Molecular profiling of stem cells

Clinica Chimica Acta 378 (2007) 24 – 32 www.elsevier.com/locate/clinchim

Invited critical review

Molecular profiling of stem cells Li Ma 1 , Bingyun Sun 1 , Leroy Hood, Qiang Tian ⁎ Institute for Systems Biology, 1441 N 34th St., Seattle, WA 98103, United States Received 15 September 2006; received in revised form 8 December 2006; accepted 21 December 2006 Available online 3 January 2007

Abstract Stem cells, with their profound implication in development and enormous potential in regenerative medicine, have been the subject of extensive molecular profiling studies in search of better markers and regulatory schema governing self-renewal versus differentiation. In this review article, we will discuss current advancement in high throughput technologies dedicated to the transcriptome, proteome and genome-wide localization analyses, and how they have been adopted in molecular profiling of stem cells with an emphasis on embryonic stem cell (ESC), hematopoietic stem cell (HSC) and neural stem cell (NSC). © 2007 Elsevier B.V. All rights reserved. Keywords: Stem Cell; transcriptome; proteome; microarray; mass spectrometry; ChIP-chip

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transcriptomic analysis of stem cells . . . . . . . . . . . . . . . . 2.1. Embryonic stem cells (ESC) . . . . . . . . . . . . . . . . . 2.2. Hematopoietic stem cells (HSCs) . . . . . . . . . . . . . . 2.3. Neural stem cells (NSCs) . . . . . . . . . . . . . . . . . . 2.4. Cross-comparison of stem cells from different tissue origins 3. Proteomic analyses of stem cells . . . . . . . . . . . . . . . . . . 3.1. Current technological platforms for proteomic research . . . 3.2. Current researches on stem cell proteomics . . . . . . . . . 4. Transcriptional regulatory networks in stem cells . . . . . . . . . . 5. Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Stem cells possess the unique properties of self-renew (to expand themselves) and multipotentiality (to give rise to multiple progenies). They hold great promise in regenerative medicine, tissue repair, as well as tumor biology. A thorough ⁎ Corresponding author. Tel.: +1 206 732 1308; fax: +1 206 732 1299. E-mail address: [email protected] (Q. Tian). 1 Equal contribution. 0009-8981/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.cca.2006.12.016

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understanding of stem cell biology calls for the elucidation of the complete genetic part list at the levels of mRNAs, proteins and regulatory networks. Advances in genomic and proteomic technologies have allowed capture of genetic information at each of these levels during the developmental process of stem cells and have contributed enormously to our understanding of the fundamental processes of stem cell development. Several review articles have depicted a comprehensive overview of molecular signatures of stem cell, focusing mainly on gene expressions at the mRNA level [1,2]. However, It has been

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documented that changes at the mRNA level capture roughly 40% of variations at the protein level [3,4]. Thus, transcriptomic analysis alone inevitably leads to incomplete and biased interpretation of the underlying biology. While proteomic analysis has been heralded to provide a wealth of information complementary to the transcriptomic data, due to technical limitations, even the most comprehensive global proteomic analysis to date in a given stem cell specie detected only less than 2000 proteins [5]. Thus, neither transcriptomic nor proteomic analysis alone is sufficient for thorough exhibition of stem cell profiles. Here, we will discuss some of the recent advances in high throughput genomic, proteomic and genomewide localization technologies and how they have been applied to the molecular profiling of various stem cells with an emphasis on embryonic stem cell (ESC), hematopoietic stem cell (HSC) and neural stem cell (NSC). 2. Transcriptomic analysis of stem cells The ever-advancing high throughput technologies measuring changes of gene expression at the mRNA levels have enabled extensive global transcriptomic profiling of several different stem cells (ESC, HSC and NSC) and their differentiated progenies. These studies employed a variety of technological platforms including subtractive cDNA library analysis [6], expressed sequence tags (EST) sequencing [7], serial analysis of gene expression (SAGE) [8], DNA microarrays [9–11] and massive parallel signature sequencing (MPSS) [12–14] as will be discussed in more detail below according to individual stem cell type. 2.1. Embryonic stem cells (ESC) Both mouse and human ESCs have been analyzed transcriptionally to reveal their molecular signatures. Tanaka et al. [15] compared mRNA expression profiles of murine ESCs, trophoblast stem cells and mouse embryonic fibroblast (MEF) using cDNA microarrays and identified a total of 346 signature genes characteristic for one or another cell type. Among these genes, 124 were mESC-specific genes, including the well-characterized Oct3/4 and Rex-1. In addition, zinc finger protein 57, SCAMP (secretory carrier membrane protein) 37, EH-domain containing protein 2, Cdk inhibitor-related protein P15RS and Esg-1 (embryonic stem cell specific gene 1) were also clustered together as mESC-specific genes. Based on functional annotation, these signature genes were further classified into nine functional categories including those involved in apoptosis, cell cycle, transcription/chromatin and signal transduction. Of note is that a large number of ESCspecific genes remain functionally unknown. Another transcriptomic analysis carried out by Sharov et al. [16] collected EST sequences from mouse oocytes, blastocysts, embryonic and adult stem cells, generated lists of signature genes for distinct early developmental stages and found 75 mESCsspecific genes including 28 genes with unknown functions. Further characterization of these unknown genes would provide new insight for maintaining mESC identity.

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Although established much later than their mouse counterparts [17], given the huge potential in regenerative medicine, human ESCs have been studied more extensively with a number of transcriptomic efforts being initiated to identify hESCspecific genes [7,8,13,18–21]. Sperger et al. compared mRNA expression patterns via cDNA microarrays five human ES cell lines to a panel of 69 other different human cell lines or tissue samples. Among the genes highly expressed in hESCs were some previously proven ESC-associated genes, i.e., transcription factors Oct3/4, FoxD3, Sox2 and a DNA methyltransferase DNMT3B. In addition, genes involved in the Wnt/β-catenin signaling pathway, such as Frizzled 7, Frizzled 8 and Tcf3 were also highly expressed, which is consistent with mouse genetics data showing that different dosage of β-catenin could modulate ES cell differentiation [22]. All four fibroblast growth factor (FGF) receptors were highly expressed in hESCs demonstrating the importance of FGF signaling in maintaining hESCs. Using oligonucleotide DNA microarrays, Bhattacharya et al. [19] identified 92 genes that were enriched in all six different hESC lines, including the known ESC markers Oct3/4, Nanog, GTCM-1, Connexin 43/GJA1, TDGF1 and Galanin. Several other studies employing oligonucleotide microarray [20,21], SAGE [8], EST enumeration [7] and MPSS [13] each established a unique transcriptomic profile for hESC. A crosscomparison of the gene lists generated by these efforts shows that Oct3/4, Nanog, Sox2, Rex1, DNMT3B, Lin28, TDGF1 and GDF3 are commonly expressed in all the hESCs. A summary of selected genes identified by these analyses is shown in Table 1. Cross-species comparisons between human and mouse ESC transcriptomes reveal both similar and distinct features [12,23]. While some known ESC self-renewal associated genes such as Oct4, Sox2, LeftB, Utf-1 and TDGF are well conserved, the correlation coefficient for 5921 homologous genes identified between mouse and human ESC is only 0.41, whereas that of the two independently-derived hESC populations is 0.90 [12]. The dramatic difference in gene expression profiles between mouse and human ESCs reflects distinct mechanisms for maintaining ESC pluripotency in mouse and human. For instance, leukemia inhibitory factor (LIF) is required for the propagation of mouse ESCs, whereas human ESCs require exogenous FGF2 for self-renewal. MPSS data [12], as well as several other analyses [7,18,24], confirm that only mouse ESCs express LIF receptor (LIFR) together with the downstream signal transducers JAK and STAT3, while no LIFR or JAK are detected in human ESCs. On the other hand, human ESCs express high levels of FGF receptor 1, 3 and 4, whereas mouse ESCs contain only a minimal level of FGFR1. Moreover, significant differences have also been observed for genes involved in Wnt, TGF-β/BMP signaling pathways between the two species [7,12,24,25]. 2.2. Hematopoietic stem cells (HSCs) HSCs are one of the best-characterized adult stem cells. Both mouse and human HSCs have been isolated according to their specific cell surface phenotype using fluorescence-activated cell sorting (FACS) [26–28]. Although representing only ∼ 0.05%

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Table 1 Selected list of stem cell markers identified by transcriptomic analyses Oct4

mESC

hESC

Nanog

mESC hESC

Sox2

mESC hESC

Zfp42 (Rex1) Utf1

Tdgf1 (Cripto)

mESC hESC mESC hESC mESC hESC

LeftB

hESC

Lin28

hESC

Dnmt3B

hESC

Gdf3 Gja1

hESC hESC

Notch1 Tal1 Lmo2 Hox A9

HSC HSC HSC HSC

Bmi1 Bmp4 Meis-1 Pax6 Lhx2 Cyclin D1

HSC HSC HSC NSC NSC NSC

Oligonucleotide microarray [10]; oligonucleotide microarray [11]; cDNA microarray [15]; in silico differential display [100] cDNA microarray [18]; oligonucleotide microarray [19]; oligonucleotide microarray [21]; SAGE [8]; MPSS [13]; oligonucleotide microarray [20] In silico differential display [100] Oligonucleotide microarray [19]; MPSS [13]; oligonucleotide microarray [20] SAGE [8]; oligonucleotide microarray [21]; MPSS [13]; oligonucleotide microarray [20] Oligonucleotide microarray [11]; cDNA microarray [15]; EST [16]; in silico differential display [100] SAGE [8]; MPSS [13]; oligonucleotide microarray [21] Oligonucleotide microarray [11]; in silico differential display [100] MPSS [13] Oligonucleotide microarray [11]; in silico differential display [100] Oligonucleotide microarray [19]; oligonucleotide microarray [21]; SAGE [8]; MPSS [13]; oligonucleotide microarray [20] Oligonucleotide microarray [21]; oligonucleotide microarray [19]; MPSS [13]; oligonucleotide microarray [20] SAGE [8]; oligonucleotide microarray [19]; oligonucleotide microarray [20] cDNA microarray [18]; SAGE [8]; MPSS [13]; oligonucleotide microarray [20] SAGE [8]; oligonucleotide microarray [19] Oligonucleotide microarray [19]; MPSS [13]; oligonucleotide microarray [20] Oligonucleotide microarray [11]; Macroarray [6] Oligonucleotide microarray [9] Oligonucleotide microarray [11] Oligonucleotide microarray [10]; oligonucleotide microarray [11]; oligonucleotide microarray [9]; cDNA microarray [40]; cDNA microarray [44] Macroarray [6]; oligonucleotide microarray [9] Macroarray [6] Macroarray [6]; oligonucleotide microarray [9] cDNA microarray [50] cDNA microarray [50] RDA substraction [47]; cDNA microarray [49]; cDNA microarray [48]; cDNA microarray [50]

of bone marrow cells, HSCs can fully reconstitute all blood cell elements. In addition to their ability of self-renewal to produce more HSCs, they give rise to both lymphoid and myeloid lineages through progressive restriction of lineage potential and acquire the characteristics of mature, fully differentiated cells [29–31]. Several groups have employed high-throughput sequencing strategies to uncover the transcripts that specify HSCs [6,32,33]. These approaches involve construction of cDNA libraries from murine HSCs followed by the subtraction of mature housekeeping genes and then sequencing and bioinformatics analyses. Philips et al. constructed a cDNA library from murine fetal liver HSCs (Lin-Sca-1+AA4.1+c-Kit+) and subtracted house keeping genes from the AA4.1-cells. Terskikh et al. built an adult murine HSCs (Lin-Sca-1+c-Kit+Thy-1lo) cDNA library that was subtracted with cDNA from bone mar-

row cells. Park et al. spotted cDNA clones from highly enriched bone marrow HSC and multipotent progenitor (MPP) populations onto nylon membranes and hybridized them with cDNA probes derived from mature lineage (spleen, thymus and bone marrow) cells to select genes with no/low hybridization signal for subsequent sequencing analysis. A cross-comparison among these efforts demonstrate that the components of evolutionally conserved and developmentally regulatory pathways were prominent in HSCs, including those from the Wnt pathway (Lef1, Tcf4, Dsh), the TGF-β super family (BMP4, Activin C, Serine and Therine kinases NIK and Ski), the Sonic hedgehog (Shh) pathway (Smoothened, SMO), the Notch family (Notch1 and Manic Fringe), members of the homeobox regulatory cascade (Hoxa9, Meis-1, TGIF and Enx-1) and Bmi-1. Accumulating evidence indicates that some of these molecules and the underlying genetic pathways are involved in the regulation of stem cell self-renewal or maintenance. For instance, Activation of Wnt pathway by expressing β-catenin in HSCs results in growth factor-independent growth and enhanced selfrenewal properties [34]. Expression of β-catenin in the granulocyte-monocyte progenitor (GMP) renders enhanced self-renewal activity – a hallmark for stem cells – and leukemic potential of these cells in chronic myelogenous leukemia (CML) patients [35]. Bmi-1, a suppressor of the Ink4 locus (encoding p16 and p19 cell growth inhibitors), is essential for the determination of the proliferation potential of HSCs [5,36]. BMP signaling controls the number of HSCs by regulating the size of HSC niche – the hematopoietic microenvironment in bone marrow that supports HSC self-renewal – through BMP receptor BMPRIA [38]. An effort sought to identify genes in the HSC niche by comparing HSC-supporting and none-HSC-supporting stromal cell lines via cDNA subtraction, sequencing, high-density array and bioinformatics analyses [39] found that, in consistent with their intrinsic counterpart in HSCs, genes involved in the Wnt, BMP and Notch pathways were also present in the subtracted stromal cell library. Thus, the basic mechanisms that mediate stem cell self-renewal and differentiation would be conserved both in stem cell compartment and in their corresponding microenvironment. Several groups have performed DNA microarray analyses to delineate the early differentiation process of mouse HSCs [9,40,41]. Akashi et al. compared gene expression profiles among HSCs, MPPs, common lymphoid progenitors (CLPs) and common myeloid progenitors (CMPs) using Affymetrix oligonucleotide arrays. They found that HSCs coexpress both hematopoietic and non-hematopoietic gene; MPPs coexpress a more limited gene set that displays a more hematopoieticspecific character. CMP coexpress myeloerythroid but not lymphoid genes whereas CLPs coexpress T-, B- and NKlymphoid but not myeloid gene. This progressive decrease in the transcription accessibility during differentiation correlates with the stepwise-restricted lineage potential of the cells. These findings support the hypothesis that open chromatin structure is maintained in early hematopoietic stem/progenitor cells, enabling multilineage differentiation programs to be readily accessible [42]. It is also consistent with Ramalho-Santos et al.'s [11] finding that chromatin-remodeling genes are

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enriched in all stem cells suggesting that the ability to modulate local chromatin states may be necessary for stem cell pluripotency. In support of this hypothesis is our recent finding of the enrichment of chromatin remodeling proteins in undifferentiated hESCs via a proteomic analysis (Tian Q et al., submitted for publication). Terskikh et al. [40] carried out cDNA microarray comparison of gene expression of HSCs, GMPs, CMPs, MEPs, CLPs, Pro-T and Pro-B cells and compiled a list of genes involved in HSC self-renewal. Another study using oligonucleotide array identified 72 genes related to the long-term reconstituting (LTR) activity of HSCs, including membrane-associated proteins, signal transduction pathway proteins and transcriptional regulatory factors, such as Fzl4, Kit and GATA3 [41]. Fewer analyses on human HSC transcriptome have been reported, partly due to difficulty in obtaining high purity HSC populations. A comparison of HSC-enriched (CD34+/CD38−/ Lin−) versus HSC-depleted, hematopoietic progenitor cell (HPC)-enriched (CD34+/[CD38/Lin]+) cells yielded 81 genes over-represented and 90 genes under-represented in human HSCs [43]. Wagner et al. further separated CD34+/CD38− hematopoietic population into slow-dividing fraction (SDF) and fast-dividing fraction (FDF) and looked for differentially expressed genes. Among the genes showing the highest expression levels in the SDF were CD133, ERG, cyclin G2, MDR1, osteopontin, CLQR1, IFI16, JAK3, FZD6 and HOXA9—a pattern compatible with their primitive function and self-renewal capacity [44]. 2.3. Neural stem cells (NSCs) NSCs are self-renewal multipotent precursors that can give rise to neuronal and glial progenitor cells, which in turn differentiate into neurons, astrocytes and oligodendrocytes [45,46]. Several large-scale gene expression analyses have revealed specific genes that are related to NSC function and differentiation. Geschwind et al. compared neurospheres – the pluripotent neural cell colonies that contain NSCs – with the more differentiated cells using representational difference analysis (RDA) combined with cDNA microarrays and identified 88 genes whose expression levels were significantly higher in the progenitors [47]. Another study using the same culture condition and different cDNA microarrays detected 126 genes being enriched and 78 genes being down-regulated in the neural progenitors [48]. Other researchers chose to purify the neural progenitor cells through FACS first, then conduct cDNA microarray analysis [49,50]: Luo et al. sorted cells using antibodies that recognize different neuronal and glial progenitor markers; Livesey et al. purified progenitor cells based on their DNA content—cells that contain double amount of DNA are under division hence defined as progenitors; both studies generated lists of neural progenitor specific genes. Although most of the NSC transcriptional profiles to date were obtained using heterogeneous cell populations which included stem cells, progenitor cells at different stages of differentiation and some differentiated cells, a cross-examination of all these microarray data could still extract important information regarding early

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development and function of NSCs. For instance, cell cycle regulators Rb and cyclin D1, D2 were found to be progenitorenriched in all of these studies. It has been shown that these genes not only played a role in cell proliferation, but also were critical for regionalization and pattern formation in cerebral cortex [47]. BMP4, an important regulator of dorsoventral patterning, was also found to be enriched in two of the analyses. In addition, the enrichment of chromatin modulating factors like GCN5, ATRX, Sirt2 re-enforced the notion that all stem cells maintain a relative open chromatin structure [11]. 2.4. Cross-comparison of stem cells from different tissue origins The idea that different stem cells may share a similar molecular signature prompted searches for the “stemness” genes that are expressed in common among ESCs, HSCs and NSCs [10,11]. Two groups employing Affymetrix GeneChip™ technology each came up with a list of more than 200 genes that are commonly expressed among these different stem cells. One of the groups went further to examine gene expression profiles of hematopoietic hierarchy and found that HSCs and early progenitors express many known HSC markers including c-Kit, Sca-1, Abcb1b/MDR1, Mpl Meis1. They also found that molecules thought to be involved in cell–cell communication such as Bmp8a, Wnt10A, EGF-family members Ereg and Hegfl, as well as ligand-receptor pairs which may be involved in signaling between HSC and their microenvironment (Wnt10A/ Frizzled, Agpt/Tek), were overrepresented in the HSCs. A cross-comparison between the two lists of “stemness” genes generated by these two groups yields only 15 common genes. This discrepancy has been attributed to differences in methodology, including cell sources and purity, transcript amplification and the analyzing algorithms. A re-analysis of the same data set using identical statistical methods found 605 overlapping genes in the two lists. 3. Proteomic analyses of stem cells Proteomics, which studies comprehensive global protein profiles in given biological systems such as cell fractions, subcellular organelles, tissues and body fluids, poises to provide complementary insights bridging the digital information of transcriptome to the physiological phenotypes and functions [51]. Proteins inherit biological information from DNAs and RNAs and form organized networks or molecular machines to execute most of the biological functions directly. With the apparent lack of linear correlation with the corresponding proteome, transcriptomic analysis, even examined at close to completeness by the highly sophisticated MPSS technique [14,52], can only partially interpret the biological network behavior in the absence of proteomic confirmation [3,53,54]. Proteomics faces enormous challenges and remains to be the bottleneck for systems-level analysis of the biological information hierarchy (from nucleotides to proteins, and to cells and organs) due to the complicated architecture arisen from the linear protein sequence, numerous post-translational modifications (PTMs) and

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Table 2 Comparison of performance characteristics for new types of tandem mass spectrometers (see ref. [58]) Instrument a

Resolution

Mass accuracy

MS scan rate

MS/MS scan rate

Linear Ion Traps Q-QIT TOF-TOF FTMS

2–15,000

100–300 ppm

2000 20–25,000 50–100,000

300–500 ppm 10–20 ppm 1–2 ppm

Moderate to fast Moderate Fast Moderate

Moderate to fast Moderate Slow Moderate

a Q-QIT, quadrupole–quadrupole ion trap; TOF, time-of-flight; FTMS, Fourier transform mass spectrometer.

the lack of effective amplification approach to overcome the concentration dynamics (ranging from 106-fold in cells to 1012fold in blood) [51,55]. The rapid advancement in peptide and/or protein capturing technologies coupled with explosive development of instrumentation and methodology in the fields of mass spectrometry and protein informatics provide ever-improving powerful new strategies for proteomic research. 3.1. Current technological platforms for proteomic research Proteomics addresses at least two levels of information, i.e., the identification of protein constituents and the quantification of protein concentration. About 30 years ago, based on traditional gel electrophoresis and mass spectrometry, 2dimensional gel electrophoresis (2-DE) was first adopted as the tool to exhibit global protein expression [57]. Since then techniques aimed at different steps of proteomic research have been renovated extensively. Mass spectrometry (MS) with its superior sensitivity, accuracy and throughput in protein and peptide identification emerges to become the most sophisticated and powerful tool for dissecting protein sequence and quantification. The MS instruments used for these studies include a number of mass spectrometers with different mass accuracy, mass resolution and detection rate, which provide a wide selection window for different analytical purposes. The respective technical parameters for each machine are summarized in Table 2 [58]. As with its transcriptomic counterpart, functional proteomic analysis of a given biological system requires global quantification of all the protein components. Quantitative proteomics primarily includes two main categories: i.e., global quantitative proteomics of the whole system being studied, and targeted quantitative proteomics of a selected subset of proteins. Spearheaded by isotope-coded affinity tag (ICAT) technology which specifically captures cysteine-containing peptides [59], there are several other chemical isotope tagging techniques to complement ICAT, such as N-terminal labeling (iTRAQ [60], SPITC [61], etc.) and C-terminal labeling (esterified with isotope labeled methanol [62]). In addition, versatile enzymatic isotope labeling techniques have also been developed which include in vivo labeling through metabolism [63–69], stable isotope labeling of amino acids in culture (SILAC) [70] and enzymatic labeling of O16 to O18 at C-terminal through proteases [71–73]. To specifically quantify individual protein or

group of proteins of interest, quantitative methods on targeted proteins have also been developed which involve the use of visible isotope-coded affinity tag [74], or isotope dilution mass spectrometry [75,76]. In the latter method, standard peptides structurally analogous to the proteins (or peptides) of interest are isotopically labeled and spiked into the sample mixture [77]; the relative ratio between the standard peptides and the targeted peptides from unknown protein are obtained and used together with the concentration of the standard peptides to deduce the absolute quantification of the unknown proteins. All of the above isotopic labeling methods are based on the essence that a distinct mass difference is tagged to a pair of (or a set of multiple) peptides by isotopes that have similar chemical properties. The quantification of one peptide in the pair relative to the other (or others in the set) is achieved by measuring the difference of signal intensity between the respective peptides with fixed mass difference. Because of the complexity and expense associated with the design and application of chemical modification for sample pairs, a label-free quantitative profiling approach which compares extracted ion chromatograms from mass spectra has been introduced [78–80]: with appropriate normalization, the MS signals of analyte ions are linearly correlated with the analyte concentrations, especially for the case of complex matrixes, forming the basis of an analytical method for global protein quantification. 3.2. Current researches on stem cell proteomics The main purpose to study stem cells using proteomic approaches is to delineate a stem cell protein signature and to identify proteins that affect the biological aspects of self-renewal and pluripotency [81–84]. Large-scale proteomic analysis alone or combined with the transcriptomic studies have been carried out on several stem cell populations and developmental stages. Early efforts on stem cell proteomics employed two-dimensional gel electrophoresis (2-DE) as the front-end fractionation prior to MS analysis [83]. The main advantage of 2-DE method is its relatively easy accessibility by most research laboratories. However, due to its limited resolving power, this separation approach usually leads to the detection of a limited number of proteins, most often the abundant proteins in a sample [84]. Even with the development of the more sensitive 2D differential in-gel electrophoresis (DiGE)-based staining procedure, the resolution of 2-DE-based approaches is still not directly comparable to the 2D separation by liquid chromatography methods (such as reverse phase chromatography and strong cation exchange chromatography) which represent the most powerful fractionation methods in large-scale proteomics [84]. Moreover, the statistical studies on the comparative proteomics by 2-DE are problematic since different statistical analysis algorisms generate very diverge results [85]. A recent review of ESC proteomics by Van Hoof et al. [84] has covered details of 2-DE techniques. We will focus our discussion more on liquid chromatographybased stem cell proteomic researches. Global proteomic analysis has been performed in stem cell lines for the purpose of establishing a reference protein catalogue of stem cells. Nagano et al. profiled mouse embryonic

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stem cells by 2D-LC quadrupole time-of-flight (Q-TOF) mass spectrometer and identified 1790 proteins from trypsin-digested total cell lysates [86]. More studies, however, are conducted in a comparative manner in which a differential protein expression profile between two or more conditions is carried out. A panel of proteins from cell membrane and cytoplasm has been established for cell type benchmarks or cell differentiation. The up- or down-regulated proteins may represent functional information that impinges upon the regulatory signaling pathways. Van Hoof et al. [87] studied the difference between the human and mouse ESC proteomes by Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer. They found a total of 191 proteins as putative ESC-specific markers by comparing to their differentiated progenies. Some of the proteins were further validated using Western blots, immunofluorescence confocal microscopy and fluorescence-activated cell sorting, and the results demonstrated the robustness of their proteomic approach. Lee et al. [88] characterized adipocytic differentiation of human mesenchymal stem cells using 2-DE matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometer and detected 32 protein spots with differential expression. Using 2-DE and LC/MS/MS, Wang et al. [89] studied the neural differentiation of mouse ESCs and identified 23 proteins with changing expression levels or phosphorylation states after differentiation. To characterize the molecular signature of hematopoietic stem cells (HSCs) and their progenies, Unwin et al. [90] carried out an iTRAQ quantitative proteomic comparison of the long term reconstituting HSCs (LSK+) and the non-long term reconstituting HSCs (LSK−). The results revealed a hypoxia-related change in proteins controlling metabolism and oxidative protection, indicating that the LSK+ cells are adapted for anaerobic environments. Salasznyk et al. [91] employed a quantitative proteomic approach to study the osteogenic differentiation process of the mesenchymal stem cells. Their results brought up an interesting idea of gene focusing, i.e., focusing of gene expression in specific functional clusters, such as the Ca2+-dependent signaling pathways, as the basis of the specific extracellular matrix directed osteogenic cell differentiation. The global proteomic data of stem and progenitor cells have been compared to their cognate transcriptomic data, which leads to the conclusion that both transcriptomic and proteomic analyses are necessary for a complete in-depth exploration of biological function. Unwin et al. [92] disclosed that 54% of proteomic changes were not seen at the transcriptome level based on their studies of hematopoietic stem cells. Tian et al. [4] conducted a concurrent transcriptomic and proteomic analysis of two bone marrow derived hematopoietic progenitor cell lines that represent two distinct hematopoietic stages via DNA microarray and ICAT mass spectrometry. Among 150 signature genes, 113 (76%) exhibited changes for mRNAs and their cognate proteins in the same direction. However, only 29 of them changed significantly at both mRNA and protein levels and were thus dubbed correlated genes; 67 genes showed significant change at the mRNA but not the protein level, whereas 52 genes showed significant change at the protein but

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not the mRNA level. The correlation coefficient between mRNA and protein is about 0.6. Both studies suggest posttranslational control as an important regulatory scheme during early hematopoietic development. Thus, a complete view of hematopoiesis requires the integration of multiple high throughput platforms assessing both transcriptome and proteome. In addition to global identification and quantification of protein constituents in stem and progenitor cells, MS-based proteomic technologies have also been applied for the identification of protein factors that promote proliferation of stem cells, and for the dissection of protein complexes or signaling pathways that regulate stem cell function. Using a ProteinChip systems, Sakaguchi et al. [93] has identified a carbohydrate-binding protein, Galectin-1, as an endogenous factor that promotes the proliferation of NSCs in the adult brain. The intricate protein interaction network is key to understanding almost any complex biological systems including stem cells. The advances in MS and quantitative proteomic technologies spawned a new way of evaluating protein interactions by using affinity purification followed by MS analysis. Using such a strategy, Tian et al. [94] discovered a number of proteins in the complex of β-catenin, a central effector of the Wnt signaling pathway, and demonstrated that one of the proteins, 14-3-3ζ, was involved in the developmental process of intestinal stem cells. 4. Transcriptional regulatory networks in stem cells Beyond the realm of sheer transcriptomic and proteomic profiling of stem cells, another important aspect of stem cell molecular profiling is the exhibition of transcriptional regulatory networks in which key transcription factors bind directly to their consensus sequences located on their target genes. Transcription factors have been known to modulate the early process of stem cell development by controlling their direct target gene expression. A critical step in dissecting the gene regulatory networks in stem cells is the identification of the consensus DNA sequences to which particular transcription factor binds. The completion of human and mouse genome projects have made feasible genome-wide localization analyses of the control regions at which particular transcription factors bind through a powerful method pioneered by Rick Young and colleagues dubbed CHIP-Chip (Chromatin immunoprecipitation followed by DNA chip) analysis [95]. To characterize all of the binding sites in the genome for a particular transcription factor, one must either have a specific antibody for the transcription factor or by genetic engineering attach to it an epitope for which an antibody is available. The idea is to isolate nuclei from cells whose state is to be investigated and crosslink with formaldehyde the transcription factor to the DNA to which it binds. Then one shears the DNA into small fragments (with the attached transcription factor), uses the antibody to pull down the transcription factor and its DNA binding sites. The associated DNA fragments are amplified and hybridized to a DNA array that ideally has all of the control regions of all genes. A control is run with DNA that does not have the transcription

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factor (the DNA is isolated, amplified and run on the DNA array in conjunction with the specific reaction) and the enrichment of specific transcription factor binding sites is measured. Using ChIP-Chip analysis, Boyer and colleagues [96] recently showed that Oct4, Sox2 and Nanog, the three key transcription factors with essential role in ESC self-renewal, collaborate to regulate hESC pluripotency and self-renewal through autoregulatory and feed-forward loops. These three transcription factors function by activating pluripotency genes including themselves and by repressing key genes involved in developmental process. Using ChIP followed by paired-end ditags (ChIP-PET) approach, Loh et al. [97] conducted a similar analysis in mouse ESCs and found that Oct4 and Nanog regulate substantially overlapping target genes. However, crosscomparison between the direct target genes of Oct4 and Nanog in human and mouse ESCs unveiled only limited overlap between the two sets of data, suggesting different control mechanisms between the two species. Another ChIP-Chip analysis examined direct target genes of Polycomb complexes in murine ESC and found that Polycomb repressive complexes (PRC1 and PRC2) co-occupied many genes encoding transcription factors with important role in development [98]. A similar set of developmental regulators in human ESCs have also been identified [99] raising the possibility that Polycomb proteins act as transcriptional repressors by collaborating with a specific set of transcription factors. A comprehensive proteomic analysis of hESCs by our own group uncovered more than 100 transcriptional regulatory proteins (Tian et al., manuscript submitted), which offer themselves as fascinating candidates for further elucidating the transcriptional circuitry in hESCs. Taken together, these studies enabled the discovery of key transcription factors and their regulatory network that govern the processes of self-renewal and development of ESCs. 5. Concluding remarks The complete molecular profiling of stem cells requires the thorough demonstration of all the genetic components at the level of mRNA expression, protein expression, protein–protein interaction and protein–DNA interaction. Current high throughput technologies have allowed a fairly deep, but not exhaustive exhibition of most of these components. Future investigations would entail the development and application of improved more sensitive genomic and proteomic technologies, rigorous functional analysis of selected candidate genes through both in vivo and in vitro assays and the development and implementation of better computational algorisms which will enable integrated systems level analysis and modeling of the collective data from the combined genomic, trancriptomic and proteomic efforts. References [1] Eckfeldt CE, Mendenhall EM, Verfaillie CM. The molecular repertoire of the ‘almighty’ stem cell. Nat Rev Mol Cell Biol 2005;6:726–37. [2] Li L, Akashi K. Unraveling the molecular components and genetic blueprints of stem cells. Biotechniques 2003;35:1233–9.

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