fold-change cutoff value was used.

CD133- and CD34- cells (data not shown). Moreover, 92 new genes were downregulated in CD133+ and CD34+ cells when compared to CD133- and CD34- cells. The role of these novel genes in the regulation of HSCs requires further investigation.

The association with a biological process was deﬁ ned for the set of differentially expressed genes in the original data and the reanalyzed data. Only the genes similarly expressed in CD133+ and CD34+ cells were considered. Annotation was performed anew to both data sets (Figure 10). In both cases, the biological processes representative of CD133+ and CD34+ cells were clearly different from their negative counterparts. The associated biological processes enriched in CD133+ and CD34+ cells have been shown to be characteristics to HSC populations106,109,199. Many of the related biological processes were the same between the original data and the reanalyzed data, especially in the processes that represent CD133- and CD34- cells.

The causes for genes with higher expression level in CD133+ and CD34+ cells than in CD133- and CD34- cells related to catalytic activity (38 genes), cytokine activity (5 genes) and endopeptidase/protease inhibitor activity (4 genes). Cytokine activity-associated genes SOCS2, TNFSF4, TRIP6, PXDN and IPO11 were the same as identiﬁ ed in the original data (studies I and II). The 4 protease inhibitor genes were SERPING1, SERPINE2, SPINK2 and TFPI. Similarly to other protease inhibitors200,201, RESULTS AND DISCUSSION 47

Figure 10. Biological processes represented by differentially expressed genes in CD133+ and CD34+ cells when compared to CD133- and CD34- cells. A) Original data (study II) and B) reanalyzed data. these genes may have a role in the modulation of cytokine expression. Their overexpression in HSCs has been previously shown112,115,188,193. Forty-nine of the genes overexpressed in CD133+ and CD34+ cells were associated with intracellular compartments, especially to cytoplasm (25 genes) (Table 4).

The genes downregulated in CD133+ and CD34+ cells when compared to CD133- and CD34- cells were associated with cell communication (32 genes), response to other organism (17 genes), apoptosis (16 genes) and cell activation (7 genes). Moreover, the encoded protein products were associated with protein binding (42 genes) and receptor activity (20 genes). The most strikingly emphasized cellular compartment for the encoded gene products was cell membrane (55 genes). CD133- and CD34- cell populations include lymphocytes, mononuclear phagocytes, dendritic cells and granulocytes that act in the immune system. It is crucial for these cells to have active signaling and communication with both other cells and the matrix. The immune response is dependent on detected changes in the environment and the activation of the cell is initiated by extrinsic factors. 48 RESULTS AND DISCUSSION

Table 4. The most common cellular compartments for products encoded by expressed genes in CD133+ and CD34+ cells. RESULTS AND DISCUSSION 49

The main functional categories related to the CD133+ and CD34+ gene expression proﬁ les were the same in the original data and the reanalyzed data using the most recent annotation. There are several reasons to explain the differences between the original data and the reanalyzed data. These are: the difference between the redesigned probe sets and original Affymetrix probe sets, different preanalysis and normalization of the data, and the criteria of the differentially expressed genes. Redesigned probe sets or sequence matched probes have shown a 30-50% discrep- ancy of results in differential gene expression analysis when compared to analysis using the original Affymetrix probe sets181,202. It is important to notice that the differences between the original and reanalyzed data sets of differentially expressed genes in CD133+/CD133- and CD34+/ CD34- cells are not necessarily faulty results. Well-characterized genes expressed in CD133+/CD34+ cells are not all among the list of the most highly expressed genes in the reanalysis due to the selection criteria that was used. Thus, the differential gene expression that was shown in the original data, but not in reanalyzed data, is still in the main part usable. However, redesigned probes have the improved capacity to detect the expressed genes. Thus, the reanalysis of the gene expression data may help in identiﬁ cation of new genes and provide novel data for further studies.

CD133+ and CD34+ cells have divergent roles in hematopoietic regeneration (studies I and II)

The divergent gene expression between CD133+ and CD34+ cells was revealed by direct pairwise analysis. The represented analysis was specially focused on genes associated with hematopoiesis, cell cycle and cell adhesion. CD133+ cells had a higher expression level of CD133, CD34, CD135 (FLT3) and CD117 (KIT) genes. The higher expression of CD34 in the CD133+ cell population may be explained by the higher abundance of CD34 on the cell surface of CD133+ cells. Nearly all CD133+ cells are CD34-positive, whereas the CD34+ cell population includes lineage committed progenitors that carry a lower number of CD34 molecules on their cell surface.

The hematopoietic lineage-speciﬁ c markers were downregulated in CD133+ and CD34+ cells when compared to their negative counterparts. However, there was a difference in the level of underexpression between CD133+ and CD34+ cells (Table 5). CD133+ cells expressed IL3R (CD123), a marker typical for common myeloid progenitors, pronormoblasts and 50 RESULTS AND DISCUSSION megakaryoblasts. The other genes encoding megakaryoblast markers, such as IL-11R or CD126, were not expressed in CD133+ cells. On the other hand, one or more of the CD34+ sample replicates expressed several genes related to hematopoietic diffentiation. The complete list of genes related to hematopoiesis is shown in Table 5.

The lineage differentiation marker expression may be connected to the cells in the CD133+ and CD34+ subpopulations. Hence, these results suggest that the CD34+ cell population contains a higher proportion of pro T cells, other differentiating T cells, promonoblasts, monoblasts and megakaryocytes than the CD133+ cell population (Figure 1). On the other hand, it has been suggested that HSCs express lineage differentiation marker genes at a low level to enable the fast differentiation of HSCs when a differentiated cell type is needed in the blood system. It may also be that CD34+ cell population consists of a higher number of short- term HSCs with better readiness to form all types of blood cells.

CD34+ cells expressed a wider variety of genes encoding cell adhesion molecules, such as selectin E and selectin P. Selectins are important for cell trafﬁ cking. CD34+ cells may thus have greater capacity to be mobilized. However, the engraftment potential of CB HSCs has been delayed when compared to HSCs from other sources203. The presence of very late antigen 4 has been proven crucial for the homing of HSCs204. The expression of gene coding for very late antigen 4 was higher in CD133+ cells.

The gene expression proﬁ le of CD133+ cells revealed the expression of many genes related to the cell cycle (study I). CD133+ cells expressed 20 cell cycle genes on a higher level than CD34+ cells. These products of the cell cycle genes have inhibitor as well as promoter effects. The results suggest that both quiescent cells and actively cycling cells are found in the CD133+ cell population. Long-term HSCs are mostly considered to be noncycling, but that is not as critical a parameter in CB HSCs91. Hence, CB HSC in an active cell cycle state can have self-renewing capacity.

CD133+ and CD34+ cells had a high number of commonly expressed genes typical of HSCs. They also had many overlapping biological processes. Both CD133+ and CD34+ cells have been successfully used in the reconstruction of the blood system in irradiated patients’ marrow. Thus both cell populations contain cells with long-term repopulation and short-term repopulation potential. The direct comparison of CD133+ and CD34+ cells revealed very delicate differences between the cell types. Previous studies have shown that differences in the gene expression RESULTS AND DISCUSSION 51 profi le refl ect true changes between closely related cell populations205,206. Thus, the differences demonstrated in expression profi les of CD133+ and CD34+ cells suggest functional divergences between the cell types.

The gene expression proﬁ les indicate that faster repopulation of the recipient could be obtained after CD34+ cell transplantation. Due to the more primitive nature of CD133+ cells, selected CD133+ cell population may not include enough cells readily available for development of all blood cell types or they may have poorer capacity to travel into the BM,

Table 5. Differential expression of hematopoietic cell marker genes in CD133+ and CD34+ cells. 52 RESULTS AND DISCUSSION or both. CD133+ cells, as a more primitive source of HSCs, could be better suited for HSC expansion and gene therapy. Due to the limited number of transplantations performed using selected CD133+ and CD34+ cells, it is not possible to say whether there is a difference in the repopulation after transplantation. It should be kept in mind that despite the divergent biological capacities, binding of an antibody to the CD133 or CD34 molecules may inﬂ uence cell function.

N-glycosylation-related gene expression is similar in CD133+ and CD34+ cells and correlates with the N- glycome proﬁ le (study III)

CD133+ and CD34+ cells had characteristic N-glycosylation-related gene expression when compared to CD133-/CD34- cells (study III). Moreover, mass spectrometric analysis of CD133+ and CD133- cells, combined with results from NMR and exoglycosidase digestion, revealed differences in the glycosylation of these cells. CD133+ cells contained signifi cantly more fully processed, especially biantennary type N-glycans than CD133- cells (Figure 11). Yet, fully processed multiantennary N-glycans were fewer in CD133+ cells than in CD133- cells. The most frequent glycosylation of CD133+ cells was unprocessed, high-mannose type N-glycans. High mannose type N-glycans were more abundant in CD133+ cells than in CD133- cells. Enrichment of high mannose type N-glycans in CD133+ cells was evident in the comparison of CD133+ cells to CD8+ cells which represent a mature leukocyte population (data not shown). However, more analyses are needed to confi rm that the differences in the relative abundance of neutral N-glycans are due to tissue-specifi c glycosylation changes.

Characteristic mannosylation pattern of CD133+ and CD34+ cells (study III)

The differential expression of one mannosidase and three glycosyltransferase encoding genes were related to the changes observed in the CD133+ and CD133- cell glycome. The differential gene expression was observed in both CD133+ and CD34+ cells as compared to their negative counterparts. CD133+ and CD34+ cells, but not CD133- and CD34- cells, lacked the expression of MAN1C1 encoding one of the key α1,2-mannosidases participating in N-glycan maturation. No difference was seen in the expression of other α1,2-mannosidase encoding genes RESULTS AND DISCUSSION 53

MAN1A1, MAN1A2 and MAN1B1, yet their expression was detected in CD133+ and CD34+ cells as well as CD133- and CD34- cells.

MAN1 enzymes remove α1,2-mannosidases from different positions in N-glycans, and their functions are highly overlapping (Figure 6). MAN1A, MAN1B and MAN1C are located in different genes on chromosomes 6 and 1, and they have independent regulatory pathways. The unique specifi city of MAN1C1 encoded α1,2mannosidase 1C is that it prefers the removal of terminal α1,2-linked mannose on the α1,3 branch in 207 Man9GlcNAc2 and Man8GlcNAc2 isomer B . The α1,2-mannosidases 1A and 1B remove terminal α1,2-linked mannose from the α1,6 branch from Man8GlcNAc2 isomer B before the α1,3 branch is demannosylated. Hence, the absence of MAN1C1 expression in CD133+ and CD34+ cells may affect the enrichment of high mannose N-glycans. It is likely that most of the high mannose N-glycans seen in the CD133+ and CD133- cell glycan profi le were intracellular. N-glycan profi les include all N- glycans, not only those on the cell surface. However, labeling with mannose binding lectins Pisum sativum agglutinin and Hippeastrum hybrid agglutinin confi rmed the presence of mannose-rich N-glycans on the cell surface.

High mannose N-glycans promote proper protein folding208. The high mannose N-glycans transported to the cell surface are known to be demannosylated. A half-life of 6.7 hours for cell surface high mannose

Figure 11. Schematic representation of the differences in CD133+ and CD133- cell N- glycan proﬁ les. In CD133+ cells, high mannose N-glycans and biantennary complex type N-glycans were the most enriched N-glycan structures. In contrast, the proportion of low mannose type N-glycans and multiantennary structures greater than biantennary complex N-glycans was increased in CD133- cells. Monosaccharide symbols: = N-acetyl-D- glucosamine; = D-mannose. Glycosidic linkages are indicated by lines connecting the monosaccharides. 54 RESULTS AND DISCUSSION

N-glycans has been observed in the hepatic cell line209. On the other hand, 4TO7 tumor cells are capable of spreading to the lungs and they have been shown to have more high mannose and N-glycans on their cell surface than nonmetastatic tumor cells210. Further, it has been shown that sialic acids have no effect on the migration and invasion properties of 4TO7 cells. Thus, high mannose N-glycans may have yet unknown functions.

The 5% increase of high mannose type N-glycans demonstrated in CD133+ cells when compared to CD133- cells may correspond to a variety of glycoproteins. There may be a greater number of recently transported proteins on the surface of CD133+ cells. Hence, there may simply be more time for trimming mannose residues to form the low mannose type N-glycans more abundant in CD133- cells. If there is a greater proportion of mannose-rich glycans on the surface of CD133+ cells, it could be due to the lower number of mannose trimming enzymes in these cells. The unique mannosylation of CD133+ cells may be useful for classiﬁ cation of a primitive cell type. The lack of MAN1C1 expression may function as a marker for HSCs.

Differential expression of N-acetylglucosaminyltransferase genes and the enrichment of biantennary complex type N-glycans in CD133+ cells (study III)

Differential expression of two genes encoding N-glycan backbone branching N-acetylglucosaminyltransferase enzymes was demonstrated between CD133+/CD133- and CD34+/CD34- cells. MGAT2 was overexpressed 1.9-fold in CD133+ cells when compared to CD133- cells. MGAT2 overexpression was 1.7-fold in CD34+ cells when compared to CD34- cells. MGAT4A was underexpressed 2.8-fold in CD133+ cells and 2.0- fold in CD34+ cells when compared to their negative counterparts. The increase in the amount of biantennary complex type N-glycans demonstrated in CD133+ cells correlated with the higher expression of MGAT2. Furthermore, a lower number of triantennary and other multiantennary N-glycans were seen in CD133+ cells. MGAT4 gene encoding N-acetylglucosaminyltransferase 4 participates in the biosynthesis of triantennary and other multiantennary complex type N-glycans. Thus, underexpression of MGAT4A may be related to the reduction of multiantennary N-glycans in CD133+ and CD34+ cells. Complex type N-glycans seem to be developmentally important as MGAT2-null mice suffer from hematological and osteogenic abnormalities and die soon after birth. This RESULTS AND DISCUSSION 55 indicates that complex type N-glycans could have a function in the interaction between HSCs and BM osteoblasts. High binding of Phaseolus vulgaris-derived Phytohemagglutinin-L lectin, speciﬁ c to large complex type N-glycans, demonstrated the presence of large complex type N- glycans on leukocytes including the CD34+ cells.

Despite the differential expression of genes coding for β1,4-galactosyltransferases there are no differences in the β1,4-galactosylation of CD133+ and CD133- cells (study III)

Type 2 N-acetyllactosamine dominated as glycan backbone structure in CD133+ and CD133- cells. Mass spectrometric analysis results were veriﬁ ed by binding of Ricinus communis agglutinin-1 lectin. Moreover, type 2 N-acetyllactosamine were β1,4-galactosylated as β1,3-linked galactose was not detected. Genes encoding β1,4-galactosyltransferases such as B4GALT1, B4GALT3 and B4GALT4 were expressed in CD133+, CD133-, CD34+ and CD34- cells. B4GALT3 was downregulated in CD133+ and CD34+ cells by 2.3- and 2.0-fold respectively, when compared to their negative counterparts.

The differential expression of β1,4-galactosyltransferase encoding genes was not shown to have any effect on N-glycosylation. Yet, it may not be ruled out that the differential expression of β1,4-galactosyltransferase encoding genes still have a signifi cant function in the galactosylation of specifi c proteins not detected in glycan profi ling, or have an impact on the galactosylation of other glycan types such as O-glycans or glycolipids.

CD133+ and CD34+ cell speciﬁ c biosynthesis of N-glycan terminal epitopes (study III)

The complex type N-glycans, biantennary and larger, were very often α2,6-sialylated in CD133+ and CD133- cells. ST6Gal1 encoding α2,6- sialyltransferase was similarly expressed in CD133+/CD133- cells, and CD34+/CD34- cells. Lectin Sambucus nigra agglutinin recognizing α2,6- linked sialic acid bound to 98% of the leukocytes including CD34+ cells. Thus, α2,6-sialylation was a dominant terminal epitope on CD133+ and CD133- cells. Moreover, α2,3-sialylation had increased on CD133+ cells when compared to CD133- cells. The α2,3-sialidase digestion disclosed the presence of some solely α2,3-sialylated N-glycans in CD133+ cells. 56 RESULTS AND DISCUSSION

The α2,3-sialidase treatment was not able to entirely desialylate N- glycans in CD133- cells. CD133+ and CD34+ cells overexpressed the α2,3- sialyltransferase encoded by ST3GAL6 when compared to their negative counterparts (CD133- and CD34- cells). ST3GAL6 expression was 3.9-fold higher in CD133+ cells than CD133- cells, and 1.6-fold higher in CD34+ cells than CD34- cells. Lectin Maackia amurensis agglutinin with α2,3- linked sialic acid speciﬁ city bound to 62% of the leukocytes and CD34+ cells demonstrating the presence of α2,3-sialyl-N-acetyllactosamine structures on the cell surface.

α2,3- and α2,6-sialyltransferases compete for the same N-glycan substrates. Lowered amount of α2,6-sialyltransferase and decreased α2,6-sialylation in murine T cells was suggested to be caused by substrate competition with α1,3-galactosyltransfase211. However, α1,3- galactosyltransferase is not present in human cells and similar substrate competition is not relevant in humans. The lower relative abundance of α2,6-sialylation in CD133+ cells is likely to be caused by increased α2,3-sialylation. ST6Gal1 or ST3Gal6 have not been shown crucial for development. B cells express CD22 (Siglec2) that speciﬁ cally binds to α2,6-linked sialic acid. However, Siclec2 functions only in activated B cells, while it is masked by the cell's own α2,6-sialylation in resting B cells212. B cells that have reduced sialylation have been shown more susceptible to apoptosis213. Similar masking by sialic acids could also exist in other cell types to prevent Fas receptor ligand driven apoptosis. HSCs upregulate Fas upon active cell cycle, but remain resistant to Fas-induced suppression214. Fas ligand coating of HSCs improves their engraftment capacity by two- fold215. In addition, HSCs have shown to secrete Fas ligand which targets the marrow cells of the recipient in transplantation215.

Sialylation has been suggested to play a role in the initial attachment of HSCs to BM osteoblasts158. However, the participation of any single adhesion molecule in the homing of HSCs has not been unequivocally shown216. Glycosylation-based recognition may be involved in the initial steps of homing, but the binding of specifi c proteins are required for engraftment. The interaction may involve expression of stem cell factor and stromal cell-derived factor 1, activation of lymphocyte function- associated antigen 1, very late antigen 4, very late antigen 5 and CD44, cytoskeleton rearrangements and secretion of membrane type 1–matrix metalloproteinases217. In the future, it would be interesting to fi nd out whether α2,3-sialylation is the only form of sialylation in certain proteins. It is also not yet known if solely α2,3-sialylated structures are typical of BM CD133+ cells as well. More studies are needed to reveal whether RESULTS AND DISCUSSION 57 the differential sialylation observed in CB CD133+ cells is benefi cial to cell function or if these cells would benefi t from a higher level of sialylation.

Only a small amount of CD34+ cells have been shown to bind to hyaluronan in BM. The α2,3-sialylation of N-glycans has been shown reduce the binding of CD44 molecule to hyaluronan in CHO cell line218. Enriched α2,3-sialylation may have a role in the mobilization of HSCs to the circulatory system. CD34+ cells carry a speciﬁ c glycoform of CD44 molecule on their cell surface165. It has been shown to have over 5-fold L-selectin binding when compared to P-selectin glycoprotein ligand 1168.

Selectin ligand sialyl-Lewisx is dependent on α2,3-sialylation and α1,3- fucosylation219. In addition to common α1,6-fucosylation of N-glycan core structures, one or two more fucose residues were revealed in CD133+ and CD133- cell N-glycans by mass spectrometric analysis. The fucose was expected to be α1,2- or α1,3-linked to the N-glycans as no type 1 N-acetyllactosamine was found on CD133+ or CD133- cells. FUT1 and FUT2 both encode α1,2-fucosyltransferase, but their expression was not reliably analyzed. FUT1 expression was not detected in CD133+/ CD34+ cells or their negative counterparts. For FUT2 there was no probe available on the array. Ulex europaeus agglutinin I lectin with speciﬁ city towards α1,2-linked fucose residues labeled 53% of the leukocytes. Impaired α1,3-fucosylation and decreased α1,3-fucosyltransferase expression has been shown to be characteristic of CB CD34+ cells163,164. In order to deﬁ ne α1,3-linked fucose on CD133+ or CD133- cell N-glycans, leukocytes were labeled with Lotus tetragonolobus agglutinin lectin. It bound to only 6% of leukocytes. Moreover only FUT4, but no FUT7 expression was detected in CD133+, CD34+, CD133- and CD34- cells. FUT4 was expressed similarly in all cell types. FUT7 encodes the main α1,3-fucosyltransferase responsible for the synthesis of sialyl-Lewisx, but FUT4 protein product is also able to synthesize it. These results suggest that poor α1,3-fucosylation is typical for the CB leukocytes including CD34+ cells. 58 CONCLUSIONS

CONCLUSIONS

The positive selection of HSCs has provided an attractive alternative to T cell depletion of transplantable cells. The selection of CD34+ cells can be used in this process. However, the selection of CD133+ cells has also shown to be promising in the enrichment of HSCs220-222. Although, CD133+ and CD34+ cells are highly similar cell populations there may be differences in their genetic regulation. In previous studies, HSCs from BM, PB and CB have shown differential gene expression related to divergent biological properties operative in HSCs10,155,223. Increased knowledge of HSC biology is crucial to improve stem cell-based therapies.

The present studies characterize human CB-derived CD133+ and CD34+ cells on a genomic level and provide gene expression profi les of CD133+ and CD34+ cells when compared to CD133-and CD34- cells, respectively. The integrated CD133+/CD34+ cell gene expression profi les reveal novel genes to specify HSCs. Moreover, the transcripts that are expressed differentially between CD133+ and CD34+ cells are associated with cell cycle, cell adhesion and differentiation. This fi nding suggests that the divergent gene expression patterns may be adjusting CD133+ and CD34+ cells in different ways in order to encompass hematopoiesis. CD133+ cells seem to be more primitive in their nature and possibly have a wider differentiation potential than CD34+ cells. CD133+/CD34+ cells express numerous transcripts coding for putative transmembrane proteins that may serve as markers for HSCs. However, during this study, specifi c markers to identify HSCs could not be found on the cell surface of CD133+ or CD34+ cells.

These studies also demonstrate N-glycan structure profi le typical for CD133+ cells. CD133+ cells have enriched amount of high mannose type and biantennary complex type N-glycans when compared to CD133- cells. CD133+ cells also have solely α2,3-sialylated N-glycans. Comparison of N-glycan structure and gene expression profi les identify key genes regulating the CD133+ cell-specifi c N-glycan synthesis. The expression pattern of genes encoding glycosyltransferases and glucosidases is similar in CD133+ and CD34+ cells, suggesting a similar N-glycosylation in CD133+ and CD34+ cells. Furthermore, the specifi c binding properties of various lectins are utilized to reveal the glycan determinants on the cell surface. CONCLUSIONS 59

Further studies to identify proteins that carry the specifi c N-glycan structures in CD133+ cells could prove useful in the recognition and isolation of HSCs. Moreover, the role of glycans and glycan binding proteins in the communication between HSCs and their environment should be elucidated. The modifi cation of HSC glycosylation could increase HSC homing and engraftment or target them to specifi c tissues. In conclusion, these results provide new knowledge of CD133+ and CD34+ cells that could help to design cells with enhanced biological properties and to further the use of HSCs in novel therapeutic applications. 60 ACKNOWLEDGEMENTS

ACKNOWLEDGEMENTS

This work was carried out at the Finnish Red Cross Blood Service, Research and Development, in Helsinki during the years 2004-2007. Partial funding support was provided by the Finnish Funding Agency for Technology and Innovation.

I am grateful to the Director of the Institute, Docent Jukka Rautonen, and Department Head, Kari Aranko, for providing excellent research facilities. I devote my warmest thanks to my supervisor, Docent Taina Jaatinen, for sharing her vast knowledge on stem cell biology, but also her enthusiasm, optimism, great patience and motivation that helped to guide me through this learning process. Very warm thanks to my supervisor, Docent Jukka Partanen, for support, numerous interesting discussions when planning the studies and all the helpful comments throughout the PhD work.

I am indebted to the reviewers, Docents Heli Skottman and Timo Tuuri, for their time, constructive criticism and valuable comments. I warmly thank Professor Riitta Lahesmaa for her kind promise to act as an opponent for the public defense of this thesis.

Acknowledgement and sincere appreciation go to Docent Jarmo Laine for his encouragement, support and valuable advice in clinical aspects, Docent Sampsa Hautaniemi for offering his expertise in data analysis, Jari Niemi for his enthusiasm in statistical analyses, Tero Satomaa and Doctor Jari Natunen for sharing their endless knowledge on glycobiology, Olli Aitio for his skillful nuclear magnetic resonance analysis, Doctor Daniel Nicorici for his ﬂ exibility in performing data analysis, Annamari Heiskanen for her timely mass spectrometric analysis and Maria Blomqvist for her work on glycan structure analysis. Additionally, Professor Olli Yli-Harja and Juhani Saarinen are acknowledged for their fruitful collaboration. My special thanks to the undergraduate student Erkka Valo for his work on data reanalysis.

I wish to thank Docent Riitta Kekomäki and the whole staff of the Finnish Red Cross Cord Blood Bank for providing CB units to this study. I am grateful to ’STR’ and all colleagues and technicians who shared their knowledge to help carry out this study, notably; Sirkka Mannelin, Doctor Tuija Kekarainen, Noora Alakulppi, Anita Laitinen, Tanja Kaartinen, Doctor ACKNOWLEDGEMENTS 61

Johanna Nystedt and Docent Leena Valmu. Doctor Hannu Turpeinen ‘transplantation specialist’, Katri Haimila, Doctor Pekka Aroviita and Doctor Suvi Natunen ‘our own glycobiologist’ are thanked for their time and productive input.

My colleagues deserve heartfelt thanks for creating a most enjoyable and variable working atmosphere. I also thank my boss, Research and Development Manager Saara Laitinen, for her sincere care and understanding.

I address special thanks to Maarit Koutonen of Lähituki for the fast and effective support, and to both Marja-Leena Hyvönen and Maija Ekholm for excellent library services. Kevin Hanley is warmly thanked for ﬂ exibly organizing the language revision of this thesis and I am grateful for the always warm and efﬁ cient assistance of Pirjo Nick.

My very sincere thanks to all my friends for their invaluable companionship and the enriching life experiences we enjoy, providing balance to life inside the laboratory. Thank you all for being there for me.

I owe my deepest gratitude to my mother and father for their love and support throughout my life and for my dear little sister Nina who has been looking after the big sister, even the times when it should be the other way around. I am grateful to my grandparents for being part of my life. My sweetest thanks are to my husband Marko for his love, presence and endless support.

January 2008, Helsinki, Finland 62 ACKNOWLEDGEMENTS

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