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Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell
Resource Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis Nina Cabezas-Wallscheid,1,2,10 Daniel Klimmeck,1,2,3,10 Jenny Hansson,3,10 Daniel B. Lipka,4,10 Alejandro Reyes,3,10 Qi Wang,5,9 Dieter Weichenhan,4 Amelie Lier,2,6 Lisa von Paleske,1,2 Simon Renders,1,2 Peer Wu¨nsche,1,2 Petra Zeisberger,1,2 David Brocks,4 Lei Gu,4,5,9 Carl Herrmann,5,9 Simon Haas,2,7 Marieke A.G. Essers,2,7 Benedikt Brors,5,8,9 Roland Eils,5,8,9 Wolfgang Huber,3,11 Michael D. Milsom,2,6,11 Christoph Plass,4,8,11 Jeroen Krijgsveld,3,11 and Andreas Trumpp1,2,8,11,* 1Division
of Stem Cells and Cancer, Deutsches Krebsforschungszentrum (DKFZ), 69120 Heidelberg, Germany Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH), 69120 Heidelberg, Germany 3European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany 4Division of Epigenomics and Cancer Risk Factors, DKFZ, 69120 Heidelberg, Germany 5Division of Theoretical Bioinformatics, Department of Bioinformatics and Functional Genomics, DKFZ, 69120 Heidelberg, Germany 6Junior Research Group Experimental Hematology, Division of Stem Cells and Cancer, DKFZ, 69120 Heidelberg, Germany 7Junior Research Group Stress-induced Activation of Hematopoietic Stem Cells, Division of Stem Cells and Cancer, DKFZ, 69120 Heidelberg, Germany 8German Cancer Consortium (DKTK), 69120 Heidelberg, Germany 9Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, 69120 Heidelberg, Germany 10Co-first author 11Co-senior author *Correspondence: [email protected] http://dx.doi.org/10.1016/j.stem.2014.07.005 2Heidelberg
SUMMARY
In this study, we present integrated quantitative proteome, transcriptome, and methylome analyses of hematopoietic stem cells (HSCs) and four multipotent progenitor (MPP) populations. From the characterization of more than 6,000 proteins, 27,000 transcripts, and 15,000 differentially methylated regions (DMRs), we identified coordinated changes associated with early differentiation steps. DMRs show continuous gain or loss of methylation during differentiation, and the overall change in DNA methylation correlates inversely with gene expression at key loci. Our data reveal the differential expression landscape of 493 transcription factors and 682 lncRNAs and highlight specific expression clusters operating in HSCs. We also found an unexpectedly dynamic pattern of transcript isoform regulation, suggesting a critical regulatory role during HSC differentiation, and a cell cycle/ DNA repair signature associated with multipotency in MPP2 cells. This study provides a comprehensive genome-wide resource for the functional exploration of molecular, cellular, and epigenetic regulation at the top of the hematopoietic hierarchy.
2010; Wilson et al., 2009). They give rise to a series of multipotent progenitors (MPPs) with decreasing self-renewal potential, followed by differentiation toward committed progenitors and more mature cells (Adolfsson et al., 2005; Forsberg et al., 2006). MPPs have been subdivided immunophenotypically into MPP1, MPP2, MPP3, and MPP4 populations based on a stepwise gain of CD34, CD48, and CD135 as well as loss of CD150 expression (Wilson et al., 2008). However, despite recent efforts to characterize changes in gene expression and epigenome modifications that occur at distinct stages of differentiation (Gazit et al., 2013; Kent et al., 2009; McKinney-Freeman et al., 2012; Bock et al., 2012), the distinct functional characteristics and the molecular programs that maintain HSC self-renewal and drive progenitor differentiation are poorly characterized. We have taken advantage of recent technological advances enabling analysis of rare cell populations to establish comprehensive mass spectrometry-based proteome, transcriptome (RNA sequencing [RNA-seq]), and genome-wide DNA methylome (tagmentation-based whole genome bisulfite sequencing, TWGBS) data for HSCs and MPPs. We provide a comprehensive insight into the molecular mechanisms that are dynamically regulated during early HSC commitment through the MPP1– MPP4 populations. We uncovered molecular changes at the protein, RNA, and DNA levels as they occur in vivo in the context of physiologic commitment processes.
RESULTS INTRODUCTION Hematopoietic stem cells (HSCs) are unique in their capacity to self-renew and replenish the entire blood system (Orkin and Zon, 2008; Purton and Scadden, 2007; Seita and Weissman,
The five stem/progenitor populations corresponding to HSC and MPP1–MPP4 (Wilson et al., 2008) were isolated by fluorescenceactivated cell sorting (FACS) from the bone marrow of C57BL/6J mice (Figure 1; Figures S1A–S1C available online). These cells Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 1
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
A
Multipotent stem and progenitor cells
Myeloid committed
Linneg Sca-1+
Linneg Sca-1-
c-Kit+
Mature effector cells Mega Plat
MEP CD34+ CD48CD150+ CD135-
MPP2
HSC
MPP1
MPP3
CD34+ CD48+ CD150CD135-
MPP4
CD34+ CD48+ CD150CD135+
105
10
4
4
Neu Mac
Lymphoid committed
103
T-cells
CLP
B-cells
4.1
10
5
10
4
0
0
FSC-A
104
105
HSC
105
103
0
0
10
3
CD48
10
4
10
5
HSC; MPP1 MPP2; MPP3 MPP4
HSC; MPP1 MPP2; MPP3/4
HSC; MPP1 MPP2; MPP3 MPP4
Fig. 2,3,4
Fig. 3,4,5,6,7
Fig. 6,7
Fig. 4
Reconstitution experiments
Data integration
105
HSC and MPP signatures (RNA, protein) RNA-protein correlation Differential exon usage lncRNAs Differential DNA methylation RNA-DNA methylation correlation d
10
103
0
105
8.6
4
CD150
CD150
10
104
CD34
MPP1 MPP2 14.1
4
HSC; MPP1
DNA methylome (TWGBS)
31.9 103
0
Sca-1
72.3 10
103
4
CD150
105
RNAsequencing
0
5.9
50K 100K 150K 200K 250K
Quantitative proteomics 31.4
103
98.1 0
(n=3)
NK
103
0
FACS sorting of primary cells
Linneg Sca-1low c-Kitlow
c-Kit
10
Ery
GMP
CD135
5
Bone marrow isolation
CMP
CD34+ CD48+ CD150+ CD135-
10
Lin neg
B
CD34CD48CD150+ CD135-
C
c-Kit+
103
MPP3 0
10
0
41.6
3
CD48
10
4
10
5
MPP4 0
83.3
103
104
Functional readout d
Differentiation potential
105
CD48
Figure 1. Global Analysis of Early Adult Hematopoiesis (A) The mouse hematopoietic system. HSCs give rise to MPPs, which commit to more mature progenitors with restricted cell fate. CMP, common myeloid progenitor; MEP, megakaryocyte erythroid progenitor; GMP, granulocyte macrophage progenitor; CLP, common lymphoid progenitor; Mega, megakaryocyte; Pla, platelet; Neu, neutrophil; Mac, macrophage; NK, natural killer cell. (B) FACS scheme used to isolate primary mouse cells: HSC (Linneg Sca-1+ c-Kit+, LSK, CD34- CD135- CD150+ CD48 ), MPP1 (LSK CD34+ CD135 CD150+ CD48 ), MPP2 (LSK CD34+ CD135 CD150+ CD48+), MPP3 (LSK CD34+ CD135 CD150 CD48+), and MPP4 (LSK CD34+ CD135+ CD150 CD48+). L, lineage-negative. (C) Experimental study design. Shown are the different generated data sets and their respective figure numbers. See also Figures S1 and S7.
were used as a source for quantitative proteomics, RNA-seq, TWGBS, and functional reconstitution experiments. Proteome Differences between HSCs and MPP1 Cells Uncovered Key Molecular Players of Long-Term Reconstitution Potential The transition from CD34 HSCs to CD34+ MPP1 is accompanied by a switch in the cells’ reconstitution capabilities. We transplanted mice with 50 HSCs and observed that 100% of primary and 80% of secondary recipients showed multilineage repopulating activity (Figures S1D–S1F). In contrast, 56% of mice transplanted with MPP1 cells showed reconstitution of the primary recipient, and no engraftment was detected in secondary recipients. This is consistent with previous reports showing that the two populations are very similar but display a measurable difference in long-term self-renewal (Ema et al., 2006; Osawa et al., 1996). To investigate the molecular basis of this difference in self-renewal, we compared the proteomes of HSC and MPP1 cells in a quantitative mass spectrometry-based approach (Figure 2A; Figure S2A). From 400,000 HSCs and MPP1 purified in biological triplicate, 6,389 protein groups were identified (Table S1). These covered a broad range of protein classes, e.g. receptors (222) and transcription factors (549) (Figure 2B), as well as low-abundance proteins, as judged from the estimated protein levels that spanned more than seven orders of magnitude (Figure S2B). 2 Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc.
In total, 4,037 proteins were quantified in all three replicates (Figure S2C). Of these, only 47 proteins were expressed differentially (false discovery rate [FDR] = 0.1), together with nine proteins detected exclusively in MPP1 (Figure 2C; Figure S2D; Table S1). This suggests that the differential self-renewal potential between HSCs and MPP1 is elicited by a relatively small number of proteins. Gene ontology (GO) enrichment analysis revealed that cell cycle as well as related processes, such as DNA replication and cell proliferation, were strongly overrepresented in MPP1 (Figure 2D). Indeed, protein-protein interaction network analysis highlighted a large group of interconnected cell cycle proteins being expressed at elevated levels in MPP1 compared with HSCs. All major processes associated with the cell cycle machinery, including DNA polymerases (Pol1a, Pole), cell cycle checkpoint proteins (Chek1), DNA methylation maintenance and cell cycle progression (Cdk1, Cdk6), and others were represented in the network (Figure 2E). In contrast, HSCs were enriched in the monosaccharide metabolic process, including the glycolytic enzymes lactate dehydrogenase b and d (Ldhb, Ldhd) as well as Pygm. In addition, cellular ion homeostasis, including two iron transporters (Fth1, Ftl2), oxidation reduction (Rrm1, Rrm2), and response to hypoxia (Mecp2) were enriched in HSCs. Together, the data are consistent with an anaerobic metabolic program employed by quiescent HSCs, whereas MPP1 cells become primed for entry into the cell cycle and start proliferating.
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
A
B
22
C Protein count 0
5
Igf2bp2
1000
1500
nucleic acid binding hydrolase transferase enzyme modulator transcription factor cytoskeletal protein oxidoreductase kinase ligase transporter receptor membrane traffic protein signaling molecule protease calcium-binding protein transfer/carrier protein chaperone phosphatase isomerase lyase defense/immunity protein cell adhesion molecule extracellular matrix protein structural protein cell junction protein tr.membr. rec. reg. surfactant storage protein viral protein
MPP1
4x10 cells, n=3 Cell lysis/protein extraction/ protein digestion
Peptide labeling Peptide fractionation
High-resolution nano LC-MS/MS
Hmga2
2
log2 ratio HSC/MPP1
HSC
500
Ldhd Hmga1 Hmga1 Gda Fscn1 Pygm Myof Irgm2 Serpinb6a Nagk Ifitm1 Cd38 Camk2d Cbr3 Gstm1 Tgm2 Slamf1 Cmas Ldhb Gstm5
0
Stmn1 Dnmt1 Pola1 Smc2 Ndrg1 Hat1 Plek Lig1 Tyms Chek1 Hells Uhrf1 Cdk6 Kif4 Serpinb1a Dut Kif2c Top2a Thy1 Ncaph Rrm2 Anxa2 Pole Gins3 Cdk1
−2
Dnajc6; Sptlc2; Mtmr9; Zc3h10 Mis12; Mllt1; Faah; Steap3; Ncf4
0.0
Hat1 20
# proteins
oxidation reduction 14 monosaccharide metabolic process 8 cellular ion homeostasis 6 regulation of chromosome organization 3 neuron projection development 4 response to hypoxia 3 0 DNA replication cell cycle 5 chromosome condensation 0 DNA packaging 2 chromosome organization 5 cell division 3 M phase 2 cellular response to stress 1 mitosis 2 cell prolif eration 1 DNA repair 0 deoxyribonucleotide metabolic process 0 cell activation 2 neg. reg. of transcription, DNA- dependent 1
0
20
40
Cdk6
Lig1 Uhrf1
Hells Plek
Dut
Stmn1 Cdk1
Smc2
19 29
1
Kif4 Rrm2
Ncf4
Top2a
Gins3 Kif2c
9 8 9
0 -1
10 16 14 13 12
F 2
Ncaph
Chek1
7
25 +9
Dnmt1 Pole
Pola1
2 1 1 1 1 1
1.5
−log10(adjusted p−value HSC/MPP1)
E HSC MPP1
1.0
log2 ratio HSC/MPP1
D
0.5
Tyms
Mis12
-2
Hmga1 Gstm1
Pygm
Hmga2 Gstm5
Ldhb
-3
Ldhd
4 7 7
Node fill color
Edge color 0.41 1 Combined score STRING
-2 2 2.7 Average log2 ratio HSC/MPP1
Node size
0.02 0.1 adj. p value
Node shape Uniquely detected MPP1
Figure 2. Proteomic Comparison of HSCs and MPP1 (A) Quantitative proteomics workflow. (B) Classification of identified proteins. Bars show the number of proteins within each functional class. (C) Differential protein expression. Proteins expressed significantly higher (FDR = 0.1) in HSCs and MPP1 are shown in red and blue, respectively. The lower right corner shows proteins exclusively detected in MPP1. (D) Overrepresented biological processes of differentially expressed proteins (202 with FDR = 0.15 and 9 exclusively detected in MPP1). Top, HSC-enriched. Bottom, MPP1-enriched. Neg. reg. of transcription, negative regulation of transcription. (E) Protein network of differentially expressed proteins. The edges show known and predicted protein-protein interactions. STRING, Search Tool for the Retrieval of Interacting Genes/Proteins; adj., adjusted. (F) CD cell surface proteins expressed differentially. The average log2 ratio ± SD is shown. See also Figure S2.
Of the 22 proteins with higher expression in HSCs, we found three high mobility group AT hook proteins; namely, two isoforms of Hmga1 as well as Hmga2 (Figure 2C; Figure S2E). These chromatin-modulating transcriptional regulators have been reported recently to be potent rheostats of tumor progression (Morishita et al., 2013; Shah et al., 2013) and HSC self-renewal, proliferation, and lineage commitment checkpoints (Battista et al., 2003; Copley et al., 2013). In addition, the protein showing the highest fold change was the Hmga2 target Igf2bp2 (Figure 2C; Cleynen et al., 2007). Hmga1, Hmga2, and Igf2bp2 are downstream mediators of the Lin28-let7 pathway that link metabolism to prolifera-
tion and drive self-renewal (Shyh-Chang et al., 2013; Yaniv and Yisraeli, 2002). High expression of this pathway in HSCs suggests a role in self-renewal of HSCs, whereas its downregulation in MPP1 cells correlates with decreasing self-renewal activity. Two glutathione S-transferases (GST), Gstm1 and Gstm5, were found to be expressed at higher levels in HSCs compared to MPP1 (Figure 2C). Moreover, elevated levels in HSCs were consistent for all 11 GSTs quantified (Figure S2F). This points to a requirement for this enzyme class in HSCs, which may relate to their ability to mediate the conjugation of xenobiotics for the purpose of detoxification and defense against environmental Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 3
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
stress and cellular damage (Tew and Townsend, 2012). This suggests that homeostatic HSCs have an array of mechanisms in place to protect their cellular integrity, extending an observation that we have made previously in hematopoietic progenitors (Klimmeck et al., 2012). Along these lines, two interferon-inducible proteins involved in host defense (Ifitm1 and Irgm2) were expressed at higher levels in HSCs compared with MPP1 (Figure 2C), suggesting that the type I interferon pathway is not only critical for the response to stress but also during homeostasis (Essers et al., 2009; Trumpp et al., 2010). Moreover, HSCs and MPP1 employ different intracellular serpins for protection against death during stress because Serpinb6a and Serpinb1a were expressed at higher levels in either HSC or MPP1, respectively (Figure 2C). Taken together, HSCs harbor proteins involved in immune defense and detoxification, indicative of an increased self-protecting repertoire compared with MPP1. Among the 86 quantified cluster of differentiation (CD) surface proteins (Table S1), nine showed a strong differential expression between HSC and MPP1 (Figure 2F). These included CD34 as well as other membrane proteins described previously in the context of stem/progenitors, namely CD38, CD41, CD49b, and CD90 (Benveniste et al., 2010; Dumon et al., 2012; Weissman and Shizuru, 2008). Additionally, our analysis identified CD82 and CD13 to be expressed at higher levels in HSCs, whereas CD11b had a higher expression level in MPP1. These findings may be used to develop additional marker combinations to distinguish HSCs and MPP1 as well as to further refine the classification of stem/progenitor intermediates. The Transcriptome and Proteome Are Highly Coordinated upon HSC Differentiation We next analyzed the transcriptome of HSCs and MPP1 using high-throughput RNA sequencing starting from 30,000 FACSsorted primary cells (Figure 3A; Figure S3A). Robust and reproducible data were obtained for all samples, with more than 2 3 108 total readings per population (Figure S3B). In total, transcripts corresponding to 27,881 genes were identified (Table S2). Those genes were classified, according to their database annotation, into 21 RNA categories, and, as expected, proteincoding transcripts were highly represented (68.9%) (19,219; Figure 3B). In line with the proteome data, the protein-coding transcripts displayed a high diversity of functionalities (Figure 3C), including transcription factors (TF, 1,776 genes), receptors (1,796), and cell adhesion molecules (584). Additionally, the expression of 8,662 noncoding RNA species was identified, including pseudogenes (4,034), microRNAs (miRNAs) (642) and long noncoding RNAs (lncRNAs) (589). We found 479 genes to be expressed differentially between HSCs and MPP1 (FDR = 0.1; Table S2; Figure 3D). Consistent with the GO terms enriched in the proteome data, we found an overrepresentation of processes related to metabolism (positive regulation of the phosphate metabolic process and sulfur compound metabolic process) and response to hypoxia (cellular response to oxygen-containing compounds) within the genes expressed at higher levels in HSCs (Figure 3E). Furthermore, TFs involved in different aspects of developmental signaling (cellular developmental process, cell differentiation Mecom, Hoxb7) were enriched in HSCs. In contrast, the DNA metabolic process, cell cycle, and nuclear division were enriched in 4 Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc.
MPP1 in agreement with the proteome findings and increasing proliferative activity. To systematically investigate potential posttranscriptional regulation at the transition from HSCs to MPP1, we correlated the transcriptome data with the proteome data (Figures 3F–3G; Table S3). Out of the quantified proteins, we were able to assign 99.3% (4,007 of 4,036) to their corresponding transcripts (Figure 3F; Figures S3D–S3E). Notably, in addition to a strong positive correlation between both data sets, all proteins that were expressed differentially were also consistently upregulated or downregulated at the transcript level (Figure 3F). For example, the response to cytokine stimulus was overrepresented at both the RNA and protein levels in HSCs, whereas genes belonging to the cell cycle and DNA replication were expressed strongly in MPP1 (Figure 3G). As a notable exception to this overall pattern, enzymes in glycolysis/gluconeogenesis (Cmas, Gstm5, and Nagk), as well as several downstream targets of the Lin28-let7 pathway (Hmga1, Hmga2, and Igf2bp2), showed only modest changes at the mRNA level (Figures 3F and 3G), in line with their suggested regulation at the posttranscriptional level (Shyh-Chang and Daley, 2013). In addition, we found the GO term chromosome enriched and several long-lived histones related to chromosome organization (e.g. H2afz, H2afy2) to be downregulated at the RNA level in MPP1. Because these were not detected as being expressed differentially at the protein level (Figures 3F and 3G), we hypothesize that the regulatory initiation of nuclear reorganization starts already in MPP1 but becomes only operational at subsequent stages. The overall high similarity between the transcriptome and proteome suggests that posttranscriptional regulation in HSC/progenitors might be used preferentially only for some pathways, including the Lin28-Hmga-Igf2bp2 axis. MPP2 Cells Are Multipotent, but MPP3 and MPP4 Show a Differentiation Lineage Bias To investigate the functional attributes of MPP2–MPP4, we performed in vivo reconstitution assays using FACS-sorted cells (Figure 4A; Figures S4A–S4B). The contribution of transplanted MPP2 cells to the T cell, B cell, and myeloid progeny in peripheral blood (PB) after 4 months was 23%, 35%, and 32%, respectively, compared with the 56%, 70%, and 82% observed for transplanted HSCs (Figures 4B and 4C; Figure S4A). In contrast, MPP3 and MPP4 cells generated only a small number of differentiated cells (mostly below 2%), although the progeny were retained in the PB for at least 4 months. MPP3-transplanted mice showed a bias toward the production of myeloid cells, which was evident from 1 week posttransplantation but decreased over time. MPP4-transplanted animals preferentially generated lymphoid B cell progeny starting at 3 weeks posttransplantation, whereas myeloid progeny remained below 1% during the entire observation period. In line with this, T cells peaked after 3 weeks in the thymi of MPP4-transplanted mice (Figure S4B). In addition, colony-forming unit (CFU) assays showed that, like MPP2 and MPP3 progenitors, MPP4 cells also generate myeloid colonies (Figure S4C), in line with earlier studies performed with CD135+ lymphoid-primed multipotent progenitors (Adolfsson et al., 2005). In summary, MPP2 generates a large number of long-term myeloid, B cell, and T cell progeny upon transplantation. In contrast, the other two populations generate only a limited number of progeny in vivo, with a significant lineage
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
Count 0
HSC MPP1 n=4 n=3 30,000 cells
polyA-RNA High throughput sequencing 1 sample/lane Paired-end reads 100bp
B
68.93% 19219 protein coding 14.47% 4034 pseudogene 3.77% 1052 antisense 2.30% 642 miRNA 2.11% 589 lncRNA 2.07% 577 snoRNA 2.02% 562 processed transcript 2.01% 560 snRNA 0.88% 244 misc RNA 0.42% 117 IG LV gene 1.02% 285 other non-coding species
HSC MPP1
E
# transcripts 180
120
60
0
response to stimulus 167 cellular developmental process 83 cell dif f erentiation 80 regulation of localization 44 cell migration 25 positive regulation of phosphate metabolic process 24 cellular response to oxygen-containing compound 16 sulf ur compound metabolic process 9 response to alcohol 8 cell cycle DNA metabolic process nuclear division microtubule-based process regulation of cell cycle DNA recombination
G
HSC/MPP1 RNA score
1.0
500
1000
1500
2500
29 17 7 7 21 3
60
120
180
111
F
4,007 19,219
32 15 63
53 50 24 12 10 7 2 4
D
277
202 Mean of normalized counts
47 +9
RNA
RNA, protein coding Δ RNA FDR=0.1
Protein
Protein Δ Protein FDR=0.1
RNA & Protein
RNA & protein Δ RNA & protein FDR=0.1
357 435
91 53 52 50 39
Gbp10
13
Vitamin C
Gstm1
vacuolar membrane
0.5
Type
cellular response to cytokine stimulus
GOBP name GOCC name KEGG name Keywords
Extracellular matrix Glycolysis/Gluconeogenesis
0.0
log10(Size) 1 2 3
nucleosome
−0.5
2000
nucleic acid binding hydrolase transferase enzyme modulator transcription factor cytoskeletal protein oxidoreductase kinase ligase transporter receptor membrane traffic protein signaling molecule protease calcium-binding protein transfer/carrier protein chaperone phosphatase isomerase lyase defense/immunity protein cell adhesion molecule extracellular matrix protein structural protein cell junction protein tr.membr. rec. reg. surfactant storage protein viral protein
Myof
Pygm Igf2bp2 Ifitm1 Gda Zc3h10 Hmga2 Mis12 Hmga1 Sptlc2 Mllt1 Cmas Anxa2 Gstm5 Faah Mtmr9 Tyms Nagk Steap3 H2afz Ncf4 Cdk6 Dnajc6 Serpinb1a H2afy2 Hmgb2 Cdk1 Top2a Hist1h1a Kif4 Stm1 Smc2
RNA log2 fold change (HSC/MPP1)
C
log2 fold change (HSC/MPP1)
A
FDR
Tricarboxylic acid cycle Chromosome Nuclear pore Cell cycle complex
0.015 0.010 0.005 0.000
−1.0
DNA-dependent DNA replication initiation
−1.0
−0.5
0.0
0.5
Protein log2 fold change (HSC/MPP1)
1.0
HSC/MPP1 protein score
Figure 3. Comparison of the Transcriptome and Proteome of HSCs and MPP1 Cells (A) RNA-seq workflow. (B) RNA categories of 27,881 quantified genes. Shown are the percentage and number of RNA species within each RNA class. (C) Classification of quantified protein-coding genes. Bars show the number of genes within each functional class, ranked based on the protein coverage (Figure 2B). (D) Differential gene expression. Genes highly expressed (FDR = 0.1) in HSCs or MPP1 are shown in red and blue, respectively. (E) Overrepresented biological processes of differentially expressed genes. (F) Overlap and correlation between protein and RNA expression changes. Top panel, integration of proteome and transcriptome data sets. Quantified proteins (blue) were mapped to protein-coding transcripts (green). Differentially expressed transcripts (dark green, 78 mapped of 435) and differentially expressed proteins (dark blue, 47 significant plus 9 exclusively detected proteins) were assessed for overlap (red). Bottom panel, significant (FDR = 0.1) changes at RNA (green), protein (blue), and both levels (red, correlation coefficient R = 0.93), respectively. The box on the left indicates exclusively detected proteins. (G) 2D GO enrichment analysis of protein and RNA expression changes. Red regions correspond to concordant enrichment or lower expression. Blue and green regions highlight terms that are enriched or lower in one direction but not in the other, whereas terms in yellow regions show anticorrelating behavior. GOBP, gene ontology biological process; GOCC, gene ontology cellular compartment; KEGG, Kyoto Encyclopedia of Genes and Genomes. See also Figure S3.
Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 5
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
A
MPP2
B
CD45.1/2 Supportive
Donor CD45.2 (%)
MPP3 CD45.2 B6J Donor
CD45.1 Recipient
MPP4
1 -16 weeks
Read-out PB, BM, SPL & THY
MPP2
100
MPP3
100
10
10
10
1
1
1
0.10
0.10
0.10
0.01
0.01
1
3
6
9
12
My-cells B-cells T-cells
MPP4
100
16
1
3
6
9
12
0.01
16
1
3
6
9
12
16
CD45.1
Weeks
MY-cells LY-cells
1
C CD45.2
800
Count
9
12
1
16
3
6
9
12
16 Fold change x0.0003 x0.1
MPP2 MPP3 MPP4
E CD34
6
x1
0
D
3
My-cells B-cells T-cells
−3
−1
1
3
Row Z-score
MP P 4
20 HSC
MPP3
3,721 1,040 MP P 2
PC 1 B cell receptor signaling pathway
Retinoic acid metabolic process
2
3
2
HSC MPP1 MPP2 MPP3 MPP4
2
1
1
1
0
0 0
-1 -1
-1
-2 -3
Retinoic acid metabolism Aldh1a1* Bco2* * * * * Aldh1a7* Aldh1a3 * * *
Adh1
Regulation of metabolism
Rbp1* * *
Crabp2
Igf2bp2* *
Lin28b* *
Cyp26b1* *
Cyp26a1
Aldh1a2*
Lin28a* *
Hmga1 * * *
Hmgb2* * * * *
Hmga2*
*
*
Insrr*
Igf1*
Rara*
Rarb*
Rxra * *
*
Hif3a* * * * *
Igf2*
*
* Irs2*
Igf2r*
* *
Foxa3* * * *
Lin28b
Let7
*
Rarg*
*
* *
*
Igf2bp2; Hmga1; Hmga2 Insr; Igf1; Igf2; Igf2r
Ppard* Ppargc1a* * * * *
Pparg
*
Anaerobic Glycolysis
*
H 1 2 3 4
* RNA DEG HSC-MPPs FDR=0.1
Mean expression
Rdh5* * * *
Ldhb*
*
*
OxPhos; TCA Proliferation
* RNA DEG HSC/MPP1,2,3 or 4 FDR=0.1 Proteome DEP HSC/MPP1 FDR=0.1
RAR / PPAR signaling
N genes
60
54
10
0
1484 247 27
0
6
86
0
7
155
16
8
497
431
9
36
0
10
57
0
11
20
0
12
22
0
13
204
50
14
28
0
15
178
57
16
17
17
84
0
18
340
261
4
19
25
0
20
170
47
21
39
0
22
14
0
23
42
0
24
3
0
25
709
324
26
227
41
27
58
0
28
4
0
1176 306
30
11
0
31
330
306
32
0
0
Enriched Non-enriched
3 Rtel1
M
L 35
84 86
14 103 62 51
5
115 40 5
301 338 113 414 152 70 12
56 38 45 22 135 136 48 201 67 17 13
82 87
9
16 13
24 11 18 12 6
7
8
5
37
21
118 39
48
77 313
18
30
35 52 63 536 77 24 48 17 45 32 51 9 47 67 41 33 35
Fanci
Rad54b
Exo1
Hmgb2
Li g 1
Fanca 6
46 49 38 47 36 157 149 53 235 76 53 70
Uhrf1
Pol d3
9
Ube2a 76 73
Br i p 1
Erc c1 Rpa2
10
5 18 16
Topb p1
Rad51
Faap24 15
6
Btbd 12 Rad18
Brc a1
Paxip1
47
7
32 35 18 18
8
Xrc c3
Gm15428 Rad51c
Rbb p8 Apitd1
3 24
Mus81
Em s y Fancb Fancc
4
38 10 12 18 7
12
27 4 7
7
22 29
6
3 22 21
8
39 10 13
3
19
16 10 19 15 10 21
ex tr re ace s l in po c lula fla ns el r m e l a reg ce m to dh io ll c a d su ev ell tor wo esi n rfa el co y r u n on op m e d i ce m m sp ng re ce re c ent uni ons pt sp ell al ca e or on p pr tio si se rol oc n gn t ife e al o s ra ss in t ti ch c g i m on ro m ell pat ulu m hw s m e ig a os ta ra y om bo cy ch t to e lic ves ion ro s o k m rg pro icle el os a e to nu ni ces om z n c s e, or leo atio g s n ce nt ce ani om ro ll za e ly m m d tio e r ivi n d e ph fe oc c D D ic r sio ns yt yt N N e n e e m ok A r A gio re e in e re n sp d e p p l i p a o ia r ca ir T ns te od ti le ce e t d im uct on ca u k ll o m i o no em res n o d b ni b pir in B cyt iffe act unit ca ry at te c e re er y l W on or rle ell ch nt ium y nt ic f sy ukin diff em iati re ore st −6 ere ota on ce lim em p n x pt b d ro tia is o e d ti m r si mo ve uc on el n gn rp lop tio an e a h m n re int os rve lingoge en sp er om d p ne t on leu e eve at si se ki or lo hw s to n−1 gan pm ay in p iz en te ro at t rfe d io r u n pr JA pro en on ctio po − e si −B K− tein doc be n tiv e ce STA se yto ta re ll T cr sis gu d ca iff ca etio la er sc n lc p tio n re ium lat co ent ade of gu i el ag ia o R la n et ul tion N tio h ac a A o t ti ER men o me iva on K1 tabf pr ost tion o an oli teo asis n d e c p ly ER ur ro si s M K2oge ces AP c n s i a e m nte k K s sis ye rfe ph ina cascad lo r o id n os se ca e le −g ph ac de uk a o t oc mm lip ry ivit yt a id lat y e p bi io di ro nd n ffe d in re uct g nt io ia n tio n
HSC (#2) MPP1 (#3) HSC-MPP1 (#4) MPP1-MPP2 (#7) HSC-MPP1-MPP2 (#8) MPP2-MPP3 (#13) MPP1-MPP2-MPP3 (#15) HSC-MPP1-MPP2-MPP3 (#16) HSC-MPP4 (#18) HSC-MPP1-MPP4 (#20) MPP3-MPP4 (#25) HSC-MPP3-MPP4 (#26) MPP2-MPP3-MPP4 (#29) MPP1-MPP2-MPP3-MPP4 (#31)
1 0 0.2 adjusted p-value
N GOs
390
29
Ldhb: RNA mean expression >700 Ldhc: RNA mean expression <700
Row Z-score 0
0
2 3 5
Hif1a
Retinoic acid metabolic process
Rdh10* * *
1
4
Insr
*
HSC
K
J
MPP1
-2
HSC MPP1 MPP2 MPP3 MPP4
Cd96 Itgax Selp Ldhb H2−Aa Ctla2b Igtp Htatip2 Sdpr Gp1bb Aqp1 Tal1 Bpgm Aldh1a1 Fhl1 Fli1 Mpl Pdzk1ip1 vWF Nckap1 Gata2 Epor Zfpm1 Tspan33 Cmas Clu Gpr64 Gstt1 Klf1 Itga2b Gata1 Hemgn Gp5 F2r Mrvi1 Rgs10 Lmna Thpo Car1 Cpa3 Il1rl1 Gadd45a
3,826
Mitosis
Mean log2 fold change
H
HSC MPP1 MPP2 MPP3 MPP4
−3
1,797
-40 -20 0 20 40
CD48
MP P 3
MP P 1
479
MPP2
-40
I
2,437
HS C
-20 CD135
1,436
3,240 4,043
MPP1
0
4,288
Cluster
mean RNA expression
CD150
G
MPP4
MPP4
PC 2
40
MPP3
F
MPP2
HSC MPP1 MPP2 MPP3 MPP4
Cl s p n Fancd2
Ogg 1 Nud t1 M lh1 Mbd4
Rad50
Rpa1
Rad52
Chek1
Chaf1a
Rad54l Cdca5
Neil3
Mcm8
Smc4
Smarc b1
Eme1 Kif22
LOC677447
Kat5
Rad9 H2afx
Msh6
Trip 13
No n o
Pol a1
Bar d1
Foxm1
Pcna
Trp53 Rad51ap1
Node size
1
56 Degrees
Dt l
Dna2
Ruvbl 1
0.4 1 Combined score STRING
Hus1b Cul 4a Baz1b
Ssr p1
Uchl 5 Nsmc e1 Po l q
Act l6a
Pole
Smc2
Nsmc e2
Edge color
Smc1a
Pif1
Po l h Eef1e1
Dek
Node color MPP1 MPP2 MPP3 MPP4 Highest expression
(legend on next page)
6 Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc.
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
bias toward mostly myeloid (MPP3) or lymphoid (MPP4) cell types. Clustering Analyses Identify Global Molecular Differences during HSC Differentiation We sequenced the transcriptomes of the MPP2, MPP3, and MPP4 populations in biological triplicates (Figures 4D–4M; Figures S4D–S4G). The surface markers used for FACS showed consistency between transcript and protein levels (Figure 4D). We detected 6,487 genes to be expressed differentially across all five populations (FDR = 0.1; Table S2). The differential expression of a set of genes was confirmed by quantitative real-time PCR (Figure S3F), validating the robustness of the data. Unsupervised clustering of gene expression profiles grouped together HSC and MPP1 as well as MPP3 and MPP4 (Figure 4E). This was verified by principal component analysis (Figure 4F) and by the numbers of differentially expressed genes in each pairwise comparison (Figure 4G). In all analyses, MPP2 was placed between the HSC-MPP1 and MPP3-MPP4 clusters (Figures 4B and C; Figures S4A and S4B). Clustering of Gene Expression Profiles Suggests Processes Operating in HSC/MPP1-MPP4 Cells The 6,487 genes with differential expression across the five cell populations were enriched in specific GO terms (http:// www-huber.embl.de/SMHSC/HSCGO/goAnalysis.html; Supplemental Information), of which we highlight three selected examples (Figure 4H). Based on the expression data, MPP2 is the mitotically most active population, whereas HSC is the most quiescent one. In agreement with the functional lymphoid differentiation bias observed in MPP4-transplanted recipients (Figures 4B and 4C), the B cell receptor signaling pathway was enriched in this population. Consistent with this, mapping of the HSC-MPP data against granulocyte macrophage or lymphoid-primed progenitor signatures described previously revealed an enrichment for MPP3 and MPP4 populations, respectively (Ma˚nsson et al., 2007; Figure S4E). In addition, genes of a
megakaryocytic erythroid signature described previously (Ma˚nsson et al., 2007; Gekas and Graf, 2013; Sanjuan-Pla et al., 2013) were expressed preferentially in HSCs, MPP1, and MPP2 compared with MPP3 and MPP4 (Figure 4I). Consistent with the differential expression of metabolic enzymes between HSCs and MPP1 (Figures 2D and 3E), we found genes belonging to the retinoic acid metabolic process to be highly expressed in HSCs compared with all other populations (Figures 4H and 4J), suggesting a role in adult HSCs. Furthermore, we found evidence for stage-dependent and isoform-specific expression of essential glycolytic enzymes in HSCs (e.g. Aldoc, Ldhb; Figure S4D), extending recent studies demonstrating the relevance of anaerobic glucose metabolism for the maintenance of HSC self-renewal (Takubo et al., 2013). In contrast, the large majority of enzymes involved in the tricarboxylic acid cycle and oxidative phosphorylation showed MPP2-enriched expression (e.g. Fh1, Aco2). Interestingly, although Lin28b and its target genes, including Hmga2 and Igf2bp2, were highly expressed in HSCs compared with MPP1, the family member Lin28a was specifically upregulated in MPP3 (Figure 4J). Therefore, our findings support the recently established role of the Lin28-let7 axis in glucose metabolism in stem cells (Shyh-Chang and Daley, 2013) and suggest Lin28b as a candidate for an upstream posttranscriptional regulator of glycolytic enzymes in HSCs (Figures 3F and 3G). Next we assigned each of the 6,487 differentially expressed genes to one of the 32 possible relative expression patterns (Figure 4K) and tested for overrepresented GO terms within each pattern (Table S4). Population-specific GO terms are displayed in Figure 4L. Consistent with the strong upregulation of cell cycle-associated genes in MPP2, we observed that 43% of the genes involved in DNA repair also showed the highest expression in MPP2 (Figure S4F). The protein-protein interaction network of the HSC-suppressed DNA repair genes comprised, e.g., Brca1 and Exo1 (Figure 4M), representing potential players for the switch in DNA repair mechanisms between HSCs and MPP2. Interestingly, the two common mechanisms for repair of DNA double strand breaks (homologous recombination and
Figure 4. Lineage Potential and Whole Transcriptome Molecular Signatures of HSC-MPP1-4 Populations (A) Experimental workflow for investigation of the in vivo reconstitution potential. MPP2, MPP3, and MPP4 were transplanted into lethally irradiated mice, and the myeloid (MY) and lymphoid (LY) outcomes were monitored over time by flow cytometry. SPL, spleen; THY, thymus. (B and C) Monitoring the myeloid and lymphoid outcomes in peripheral blood after transplantation of MPP2, MPP3, and MPP4. The relative percentage (B) and contribution (C) of donor myeloid (blue), B cells (gray), and T cells (purple) were monitored from the peripheral blood of MPP2-, MPP3-, and MPP4-transplanted animals at 1–16 weeks. n = 8-12 per group. The sizes of the circles in the right plot represent fold change relative to HSC contribution after 3 weeks. (D) Differential RNA expression of the surface markers used for FACS. Average RNA expression ± SD is shown. (E) Clustered heat map. The colors represent the normalized average read count in each of the five cell populations for all differentially expressed genes (FDR = 0.1). (F) Principal component analysis. (G) Relative distances are shown based on numbers of differentially expressed genes in pairwise comparisons. (H) GO enrichment analysis of 6,387 genes with differential expression. Three representative examples of significantly enriched GO terms out of 1,001 terms are shown. (I) Expression of megakaryocytic/erythrocytic genes. (J) Transcriptome and proteome signatures of metabolism. Average RNA expression ± SD is shown (arbitrary units). RAR, retinoic acid receptor; PPAR, peroxisome proliferator-activated receptor; OxPhos, oxidative phosphorylation; DEG, differentially expressed genes. (K) Gene expression clusters. 6,487 differentially expressed genes were grouped into 32 clusters based on higher expression (enriched) in one or several cell population(s) compared with mean expression level across all cell populations. (L) Representative overrepresented GO terms of gene expression clusters. The number of genes within each cluster is shown for significantly enriched GO terms (FDR = 0.1). (M) Network of HSC-suppressed DNA repair genes. Genes with low expression in HSCs (Z-score < 1.5) and with known and predicted protein-protein interactions are shown. The colors depict the MPP population with the highest expression. Note that most genes are most highly expressed in MPP2. See also Figure S4.
Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 7
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
A
HSC
B
-log10(p value)
0
1
Nuclear Receptors Monoamine GPCRs TGF Beta Signaling Pathway Retinol metabolism GPCRs, Other Diurnally Regulated Genes with Circadian Orthologs Ovarian Inf ertility Genes Exercise-induced Circadian Regulation Senescence and Autophagy Splicing f actor NOVA regulated synpatic proteins
2
Frizzled ligands
Wnt1
Wnt2a
Wnt2b
Wnt3
Wnt3a
Wnt4
Wnt5a
HSC
Wnt5b
MPP1 MPP2
3
MPP3
4
Wnt6*
2
Wnt7a
Wnt7b
Wnt10a* Wnt10b
Wnt11
Wnt16
MPP4
*
4 2 3 3 2 3 4 2
HSC-MPP1
Wnt receptors
Fzd1
Fzd2*
Fzd3*
Fzd4*
-log10(p value)
0
2
Cytoplasmic Ribosomal Proteins Retinol metabolism Matrix Metalloproteinases Endochondral Ossif ication G Protein Signaling Pathways Ovarian Inf ertility Genes ACE Inhibitor Pathway Nuclear receptors in lipid metabolism and toxicity Dopminergic Neurogenesis Adipogenesis Focal Adhesion Regulation of Actin Cytoskeleton Nuclear Receptors Glutathione metabolism Wnt Signaling Pathway Purine metabolism Wnt Signaling Pathway and Pluripotency
DEG HSC/MPP1 FDR=0.1
Myc* DEG HSC-MPPs FDR=0.1 Wnt1 RNA mean expression <700 Fzd7 RNA mean expression >700
Protein Kinase C
4 20
Prkca*
Prkcb*
Prkcc
Prkcd*
Fzd5*
Fzd6
Fzd7
Dvl1
Dvl2
Dvl3
Fzd8*
Fzd9*
Ldlr
Fzd10
Rhoa
Racgap1* *
6 4 9 13
Mapk9
Ppp2r5c Ppp2r5e
Mapk10
Cytoskeleton Axin1
5 2 4 3 14 19 14 5 3 8 15 10
Prkce*
Prkci
Prkch*
Prkd1*
Prkcq
Prkcz
Frat1
Csnk1e
Gsk3b
Apoptosis
Apc B-catenin Fbxw2
Pafah1b1
P Ctnnb1 Phosphorylated Ubiquitin tagged B-catenin
Tcf1
C
Counts nucleic acid binding enzyme modulator receptor transcription factor transferase hydrolase cytoskeletal transporter signaling kinase oxidoreductase cell adhesion ligase defense/immunity extracellular matrix phosphatase lyase calcium-binding membrane traffic structural transfer/carrier isomerase protease chaperone cell junction viral surfactant tr.membr. rec. reg.
50
Transcriptional activation
Nucleus
26S Proteosome degradation
100 Mycn*
Myc*
D Fitted expression
0
Ccnd1*
Ccnd2*
Ccnd3*
Fosl1
Jun*
Plau*
Foxj3 (ENSMUSG00000032998+)
5000 2000 1000
HSC MPP1 differential exon usage
100
exons 10
1 2 3 4 5 6 7 8 9 10 119537004
119557474
119577944
119598414
119618884
Genomic region
Figure 5. Gene Expression Signatures and Transcript Isoform Regulation in HSCs and MPP1–MPP4 Cells (A) Signaling pathway enrichment analysis. Shown is the overrepresentation of signaling pathways within HSC- and HSC-MPP1 clusters. NOVA, neurooncological ventral antigen; ACE, angiotensin-converting enzyme. (B) Wnt signaling. Shown is a pathway analysis based on WikiPathways (WP403). Average RNA expression ± SD is shown (arbitrary units). (C) Association of differentially spliced genes to protein classes. (D) Representative example for a TF (Foxj3) showing differential exon use. The first Foxj3 exon was detected at higher levels in HSC (red) compared with MPP1 (blue).
nonhomologous end joining) also showed a different expression pattern across the five cell populations. Both repair processes are low in HSCs and are most enriched in MPP2 (Figure S4G). In summary, our data indicate that the increased mitotic activity during HSC differentiation, starting in MPP1 and peaking in MPP2, is associated with a parallel increase in expression of DNA repair pathway genes. 8 Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc.
To investigate the signaling pathway complexity of both the HSC and the HSC-MPP1 self-renewal clusters, we tested for overrepresentation of differentially expressed genes in pathways annotated in REACTOME within each group of genes. We found, among others, G protein-coupled receptor (Gpr143, Pde1c) and transforming growth factor b (Inhba, Bmp4) signaling to be enriched (Figure 5A; Table S4). In accordance with the GO
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
enrichments (Figure 4L), Wnt signaling was prominent in selfrenewing HSCs and MPP1. Although the role of canonical b -catenin-mediated Wnt signaling in adult HSCs remains controversial (Koch et al., 2008; Luis et al., 2012), noncanonical signaling has recently been suggested to mediate critical interactions between HSCs and their niches (Sugimura et al., 2012). Therefore, we interrogated individual expression patterns of the entire Wnt signaling pathway during differentiation of HSCs toward MPP4. Although the pathway was highly overrepresented in HSC-MPP1, none of the Wnt ligands were expressed at high levels, arguing against autocrine production of Wnt ligands (Figure 5B). However, even low levels of Wnt ligands support stem cell self-renewal (Luis et al., 2012). In agreement with this, three frizzled receptors (Fzd4, Fzd8, and Fzd9) were highly expressed in HSC-MPP1, consistent with reported Fzd4 expression in human CD34+ cells (Tickenbrock et al., 2008) and a role of noncanonical Fzd8 in the maintenance of quiescent long-term HSCs (Sugimura et al., 2012). Taken together, our analysis underscores the relevance of Wnt signaling for HSCs but also outlines the complex isoform-specific enzyme utilization operational in HSCs and the different MPPs. This likely contributes to the dynamic regulation of this pathway, which is critical for most stem/ progenitor cell types. Transcription Factor and Transcript Isoform Regulatory Landscape Among the genes expressed differentially across the five populations, we identified 490 TFs, including members of the Fox, Gata, and Hox families (Table S2). TF splice isoforms can have stage- and tissue-specific expression patterns throughout development (Lo´pez, 1995; Sebastian et al., 2013) and alternative splicing has been shown to trigger switches between activating and repressive TF isoforms (Taneri et al., 2004). We tested for differential exon use for detection of alternative transcription start sites, alternative splicing, and alternative terminations sites (Anders et al., 2012) in the HSC MPP1–MPP4 transcriptome data. Among the 497 genes expressing transcript isoform variants across HSC MPPs (FDR = 0.1), we identified 46 TFs (Figure 5C; http://www-huber.embl.de/SMHSC/HSCDEU/overall/ testForDEU.html; Supplemental Information). We observed that the first exon of the TF forkhead box J3, Foxj3, was included more frequently in the HSC transcripts compared with MPP1, suggesting the expression of a specific Foxj3 isoform in HSCs (Figure 5D). Although the role of this Foxj3 variant in hematopoiesis is uncharacterized, an alternative splicing switch for another forkhead family member, Foxp1, has recently been shown to regulate embryonic stem cell pluripotency and reprogramming by changing its DNA-binding preference (Gabut et al., 2011). Genome-wide DNA Methylation Analysis of HSCs/MPPs Identifies Candidate Regions for Epigenetic Regulation To investigate the methylation status of all cytosine residues within the genome of HSCs and MPPs, we subjected R10,000 FACS-sorted cells per biological replicate to TWGBS (Wang et al., 2013) (Figure 6A; Figure S5A). Robust data were obtained for all samples, with more than 6 3 108 reads and a combined genomic cytosine-phosphate-guanine (CpG) coverage of more than 33-fold per population across the three biological replicates (Figure S5B). Global levels of DNA methylation ranged between
81% and 83% and were not significantly different across populations, whereas pairwise comparisons identified a total of 15,887 distinct differentially methylated regions (DMRs) (Figure 6B; Table S5). Mapping these DMRs to previous DNA methylome data generated on HSC/MPPs using reduced representation bisulfite sequencing (RRBS) (Bock et al., 2012), we found that 85% of all DMRs were exclusively identified using the TWGBS analysis (e.g. Mecom; Figure S5C), demonstrating the additional coverage of our data set. Early commitment steps correlated with lower numbers of DMRs (1,121, HSC-MPP1), which likely reflects close ontogenic and functional relationships, and were mainly associated with loss of methylation (71%, HSCMPP1). In contrast, transitions between more differentiated MPP populations showed higher numbers of DMRs (1,874, MPP2MPP3/MPP4) and gain of methylation (75%, MPP2-MPP3/ MPP4; Figure 6C; Figure S5D; Table S5). Globally, methylation changes showed continuous progressive behavior (gain or loss) through HSC differentiation (Figure 6C). Next we investigated the overlap of DMRs with annotated genomic features derived from Refseq (Figure 6D; Pruitt et al., 2009). The majority of DMRs were located in intragenic regions (57%), whereas promoters and intergenic regions comprised 9% and 34%, respectively. Comparison of our data set with experimentally defined functional genomic elements (Stamatoyannopoulos et al., 2012; Shen et al., 2012) demonstrated a significant overrepresentation of DNaseI hypersensitivity sites (DHSs), promoters, enhancers, and/or transcription factor binding sites (TFBSs) across DMRs (Figure S5E; all p values < 10 16). Interestingly, 74% of all DMRs that overlapped with DHSs (10,887) mapped to regions annotated as TFBSs, promoters, and/or enhancers, suggesting that dynamic methylation upon hematopoietic commitment occurs predominantly at cis-regulatory elements (Figure 6E). Together, these data support the hypothesis that many DMRs represent cis-regulatory elements in HSC-MPPs. Gene Expression Anticorrelates Globally with Changes in DNA Methylation Given the significant overlap between DMRs and functionally important regulatory regions of the genome, we postulated that the methylation changes could be associated with gene expression during HSC differentiation. Indeed, we found that DMRs coincided with previously identified cis-acting regulatory sites at the Sfpi1 (Rosenbauer et al., 2004) and Gata2 (Gao et al., 2013) loci (Figure S5F), which both encode TFs described as effectors of hematopoietic commitment. We generated gene sets based on DMRs that were stratified for methylation increase or decrease and interrogated the associated gene expression levels between HSCs and each of the MPP populations. DMRs detected at the early transition of HSC to MPP1 were significantly associated with anticorrelated gene expression along differentiation (Figure 6F). In addition, pairwise comparisons demonstrated a global anticorrelation between transcription and DNA methylation (Figure 6G; Supplemental Information). Among the top anticorrelating genes between HSC-MPP1 (80; Table S6), we found a number of well documented markers of early hematopoietic differentiation and HSC function (e.g. Hoxb2, Rorc, Cd34). For example, the increasing expression of Cd34 and Wnt-inhibitory factor (Wif1) from HSC to MPP1 is Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 9
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
A
B HSC
MPP1
C
HSC
MPP1
MPP2
MPP3/4
HSC
R1 R2 R3 R1 R2 R3 R1 R2 R3 R1 R2 R3
MPP1
MPP2
MPP3/4
R1 R2 R3 R1 R2 R3 R1 R2 R3 R1 R2 R3
MPP2 MPP3/4 4,712
n=3
DNA methylation 1.0
10-30 ng DNA/replicate HSC
Tagmentation-based wholegenome bisulfite sequencing (TWGBS) 100bp paired-end reads 6.7 - 19.9 x 108 aligned reads
MPP1
1,121
MPP2
1,412
1,874
MPP3/4
0.8
0.6
0.4
7,411
0.2
gain of methylation 0
12,013
loss of methylation
DMRs (MPP2 – MPP3/4)
DMRs (HSC – MPP1)
D
E
57.0%
F Promoter n=4,745 (29.7%)
8.7%
DHS n=14,617 (91.4%)
34.3%
Enhancer n=4,561 (28.5%)
111 12
778
HSC
MPP1
3730
MPP3/4
MPP2
TFBS n=8,798 (55.0%)
99 23
258
7
162
1313 1296
51 1553
Intergenic Promoter Intragenic
3474
Hoxd
1.0 0.8 0.6 0.4
Relative Methylation
0.2
b8 b7
Mir10a b4
b6 b5
b3
b2
b1
HoxD
Chr2:74,660,000
0.8
1.0
2
0.6
0
Z-score
b9 Gene Promoter DMR Enhancer
0.4
−2
* DEG HSC-MPPs FDR=0.1 Hoxb1 with DMRs Hoxb7 no DMRs
Chr11: 96,270,000
0.0
Hoxb1* Hoxb2* Hoxb3* Hoxb4* Hoxb5* Hoxb6* Hoxb7* Hoxb8 Hoxb9* Hoxb13 Hoxd1 Hoxd3 Hoxd8 Hoxd9 Hoxd10 Hoxd11 Hoxd12 Hoxd13
Hoxb
HoxB
I
0.2
Spns3 Wdr16 F2rl2 Zfat Sept3 Endou Rfc2 Poc1a Zscan10 Gfra1 Tyms Rabgap1l
H
HSC MPP1 MPP2 MPP3/4
0.0
Haao Mrvi1 Mc5r Cd163 Tex22 Alx3 Anp32e Cd34 Slc16a7 Wif1 Ltc4s Mrc2
Eomes Tenc1 Plekhg6 Clec14a Flrt1 Ndnf Cpne8 Kcnc1 Cttnbp2nl Sox7 Hoxb2 Dpy19l2
Relative Methylation
Il4 Lsr Slc9a3r2 Perp Doc2b Pde1b Efna1 Pdgfc Rorc Tinagl1 Gfi1 St8sia1
HSC MPP1 MPP2 MPP3 MPP4
G
2215
Gene Promoter DMR Enhancer
d13 d12 d11
d10 d9
d8
1700109F18Rik d3
d1
b1 Hoxb1 with DMRs d7 Hoxd7 no DMRs Transcription direction
Figure 6. Global DNA Methylation Analysis and Anticorrelation with Gene Expression (A) DNA methylome workflow. (B) Gain or loss of methylation DMRs between HSC-MPPs. The numbers indicate total DMRs. (C) Clustering of DMRs identified between HSC-MPP1 or MPP2-MPP3/MPP4. Each horizontal dash represents a DMR. R1–R3, replicates 1–3. (D) Overlap of DMRs with gene-centric Refseq genomic regions. (E) Percent overlap of all DMRs with experimentally defined functional genomic elements based on available data from the mouse ENCODE project. (F) Overall comparison of DNA methylation to gene expression. The box plots represent relative gene expression associated with either gain (left) or loss (right) of methylation in the transition from HSC and MPP1. (G) Pairwise comparison of DNA methylation to gene expression. The box plots represent log2 fold change of gene expression associated with either gain or loss of methylation in the transition from HSC to MPP1. The top 48 anticorrelated genes (24 highest/24 lowest expression in HSCs) are indicated. (H) Gene expression levels of all members of the HoxB and HoxD clusters. (I) Relative DNA methylation profile for the HoxB and HoxD clusters. Red arrows indicate examples of DMRs. See also Figure S5.
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Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
associated with decreasing methylation at the DMRs of these loci. In contrast, the cyclic AMP-mediating cyclic nucleotide phosphodiesterase 1b (Pde1b), as well as the retinoic acid receptor orphan receptor g(Rorc), become methylated and downregulated during differentiation toward MPP1. Hox TF clusters are critical during normal and malignant hematopoiesis (Argiropoulos and Humphries, 2007), but little is known about their epigenetic regulation. We found that most members of the HoxB family showed the highest expression in HSCs and were associated with DMRs (seven out of nine; Figures 6H and 6I). Notably, HSC differentiation showed a continuous increase in DMR methylation and paralleled decrease of RNA expression (Figure 6I, see arrows for Hoxb2 and Hoxb4). In contrast, the members within the HoxD cluster showed no specific expression pattern in HSCs and MPPs, and none of its genes were associated with a DMR (Figure 6I). Similar results were found for HoxA and HoxC clusters, respectively (Figures S5G and S5H). In conclusion, the integrated analysis of the methylome and transcriptome provides a resource of genes whose expression is, at least partially, regulated by DNA methylation and that are possibly involved in the hard wiring of the hierarchical organization of the HSC/progenitor populations. Differential lncRNA Expression and the Imprinted Gene Network in Stem/Progenitors Long noncoding RNAs (lncRNAs) have been implicated in guiding chromatin remodeling complexes to specific target genes and mediating gene activation or silencing by recruiting the DNA demethylation machinery to the promoter (Arab et al., 2014) or recruiting repressive complexes such as PRC2, respectively (Rinn and Chang, 2012). However, little is known about their functional role or regulation in stem cells and hematopoiesis (Paralkar and Weiss, 2013). We identified 682 lncRNAs expressed in HSC-MPPs, 79 of which were differentially expressed across all five populations (FDR = 0.1; Figure 7A; Table S2). Unsupervised clustering (Figure 7B) and pairwise comparisons of differentially expressed lncRNAs revealed the same population relationship as using the entire transcriptome data set, again placing MPP2 between the HSC-MPP1 and the MPP3-MPP4 populations (compare Figures 4E–4G and S6A). Next we clustered the significantly changed lncRNAs based on their relative expression levels across all populations (Figure 7C; Table S4). As shown in cluster 2, 12 lncRNAs are strongly expressed in HSCs compared with the rest of the populations, and none of these has yet been functionally annotated or studied (e.g. 2410080I02Rik, Gm12474). In addition, 14 lncRNAs are coexpressed in HSC-MPP1 (cluster 4), representing additional candidates for regulation of self-renewal (e.g. H19, Malat1, Meg3). In agreement, the imprinted H19 lncRNA has recently been shown to mediate HSC quiescence by inhibiting insulin growth factor (IGF) signaling (Venkatraman et al., 2013). Thirteen lncRNAs were enriched in MPP3-MPP4, suggesting regulatory roles in these lineage-restricted populations (cluster 25; e.g. Gm568, Neat1). Although Neat1 is essential for the integrity of the nuclear substructure, it has also been linked to the immune response after HIV-1 infection (Zhang et al., 2013) and might, therefore, be involved in the maintenance of immune regulatory circuits in MPP3-MPP4. Notably, most of the differentially expressed lncRNAs identified here have not been studied in hematopoiesis
and/or have unknown functions. The lncRNAs H19 and Meg3 that are highly expressed in HSCs compared with MPPs (Figure 7C, validated by quantitative RT-PCR; Figure S6B) are core members of the transcriptional imprinted gene network (IGN), which has been postulated to regulate embryonic growth (Varrault et al., 2006). In adult hematopoiesis, the genetic deletion of several members of this network affects HSC self-renewal integrity (e.g. Cdkn1c/p57KIP2) (Berg et al., 2011; Rossi et al., 2012). Therefore, we further investigated the expression of the IGN members and found an overall strong expression in HSCs but a steady decrease during the differentiation process (Figure 7D), suggesting a contribution of the IGN in the maintenance of self-renewal and/or quiescence of HSCs. Finally, we interrogated the DMRs within the loci encoding the 682 quantified lncRNAs. This revealed a significant enrichment of DMRs in the differentially expressed lncRNAs. These DMRs were enriched within a 10 kb window centered on the transcription start site (Figure 7E). A notable example is the H19 locus, which exhibits a DMR in an enhancer region outside of its imprinting control region (Figures 7F and 7G). The increasing level of methylation at this enhancer during the transition from HSCs to MPPs correlates with decreasing expression during differentiation (Figure 7F). Overall, these data suggest that differential DNA methylation of regulatory regions is a likely mechanism by which lncRNA expression is controlled in HSCs and their progeny. DISCUSSION In this study, we describe a combined proteome, transcriptome, and DNA methylome analysis of highly purified primary HSCs and four downstream MPPs, which we characterized additionally using in vitro and in vivo functional assays (Figure S7). Our data sets uncover progressively changing cell type-specific methylation, gene, and protein expression landscapes starting with quiescent CD34-CD150+CD48-LSK HSCs that sit at the pinnacle of the hematopoietic hierarchy. These differentiate toward slowly cycling multipotent MPP1, followed by multipotently cycling MPP2. The steady increase in the activity of the cell cycle and proliferation machinery is paralleled by the robust upregulation of the entire DNA repair machinery. This raises the possibility that physiological DNA replication in proliferating early progenitors generates significant replicative stress that needs to be counteracted by the activation of the DNA repair machinery to ensure genome integrity (Bakker and Passegue´, 2013). We found that the majority of DMRs either progressively gain or lose DNA methylation through early HSC differentiation. Moreover, we observed a global anticorrelation between DNA methylation and gene expression. Because this was observed at a global level by the whole genome DNA methylome analysis (TWGBS) but not using previous approaches (array-based and RRBS) (Ji et al., 2010; Bock et al., 2012), our data suggest that many regulatory regions, including, e.g., distal enhancers critical for gene expression, are only covered by TWGBS analysis. The observed high overall correlation between RNA and protein levels suggests that posttranscriptional regulation is not a predominant mechanism by which gene expression is regulated in homeostatic HSCs. However, a small number of specific functions in the stem/progenitor compartment may be regulated in Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 11
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
D
C
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Mus musculus
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Figure 7. Expression Landscape of lncRNAs and the Imprinted Gene Network (A) Workflow for lncRNA analysis. (B) Clustering of 79 differentially expressed lncRNAs. (C) LncRNA expression clusters. Differentially expressed lncRNAs were grouped into 32 clusters based on higher expression (enriched) in one or several cell population(s) compared to mean expression level across all cell populations. Right, an example diagram for each of the most enriched clusters. Average RNA expression ± SD is shown. (D) Expression of imprinted gene network genes. (E) Differential DNA methylation of quantified lncRNAs. Top panel, comparison of DMRs between differentially and nondifferentially expressed lncRNAs. Bottom panel, heatmap showing DMRs in red. LncRNAs are ranked by increasing adjusted p value (gray scale). Distances are relative to transcription start sites. *p = 0.01–0.001; **p < 0.001. (F) Differential methylation of the lncRNA H19 locus. The H19 locus shows increasing methylation from HSC (red) to MPP1 (blue), MPP2 (green), and MPP3/4 (brown) in two DMRs. The inset shows a diagram depicting average H19 RNA expression (mean ± SD). (G) Differential methylation of H19 locus at enhancer region 3. Black and white dots represent methylated and unmethylated CpGs, respectively. The red box indicates DMR. See also Figure S6.
12 Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc.
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
this way. For example, our results point to a potential posttranscriptional regulation of glycolytic metabolism in HSCs via Lin28b-Hmga-Igfbp2 signaling (Shyh-Chang and Daley, 2013), likely because of differences in relative protein synthesis rather than degradation rate (Kristensen et al., 2013; Signer et al., 2014). Moreover, our data suggest that HSC self-renewal and quiescence are regulated by an interplay between the Lin28blet7-Hmga-Igfbp2 axis, the IGN regulatory network, and HSCenriched pathways such as Wnt and retinoic acid (RA) signaling. Although some of these pathways are also implicated in embryonic HSC emergence from the hemogenic endothelium (Varrault et al., 2006; Chanda et al., 2013; Copley et al., 2013), the individual players and mechanisms governing HSC function remain to be explored in the embryo and in the adult. As an example, the Lin28b target Igf2bp2 was found to be one of the most differentially expressed transcripts and proteins in HSCs. It is known to modulate expression of the lncRNA H19, leading to suppression of proproliferative IGF signaling as well as Let7 miRNAs and has been suggested to mediate HSC quiescence (Runge et al., 2000; Kallen et al., 2013; Venkatraman et al., 2013). In agreement, our data show increasing H19 enhancer methylation during the transition from HSC to MPP1, providing an explanation for the release of HSCs out of quiescence associated with loss of selfrenewal, which might be enhanced further by suppression of the IGN activity (i.e. p57; Zou et al., 2011; Tesio and Trumpp, 2011). RA signaling is not only known to be critical for embryonic HSC emergence (Chanda et al., 2013) but also for the regulation of Hox gene expression by chromatin reorganization in embryonic stem cells (Kashyap et al., 2011). Because we observed high expression and low DNA methylation of most members of the HoxA/B clusters in HSCs, it is plausible that RA signaling contributes to the control of the epigenetic landscape of HoxA/ B transcription factors in HSCs. Moreover, because many Hox genes are mutated in leukemias, these mechanisms may also be relevant for leukemic stem cells (Alharbi et al., 2013). An unexpected finding is the degree of alternatively spliced transcript isoforms present in HSCs and their progeny. To date, only rare cases of HSC regulation through alternative splicing have been reported (Bowman et al., 2006). In this study, we identified almost 500 genes with alternative transcript isoform regulation. Although the underlying regulatory mechanism remains unknown, the lncRNA Malat1, which is highly expressed in HSCs, has been suggested to be a regulator of alternative splicing (Tripathi et al., 2010). In line with this, Malat1 has been implicated in multiple types of human cancer (Gutschner et al., 2013), and numerous genetic mutations encoding factors of the splicing machinery have been detected in patients with chronic lymphoid leukemia (Martı´n-Subero et al., 2013) and myelodysplastic syndrome, a disease derived directly from HSCs (Lindsley and Ebert, 2013; Medyouf et al., 2014). Our catalog of splicing variants, with Foxj3 as an example of an HSC-specific splice isoform, will serve as a starting point to explore this largely uncharted area of regulation in HSCs and their immediate progeny. In addition to Malat1, we identified more than 70 differentially expressed lncRNAs, the vast majority of which have no documented role in hematopoiesis. The variety of molecular functions assigned to lncRNAs is expanding steadily, and their biological roles include regulation of genomic imprinting, differentiation, and self-renewal (Fatica and Bozzoni, 2014).
In summary, the global signatures for stemness and multipotency generated in this study represent not only a comprehensive reference but also suggest distinct areas of stem cell regulation (progressive DNA methylation, alternative splicing, and lncRNAs). This study significantly extends the current understanding of HSC progenitor biology at the global level and provides a solid basis for functional studies exploring the networks responsible for stem cell quiescence, self-renewal, and differentiation. EXPERIMENTAL PROCEDURES Animals Eight- to twelve-week-old female C57BL/6 (CD45.2) mice purchased from Harlan Laboratories and B6.SJL-Ptprca Pepcb/BoyJ (CD45.1) animals purchased from Charles River Laboratories were used throughout the study. CD45.1/CD45.2 heterozygotes (F1) for transplant auxiliary bone marrow were bred in-house at the Deutsches Krebsforschungszentrum. Bone Marrow Reconstitution Experiments Fifty (HSC, MPP1) or 2,000 cells (HSC, MPP2, MPP3, MPP4) were FACSsorted and injected intravenously together with 2 3 105 supportive bone marrow (BM) cells (CD45.1/2) into lethally irradiated (2 3 450 Gy) (CD45.1) recipient mice. CD45.2-donor cells were monitored at 1, 3–4, 6, 9, 12, and 16 weeks posttransplantation. For secondary transplantations, whole BM was isolated at 16 weeks posttransplant, and 1 3 106 cells were retransplanted into lethally irradiated CD45.1 recipient mice. Myeloid (CD11b+Gr1+) and lymphoid (T cells, CD4+CD8+; B cells, B220+) lineages were addressed by FACS. Proteomic Analysis FACS-sorted HSCs and MPP1 (4 3 105) were lysed, and proteins were extracted, reduced/alkylated, and digested with trypsin. Peptides were labeled differentially with stable isotope dimethyl labeling on a column as described previously (Boersema et al., 2009) and fractionated by OFFGEL isoelectric focusing (Agilent Technologies). In technical duplicates, peptides were separated by nanoflow ultra-high-performance liquid chromatography on a 120 min gradient and analyzed by electrospray ionization-tandem mass spectrometry (ESI-MS/MS) on a linear trap quadrupole Orbitrap Velos or Orbitrap Velos Pro (Thermo Fisher Scientific). MS raw data files were processed with MaxQuant (version 1.3.0.5) (Cox and Mann, 2008). The derived peak list was searched using the built-in Andromeda search engine (version 1.3.0.5) in MaxQuant against the Uniprot mouse database (2013.02.20). A 1% FDR was required at both the protein level and the peptide level. Differential expression was assessed using the Limma package in R/Bioconductor (Gentleman et al., 2004; Smyth, 2004), and proteins with an adjusted p value of less than 0.1 were considered expressed differentially between HSC and MPP1. RNA-seq Total RNA isolation was performed using an ARCTURUS PicoPure RNA isolation kit (Life Technologies, Invitrogen) according to the manufacturer’s instructions. Total RNA was used for quality controls and for normalization of the starting material (Figure S3). cDNA libraries were generated with 10 ng of total RNA for HSC-MPP using the SMARTer Ultra Low RNA kit for Illumina sequencing (Clontech Laboratories) according to the manufacturer’s indications. Sequencing was performed with a HiSeq2000 device (Illumina) and one sample per lane. Sequenced read fragments were mapped to the mouse reference genome GRCm38 (ENSEMBL release 69) using the Genomic ShortRead Nucleotide Alignment program (version 2012-07-20). DESeq2 (Love et al., 2014) and DEXSeq (Anders et al., 2012) were used to test for differential expression (FDR = 0.1) and differential exon use, respectively. TWGBS DNA methylation analysis using TWGBS (Adey and Shendure, 2012) was performed as described previously (Wang et al., 2013). Genomic mouse DNA (10-30 ng) was used as input, and each sequencing library was subjected
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Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
to 101 bp paired-end sequencing on a single lane of a HiSeq2000 device (Illumina). Animal procedures were performed according to protocols approved by the German authorities (Regierungspra¨sidium Karlsruhe [no. Z110/02, DKFZ nos. 261, G175-12, and G140-13]).
Adolfsson, J., Ma˚nsson, R., Buza-Vidas, N., Hultquist, A., Liuba, K., Jensen, C.T., Bryder, D., Yang, L., Borge, O.-J., Thoren, L.A., et al. (2005). Identification of Flt3+ lympho-myeloid stem cells lacking erythro-megakaryocytic potential a revised road map for adult blood lineage commitment. Cell 121, 295–306.
ACCESSION NUMBERS
Alharbi, R.A., Pettengell, R., Pandha, H.S., and Morgan, R. (2013). The role of HOX genes in normal hematopoiesis and acute leukemia. Leukemia 27, 1000– 1008.
Proteome data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the Proteomics Identifications Database (PRIDE) partner repository (Vizcaı´no et al., 2013) with the data set identifier PXD000572. RNA-seq data are available in the ArrayExpress database (http://www.ebi.ac.uk/arrayexpress) under accession number EMTAB-2262. TWGBS data can be accessed under Gene Expression Omnibus under accession no. GSE52709.
Anders, S., Reyes, A., and Huber, W. (2012). Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008–2017. Arab, K., Park, Y.J., Lindroth, A.M., Scha¨fer, A., Oakes, C., Weichenhan, D., Lukanova, A., Lundin, E., Risch, A., Meister, M., et al. (2014). Long Noncoding RNA TARID Directs Demethylation and Activation of the Tumor Suppressor TCF21 via GADD45A. Mol. Cell, in press. Published online July 30, 2014. http://dx.doi.org/10.1016/j.molcel.2014.06.031.
SUPPLEMENTAL INFORMATION
Argiropoulos, B., and Humphries, R.K. (2007). Hox genes in hematopoiesis and leukemogenesis. Oncogene 26, 6766–6776.
Supplemental Information includes Supplemental Experimental Procedures, seven figures, six tables, one item and two online resources can be found with this article online at http://dx.doi.org/10.1016/j.stem.2014.07.005.
Bakker, S.T., and Passegue´, E. (2013). Resilient and resourceful: genome maintenance strategies in hematopoietic stem cells. Exp. Hematol. 41, 915–923.
AUTHOR CONTRIBUTIONS N.C.-W., D.K., and A.T. designed and coordinated the study. A.T., J.K. (proteome), C.P. (methylome), M.D.M. (methylome), and W.H. (bioinformatics) designed and supervised the experiments and interpreted the data. J.H., D.K., and N.C.-W. performed the proteome experiments, bioinformatic analysis, and/or data interpretation. N.C.-W., A.R., D.K., D.B.L., and J.H. performed the RNA-seq experiments, bioinformatic analysis, and/or data interpretation. D.B.L., Q.W., N.C.-W., D.K., A.R., D.W., A.L., D.B., L.G., C.H., S.H., M.A.G.E., B.B., and R.E. performed the methylome experiments, bioinformatic analysis, and/or data interpretation. D.K., N.C.-W., and A.L. (methylome) performed FACS. N.C.-W. and D.K. coordinated the animal experiments and performed reconstitution experiments, FACS analysis, qPCR, and CFU assays. L.v.P., S.R., P.W., and P.Z. assisted with FACS analysis, reconstitution experiments, and qPCR. N.C.-W., D.K., J.H., D.B.L., and A.R., together with W.H., J.K., C.P., M.D.M., and A.T., wrote the manuscript. ACKNOWLEDGMENTS We thank A. Rathgeb from the Central Animal Laboratory; S. Schmidt, U. Ernst, A. Hotz-Wagenblatt, C. Previti, and S. Wolf from the Genomics and Proteomics Core Facility; S. Schmitt and G. de la Cruz from the Flow Cytometry Core Facility at the DKFZ; and the EMBL Proteomics Core Facility for expert assistance. We also thank M. Ba¨hr and M. Helf for technical assistance. We further thank C.C. Oakes, A.M. Lindroth, and R. Claus for helpful discussions as well as P. Komardin for support in handling data submission to the GEO database. This work was supported by the BioRN Spitzencluster ‘‘Molecular and Cell Based Medicine’’ supported by the German Bundesministerium fu¨r Bildung und Forschung (to A.T.), by the EU FP7 Programs ‘‘EuroSyStem’’ (to A.T.) and ‘‘Radiant’’ (to W.H. and A.R.), by the Dietmar Hopp Foundation (to A.T.), by the Sonderforschungsbereich SFB 873 funded by the Deutsche Forschungsgemeinschaft (to A.T.), by a Vidi grant from the Netherlands Organization for Scientific Research (NWO) (to J.K.), by the DFG Priority Program SP1463 (to D.B.L. and C.P.), by an intramural grant from the DKFZ (Epigenetics@DKFZ) (to D.B.L. and M.D.M.), and by a Humboldt research fellowship for postdoctoral researchers (to Q.W.). Received: February 11, 2014 Revised: June 25, 2014 Accepted: July 18, 2014 Published: August 21, 2014
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Cell Stem Cell
Resource Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis Nina Cabezas-Wallscheid,1,2,10 Daniel Klimmeck,1,2,3,10 Jenny Hansson,3,10 Daniel B. Lipka,4,10 Alejandro Reyes,3,10 Qi Wang,5,9 Dieter Weichenhan,4 Amelie Lier,2,6 Lisa von Paleske,1,2 Simon Renders,1,2 Peer Wu¨nsche,1,2 Petra Zeisberger,1,2 David Brocks,4 Lei Gu,4,5,9 Carl Herrmann,5,9 Simon Haas,2,7 Marieke A.G. Essers,2,7 Benedikt Brors,5,8,9 Roland Eils,5,8,9 Wolfgang Huber,3,11 Michael D. Milsom,2,6,11 Christoph Plass,4,8,11 Jeroen Krijgsveld,3,11 and Andreas Trumpp1,2,8,11,* 1Division
of Stem Cells and Cancer, Deutsches Krebsforschungszentrum (DKFZ), 69120 Heidelberg, Germany Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH), 69120 Heidelberg, Germany 3European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany 4Division of Epigenomics and Cancer Risk Factors, DKFZ, 69120 Heidelberg, Germany 5Division of Theoretical Bioinformatics, Department of Bioinformatics and Functional Genomics, DKFZ, 69120 Heidelberg, Germany 6Junior Research Group Experimental Hematology, Division of Stem Cells and Cancer, DKFZ, 69120 Heidelberg, Germany 7Junior Research Group Stress-induced Activation of Hematopoietic Stem Cells, Division of Stem Cells and Cancer, DKFZ, 69120 Heidelberg, Germany 8German Cancer Consortium (DKTK), 69120 Heidelberg, Germany 9Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, 69120 Heidelberg, Germany 10Co-first author 11Co-senior author *Correspondence: [email protected] http://dx.doi.org/10.1016/j.stem.2014.07.005 2Heidelberg
SUMMARY
In this study, we present integrated quantitative proteome, transcriptome, and methylome analyses of hematopoietic stem cells (HSCs) and four multipotent progenitor (MPP) populations. From the characterization of more than 6,000 proteins, 27,000 transcripts, and 15,000 differentially methylated regions (DMRs), we identified coordinated changes associated with early differentiation steps. DMRs show continuous gain or loss of methylation during differentiation, and the overall change in DNA methylation correlates inversely with gene expression at key loci. Our data reveal the differential expression landscape of 493 transcription factors and 682 lncRNAs and highlight specific expression clusters operating in HSCs. We also found an unexpectedly dynamic pattern of transcript isoform regulation, suggesting a critical regulatory role during HSC differentiation, and a cell cycle/ DNA repair signature associated with multipotency in MPP2 cells. This study provides a comprehensive genome-wide resource for the functional exploration of molecular, cellular, and epigenetic regulation at the top of the hematopoietic hierarchy.
2010; Wilson et al., 2009). They give rise to a series of multipotent progenitors (MPPs) with decreasing self-renewal potential, followed by differentiation toward committed progenitors and more mature cells (Adolfsson et al., 2005; Forsberg et al., 2006). MPPs have been subdivided immunophenotypically into MPP1, MPP2, MPP3, and MPP4 populations based on a stepwise gain of CD34, CD48, and CD135 as well as loss of CD150 expression (Wilson et al., 2008). However, despite recent efforts to characterize changes in gene expression and epigenome modifications that occur at distinct stages of differentiation (Gazit et al., 2013; Kent et al., 2009; McKinney-Freeman et al., 2012; Bock et al., 2012), the distinct functional characteristics and the molecular programs that maintain HSC self-renewal and drive progenitor differentiation are poorly characterized. We have taken advantage of recent technological advances enabling analysis of rare cell populations to establish comprehensive mass spectrometry-based proteome, transcriptome (RNA sequencing [RNA-seq]), and genome-wide DNA methylome (tagmentation-based whole genome bisulfite sequencing, TWGBS) data for HSCs and MPPs. We provide a comprehensive insight into the molecular mechanisms that are dynamically regulated during early HSC commitment through the MPP1– MPP4 populations. We uncovered molecular changes at the protein, RNA, and DNA levels as they occur in vivo in the context of physiologic commitment processes.
RESULTS INTRODUCTION Hematopoietic stem cells (HSCs) are unique in their capacity to self-renew and replenish the entire blood system (Orkin and Zon, 2008; Purton and Scadden, 2007; Seita and Weissman,
The five stem/progenitor populations corresponding to HSC and MPP1–MPP4 (Wilson et al., 2008) were isolated by fluorescenceactivated cell sorting (FACS) from the bone marrow of C57BL/6J mice (Figure 1; Figures S1A–S1C available online). These cells Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 1
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
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10
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Figure 1. Global Analysis of Early Adult Hematopoiesis (A) The mouse hematopoietic system. HSCs give rise to MPPs, which commit to more mature progenitors with restricted cell fate. CMP, common myeloid progenitor; MEP, megakaryocyte erythroid progenitor; GMP, granulocyte macrophage progenitor; CLP, common lymphoid progenitor; Mega, megakaryocyte; Pla, platelet; Neu, neutrophil; Mac, macrophage; NK, natural killer cell. (B) FACS scheme used to isolate primary mouse cells: HSC (Linneg Sca-1+ c-Kit+, LSK, CD34- CD135- CD150+ CD48 ), MPP1 (LSK CD34+ CD135 CD150+ CD48 ), MPP2 (LSK CD34+ CD135 CD150+ CD48+), MPP3 (LSK CD34+ CD135 CD150 CD48+), and MPP4 (LSK CD34+ CD135+ CD150 CD48+). L, lineage-negative. (C) Experimental study design. Shown are the different generated data sets and their respective figure numbers. See also Figures S1 and S7.
were used as a source for quantitative proteomics, RNA-seq, TWGBS, and functional reconstitution experiments. Proteome Differences between HSCs and MPP1 Cells Uncovered Key Molecular Players of Long-Term Reconstitution Potential The transition from CD34 HSCs to CD34+ MPP1 is accompanied by a switch in the cells’ reconstitution capabilities. We transplanted mice with 50 HSCs and observed that 100% of primary and 80% of secondary recipients showed multilineage repopulating activity (Figures S1D–S1F). In contrast, 56% of mice transplanted with MPP1 cells showed reconstitution of the primary recipient, and no engraftment was detected in secondary recipients. This is consistent with previous reports showing that the two populations are very similar but display a measurable difference in long-term self-renewal (Ema et al., 2006; Osawa et al., 1996). To investigate the molecular basis of this difference in self-renewal, we compared the proteomes of HSC and MPP1 cells in a quantitative mass spectrometry-based approach (Figure 2A; Figure S2A). From 400,000 HSCs and MPP1 purified in biological triplicate, 6,389 protein groups were identified (Table S1). These covered a broad range of protein classes, e.g. receptors (222) and transcription factors (549) (Figure 2B), as well as low-abundance proteins, as judged from the estimated protein levels that spanned more than seven orders of magnitude (Figure S2B). 2 Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc.
In total, 4,037 proteins were quantified in all three replicates (Figure S2C). Of these, only 47 proteins were expressed differentially (false discovery rate [FDR] = 0.1), together with nine proteins detected exclusively in MPP1 (Figure 2C; Figure S2D; Table S1). This suggests that the differential self-renewal potential between HSCs and MPP1 is elicited by a relatively small number of proteins. Gene ontology (GO) enrichment analysis revealed that cell cycle as well as related processes, such as DNA replication and cell proliferation, were strongly overrepresented in MPP1 (Figure 2D). Indeed, protein-protein interaction network analysis highlighted a large group of interconnected cell cycle proteins being expressed at elevated levels in MPP1 compared with HSCs. All major processes associated with the cell cycle machinery, including DNA polymerases (Pol1a, Pole), cell cycle checkpoint proteins (Chek1), DNA methylation maintenance and cell cycle progression (Cdk1, Cdk6), and others were represented in the network (Figure 2E). In contrast, HSCs were enriched in the monosaccharide metabolic process, including the glycolytic enzymes lactate dehydrogenase b and d (Ldhb, Ldhd) as well as Pygm. In addition, cellular ion homeostasis, including two iron transporters (Fth1, Ftl2), oxidation reduction (Rrm1, Rrm2), and response to hypoxia (Mecp2) were enriched in HSCs. Together, the data are consistent with an anaerobic metabolic program employed by quiescent HSCs, whereas MPP1 cells become primed for entry into the cell cycle and start proliferating.
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
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nucleic acid binding hydrolase transferase enzyme modulator transcription factor cytoskeletal protein oxidoreductase kinase ligase transporter receptor membrane traffic protein signaling molecule protease calcium-binding protein transfer/carrier protein chaperone phosphatase isomerase lyase defense/immunity protein cell adhesion molecule extracellular matrix protein structural protein cell junction protein tr.membr. rec. reg. surfactant storage protein viral protein
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oxidation reduction 14 monosaccharide metabolic process 8 cellular ion homeostasis 6 regulation of chromosome organization 3 neuron projection development 4 response to hypoxia 3 0 DNA replication cell cycle 5 chromosome condensation 0 DNA packaging 2 chromosome organization 5 cell division 3 M phase 2 cellular response to stress 1 mitosis 2 cell prolif eration 1 DNA repair 0 deoxyribonucleotide metabolic process 0 cell activation 2 neg. reg. of transcription, DNA- dependent 1
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Figure 2. Proteomic Comparison of HSCs and MPP1 (A) Quantitative proteomics workflow. (B) Classification of identified proteins. Bars show the number of proteins within each functional class. (C) Differential protein expression. Proteins expressed significantly higher (FDR = 0.1) in HSCs and MPP1 are shown in red and blue, respectively. The lower right corner shows proteins exclusively detected in MPP1. (D) Overrepresented biological processes of differentially expressed proteins (202 with FDR = 0.15 and 9 exclusively detected in MPP1). Top, HSC-enriched. Bottom, MPP1-enriched. Neg. reg. of transcription, negative regulation of transcription. (E) Protein network of differentially expressed proteins. The edges show known and predicted protein-protein interactions. STRING, Search Tool for the Retrieval of Interacting Genes/Proteins; adj., adjusted. (F) CD cell surface proteins expressed differentially. The average log2 ratio ± SD is shown. See also Figure S2.
Of the 22 proteins with higher expression in HSCs, we found three high mobility group AT hook proteins; namely, two isoforms of Hmga1 as well as Hmga2 (Figure 2C; Figure S2E). These chromatin-modulating transcriptional regulators have been reported recently to be potent rheostats of tumor progression (Morishita et al., 2013; Shah et al., 2013) and HSC self-renewal, proliferation, and lineage commitment checkpoints (Battista et al., 2003; Copley et al., 2013). In addition, the protein showing the highest fold change was the Hmga2 target Igf2bp2 (Figure 2C; Cleynen et al., 2007). Hmga1, Hmga2, and Igf2bp2 are downstream mediators of the Lin28-let7 pathway that link metabolism to prolifera-
tion and drive self-renewal (Shyh-Chang et al., 2013; Yaniv and Yisraeli, 2002). High expression of this pathway in HSCs suggests a role in self-renewal of HSCs, whereas its downregulation in MPP1 cells correlates with decreasing self-renewal activity. Two glutathione S-transferases (GST), Gstm1 and Gstm5, were found to be expressed at higher levels in HSCs compared to MPP1 (Figure 2C). Moreover, elevated levels in HSCs were consistent for all 11 GSTs quantified (Figure S2F). This points to a requirement for this enzyme class in HSCs, which may relate to their ability to mediate the conjugation of xenobiotics for the purpose of detoxification and defense against environmental Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 3
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
stress and cellular damage (Tew and Townsend, 2012). This suggests that homeostatic HSCs have an array of mechanisms in place to protect their cellular integrity, extending an observation that we have made previously in hematopoietic progenitors (Klimmeck et al., 2012). Along these lines, two interferon-inducible proteins involved in host defense (Ifitm1 and Irgm2) were expressed at higher levels in HSCs compared with MPP1 (Figure 2C), suggesting that the type I interferon pathway is not only critical for the response to stress but also during homeostasis (Essers et al., 2009; Trumpp et al., 2010). Moreover, HSCs and MPP1 employ different intracellular serpins for protection against death during stress because Serpinb6a and Serpinb1a were expressed at higher levels in either HSC or MPP1, respectively (Figure 2C). Taken together, HSCs harbor proteins involved in immune defense and detoxification, indicative of an increased self-protecting repertoire compared with MPP1. Among the 86 quantified cluster of differentiation (CD) surface proteins (Table S1), nine showed a strong differential expression between HSC and MPP1 (Figure 2F). These included CD34 as well as other membrane proteins described previously in the context of stem/progenitors, namely CD38, CD41, CD49b, and CD90 (Benveniste et al., 2010; Dumon et al., 2012; Weissman and Shizuru, 2008). Additionally, our analysis identified CD82 and CD13 to be expressed at higher levels in HSCs, whereas CD11b had a higher expression level in MPP1. These findings may be used to develop additional marker combinations to distinguish HSCs and MPP1 as well as to further refine the classification of stem/progenitor intermediates. The Transcriptome and Proteome Are Highly Coordinated upon HSC Differentiation We next analyzed the transcriptome of HSCs and MPP1 using high-throughput RNA sequencing starting from 30,000 FACSsorted primary cells (Figure 3A; Figure S3A). Robust and reproducible data were obtained for all samples, with more than 2 3 108 total readings per population (Figure S3B). In total, transcripts corresponding to 27,881 genes were identified (Table S2). Those genes were classified, according to their database annotation, into 21 RNA categories, and, as expected, proteincoding transcripts were highly represented (68.9%) (19,219; Figure 3B). In line with the proteome data, the protein-coding transcripts displayed a high diversity of functionalities (Figure 3C), including transcription factors (TF, 1,776 genes), receptors (1,796), and cell adhesion molecules (584). Additionally, the expression of 8,662 noncoding RNA species was identified, including pseudogenes (4,034), microRNAs (miRNAs) (642) and long noncoding RNAs (lncRNAs) (589). We found 479 genes to be expressed differentially between HSCs and MPP1 (FDR = 0.1; Table S2; Figure 3D). Consistent with the GO terms enriched in the proteome data, we found an overrepresentation of processes related to metabolism (positive regulation of the phosphate metabolic process and sulfur compound metabolic process) and response to hypoxia (cellular response to oxygen-containing compounds) within the genes expressed at higher levels in HSCs (Figure 3E). Furthermore, TFs involved in different aspects of developmental signaling (cellular developmental process, cell differentiation Mecom, Hoxb7) were enriched in HSCs. In contrast, the DNA metabolic process, cell cycle, and nuclear division were enriched in 4 Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc.
MPP1 in agreement with the proteome findings and increasing proliferative activity. To systematically investigate potential posttranscriptional regulation at the transition from HSCs to MPP1, we correlated the transcriptome data with the proteome data (Figures 3F–3G; Table S3). Out of the quantified proteins, we were able to assign 99.3% (4,007 of 4,036) to their corresponding transcripts (Figure 3F; Figures S3D–S3E). Notably, in addition to a strong positive correlation between both data sets, all proteins that were expressed differentially were also consistently upregulated or downregulated at the transcript level (Figure 3F). For example, the response to cytokine stimulus was overrepresented at both the RNA and protein levels in HSCs, whereas genes belonging to the cell cycle and DNA replication were expressed strongly in MPP1 (Figure 3G). As a notable exception to this overall pattern, enzymes in glycolysis/gluconeogenesis (Cmas, Gstm5, and Nagk), as well as several downstream targets of the Lin28-let7 pathway (Hmga1, Hmga2, and Igf2bp2), showed only modest changes at the mRNA level (Figures 3F and 3G), in line with their suggested regulation at the posttranscriptional level (Shyh-Chang and Daley, 2013). In addition, we found the GO term chromosome enriched and several long-lived histones related to chromosome organization (e.g. H2afz, H2afy2) to be downregulated at the RNA level in MPP1. Because these were not detected as being expressed differentially at the protein level (Figures 3F and 3G), we hypothesize that the regulatory initiation of nuclear reorganization starts already in MPP1 but becomes only operational at subsequent stages. The overall high similarity between the transcriptome and proteome suggests that posttranscriptional regulation in HSC/progenitors might be used preferentially only for some pathways, including the Lin28-Hmga-Igf2bp2 axis. MPP2 Cells Are Multipotent, but MPP3 and MPP4 Show a Differentiation Lineage Bias To investigate the functional attributes of MPP2–MPP4, we performed in vivo reconstitution assays using FACS-sorted cells (Figure 4A; Figures S4A–S4B). The contribution of transplanted MPP2 cells to the T cell, B cell, and myeloid progeny in peripheral blood (PB) after 4 months was 23%, 35%, and 32%, respectively, compared with the 56%, 70%, and 82% observed for transplanted HSCs (Figures 4B and 4C; Figure S4A). In contrast, MPP3 and MPP4 cells generated only a small number of differentiated cells (mostly below 2%), although the progeny were retained in the PB for at least 4 months. MPP3-transplanted mice showed a bias toward the production of myeloid cells, which was evident from 1 week posttransplantation but decreased over time. MPP4-transplanted animals preferentially generated lymphoid B cell progeny starting at 3 weeks posttransplantation, whereas myeloid progeny remained below 1% during the entire observation period. In line with this, T cells peaked after 3 weeks in the thymi of MPP4-transplanted mice (Figure S4B). In addition, colony-forming unit (CFU) assays showed that, like MPP2 and MPP3 progenitors, MPP4 cells also generate myeloid colonies (Figure S4C), in line with earlier studies performed with CD135+ lymphoid-primed multipotent progenitors (Adolfsson et al., 2005). In summary, MPP2 generates a large number of long-term myeloid, B cell, and T cell progeny upon transplantation. In contrast, the other two populations generate only a limited number of progeny in vivo, with a significant lineage
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
Count 0
HSC MPP1 n=4 n=3 30,000 cells
polyA-RNA High throughput sequencing 1 sample/lane Paired-end reads 100bp
B
68.93% 19219 protein coding 14.47% 4034 pseudogene 3.77% 1052 antisense 2.30% 642 miRNA 2.11% 589 lncRNA 2.07% 577 snoRNA 2.02% 562 processed transcript 2.01% 560 snRNA 0.88% 244 misc RNA 0.42% 117 IG LV gene 1.02% 285 other non-coding species
HSC MPP1
E
# transcripts 180
120
60
0
response to stimulus 167 cellular developmental process 83 cell dif f erentiation 80 regulation of localization 44 cell migration 25 positive regulation of phosphate metabolic process 24 cellular response to oxygen-containing compound 16 sulf ur compound metabolic process 9 response to alcohol 8 cell cycle DNA metabolic process nuclear division microtubule-based process regulation of cell cycle DNA recombination
G
HSC/MPP1 RNA score
1.0
500
1000
1500
2500
29 17 7 7 21 3
60
120
180
111
F
4,007 19,219
32 15 63
53 50 24 12 10 7 2 4
D
277
202 Mean of normalized counts
47 +9
RNA
RNA, protein coding Δ RNA FDR=0.1
Protein
Protein Δ Protein FDR=0.1
RNA & Protein
RNA & protein Δ RNA & protein FDR=0.1
357 435
91 53 52 50 39
Gbp10
13
Vitamin C
Gstm1
vacuolar membrane
0.5
Type
cellular response to cytokine stimulus
GOBP name GOCC name KEGG name Keywords
Extracellular matrix Glycolysis/Gluconeogenesis
0.0
log10(Size) 1 2 3
nucleosome
−0.5
2000
nucleic acid binding hydrolase transferase enzyme modulator transcription factor cytoskeletal protein oxidoreductase kinase ligase transporter receptor membrane traffic protein signaling molecule protease calcium-binding protein transfer/carrier protein chaperone phosphatase isomerase lyase defense/immunity protein cell adhesion molecule extracellular matrix protein structural protein cell junction protein tr.membr. rec. reg. surfactant storage protein viral protein
Myof
Pygm Igf2bp2 Ifitm1 Gda Zc3h10 Hmga2 Mis12 Hmga1 Sptlc2 Mllt1 Cmas Anxa2 Gstm5 Faah Mtmr9 Tyms Nagk Steap3 H2afz Ncf4 Cdk6 Dnajc6 Serpinb1a H2afy2 Hmgb2 Cdk1 Top2a Hist1h1a Kif4 Stm1 Smc2
RNA log2 fold change (HSC/MPP1)
C
log2 fold change (HSC/MPP1)
A
FDR
Tricarboxylic acid cycle Chromosome Nuclear pore Cell cycle complex
0.015 0.010 0.005 0.000
−1.0
DNA-dependent DNA replication initiation
−1.0
−0.5
0.0
0.5
Protein log2 fold change (HSC/MPP1)
1.0
HSC/MPP1 protein score
Figure 3. Comparison of the Transcriptome and Proteome of HSCs and MPP1 Cells (A) RNA-seq workflow. (B) RNA categories of 27,881 quantified genes. Shown are the percentage and number of RNA species within each RNA class. (C) Classification of quantified protein-coding genes. Bars show the number of genes within each functional class, ranked based on the protein coverage (Figure 2B). (D) Differential gene expression. Genes highly expressed (FDR = 0.1) in HSCs or MPP1 are shown in red and blue, respectively. (E) Overrepresented biological processes of differentially expressed genes. (F) Overlap and correlation between protein and RNA expression changes. Top panel, integration of proteome and transcriptome data sets. Quantified proteins (blue) were mapped to protein-coding transcripts (green). Differentially expressed transcripts (dark green, 78 mapped of 435) and differentially expressed proteins (dark blue, 47 significant plus 9 exclusively detected proteins) were assessed for overlap (red). Bottom panel, significant (FDR = 0.1) changes at RNA (green), protein (blue), and both levels (red, correlation coefficient R = 0.93), respectively. The box on the left indicates exclusively detected proteins. (G) 2D GO enrichment analysis of protein and RNA expression changes. Red regions correspond to concordant enrichment or lower expression. Blue and green regions highlight terms that are enriched or lower in one direction but not in the other, whereas terms in yellow regions show anticorrelating behavior. GOBP, gene ontology biological process; GOCC, gene ontology cellular compartment; KEGG, Kyoto Encyclopedia of Genes and Genomes. See also Figure S3.
Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 5
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
A
MPP2
B
CD45.1/2 Supportive
Donor CD45.2 (%)
MPP3 CD45.2 B6J Donor
CD45.1 Recipient
MPP4
1 -16 weeks
Read-out PB, BM, SPL & THY
MPP2
100
MPP3
100
10
10
10
1
1
1
0.10
0.10
0.10
0.01
0.01
1
3
6
9
12
My-cells B-cells T-cells
MPP4
100
16
1
3
6
9
12
0.01
16
1
3
6
9
12
16
CD45.1
Weeks
MY-cells LY-cells
1
C CD45.2
800
Count
9
12
1
16
3
6
9
12
16 Fold change x0.0003 x0.1
MPP2 MPP3 MPP4
E CD34
6
x1
0
D
3
My-cells B-cells T-cells
−3
−1
1
3
Row Z-score
MP P 4
20 HSC
MPP3
3,721 1,040 MP P 2
PC 1 B cell receptor signaling pathway
Retinoic acid metabolic process
2
3
2
HSC MPP1 MPP2 MPP3 MPP4
2
1
1
1
0
0 0
-1 -1
-1
-2 -3
Retinoic acid metabolism Aldh1a1* Bco2* * * * * Aldh1a7* Aldh1a3 * * *
Adh1
Regulation of metabolism
Rbp1* * *
Crabp2
Igf2bp2* *
Lin28b* *
Cyp26b1* *
Cyp26a1
Aldh1a2*
Lin28a* *
Hmga1 * * *
Hmgb2* * * * *
Hmga2*
*
*
Insrr*
Igf1*
Rara*
Rarb*
Rxra * *
*
Hif3a* * * * *
Igf2*
*
* Irs2*
Igf2r*
* *
Foxa3* * * *
Lin28b
Let7
*
Rarg*
*
* *
*
Igf2bp2; Hmga1; Hmga2 Insr; Igf1; Igf2; Igf2r
Ppard* Ppargc1a* * * * *
Pparg
*
Anaerobic Glycolysis
*
H 1 2 3 4
* RNA DEG HSC-MPPs FDR=0.1
Mean expression
Rdh5* * * *
Ldhb*
*
*
OxPhos; TCA Proliferation
* RNA DEG HSC/MPP1,2,3 or 4 FDR=0.1 Proteome DEP HSC/MPP1 FDR=0.1
RAR / PPAR signaling
N genes
60
54
10
0
1484 247 27
0
6
86
0
7
155
16
8
497
431
9
36
0
10
57
0
11
20
0
12
22
0
13
204
50
14
28
0
15
178
57
16
17
17
84
0
18
340
261
4
19
25
0
20
170
47
21
39
0
22
14
0
23
42
0
24
3
0
25
709
324
26
227
41
27
58
0
28
4
0
1176 306
30
11
0
31
330
306
32
0
0
Enriched Non-enriched
3 Rtel1
M
L 35
84 86
14 103 62 51
5
115 40 5
301 338 113 414 152 70 12
56 38 45 22 135 136 48 201 67 17 13
82 87
9
16 13
24 11 18 12 6
7
8
5
37
21
118 39
48
77 313
18
30
35 52 63 536 77 24 48 17 45 32 51 9 47 67 41 33 35
Fanci
Rad54b
Exo1
Hmgb2
Li g 1
Fanca 6
46 49 38 47 36 157 149 53 235 76 53 70
Uhrf1
Pol d3
9
Ube2a 76 73
Br i p 1
Erc c1 Rpa2
10
5 18 16
Topb p1
Rad51
Faap24 15
6
Btbd 12 Rad18
Brc a1
Paxip1
47
7
32 35 18 18
8
Xrc c3
Gm15428 Rad51c
Rbb p8 Apitd1
3 24
Mus81
Em s y Fancb Fancc
4
38 10 12 18 7
12
27 4 7
7
22 29
6
3 22 21
8
39 10 13
3
19
16 10 19 15 10 21
ex tr re ace s l in po c lula fla ns el r m e l a reg ce m to dh io ll c a d su ev ell tor wo esi n rfa el co y r u n on op m e d i ce m m sp ng re ce re c ent uni ons pt sp ell al ca e or on p pr tio si se rol oc n gn t ife e al o s ra ss in t ti ch c g i m on ro m ell pat ulu m hw s m e ig a os ta ra y om bo cy ch t to e lic ves ion ro s o k m rg pro icle el os a e to nu ni ces om z n c s e, or leo atio g s n ce nt ce ani om ro ll za e ly m m d tio e r ivi n d e ph fe oc c D D ic r sio ns yt yt N N e n e e m ok A r A gio re e in e re n sp d e p p l i p a o ia r ca ir T ns te od ti le ce e t d im uct on ca u k ll o m i o no em res n o d b ni b pir in B cyt iffe act unit ca ry at te c e re er y l W on or rle ell ch nt ium y nt ic f sy ukin diff em iati re ore st −6 ere ota on ce lim em p n x pt b d ro tia is o e d ti m r si mo ve uc on el n gn rp lop tio an e a h m n re int os rve lingoge en sp er om d p ne t on leu e eve at si se ki or lo hw s to n−1 gan pm ay in p iz en te ro at t rfe d io r u n pr JA pro en on ctio po − e si −B K− tein doc be n tiv e ce STA se yto ta re ll T cr sis gu d ca iff ca etio la er sc n lc p tio n re ium lat co ent ade of gu i el ag ia o R la n et ul tion N tio h ac a A o t ti ER men o me iva on K1 tabf pr ost tion o an oli teo asis n d e c p ly ER ur ro si s M K2oge ces AP c n s i a e m nte k K s sis ye rfe ph ina cascad lo r o id n os se ca e le −g ph ac de uk a o t oc mm lip ry ivit yt a id lat y e p bi io di ro nd n ffe d in re uct g nt io ia n tio n
HSC (#2) MPP1 (#3) HSC-MPP1 (#4) MPP1-MPP2 (#7) HSC-MPP1-MPP2 (#8) MPP2-MPP3 (#13) MPP1-MPP2-MPP3 (#15) HSC-MPP1-MPP2-MPP3 (#16) HSC-MPP4 (#18) HSC-MPP1-MPP4 (#20) MPP3-MPP4 (#25) HSC-MPP3-MPP4 (#26) MPP2-MPP3-MPP4 (#29) MPP1-MPP2-MPP3-MPP4 (#31)
1 0 0.2 adjusted p-value
N GOs
390
29
Ldhb: RNA mean expression >700 Ldhc: RNA mean expression <700
Row Z-score 0
0
2 3 5
Hif1a
Retinoic acid metabolic process
Rdh10* * *
1
4
Insr
*
HSC
K
J
MPP1
-2
HSC MPP1 MPP2 MPP3 MPP4
Cd96 Itgax Selp Ldhb H2−Aa Ctla2b Igtp Htatip2 Sdpr Gp1bb Aqp1 Tal1 Bpgm Aldh1a1 Fhl1 Fli1 Mpl Pdzk1ip1 vWF Nckap1 Gata2 Epor Zfpm1 Tspan33 Cmas Clu Gpr64 Gstt1 Klf1 Itga2b Gata1 Hemgn Gp5 F2r Mrvi1 Rgs10 Lmna Thpo Car1 Cpa3 Il1rl1 Gadd45a
3,826
Mitosis
Mean log2 fold change
H
HSC MPP1 MPP2 MPP3 MPP4
−3
1,797
-40 -20 0 20 40
CD48
MP P 3
MP P 1
479
MPP2
-40
I
2,437
HS C
-20 CD135
1,436
3,240 4,043
MPP1
0
4,288
Cluster
mean RNA expression
CD150
G
MPP4
MPP4
PC 2
40
MPP3
F
MPP2
HSC MPP1 MPP2 MPP3 MPP4
Cl s p n Fancd2
Ogg 1 Nud t1 M lh1 Mbd4
Rad50
Rpa1
Rad52
Chek1
Chaf1a
Rad54l Cdca5
Neil3
Mcm8
Smc4
Smarc b1
Eme1 Kif22
LOC677447
Kat5
Rad9 H2afx
Msh6
Trip 13
No n o
Pol a1
Bar d1
Foxm1
Pcna
Trp53 Rad51ap1
Node size
1
56 Degrees
Dt l
Dna2
Ruvbl 1
0.4 1 Combined score STRING
Hus1b Cul 4a Baz1b
Ssr p1
Uchl 5 Nsmc e1 Po l q
Act l6a
Pole
Smc2
Nsmc e2
Edge color
Smc1a
Pif1
Po l h Eef1e1
Dek
Node color MPP1 MPP2 MPP3 MPP4 Highest expression
(legend on next page)
6 Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc.
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
bias toward mostly myeloid (MPP3) or lymphoid (MPP4) cell types. Clustering Analyses Identify Global Molecular Differences during HSC Differentiation We sequenced the transcriptomes of the MPP2, MPP3, and MPP4 populations in biological triplicates (Figures 4D–4M; Figures S4D–S4G). The surface markers used for FACS showed consistency between transcript and protein levels (Figure 4D). We detected 6,487 genes to be expressed differentially across all five populations (FDR = 0.1; Table S2). The differential expression of a set of genes was confirmed by quantitative real-time PCR (Figure S3F), validating the robustness of the data. Unsupervised clustering of gene expression profiles grouped together HSC and MPP1 as well as MPP3 and MPP4 (Figure 4E). This was verified by principal component analysis (Figure 4F) and by the numbers of differentially expressed genes in each pairwise comparison (Figure 4G). In all analyses, MPP2 was placed between the HSC-MPP1 and MPP3-MPP4 clusters (Figures 4B and C; Figures S4A and S4B). Clustering of Gene Expression Profiles Suggests Processes Operating in HSC/MPP1-MPP4 Cells The 6,487 genes with differential expression across the five cell populations were enriched in specific GO terms (http:// www-huber.embl.de/SMHSC/HSCGO/goAnalysis.html; Supplemental Information), of which we highlight three selected examples (Figure 4H). Based on the expression data, MPP2 is the mitotically most active population, whereas HSC is the most quiescent one. In agreement with the functional lymphoid differentiation bias observed in MPP4-transplanted recipients (Figures 4B and 4C), the B cell receptor signaling pathway was enriched in this population. Consistent with this, mapping of the HSC-MPP data against granulocyte macrophage or lymphoid-primed progenitor signatures described previously revealed an enrichment for MPP3 and MPP4 populations, respectively (Ma˚nsson et al., 2007; Figure S4E). In addition, genes of a
megakaryocytic erythroid signature described previously (Ma˚nsson et al., 2007; Gekas and Graf, 2013; Sanjuan-Pla et al., 2013) were expressed preferentially in HSCs, MPP1, and MPP2 compared with MPP3 and MPP4 (Figure 4I). Consistent with the differential expression of metabolic enzymes between HSCs and MPP1 (Figures 2D and 3E), we found genes belonging to the retinoic acid metabolic process to be highly expressed in HSCs compared with all other populations (Figures 4H and 4J), suggesting a role in adult HSCs. Furthermore, we found evidence for stage-dependent and isoform-specific expression of essential glycolytic enzymes in HSCs (e.g. Aldoc, Ldhb; Figure S4D), extending recent studies demonstrating the relevance of anaerobic glucose metabolism for the maintenance of HSC self-renewal (Takubo et al., 2013). In contrast, the large majority of enzymes involved in the tricarboxylic acid cycle and oxidative phosphorylation showed MPP2-enriched expression (e.g. Fh1, Aco2). Interestingly, although Lin28b and its target genes, including Hmga2 and Igf2bp2, were highly expressed in HSCs compared with MPP1, the family member Lin28a was specifically upregulated in MPP3 (Figure 4J). Therefore, our findings support the recently established role of the Lin28-let7 axis in glucose metabolism in stem cells (Shyh-Chang and Daley, 2013) and suggest Lin28b as a candidate for an upstream posttranscriptional regulator of glycolytic enzymes in HSCs (Figures 3F and 3G). Next we assigned each of the 6,487 differentially expressed genes to one of the 32 possible relative expression patterns (Figure 4K) and tested for overrepresented GO terms within each pattern (Table S4). Population-specific GO terms are displayed in Figure 4L. Consistent with the strong upregulation of cell cycle-associated genes in MPP2, we observed that 43% of the genes involved in DNA repair also showed the highest expression in MPP2 (Figure S4F). The protein-protein interaction network of the HSC-suppressed DNA repair genes comprised, e.g., Brca1 and Exo1 (Figure 4M), representing potential players for the switch in DNA repair mechanisms between HSCs and MPP2. Interestingly, the two common mechanisms for repair of DNA double strand breaks (homologous recombination and
Figure 4. Lineage Potential and Whole Transcriptome Molecular Signatures of HSC-MPP1-4 Populations (A) Experimental workflow for investigation of the in vivo reconstitution potential. MPP2, MPP3, and MPP4 were transplanted into lethally irradiated mice, and the myeloid (MY) and lymphoid (LY) outcomes were monitored over time by flow cytometry. SPL, spleen; THY, thymus. (B and C) Monitoring the myeloid and lymphoid outcomes in peripheral blood after transplantation of MPP2, MPP3, and MPP4. The relative percentage (B) and contribution (C) of donor myeloid (blue), B cells (gray), and T cells (purple) were monitored from the peripheral blood of MPP2-, MPP3-, and MPP4-transplanted animals at 1–16 weeks. n = 8-12 per group. The sizes of the circles in the right plot represent fold change relative to HSC contribution after 3 weeks. (D) Differential RNA expression of the surface markers used for FACS. Average RNA expression ± SD is shown. (E) Clustered heat map. The colors represent the normalized average read count in each of the five cell populations for all differentially expressed genes (FDR = 0.1). (F) Principal component analysis. (G) Relative distances are shown based on numbers of differentially expressed genes in pairwise comparisons. (H) GO enrichment analysis of 6,387 genes with differential expression. Three representative examples of significantly enriched GO terms out of 1,001 terms are shown. (I) Expression of megakaryocytic/erythrocytic genes. (J) Transcriptome and proteome signatures of metabolism. Average RNA expression ± SD is shown (arbitrary units). RAR, retinoic acid receptor; PPAR, peroxisome proliferator-activated receptor; OxPhos, oxidative phosphorylation; DEG, differentially expressed genes. (K) Gene expression clusters. 6,487 differentially expressed genes were grouped into 32 clusters based on higher expression (enriched) in one or several cell population(s) compared with mean expression level across all cell populations. (L) Representative overrepresented GO terms of gene expression clusters. The number of genes within each cluster is shown for significantly enriched GO terms (FDR = 0.1). (M) Network of HSC-suppressed DNA repair genes. Genes with low expression in HSCs (Z-score < 1.5) and with known and predicted protein-protein interactions are shown. The colors depict the MPP population with the highest expression. Note that most genes are most highly expressed in MPP2. See also Figure S4.
Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 7
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
A
HSC
B
-log10(p value)
0
1
Nuclear Receptors Monoamine GPCRs TGF Beta Signaling Pathway Retinol metabolism GPCRs, Other Diurnally Regulated Genes with Circadian Orthologs Ovarian Inf ertility Genes Exercise-induced Circadian Regulation Senescence and Autophagy Splicing f actor NOVA regulated synpatic proteins
2
Frizzled ligands
Wnt1
Wnt2a
Wnt2b
Wnt3
Wnt3a
Wnt4
Wnt5a
HSC
Wnt5b
MPP1 MPP2
3
MPP3
4
Wnt6*
2
Wnt7a
Wnt7b
Wnt10a* Wnt10b
Wnt11
Wnt16
MPP4
*
4 2 3 3 2 3 4 2
HSC-MPP1
Wnt receptors
Fzd1
Fzd2*
Fzd3*
Fzd4*
-log10(p value)
0
2
Cytoplasmic Ribosomal Proteins Retinol metabolism Matrix Metalloproteinases Endochondral Ossif ication G Protein Signaling Pathways Ovarian Inf ertility Genes ACE Inhibitor Pathway Nuclear receptors in lipid metabolism and toxicity Dopminergic Neurogenesis Adipogenesis Focal Adhesion Regulation of Actin Cytoskeleton Nuclear Receptors Glutathione metabolism Wnt Signaling Pathway Purine metabolism Wnt Signaling Pathway and Pluripotency
DEG HSC/MPP1 FDR=0.1
Myc* DEG HSC-MPPs FDR=0.1 Wnt1 RNA mean expression <700 Fzd7 RNA mean expression >700
Protein Kinase C
4 20
Prkca*
Prkcb*
Prkcc
Prkcd*
Fzd5*
Fzd6
Fzd7
Dvl1
Dvl2
Dvl3
Fzd8*
Fzd9*
Ldlr
Fzd10
Rhoa
Racgap1* *
6 4 9 13
Mapk9
Ppp2r5c Ppp2r5e
Mapk10
Cytoskeleton Axin1
5 2 4 3 14 19 14 5 3 8 15 10
Prkce*
Prkci
Prkch*
Prkd1*
Prkcq
Prkcz
Frat1
Csnk1e
Gsk3b
Apoptosis
Apc B-catenin Fbxw2
Pafah1b1
P Ctnnb1 Phosphorylated Ubiquitin tagged B-catenin
Tcf1
C
Counts nucleic acid binding enzyme modulator receptor transcription factor transferase hydrolase cytoskeletal transporter signaling kinase oxidoreductase cell adhesion ligase defense/immunity extracellular matrix phosphatase lyase calcium-binding membrane traffic structural transfer/carrier isomerase protease chaperone cell junction viral surfactant tr.membr. rec. reg.
50
Transcriptional activation
Nucleus
26S Proteosome degradation
100 Mycn*
Myc*
D Fitted expression
0
Ccnd1*
Ccnd2*
Ccnd3*
Fosl1
Jun*
Plau*
Foxj3 (ENSMUSG00000032998+)
5000 2000 1000
HSC MPP1 differential exon usage
100
exons 10
1 2 3 4 5 6 7 8 9 10 119537004
119557474
119577944
119598414
119618884
Genomic region
Figure 5. Gene Expression Signatures and Transcript Isoform Regulation in HSCs and MPP1–MPP4 Cells (A) Signaling pathway enrichment analysis. Shown is the overrepresentation of signaling pathways within HSC- and HSC-MPP1 clusters. NOVA, neurooncological ventral antigen; ACE, angiotensin-converting enzyme. (B) Wnt signaling. Shown is a pathway analysis based on WikiPathways (WP403). Average RNA expression ± SD is shown (arbitrary units). (C) Association of differentially spliced genes to protein classes. (D) Representative example for a TF (Foxj3) showing differential exon use. The first Foxj3 exon was detected at higher levels in HSC (red) compared with MPP1 (blue).
nonhomologous end joining) also showed a different expression pattern across the five cell populations. Both repair processes are low in HSCs and are most enriched in MPP2 (Figure S4G). In summary, our data indicate that the increased mitotic activity during HSC differentiation, starting in MPP1 and peaking in MPP2, is associated with a parallel increase in expression of DNA repair pathway genes. 8 Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc.
To investigate the signaling pathway complexity of both the HSC and the HSC-MPP1 self-renewal clusters, we tested for overrepresentation of differentially expressed genes in pathways annotated in REACTOME within each group of genes. We found, among others, G protein-coupled receptor (Gpr143, Pde1c) and transforming growth factor b (Inhba, Bmp4) signaling to be enriched (Figure 5A; Table S4). In accordance with the GO
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
enrichments (Figure 4L), Wnt signaling was prominent in selfrenewing HSCs and MPP1. Although the role of canonical b -catenin-mediated Wnt signaling in adult HSCs remains controversial (Koch et al., 2008; Luis et al., 2012), noncanonical signaling has recently been suggested to mediate critical interactions between HSCs and their niches (Sugimura et al., 2012). Therefore, we interrogated individual expression patterns of the entire Wnt signaling pathway during differentiation of HSCs toward MPP4. Although the pathway was highly overrepresented in HSC-MPP1, none of the Wnt ligands were expressed at high levels, arguing against autocrine production of Wnt ligands (Figure 5B). However, even low levels of Wnt ligands support stem cell self-renewal (Luis et al., 2012). In agreement with this, three frizzled receptors (Fzd4, Fzd8, and Fzd9) were highly expressed in HSC-MPP1, consistent with reported Fzd4 expression in human CD34+ cells (Tickenbrock et al., 2008) and a role of noncanonical Fzd8 in the maintenance of quiescent long-term HSCs (Sugimura et al., 2012). Taken together, our analysis underscores the relevance of Wnt signaling for HSCs but also outlines the complex isoform-specific enzyme utilization operational in HSCs and the different MPPs. This likely contributes to the dynamic regulation of this pathway, which is critical for most stem/ progenitor cell types. Transcription Factor and Transcript Isoform Regulatory Landscape Among the genes expressed differentially across the five populations, we identified 490 TFs, including members of the Fox, Gata, and Hox families (Table S2). TF splice isoforms can have stage- and tissue-specific expression patterns throughout development (Lo´pez, 1995; Sebastian et al., 2013) and alternative splicing has been shown to trigger switches between activating and repressive TF isoforms (Taneri et al., 2004). We tested for differential exon use for detection of alternative transcription start sites, alternative splicing, and alternative terminations sites (Anders et al., 2012) in the HSC MPP1–MPP4 transcriptome data. Among the 497 genes expressing transcript isoform variants across HSC MPPs (FDR = 0.1), we identified 46 TFs (Figure 5C; http://www-huber.embl.de/SMHSC/HSCDEU/overall/ testForDEU.html; Supplemental Information). We observed that the first exon of the TF forkhead box J3, Foxj3, was included more frequently in the HSC transcripts compared with MPP1, suggesting the expression of a specific Foxj3 isoform in HSCs (Figure 5D). Although the role of this Foxj3 variant in hematopoiesis is uncharacterized, an alternative splicing switch for another forkhead family member, Foxp1, has recently been shown to regulate embryonic stem cell pluripotency and reprogramming by changing its DNA-binding preference (Gabut et al., 2011). Genome-wide DNA Methylation Analysis of HSCs/MPPs Identifies Candidate Regions for Epigenetic Regulation To investigate the methylation status of all cytosine residues within the genome of HSCs and MPPs, we subjected R10,000 FACS-sorted cells per biological replicate to TWGBS (Wang et al., 2013) (Figure 6A; Figure S5A). Robust data were obtained for all samples, with more than 6 3 108 reads and a combined genomic cytosine-phosphate-guanine (CpG) coverage of more than 33-fold per population across the three biological replicates (Figure S5B). Global levels of DNA methylation ranged between
81% and 83% and were not significantly different across populations, whereas pairwise comparisons identified a total of 15,887 distinct differentially methylated regions (DMRs) (Figure 6B; Table S5). Mapping these DMRs to previous DNA methylome data generated on HSC/MPPs using reduced representation bisulfite sequencing (RRBS) (Bock et al., 2012), we found that 85% of all DMRs were exclusively identified using the TWGBS analysis (e.g. Mecom; Figure S5C), demonstrating the additional coverage of our data set. Early commitment steps correlated with lower numbers of DMRs (1,121, HSC-MPP1), which likely reflects close ontogenic and functional relationships, and were mainly associated with loss of methylation (71%, HSCMPP1). In contrast, transitions between more differentiated MPP populations showed higher numbers of DMRs (1,874, MPP2MPP3/MPP4) and gain of methylation (75%, MPP2-MPP3/ MPP4; Figure 6C; Figure S5D; Table S5). Globally, methylation changes showed continuous progressive behavior (gain or loss) through HSC differentiation (Figure 6C). Next we investigated the overlap of DMRs with annotated genomic features derived from Refseq (Figure 6D; Pruitt et al., 2009). The majority of DMRs were located in intragenic regions (57%), whereas promoters and intergenic regions comprised 9% and 34%, respectively. Comparison of our data set with experimentally defined functional genomic elements (Stamatoyannopoulos et al., 2012; Shen et al., 2012) demonstrated a significant overrepresentation of DNaseI hypersensitivity sites (DHSs), promoters, enhancers, and/or transcription factor binding sites (TFBSs) across DMRs (Figure S5E; all p values < 10 16). Interestingly, 74% of all DMRs that overlapped with DHSs (10,887) mapped to regions annotated as TFBSs, promoters, and/or enhancers, suggesting that dynamic methylation upon hematopoietic commitment occurs predominantly at cis-regulatory elements (Figure 6E). Together, these data support the hypothesis that many DMRs represent cis-regulatory elements in HSC-MPPs. Gene Expression Anticorrelates Globally with Changes in DNA Methylation Given the significant overlap between DMRs and functionally important regulatory regions of the genome, we postulated that the methylation changes could be associated with gene expression during HSC differentiation. Indeed, we found that DMRs coincided with previously identified cis-acting regulatory sites at the Sfpi1 (Rosenbauer et al., 2004) and Gata2 (Gao et al., 2013) loci (Figure S5F), which both encode TFs described as effectors of hematopoietic commitment. We generated gene sets based on DMRs that were stratified for methylation increase or decrease and interrogated the associated gene expression levels between HSCs and each of the MPP populations. DMRs detected at the early transition of HSC to MPP1 were significantly associated with anticorrelated gene expression along differentiation (Figure 6F). In addition, pairwise comparisons demonstrated a global anticorrelation between transcription and DNA methylation (Figure 6G; Supplemental Information). Among the top anticorrelating genes between HSC-MPP1 (80; Table S6), we found a number of well documented markers of early hematopoietic differentiation and HSC function (e.g. Hoxb2, Rorc, Cd34). For example, the increasing expression of Cd34 and Wnt-inhibitory factor (Wif1) from HSC to MPP1 is Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 9
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
A
B HSC
MPP1
C
HSC
MPP1
MPP2
MPP3/4
HSC
R1 R2 R3 R1 R2 R3 R1 R2 R3 R1 R2 R3
MPP1
MPP2
MPP3/4
R1 R2 R3 R1 R2 R3 R1 R2 R3 R1 R2 R3
MPP2 MPP3/4 4,712
n=3
DNA methylation 1.0
10-30 ng DNA/replicate HSC
Tagmentation-based wholegenome bisulfite sequencing (TWGBS) 100bp paired-end reads 6.7 - 19.9 x 108 aligned reads
MPP1
1,121
MPP2
1,412
1,874
MPP3/4
0.8
0.6
0.4
7,411
0.2
gain of methylation 0
12,013
loss of methylation
DMRs (MPP2 – MPP3/4)
DMRs (HSC – MPP1)
D
E
57.0%
F Promoter n=4,745 (29.7%)
8.7%
DHS n=14,617 (91.4%)
34.3%
Enhancer n=4,561 (28.5%)
111 12
778
HSC
MPP1
3730
MPP3/4
MPP2
TFBS n=8,798 (55.0%)
99 23
258
7
162
1313 1296
51 1553
Intergenic Promoter Intragenic
3474
Hoxd
1.0 0.8 0.6 0.4
Relative Methylation
0.2
b8 b7
Mir10a b4
b6 b5
b3
b2
b1
HoxD
Chr2:74,660,000
0.8
1.0
2
0.6
0
Z-score
b9 Gene Promoter DMR Enhancer
0.4
−2
* DEG HSC-MPPs FDR=0.1 Hoxb1 with DMRs Hoxb7 no DMRs
Chr11: 96,270,000
0.0
Hoxb1* Hoxb2* Hoxb3* Hoxb4* Hoxb5* Hoxb6* Hoxb7* Hoxb8 Hoxb9* Hoxb13 Hoxd1 Hoxd3 Hoxd8 Hoxd9 Hoxd10 Hoxd11 Hoxd12 Hoxd13
Hoxb
HoxB
I
0.2
Spns3 Wdr16 F2rl2 Zfat Sept3 Endou Rfc2 Poc1a Zscan10 Gfra1 Tyms Rabgap1l
H
HSC MPP1 MPP2 MPP3/4
0.0
Haao Mrvi1 Mc5r Cd163 Tex22 Alx3 Anp32e Cd34 Slc16a7 Wif1 Ltc4s Mrc2
Eomes Tenc1 Plekhg6 Clec14a Flrt1 Ndnf Cpne8 Kcnc1 Cttnbp2nl Sox7 Hoxb2 Dpy19l2
Relative Methylation
Il4 Lsr Slc9a3r2 Perp Doc2b Pde1b Efna1 Pdgfc Rorc Tinagl1 Gfi1 St8sia1
HSC MPP1 MPP2 MPP3 MPP4
G
2215
Gene Promoter DMR Enhancer
d13 d12 d11
d10 d9
d8
1700109F18Rik d3
d1
b1 Hoxb1 with DMRs d7 Hoxd7 no DMRs Transcription direction
Figure 6. Global DNA Methylation Analysis and Anticorrelation with Gene Expression (A) DNA methylome workflow. (B) Gain or loss of methylation DMRs between HSC-MPPs. The numbers indicate total DMRs. (C) Clustering of DMRs identified between HSC-MPP1 or MPP2-MPP3/MPP4. Each horizontal dash represents a DMR. R1–R3, replicates 1–3. (D) Overlap of DMRs with gene-centric Refseq genomic regions. (E) Percent overlap of all DMRs with experimentally defined functional genomic elements based on available data from the mouse ENCODE project. (F) Overall comparison of DNA methylation to gene expression. The box plots represent relative gene expression associated with either gain (left) or loss (right) of methylation in the transition from HSC and MPP1. (G) Pairwise comparison of DNA methylation to gene expression. The box plots represent log2 fold change of gene expression associated with either gain or loss of methylation in the transition from HSC to MPP1. The top 48 anticorrelated genes (24 highest/24 lowest expression in HSCs) are indicated. (H) Gene expression levels of all members of the HoxB and HoxD clusters. (I) Relative DNA methylation profile for the HoxB and HoxD clusters. Red arrows indicate examples of DMRs. See also Figure S5.
10 Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc.
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
associated with decreasing methylation at the DMRs of these loci. In contrast, the cyclic AMP-mediating cyclic nucleotide phosphodiesterase 1b (Pde1b), as well as the retinoic acid receptor orphan receptor g(Rorc), become methylated and downregulated during differentiation toward MPP1. Hox TF clusters are critical during normal and malignant hematopoiesis (Argiropoulos and Humphries, 2007), but little is known about their epigenetic regulation. We found that most members of the HoxB family showed the highest expression in HSCs and were associated with DMRs (seven out of nine; Figures 6H and 6I). Notably, HSC differentiation showed a continuous increase in DMR methylation and paralleled decrease of RNA expression (Figure 6I, see arrows for Hoxb2 and Hoxb4). In contrast, the members within the HoxD cluster showed no specific expression pattern in HSCs and MPPs, and none of its genes were associated with a DMR (Figure 6I). Similar results were found for HoxA and HoxC clusters, respectively (Figures S5G and S5H). In conclusion, the integrated analysis of the methylome and transcriptome provides a resource of genes whose expression is, at least partially, regulated by DNA methylation and that are possibly involved in the hard wiring of the hierarchical organization of the HSC/progenitor populations. Differential lncRNA Expression and the Imprinted Gene Network in Stem/Progenitors Long noncoding RNAs (lncRNAs) have been implicated in guiding chromatin remodeling complexes to specific target genes and mediating gene activation or silencing by recruiting the DNA demethylation machinery to the promoter (Arab et al., 2014) or recruiting repressive complexes such as PRC2, respectively (Rinn and Chang, 2012). However, little is known about their functional role or regulation in stem cells and hematopoiesis (Paralkar and Weiss, 2013). We identified 682 lncRNAs expressed in HSC-MPPs, 79 of which were differentially expressed across all five populations (FDR = 0.1; Figure 7A; Table S2). Unsupervised clustering (Figure 7B) and pairwise comparisons of differentially expressed lncRNAs revealed the same population relationship as using the entire transcriptome data set, again placing MPP2 between the HSC-MPP1 and the MPP3-MPP4 populations (compare Figures 4E–4G and S6A). Next we clustered the significantly changed lncRNAs based on their relative expression levels across all populations (Figure 7C; Table S4). As shown in cluster 2, 12 lncRNAs are strongly expressed in HSCs compared with the rest of the populations, and none of these has yet been functionally annotated or studied (e.g. 2410080I02Rik, Gm12474). In addition, 14 lncRNAs are coexpressed in HSC-MPP1 (cluster 4), representing additional candidates for regulation of self-renewal (e.g. H19, Malat1, Meg3). In agreement, the imprinted H19 lncRNA has recently been shown to mediate HSC quiescence by inhibiting insulin growth factor (IGF) signaling (Venkatraman et al., 2013). Thirteen lncRNAs were enriched in MPP3-MPP4, suggesting regulatory roles in these lineage-restricted populations (cluster 25; e.g. Gm568, Neat1). Although Neat1 is essential for the integrity of the nuclear substructure, it has also been linked to the immune response after HIV-1 infection (Zhang et al., 2013) and might, therefore, be involved in the maintenance of immune regulatory circuits in MPP3-MPP4. Notably, most of the differentially expressed lncRNAs identified here have not been studied in hematopoiesis
and/or have unknown functions. The lncRNAs H19 and Meg3 that are highly expressed in HSCs compared with MPPs (Figure 7C, validated by quantitative RT-PCR; Figure S6B) are core members of the transcriptional imprinted gene network (IGN), which has been postulated to regulate embryonic growth (Varrault et al., 2006). In adult hematopoiesis, the genetic deletion of several members of this network affects HSC self-renewal integrity (e.g. Cdkn1c/p57KIP2) (Berg et al., 2011; Rossi et al., 2012). Therefore, we further investigated the expression of the IGN members and found an overall strong expression in HSCs but a steady decrease during the differentiation process (Figure 7D), suggesting a contribution of the IGN in the maintenance of self-renewal and/or quiescence of HSCs. Finally, we interrogated the DMRs within the loci encoding the 682 quantified lncRNAs. This revealed a significant enrichment of DMRs in the differentially expressed lncRNAs. These DMRs were enriched within a 10 kb window centered on the transcription start site (Figure 7E). A notable example is the H19 locus, which exhibits a DMR in an enhancer region outside of its imprinting control region (Figures 7F and 7G). The increasing level of methylation at this enhancer during the transition from HSCs to MPPs correlates with decreasing expression during differentiation (Figure 7F). Overall, these data suggest that differential DNA methylation of regulatory regions is a likely mechanism by which lncRNA expression is controlled in HSCs and their progeny. DISCUSSION In this study, we describe a combined proteome, transcriptome, and DNA methylome analysis of highly purified primary HSCs and four downstream MPPs, which we characterized additionally using in vitro and in vivo functional assays (Figure S7). Our data sets uncover progressively changing cell type-specific methylation, gene, and protein expression landscapes starting with quiescent CD34-CD150+CD48-LSK HSCs that sit at the pinnacle of the hematopoietic hierarchy. These differentiate toward slowly cycling multipotent MPP1, followed by multipotently cycling MPP2. The steady increase in the activity of the cell cycle and proliferation machinery is paralleled by the robust upregulation of the entire DNA repair machinery. This raises the possibility that physiological DNA replication in proliferating early progenitors generates significant replicative stress that needs to be counteracted by the activation of the DNA repair machinery to ensure genome integrity (Bakker and Passegue´, 2013). We found that the majority of DMRs either progressively gain or lose DNA methylation through early HSC differentiation. Moreover, we observed a global anticorrelation between DNA methylation and gene expression. Because this was observed at a global level by the whole genome DNA methylome analysis (TWGBS) but not using previous approaches (array-based and RRBS) (Ji et al., 2010; Bock et al., 2012), our data suggest that many regulatory regions, including, e.g., distal enhancers critical for gene expression, are only covered by TWGBS analysis. The observed high overall correlation between RNA and protein levels suggests that posttranscriptional regulation is not a predominant mechanism by which gene expression is regulated in homeostatic HSCs. However, a small number of specific functions in the stem/progenitor compartment may be regulated in Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 11
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
D
C
A
Mus musculus
E
B
F
G
Figure 7. Expression Landscape of lncRNAs and the Imprinted Gene Network (A) Workflow for lncRNA analysis. (B) Clustering of 79 differentially expressed lncRNAs. (C) LncRNA expression clusters. Differentially expressed lncRNAs were grouped into 32 clusters based on higher expression (enriched) in one or several cell population(s) compared to mean expression level across all cell populations. Right, an example diagram for each of the most enriched clusters. Average RNA expression ± SD is shown. (D) Expression of imprinted gene network genes. (E) Differential DNA methylation of quantified lncRNAs. Top panel, comparison of DMRs between differentially and nondifferentially expressed lncRNAs. Bottom panel, heatmap showing DMRs in red. LncRNAs are ranked by increasing adjusted p value (gray scale). Distances are relative to transcription start sites. *p = 0.01–0.001; **p < 0.001. (F) Differential methylation of the lncRNA H19 locus. The H19 locus shows increasing methylation from HSC (red) to MPP1 (blue), MPP2 (green), and MPP3/4 (brown) in two DMRs. The inset shows a diagram depicting average H19 RNA expression (mean ± SD). (G) Differential methylation of H19 locus at enhancer region 3. Black and white dots represent methylated and unmethylated CpGs, respectively. The red box indicates DMR. See also Figure S6.
12 Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc.
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
this way. For example, our results point to a potential posttranscriptional regulation of glycolytic metabolism in HSCs via Lin28b-Hmga-Igfbp2 signaling (Shyh-Chang and Daley, 2013), likely because of differences in relative protein synthesis rather than degradation rate (Kristensen et al., 2013; Signer et al., 2014). Moreover, our data suggest that HSC self-renewal and quiescence are regulated by an interplay between the Lin28blet7-Hmga-Igfbp2 axis, the IGN regulatory network, and HSCenriched pathways such as Wnt and retinoic acid (RA) signaling. Although some of these pathways are also implicated in embryonic HSC emergence from the hemogenic endothelium (Varrault et al., 2006; Chanda et al., 2013; Copley et al., 2013), the individual players and mechanisms governing HSC function remain to be explored in the embryo and in the adult. As an example, the Lin28b target Igf2bp2 was found to be one of the most differentially expressed transcripts and proteins in HSCs. It is known to modulate expression of the lncRNA H19, leading to suppression of proproliferative IGF signaling as well as Let7 miRNAs and has been suggested to mediate HSC quiescence (Runge et al., 2000; Kallen et al., 2013; Venkatraman et al., 2013). In agreement, our data show increasing H19 enhancer methylation during the transition from HSC to MPP1, providing an explanation for the release of HSCs out of quiescence associated with loss of selfrenewal, which might be enhanced further by suppression of the IGN activity (i.e. p57; Zou et al., 2011; Tesio and Trumpp, 2011). RA signaling is not only known to be critical for embryonic HSC emergence (Chanda et al., 2013) but also for the regulation of Hox gene expression by chromatin reorganization in embryonic stem cells (Kashyap et al., 2011). Because we observed high expression and low DNA methylation of most members of the HoxA/B clusters in HSCs, it is plausible that RA signaling contributes to the control of the epigenetic landscape of HoxA/ B transcription factors in HSCs. Moreover, because many Hox genes are mutated in leukemias, these mechanisms may also be relevant for leukemic stem cells (Alharbi et al., 2013). An unexpected finding is the degree of alternatively spliced transcript isoforms present in HSCs and their progeny. To date, only rare cases of HSC regulation through alternative splicing have been reported (Bowman et al., 2006). In this study, we identified almost 500 genes with alternative transcript isoform regulation. Although the underlying regulatory mechanism remains unknown, the lncRNA Malat1, which is highly expressed in HSCs, has been suggested to be a regulator of alternative splicing (Tripathi et al., 2010). In line with this, Malat1 has been implicated in multiple types of human cancer (Gutschner et al., 2013), and numerous genetic mutations encoding factors of the splicing machinery have been detected in patients with chronic lymphoid leukemia (Martı´n-Subero et al., 2013) and myelodysplastic syndrome, a disease derived directly from HSCs (Lindsley and Ebert, 2013; Medyouf et al., 2014). Our catalog of splicing variants, with Foxj3 as an example of an HSC-specific splice isoform, will serve as a starting point to explore this largely uncharted area of regulation in HSCs and their immediate progeny. In addition to Malat1, we identified more than 70 differentially expressed lncRNAs, the vast majority of which have no documented role in hematopoiesis. The variety of molecular functions assigned to lncRNAs is expanding steadily, and their biological roles include regulation of genomic imprinting, differentiation, and self-renewal (Fatica and Bozzoni, 2014).
In summary, the global signatures for stemness and multipotency generated in this study represent not only a comprehensive reference but also suggest distinct areas of stem cell regulation (progressive DNA methylation, alternative splicing, and lncRNAs). This study significantly extends the current understanding of HSC progenitor biology at the global level and provides a solid basis for functional studies exploring the networks responsible for stem cell quiescence, self-renewal, and differentiation. EXPERIMENTAL PROCEDURES Animals Eight- to twelve-week-old female C57BL/6 (CD45.2) mice purchased from Harlan Laboratories and B6.SJL-Ptprca Pepcb/BoyJ (CD45.1) animals purchased from Charles River Laboratories were used throughout the study. CD45.1/CD45.2 heterozygotes (F1) for transplant auxiliary bone marrow were bred in-house at the Deutsches Krebsforschungszentrum. Bone Marrow Reconstitution Experiments Fifty (HSC, MPP1) or 2,000 cells (HSC, MPP2, MPP3, MPP4) were FACSsorted and injected intravenously together with 2 3 105 supportive bone marrow (BM) cells (CD45.1/2) into lethally irradiated (2 3 450 Gy) (CD45.1) recipient mice. CD45.2-donor cells were monitored at 1, 3–4, 6, 9, 12, and 16 weeks posttransplantation. For secondary transplantations, whole BM was isolated at 16 weeks posttransplant, and 1 3 106 cells were retransplanted into lethally irradiated CD45.1 recipient mice. Myeloid (CD11b+Gr1+) and lymphoid (T cells, CD4+CD8+; B cells, B220+) lineages were addressed by FACS. Proteomic Analysis FACS-sorted HSCs and MPP1 (4 3 105) were lysed, and proteins were extracted, reduced/alkylated, and digested with trypsin. Peptides were labeled differentially with stable isotope dimethyl labeling on a column as described previously (Boersema et al., 2009) and fractionated by OFFGEL isoelectric focusing (Agilent Technologies). In technical duplicates, peptides were separated by nanoflow ultra-high-performance liquid chromatography on a 120 min gradient and analyzed by electrospray ionization-tandem mass spectrometry (ESI-MS/MS) on a linear trap quadrupole Orbitrap Velos or Orbitrap Velos Pro (Thermo Fisher Scientific). MS raw data files were processed with MaxQuant (version 1.3.0.5) (Cox and Mann, 2008). The derived peak list was searched using the built-in Andromeda search engine (version 1.3.0.5) in MaxQuant against the Uniprot mouse database (2013.02.20). A 1% FDR was required at both the protein level and the peptide level. Differential expression was assessed using the Limma package in R/Bioconductor (Gentleman et al., 2004; Smyth, 2004), and proteins with an adjusted p value of less than 0.1 were considered expressed differentially between HSC and MPP1. RNA-seq Total RNA isolation was performed using an ARCTURUS PicoPure RNA isolation kit (Life Technologies, Invitrogen) according to the manufacturer’s instructions. Total RNA was used for quality controls and for normalization of the starting material (Figure S3). cDNA libraries were generated with 10 ng of total RNA for HSC-MPP using the SMARTer Ultra Low RNA kit for Illumina sequencing (Clontech Laboratories) according to the manufacturer’s indications. Sequencing was performed with a HiSeq2000 device (Illumina) and one sample per lane. Sequenced read fragments were mapped to the mouse reference genome GRCm38 (ENSEMBL release 69) using the Genomic ShortRead Nucleotide Alignment program (version 2012-07-20). DESeq2 (Love et al., 2014) and DEXSeq (Anders et al., 2012) were used to test for differential expression (FDR = 0.1) and differential exon use, respectively. TWGBS DNA methylation analysis using TWGBS (Adey and Shendure, 2012) was performed as described previously (Wang et al., 2013). Genomic mouse DNA (10-30 ng) was used as input, and each sequencing library was subjected
Cell Stem Cell 15, 1–16, October 2, 2014 ª2014 Elsevier Inc. 13
Please cite this article in press as: Cabezas-Wallscheid et al., Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis, Cell Stem Cell (2014), http://dx.doi.org/10.1016/j.stem.2014.07.005
Cell Stem Cell Molecular Landscape of Early Hematopoiesis
to 101 bp paired-end sequencing on a single lane of a HiSeq2000 device (Illumina). Animal procedures were performed according to protocols approved by the German authorities (Regierungspra¨sidium Karlsruhe [no. Z110/02, DKFZ nos. 261, G175-12, and G140-13]).
Adolfsson, J., Ma˚nsson, R., Buza-Vidas, N., Hultquist, A., Liuba, K., Jensen, C.T., Bryder, D., Yang, L., Borge, O.-J., Thoren, L.A., et al. (2005). Identification of Flt3+ lympho-myeloid stem cells lacking erythro-megakaryocytic potential a revised road map for adult blood lineage commitment. Cell 121, 295–306.
ACCESSION NUMBERS
Alharbi, R.A., Pettengell, R., Pandha, H.S., and Morgan, R. (2013). The role of HOX genes in normal hematopoiesis and acute leukemia. Leukemia 27, 1000– 1008.
Proteome data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the Proteomics Identifications Database (PRIDE) partner repository (Vizcaı´no et al., 2013) with the data set identifier PXD000572. RNA-seq data are available in the ArrayExpress database (http://www.ebi.ac.uk/arrayexpress) under accession number EMTAB-2262. TWGBS data can be accessed under Gene Expression Omnibus under accession no. GSE52709.
Anders, S., Reyes, A., and Huber, W. (2012). Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008–2017. Arab, K., Park, Y.J., Lindroth, A.M., Scha¨fer, A., Oakes, C., Weichenhan, D., Lukanova, A., Lundin, E., Risch, A., Meister, M., et al. (2014). Long Noncoding RNA TARID Directs Demethylation and Activation of the Tumor Suppressor TCF21 via GADD45A. Mol. Cell, in press. Published online July 30, 2014. http://dx.doi.org/10.1016/j.molcel.2014.06.031.
SUPPLEMENTAL INFORMATION
Argiropoulos, B., and Humphries, R.K. (2007). Hox genes in hematopoiesis and leukemogenesis. Oncogene 26, 6766–6776.
Supplemental Information includes Supplemental Experimental Procedures, seven figures, six tables, one item and two online resources can be found with this article online at http://dx.doi.org/10.1016/j.stem.2014.07.005.
Bakker, S.T., and Passegue´, E. (2013). Resilient and resourceful: genome maintenance strategies in hematopoietic stem cells. Exp. Hematol. 41, 915–923.
AUTHOR CONTRIBUTIONS N.C.-W., D.K., and A.T. designed and coordinated the study. A.T., J.K. (proteome), C.P. (methylome), M.D.M. (methylome), and W.H. (bioinformatics) designed and supervised the experiments and interpreted the data. J.H., D.K., and N.C.-W. performed the proteome experiments, bioinformatic analysis, and/or data interpretation. N.C.-W., A.R., D.K., D.B.L., and J.H. performed the RNA-seq experiments, bioinformatic analysis, and/or data interpretation. D.B.L., Q.W., N.C.-W., D.K., A.R., D.W., A.L., D.B., L.G., C.H., S.H., M.A.G.E., B.B., and R.E. performed the methylome experiments, bioinformatic analysis, and/or data interpretation. D.K., N.C.-W., and A.L. (methylome) performed FACS. N.C.-W. and D.K. coordinated the animal experiments and performed reconstitution experiments, FACS analysis, qPCR, and CFU assays. L.v.P., S.R., P.W., and P.Z. assisted with FACS analysis, reconstitution experiments, and qPCR. N.C.-W., D.K., J.H., D.B.L., and A.R., together with W.H., J.K., C.P., M.D.M., and A.T., wrote the manuscript. ACKNOWLEDGMENTS We thank A. Rathgeb from the Central Animal Laboratory; S. Schmidt, U. Ernst, A. Hotz-Wagenblatt, C. Previti, and S. Wolf from the Genomics and Proteomics Core Facility; S. Schmitt and G. de la Cruz from the Flow Cytometry Core Facility at the DKFZ; and the EMBL Proteomics Core Facility for expert assistance. We also thank M. Ba¨hr and M. Helf for technical assistance. We further thank C.C. Oakes, A.M. Lindroth, and R. Claus for helpful discussions as well as P. Komardin for support in handling data submission to the GEO database. This work was supported by the BioRN Spitzencluster ‘‘Molecular and Cell Based Medicine’’ supported by the German Bundesministerium fu¨r Bildung und Forschung (to A.T.), by the EU FP7 Programs ‘‘EuroSyStem’’ (to A.T.) and ‘‘Radiant’’ (to W.H. and A.R.), by the Dietmar Hopp Foundation (to A.T.), by the Sonderforschungsbereich SFB 873 funded by the Deutsche Forschungsgemeinschaft (to A.T.), by a Vidi grant from the Netherlands Organization for Scientific Research (NWO) (to J.K.), by the DFG Priority Program SP1463 (to D.B.L. and C.P.), by an intramural grant from the DKFZ (Epigenetics@DKFZ) (to D.B.L. and M.D.M.), and by a Humboldt research fellowship for postdoctoral researchers (to Q.W.). Received: February 11, 2014 Revised: June 25, 2014 Accepted: July 18, 2014 Published: August 21, 2014
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