International Journal of Hematology

, Volume 89, Issue 1, pp 24–33 | Cite as

Transcriptional profiling of hematopoietic stem cells by high-throughput sequencing

  • Yoshimi Yashiro
  • Hideo Bannai
  • Takashi Minowa
  • Tomohide Yabiku
  • Satoru Miyano
  • Mitsujiro Osawa
  • Atsushi Iwama
  • Hiromitsu Nakauchi
Original Article

Abstract

Microarray analysis has made it feasible to carry out extensive gene expression profiling in a single assay. Various hematopoietic stem cell (HSC) populations have been subjected to microarray analyses and their profiles of gene expression have been reported. However, this approach is not suitable to identify novel transcripts or for profiling of genes with low expression levels. To obtain a detailed gene expression profile of CD34c-Kit+Sca-1+lineage marker-negative (Lin) (CD34KSL) HSCs, we constructed a CD34KSL cDNA library, performed high-throughput sequencing, and compared the generated profile with that of another HSC fraction, side population (SP) Lin (SP Lin) cells. Sequencing of the 5′-termini of about 9,500 cDNAs from each HSC library identified 1,424 and 2,078 different genes from the CD34KSL and SP Lin libraries, respectively. To exclude ubiquitously expressed genes including housekeeping genes, digital subtraction was successfully performed against EST databases of other organs, leaving 25 HSC-specific genes including five novel genes. Among 4,450 transcripts from the CD34KSL cDNA library that showed no homology to the presumable protein-coding genes, 29 were identified as strong candidates for mRNA-like non-coding RNAs by in silico analyses. Our cyclopedic approaches may contribute to understanding of novel molecular aspects of HSC function.

Keywords

Hematopoietic stem cells High-throughput sequencing Non-coding RNA 

1 Introduction

Hematopoietic stem cells (HSCs) have the capacity to self-renew as well as the ability to differentiate into all adult hematopoietic lineages and to maintain hematopoiesis throughout the lifetime of the animal. With recent advances in cell separation systems, we now have access to highly purified HSCs. We have previously reported that in adult mouse bone marrow (BM), CD34low/−c-Kit+Sca-1+lineage markers-negative (Lin) (CD34KSL) cells represent HSCs with long-term marrow repopulating ability [1]. ‘Side population’ (SP) cell sorting also was applied to identify HSCs [2]. SP cells are detected by their ability to efflux Hoechst 33342 dye through an adenosine triphosphate-binding cassette membrane transporter [3]. Both fractions, CD34KSL and SP Lin, in mouse BM are highly enriched for long-term BM repopulating cells. The very low numbers of such repopulating cells, however, have hampered studies of HSCs, leaving the molecular nature of HSCs unknown. Recent technological innovation is overcoming this disadvantage. Microarray analyses in particular have made it feasible to carry out extensive gene expression profiling in a single assay. However, this approach is not suitable to identify novel transcripts or for profiling of genes with low expression levels. Various hematopoietic stem/progenitor cell fractions have been characterized by microarray analyses and cDNA subtraction, including mouse SP c-Kit+Sca-1hiLin, Thy1.1loc-Kit+Sca-1hiLin, c-Kit+Sca-1hiLinRholo, and fetal liver c-Kit+Sca-1hiLinAA4.1+ [4, 5, 6, 7, 8, 9, 10, 11, 12, 13]. Lists of HSC-specific genes are now available from several online databases such as Stem Cell Database (SCDb; http://stemcell.princeton.edu/) [12]. However, the gene expression profiles of CD34KSL cells have never been directly compared with those of other HSC populations.

In this study, we constructed cDNA libraries from CD34KSL and SP Lin cells by using long-distance PCR amplification of full-length cDNA, and performed high-throughput sequencing of the cDNAs yielded. Using digital subtraction against ESTs from other organ databases, we detected 25 novel genes. Furthermore, we identified 29 candidates for mRNA-like non-coding RNAs by in silico analysis. Our cyclopedic approach provides information valuable in understanding molecular aspects of HSC regulation.

2 Materials and methods

2.1 Mice

C57BL/6 (B6-Cre) mice were purchased from SLC Japan, Inc. (Hamamatsu, Japan).

2.2 Isolation of HSCs

Mouse CD34KSL and SP Lin cells were purified from BM cells of 2-month-old mice. Low-density cells were isolated on Lymphoprep (1.086 g/ml; Nycomed, Oslo, Norway), and were stained with an antibody cocktail consisting of biotinylated anti-Gr-1, -Mac-1, -B220, -CD4, -CD8, and -Ter-119 mAbs (PharMingen, San Diego, CA, USA). Lineage-positive cells were depleted by passage over a MACS separation column with goat anti-rat IgG microbeads (Miltenyi Biotec, Bergisch Gladbach, Germany). The cells were further stained with fluorescein isothiocyanate (FITC)-conjugated anti-CD34, phycoerythrin (PE)-conjugated anti-Sca-1, and allophycocyanin (APC)-conjugated anti-c-Kit antibodies (PharMingen). Biotinylated antibodies were detected with streptavidin-APC-Cy7 (Molecular Probes, Eugene, OR, USA). SP Lin cells were stained with Hoechst 33342 after depleting lineage-positive cells. HSCs were isolated by fluorescence-activated cell sorting (FACS) using a MoFlo flow cytometer (DAKO Cytomation, Fort Collins, CO, USA).

2.3 Construction of HSC cDNA libraries

Total RNA was isolated from 6,000 CD34KSL and 10,000 SP Lin cells using ISOGEN-LS solution (Nippon Gene, Tokyo, Japan). Total RNA was subjected to full-length cDNA synthesis using a SMART™ PCR cDNA synthesis kit (Clontech, Palo Alto, CA, USA), which is based on SMART cDNA technology and cDNA amplification by long-distance PCR. CD34KSL cDNA was subcloned into the λTripEx2 phage vector (Clontech). SP Lin cDNA was subcloned into the pMX retrovirus vector [14].

2.4 Sequencing

XL1-Blue Escherichia coli cells were infected with the CD34KSL phage library and were subcloned by plating on L Broth (LB) plates. cDNA inserts were initially amplified by standard PCR procedures on a PE 9600 thermal cycler (Applied Biosystems, Foster City, CA, USA) using the following primers: sense, 5′-CTCCGAGATCTGGACGAGC-3′, and antisense, 5′-CGTT GTAAAACGACGGCCAGTG-3′. PCR products were purified using ExoSAP-IT (Amersham Biosciences, Uppsala, Sweden) and were sequenced using the following primer: 5′-TCTCGGGAAGCGCGCCAT-3′. The SP Lin library was transfected by electroporation into JM109 E. coli cells, which then were plated on LB plates. Plasmid DNA was prepared by the alkaline-SDS method and purified by multiscreen FB filtration (Millipore, Billerica, MA, USA). Products were sequenced using the following primer: 5′-GACCTTACACAGTCCTGCTGAC-3′.

2.5 Analysis of sequence data

A homology search of HSC library clones was performed against the RefSeq nucleotide sequence databases from the NCBI website (http://www.ncbi.nlm.nih.gov/BLAST/) using the BLASTN algorithm. Each HSC clone was assigned a Refseq identification number (ID) [15]. The clones showing the highest-scoring hits for both Identity (>95%) and Bitscore (>300.0) were selected for further analyses. Additional databases used for analyses included the Gene Ontology (GO) database from the Gene Ontology Consortium website (http://geneontology.org/) [16].

2.6 In silico subtraction

The EST databases for multiple organs, including small intestine (dbEST ID: 2601, 7229), heart (509, 5430), kidney (7215, 1300), liver (9742, 1299), and muscle (8902) were downloaded from the mouse UniGene website (http://www.ncbi.nlm.nih.gov/UniGene/). All genes were assigned a Refseq ID by BLASTN searching. Multi-organ ESTs were digitally subtracted from the HSC library clones. SCDb clones also were downloaded, assigned Refseq IDs, and compared with the HSC library clones.

2.7 Identification of putative non-coding RNAs

After homology with known protein-coding sequences according to BLASTN had been sought, remaining sequences were aligned to genomic sequence by using BLAT (http://www.genomeblat.com/genomeblat/index.asp). If they were aligned at >90% identify over >90% of their length, their cDNAs were kept; otherwise they were discarded. All of the homology searches against publicly available EST sequences were performed by BLASTN. Only EST sequences with an E < 1.0e-100 were regarded as corresponding to homologous mouse ESTs (ftp://ftp.ncbi.nih.gov/blast/db/est_mouse.Z). Sequences with E-values lower than 1.0e-50 were regarded as likely human and rat orthologous ESTs. Reverse hits were not considered.

2.8 Semi-quantitative RT-PCR

Semi-quantitative RT-PCR was carried out with normalized cDNA by quantitative PCR using TaqMan rodent GAPDH control reagent (Applied Biosystems) as described [17]. PCR products were separated on agarose gels and visualized by ethidium bromide staining.

3 Results

3.1 Cell sorting and library construction

Total RNA from 6,000 CD34KSL and 10,000 SP Lin cells was subjected to full-length cDNA synthesis coupled with cDNA amplification by long-distance PCR. CD34KSL and SP Lin cDNAs were, respectively, subcloned into the λTripEx2 phage vector and the pMX retrovirus vector. The sizes of the library were, respectively, 1.15 × 106 and 1.16 kb (Fig. 1). Although the two libraries were in different vectors, we used the same method for cDNA amplification and the average insert sizes were comparable with each other (CD34KSL 1.07 kb and SP Lin 1.16 kb). Thus, we reasoned that the difference in the library construction methods did not cause any significant biases in gene expression profiles between the two populations.
Fig. 1

Construction of the HSC cDNA libraries. Cell sorting gates for the two HSC fractions are depicted. Total RNA isolated from 6,000 CD34KSL and 10,000 SP Lin cells was subjected to full-length cDNA synthesis and to cDNA amplification by long-distance PCR. CD34KSL and SP Lin cDNAs were subcloned, respectively, into the λTripEx2 phage vector and the pMX retrovirus vector

3.2 Gene expression analysis

We sequenced the 5′-termini of about 9,500 cDNAs from each HSC library and compared them, using the BLASTN search algorithm, to a non-redundant database made available from the National Center for Biotechnology Information (NCBI). About 5,000 cDNAs from each library were determined to be identical to known genes in the NCBI database. These were sorted into non-overlapping sets of 1,424 and 2,077 different genes for CD34KSL and SP Lin, respectively (Fig. 2a). Most of the genes in our original collection (CD34KSL 775, SP Lin 1,385) were represented by a single clone. In contrast, most of the genes represented by multiple cDNAs were housekeeping genes, including ubiquitin B (CD34KSL 43, SP Lin 46), β-actin (CD34KSL 33, SP Lin 36), ribosomal protein, and so on (Fig. 2b).
Fig. 2

Profiling of gene expression of hematopoietic stem cells by high-throughput sequencing. a Summary of the HSC clones identified by high-throughput sequencing. b Non-overlapping ESTs from CD34KSL and SP Lin cells were assembled into clusters of singletons and contigs. The number of clusters (Y-axis) is plotted versus the number of clones in each cluster (X-axis)

After assigning gene identities, we used reported GO to assign predictable functions to 1,034 of the CD34KSL cDNAs and 1,510 of the SP Lin cDNAs in our set. We categorized genes by their products’ subcellular localizations, biological processes, and molecular functions (Fig. 3).
Fig. 3

Distribution of known or putative locus and functions of gene products for CD34KSL and SP Lin genes determined by gene ontology. a Total, b cellular component, c molecular function, and d biological process

We then compared gene profiles between the two HSC libraries and also with SCDb-listed HSC-specific genes [12] by using UniGene numbers to determine overlaps (Fig. 4a). To exclude ubiquitously expressed genes including housekeeping genes, digital subtraction was performed against ESTs from heart, muscle, liver, kidney, and intestine EST databases. After subtraction, 31 genes were determined to be in common between the two HSC libraries. Six of them were also included in the SCDb database (Fig. 4b). A detailed list of these genes is presented in Tables 1 and 2. Among 31 genes shared between the two HSC libraries, 25 genes appeared HSC-specific, a feature not previously reported. Of note was that five of them were novel (Fig. 4b). We next used RT-PCR to analyze these genes’ expression profiles in CD34KSL and SP Lin HSC populations. As shown in Fig. 5, we confirmed that several genes are specific to CD34KSL or to SP Lin. Others were expressed in both populations, although some genes that were not included in the SCDb database showed no HSC specificity (Table 3).
Fig. 4

Overlapping gene expression in HSCs. a Venn diagram detailing shared and distinct genes listed in SCDb or expressed by CD34KSL cells or by SP Lin cells. b Venn diagram detailing shared and distinct genes expressed among CD34KSL cells, SP Lin cells, and SCDb after in silico subtraction. The genes in EST databases for brain, heart, muscle, liver, kidney, and intestine were subtracted in silico from the HSC cDNAs

Table 1

Lists of genes identified as expressed in common among SCDb, CD34KSL, and SP Lin libraries

Accession no.

Gene

NM 008114

Growth factor independent 1B

NM 028460

RIKEN cDNA 3110045G13 gene (3110045G13Rik)

NM 008595

Manic fringe homolog (Drosophila) (Mfng)

NM 022881

Regulator of G-protein signaling 18 (Rgs18)

NM 144886

Exosome component 2 (Exosc2)

XM 354694

Serine (or cysteine) proteinase inhibitor, clade A, member 3G (Serpina3g)

Table 2

Lists of genes identified as shared only between CD34KSL and SP Lin libraries

Accession no.

Gene

NM 008187

Gene trap locus 3 (Gtl3)

NM 026042

RIKEN cDNA 2810405O22 gene (2810405O22Rik)

NM 026753

RIKEN cDNA 1110019N10 gene (1110019N10Rik)

XM 127929

RIKEN cDNA 4933421G18 gene (4933421G18Rik)

NM 144541

Brain and reproductive organ-expressed protein (Bre)

NM 145711

Thymocyte selection-associated HMG box gene (Tox)

NM 172148

cDNA sequence BC028440 (BC028440)

NM 009342

t-Complex testis expressed 1 (Tctex1)

NM 009821

Runt related transcription factor 1 (Runx1)

NM 010149

Erythropoietin receptor (Rpor)

NM 011178

Proteinase 3 (Prtn3)

NM 013585

Proteosome (prosome, macrepain) subunit, beta type 9 (large multifunctional protease 2) (Psmb9)

NM 013814

UDP-N-acetyl-alpha-d-galactosamine:polypeptide N-acetylgalactosaminyltransferase 1 (Galnt1)

NM 013899

Translocase of inner mitochondrial membrane 13 homolog a (yeast) (Timm13a)

NM 018782

Calcitonin receptor-like (Calcrl)

NM 025570

Mitochondrial ribosomal protein L20 (Mrpl20)

NM 026479

DNA segment, Chr 11, ERATO Doi 416, expressed (D11Ertd416e)

NM 026965

Catechol-O-methyltransferase domain containing 1 (Comtd1)

NM 028906

Dipeptidylpeptidase 8 (Dpp8)

NM 030066

Armadillo repeat containing, X-linked 1 (Armcx1)

NM 133786

SMC4 structural maintenance of chromosomes 4-like 1 (yeast) (Smc4l1)

NM 148934

Gene trap ROSA b-geo 22 (Gtrgeo22)

NM 172562

Transcriptional adaptor 2 (ADA2 homolog, yeast)-like (Tada2l)

NM 173440

Nuclear receptor interacting protein 1 (Nrip1)

NM 177342

TAF5 RNA polymerase II, TATA box binding protein (TBP)-associated factor (TAF5)

Fig. 5

Expression of identified genes. Expression of selected SCDb-listed genes shared between CD34KSL cells and SP Lin cells and selected genes shared only between CD34KSL cells and SP Lin cells was analyzed by RT-PCR. Cells analyzed are BM CD34KSL and SP Lin HSCs, CD34+KSL and Lineage marker progenitors, TER119+ erythroblasts, Mac-1+ monocytes/macrophages, Gr-1+ neutrophils, and B220+ B cells

Table 3

Summary of the expression profiles of selected genes determined by RT-PCR

Gene/lineage

34KSL

SP-KSL

34+KSL

Lin

Ter119

Mac-1

Gr-1

B220

Expression of all six genes identified as in common among SCDb, CD34KSL, and SP Lin libraries

 Gfi1b

+++

+++

++

+

++

±

 3110045G13Rik

±

+++

+

 Mfng

++

++

+

 Rgs18

+++

±

+

++

±

 Exosc2

±

±

±

±

±

±

 Serpina3g

++

++

+

±

±

Expression of seven selected genes identified as in common only between CD34KSL and SP Lin libraries

 Armcx

+++

++

+

+

±

 BC28440

+++

+++

++

+

±

±

+

 D11Erf416e

+

+

±

++

±

 1110019N10Rik

++

+

+

++

±

±

±

++

 2810405O22Rik

++

++

++

++

++

±

++−

++

 Bre

++

+++

+

+++

+++

+

+

+++

 Calcrl

±

+

++

+

+++

±

±

3.3 Identification of mRNA-like putative non-coding RNAs

Four thousand and four hundred and fifty clones from the CD34KSL cDNA library did not show any homology to the known protein-encoding genes in the NCBI database. We therefore computationally screened them to see if their products might include putative non-coding RNAs, the biological significance of which has recently been recognized [18, 19] (Fig. 6a).
Fig. 6

Identification of putative mRNA-like non-coding RNAs. a Scheme of the computational screening of non-coding RNA candidates. b List of non-coding RNA candidates. Chromosomal location, number of clones identified, and accession number of each candidate are indicated

Of these, 211 bore candidate sequences that matched the three-step criteria described in Methods (above). Among these 211, 112 clones (82 independent sequences) showed homology with known UniGene mouse ESTs. Of these 82, 55 sequences were hypothesized to represent protein-coding RNA, leaving 43 clones (29 independent sequences) as candidates for non-coding RNAs (Fig. 6b).

4 Discussion

With advances in technologies for HSC purification, many HSC populations have been subjected to gene expression profiling analyses. SCDb, one of the representative HSC databases, lists HSC-specific genes screened by high-throughput sequencing and microarray analysis of fetal liver AA4.1+KSL cells and by microarray analysis of BM Rhodamine-123lowKSL cells. A list of HSC-specific genes has also been provided by detailed microarray analyses of CD34KSL cells in comparison with progenitor cells and differentiated cells [20]. Furthermore, gene expression profiles have been compared among different stem cells (SP CD34KSL HSCs, neural stem cells, and ES cells), with identification of genes expressed in common [21]. Both SP CD34KSL and CD34KSL cells are highly enriched for HSCs compared with fetal liver AA4.1+KSL cells. However, the paucity of SP CD34KSL and CD34KSL cells in BM hampered approaches to expression profiling other than microarray analysis. Cyclopedic full-length cDNA sequencing projects, however, have provided us with an abundance of cDNA data for many kinds of organs, tissues, and cells, HSCs excepted [22]. To obtain a detailed gene expression profile of CD34KSL HSCs, we constructed a CD34KSL cDNA library and performed high-throughput sequencing. We then compared the resultant profile with that similarly obtained for another HSC fraction, SP Lin cells.

The HSC libraries we constructed contained independent clones in numbers comparable with those in libraries made using similar methods (Figs. 1, 2) [23]. Successful subtraction of housekeeping genes in silico allowed us to focus on genes specific to hematopoietic cells (Fig. 4). As expected, most of the genes identified as in common among SCDb, CD34KSL, and SP Lin libraries appeared to be HSC-specific by RT-PCR analysis, while the genes identified as in common only between CD34KSL and SP Lin libraries contained those non-specific to HSCs (Fig. 5, Table 3). Contamination with genes that are not HSC-specific also indicated the limitations of our in silico subtraction approach (Table 3). Furthermore, representative HSC genes, including GATA-2 and Bmi1, were identified in only one HSC library. This might be because too few clones were sequenced. However, this approach is suitable to identify novel transcripts or for profiling of genes with low expression levels, and indeed we could identify five novel genes that are HSC-specific in expression.

By GO assignment, we predicted functions of the identified genes (Fig. 3). Among genes assessed as encoding a membrane protein (CD34KSL: 122, SP Lin: 291, both populations: 118), we could use cell-function classifications to identify novel HSC cell surface marker candidates. These included cell adhesion, a biological process (CD34KSL: 10, SP Lin: 15, both populations: 7), and receptor activity, a molecular function (CD34KSL: 29, SP Lin: 72, both populations: 22). Indeed, among genes specific to CD34KSL and/or SP Lin cells, the deduced amino acid sequence of RIKEN cDNA 9930117H01 contains both a putative signal peptide sequence and a transmembrane domain (Fig. 5). RIKEN cDNA 3110045G13 is similarly predicted to encode a cell surface transmembrane protein with extracellular EGF-like domains, and RIKEN cDNA 2700079M14 to encode a transmembrane protein with an extracellular immunoglobulin-like domain. To analyze expression specificities and functions of these putative cell surface proteins in HSCs would be intriguing. The GO profiling may help in understanding the molecular machineries operating in HSCs.

One of the most surprising results to emerge from mammalian cDNA sequencing projects is that thousands of mRNA-like non-coding RNAs are expressed, constituting at least 10% of poly(A)+ RNAs [24, 25]. Non-coding RNAs are involved in the regulation of epigenetic functions, including chromatin structure and genome imprinting. Inactivation of the X chromosome by Xist RNA is a representative function of non-coding RNAs [26]. Some functions of non-coding RNAs in hematopoiesis have been reported [18, 19]. In most cases, however, the functions of these RNA molecules remain unclear. The biological significance of mRNA-like non-coding RNAs in HSCs in particular has not been clarified. We screened HSC clones for mRNA-like non-coding RNAs and identified 29 candidates. Our data suggest that some mRNA-like non-coding RNAs function in an HSC-specific manner. Understanding of the functions of HSC-specific mRNA-like non-coding RNAs would break open a new field of HSC biology.

By high-throughput sequencing analysis, we have added a number of genes to the list of HSC-specific genes and have identified HSC-specific putative mRNA-like non-coding RNAs. Further characterization of identified HSC-specific genes and their products, particularly with regard to their functional aspects in HSCs, will be highly helpful in elucidating the molecular mechanisms of HSC regulation.

Supplementary material

12185_2008_212_MOESM1_ESM.xls (204 kb)
MOESM1 ESM 1 (XLS 204 kb)
12185_2008_212_MOESM2_ESM.xls (286 kb)
MOESM2 ESM 2 (XLS 286 kb)

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Copyright information

© The Japanese Society of Hematology 2008

Authors and Affiliations

  • Yoshimi Yashiro
    • 1
  • Hideo Bannai
    • 2
    • 4
  • Takashi Minowa
    • 6
    • 7
  • Tomohide Yabiku
    • 2
    • 5
  • Satoru Miyano
    • 2
  • Mitsujiro Osawa
    • 1
    • 8
  • Atsushi Iwama
    • 1
    • 3
  • Hiromitsu Nakauchi
    • 1
    • 9
  1. 1.Division of Stem Cell Therapy, Center for Stem Cell and Regenerative Medicine, The Institute of Medical ScienceUniversity of TokyoTokyoJapan
  2. 2.Laboratory of DNA Information Analysis, Human Genome Center, The Institute of Medical ScienceUniversity of TokyoTokyoJapan
  3. 3.Department of Cellular and Molecular Medicine, Graduate School of MedicineChiba UniversityChibaJapan
  4. 4.Department of InformaticsKyushu UniversityFukuokaJapan
  5. 5.Interdisciplinary Intelligent Systems Engineering Course, Graduate School of Engineering and ScienceRyukyu UniversityOkinawaJapan
  6. 6.Hitachi, Ltd, Life Science GroupSaitamaJapan
  7. 7.Nanotechnology Innovation CenterNational Institute for Materials ScienceIbarakiJapan
  8. 8.Department of Developmental BiologyUniversity of Texas Southwestern Medical CenterDallasUSA
  9. 9.Laboratory of Stem Cell Therapy, Center for Experimental Medicine, The Institute of Medical ScienceUniversity of TokyoTokyoJapan

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