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Identification of Regulatory Networks in HSCs andTheir Immediate Progeny via Integrated Proteome,Transcriptome, and DNA Methylome AnalysisNina 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 Wunsche,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, Germany2Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH), 69120 Heidelberg, Germany3European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany4Division of Epigenomics and Cancer Risk Factors, DKFZ, 69120 Heidelberg, Germany5Division of Theoretical Bioinformatics, Department of Bioinformatics and Functional Genomics, DKFZ, 69120 Heidelberg, Germany6Junior Research Group Experimental Hematology, Division of Stem Cells and Cancer, DKFZ, 69120 Heidelberg, Germany7Junior ResearchGroupStress-induced Activation of Hematopoietic StemCells, Division of StemCells andCancer, DKFZ, 69120Heidelberg,Germany8German Cancer Consortium (DKTK), 69120 Heidelberg, Germany9Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, 69120 Heidelberg, Germany10Co-first author11Co-senior author

*Correspondence: a.trumpp@dkfz.de

http://dx.doi.org/10.1016/j.stem.2014.07.005

SUMMARY

In this study, we present integrated quantitative pro-teome, transcriptome, andmethylomeanalysesofhe-matopoietic stem cells (HSCs) and four multipotentprogenitor (MPP) populations. From the characteriza-tion of more than 6,000 proteins, 27,000 transcripts,and 15,000 differentially methylated regions (DMRs),we identified coordinated changes associated withearly differentiation steps. DMRs show continuousgain or loss of methylation during differentiation,and the overall change in DNAmethylation correlatesinversely with gene expression at key loci. Our datareveal the differential expression landscape of 493transcription factors and 682 lncRNAs and highlightspecific expression clusters operating in HSCs. Wealso found an unexpectedly dynamic pattern of tran-script isoform regulation, suggesting a critical regula-tory role during HSC differentiation, and a cell cycle/DNA repair signature associated with multipotencyin MPP2 cells. This study provides a comprehensivegenome-wide resource for the functional explorationof molecular, cellular, and epigenetic regulation atthe top of the hematopoietic hierarchy.

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,

Ce

2010;Wilson et al., 2009). They give rise to a series ofmultipotent

progenitors (MPPs) with decreasing self-renewal potential, fol-

lowed 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 step-

wise 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 compre-

hensive mass spectrometry-based proteome, transcriptome

(RNA sequencing [RNA-seq]), and genome-wide DNA methyl-

ome (tagmentation-based whole genome bisulfite sequencing,

TWGBS) data for HSCs andMPPs.We provide a comprehensive

insight into the molecular mechanisms that are dynamically

regulated during early HSC commitment through the MPP1–

MPP4 populations.We uncoveredmolecular changes at the pro-

tein, RNA, and DNA levels as they occur in vivo in the context of

physiologic commitment processes.

RESULTS

The five stem/progenitor populations corresponding to HSC and

MPP1–MPP4 (Wilson et al., 2008) were isolated by fluorescence-

activated cell sorting (FACS) from the bone marrow of C57BL/6J

mice (Figure 1; Figures S1A–S1C available online). These cells

ll Stem Cell 15, 507–522, October 2, 2014 ª2014 Elsevier Inc. 507

CD34-

CD48-

CD150+

CD135-

HSC

CMP MEP

GMP

Mega Plat

Ery

Neu

Mac

Linneg Sca-1low c-Kitlow

CLP T-cells

NK

B-cells

Lymphoid committed

Linneg Sca-1+ c-Kit+

Myeloid committed

Multipotent stem and progenitor cells

Mature effector cells

Linneg Sca-1- c-Kit+

CD34+

CD48-

CD150+

CD135-

MPP1

CD34+

CD48+

CD150+

CD135-

MPP2

CD34+

CD48+

CD150-

CD135-

MPP3

CD34+

CD48+

CD150-

CD135+

MPP4

A

B

C

Bone marrow isolation

FACS sorting of primary cells

(n=3)

Data integrationdHSC and MPP signatures (RNA, protein)

RNA-protein correlation

lncRNAsDifferential exon usage

Differential DNA methylation

Reconstitution experiments

HSC; MPP1MPP2; MPP3

MPP4

Functional readout

d

Differentiation potential

Fig. 4

Quantitative proteomics

HSC; MPP1

Fig. 2,3,4 Fig. 3,4,5,6,70 103 104 105

CD34

0

103

104

105C

D13

5

0 103 104 105

Sca-1

0

103

104

105

c-K

it

0 50K 100K 150K 200K 250KFSC-A

0

103

104

105

Lin

neg

0 103 104 105

CD48

0

103

104

105

CD

150

0 103 104 105

CD48

0

103

104

105

CD

150

0 103 104 105

CD48

0

103

104

105

CD

150

HSC MPP1 MPP2

MPP3 MPP4

98.1

4.1 31.4

5.9 31.9

72.3 14.1 8.6

41.6 83.3

(TWGBS)

DNAmethylome

RNA-sequencing

HSC; MPP1MPP2; MPP3

MPP4HSC; MPP1

MPP2; MPP3/4

Fig. 6,7

RNA-DNA methylation correlation

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.

Cell Stem Cell

Molecular Landscape of Early Hematopoiesis

were used as a source for quantitative proteomics, RNA-seq,

TWGBS, and functional reconstitution experiments.

Proteome Differences between HSCs and MPP1 CellsUncovered Key Molecular Players of Long-TermReconstitution PotentialThe transition from CD34� HSCs to CD34+ MPP1 is accompa-

nied by a switch in the cells’ reconstitution capabilities.We trans-

planted mice with 50 HSCs and observed that 100% of primary

and 80% of secondary recipients showed multilineage repopu-

lating activity (Figures S1D–S1F). In contrast, 56% of mice trans-

planted with MPP1 cells showed reconstitution of the primary

recipient, and no engraftment was detected in secondary recip-

ients. This is consistent with previous reports showing that the

two populations are very similar but display a measurable differ-

ence 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 (Fig-

ure 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. recep-

tors (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).

508 Cell Stem Cell 15, 507–522, 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 differen-

tially (false discovery rate [FDR] = 0.1), together with nine pro-

teins detected exclusively inMPP1 (Figure 2C; Figure S2D; Table

S1). This suggests that the differential self-renewal potential be-

tween HSCs and MPP1 is elicited by a relatively small number of

proteins.

Gene ontology (GO) enrichment analysis revealed that cell cy-

cle aswell as related processes, such asDNA replication and cell

proliferation, were strongly overrepresented inMPP1 (Figure 2D).

Indeed, protein-protein interaction network analysis highlighted

a large group of interconnected cell cycle proteins being ex-

pressed at elevated levels inMPP1 comparedwith HSCs. All ma-

jor processes associated with the cell cycle machinery, including

DNA polymerases (Pol1a, Pole), cell cycle checkpoint proteins

(Chek1), DNA methylation maintenance and cell cycle progres-

sion (Cdk1, Cdk6), and others were represented in the network

(Figure 2E). In contrast, HSCswere enriched in themonosaccha-

ride metabolic process, including the glycolytic enzymes lactate

dehydrogenase b and d (Ldhb, Ldhd) as well as Pygm. In addi-

tion, 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.

A B

ED

C

Cell lysis/protein extraction/protein digestion

Peptide labeling

Peptide fractionation

High-resolution nano LC-MS/MS

4x10 cells,n=3

HSC MPP1

−2

0

2

0.0 0.5 1.0 1.5−log10(adjusted p−value HSC/MPP1)

log2

ratio

HS

C/M

PP

1

Gda

Ifitm1

Hmga2

Igf2bp2

Hmga1

Rrm2

Uhrf1

22

25

Cd38

Hmga1Pygm

MyofSerpinb6a

Cbr3Gstm1

Tgm2

LdhdFscn1

LdhbSlamf1

CmasGstm5

Irgm2Nagk

Camk2d

Cdk6Hells

Plek

Top2aSerpinb1a

Chek1Tyms

Dut

Lig1

Ndrg1

Ncaph

Pola1Hat1

Anxa2

Smc2

Cdk1

Kif2c

PoleThy1

Kif4

Gins3

Stmn1 Dnmt1

nucleic acid bindinghydrolase

transferaseenzyme modulatortranscription factor

cytoskeletal proteinoxidoreductase

kinaseligase

transporterreceptor

membrane traffic proteinsignaling molecule

proteasecalcium-binding proteintransfer/carrier protein

chaperonephosphatase

isomeraselyase

defense/immunity proteincell adhesion molecule

extracellular matrix proteinstructural protein

cell junction proteintr.membr. rec. reg.

surfactantstorage protein

viral protein

0 500 1000 1500Protein count

148

63

43

05

02

5321

21

00

21

211111

1929

710

1614

1312

989

477

20 0 20 40

oxidation reductionmonosaccharide metabolic processcellular ion homeostasisregulation of chromosome organizationneuron projection developmentresponse to hypoxia

DNA replicationcell cyclechromosome condensationDNA packagingchromosome organizationcell divisionM phasecellular response to stressmitosiscell proliferationDNA repairdeoxyribonucleotide metabolic processcell activationneg. reg. of transcription, DNA-dependent

# proteinsHSCMPP1

F

-3

-2

-1

0

1

2

log 2

r atio

HSC

/MPP

1

Dnajc6; Sptlc2; Mtmr9; Zc3h10Mis12; Mllt1; Faah; Steap3; Ncf4

10.41Combined score

STRING

Edge color

Average log2 ratioHSC/MPP1

2 2.7-2

Node fill color Node size

0.10.02adj. p value

Node shape

Uniquelydetected

MPP1

Gstm1

Hmga2

Ncf4

Hmga1

Gstm5

Smc2

Top2a

Tyms

Rrm2

Chek1

Pygm

NcaphKif4

Mis12

Stmn1

Kif2c

Ldhb

Gins3

Ldhd

Cdk1

Lig1Uhrf1

Pole

Hells

Dnmt1

Plek

Pola1

Dut

Cdk6Hat1

+9

5

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 andMPP1 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.

Cell Stem Cell

Molecular Landscape of Early Hematopoiesis

Of the 22 proteins with higher expression in HSCs, we found

three highmobility group AT hook proteins; namely, two isoforms

of Hmga1 as well as Hmga2 (Figure 2C; Figure S2E). These chro-

matin-modulating transcriptional regulators have been reported

recently to be potent rheostats of tumor progression (Morishita

et al., 2013;Shahet al., 2013) andHSCself-renewal, proliferation,

and lineage commitment checkpoints (Battista et al., 2003; Cop-

ley 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 downstreammedi-

ators of the Lin28-let7 pathway that link metabolism to prolifera-

Ce

tion and drive self-renewal (Shyh-Chang et al., 2013; Yaniv and

Yisraeli, 2002).Highexpressionof this pathway inHSCssuggests

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, whichmay relate

to their ability to mediate the conjugation of xenobiotics for the

purpose of detoxification and defense against environmental

ll Stem Cell 15, 507–522, October 2, 2014 ª2014 Elsevier Inc. 509

Cell Stem Cell

Molecular Landscape of Early Hematopoiesis

stress and cellular damage (Tew and Townsend, 2012). This sug-

gests 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-induc-

ible proteins involved in host defense (Ifitm1 and Irgm2) were

expressed at higher levels in HSCs compared with MPP1 (Fig-

ure 2C), suggesting that the type I interferon pathway is not

only critical for the response to stress but also during homeosta-

sis (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, respec-

tively (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 clas-

sification of stem/progenitor intermediates.

The Transcriptome and Proteome Are HighlyCoordinated upon HSC DifferentiationWe next analyzed the transcriptome of HSCs and MPP1 using

high-throughput RNA sequencing starting from 30,000 FACS-

sorted primary cells (Figure 3A; Figure S3A). Robust and repro-

ducible data were obtained for all samples, with more than 2 3

108 total readings per population (Figure S3B). In total, tran-

scripts 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, protein-

coding 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 (Fig-

ure 3C), including transcription factors (TF, 1,776 genes), recep-

tors (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 (pos-

itive 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

510 Cell Stem Cell 15, 507–522, 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 (Fig-

ure 3F; Figures S3D–S3E). Notably, in addition to a strong positive

correlation between both data sets, all proteins that were ex-

presseddifferentiallywerealsoconsistently upregulatedordown-

regulated 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 regulationat theposttranscriptional level (Shyh-Chang

andDaley, 2013). In addition, we found theGO termchromosome

enriched and several long-lived histones related to chromosome

organization (e.g. H2afz, H2afy2) to be downregulated at the

RNA level inMPP1.Because thesewerenotdetectedasbeingex-

pressed 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 subse-

quent stages. The overall high similarity between the transcrip-

tome 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 Showa Differentiation Lineage BiasTo investigate the functional attributes of MPP2–MPP4, we per-

formed 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, andmyeloid progeny in peripheral

blood (PB) after 4 months was 23%, 35%, and 32%, respec-

tively, 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 differ-

entiated cells (mostly below 2%), although the progeny were re-

tained 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-transplantedmice (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 transplanta-

tion. In contrast, the other two populations generate only a

limited number of progeny in vivo, with a significant lineage

polyA-RNA

HSC MPP1

High throughput sequencing 1 sample/lane

Paired-end reads 100bp

A

B

C

30,000 cellsn=4 n=3

68.93% 19219 protein coding14.47% 4034 pseudogene3.77% 1052 antisense2.30% 642 miRNA2.11% 589 lncRNA2.07% 577 snoRNA2.02% 562 processed transcript2.01% 560 snRNA0.88% 244 misc RNA0.42% 117 IG LV gene1.02% 285 other non-coding species

TypeGOBP nameGOCC nameKEGG nameKeywords

log10(Size)123

0.0000.0050.0100.015

FDR

−1.0

−0.5

0.0

0.5

1.0

−1.0 −0.5 0.0 0.5 1.0HSC/MPP1 protein score

HSC

/MPP

1 R

NA

sco

re

cellular response tocytokine stimulus

Tricarboxylic acid cycle

nucleosome

Cell cycle

DNA-dependent DNAreplication initiation

Extracellular matrix

Vitamin C

vacuolarmembrane

Glycolysis/Gluconeogenesis

Nuclear porecomplex

Chromosome

nucleic acid bindinghydrolase

transferaseenzyme modulatortranscription factor

cytoskeletal proteinoxidoreductase

kinaseligase

transporterreceptor

membrane traffic proteinsignaling molecule

proteasecalcium-binding proteintransfer/carrier protein

chaperonephosphatase

isomeraselyase

defense/immunity proteincell adhesion molecule

extracellular matrix proteinstructural protein

cell junction proteintr.membr. rec. reg.

surfactantstorage protein

viral protein

0 500 1000 1500 2000 2500Count

D

EHSCMPP1

321

77

1729

89

162425

448083

167

1339505253

91

4271012

245053

111

180 120 60 0 60 120 180

DNA recombinationregulation of cell cyclemicrotubule-based processnuclear divisionDNA metabolic processcell cycle

response to alcoholsulfur compound metabolic processcellular response to oxygen-containing compoundpositive regulation of phosphate metabolic processcell migrationregulation of localizationcell dif ferentiationcellular developmental processresponse to stimulus

# transcripts F

G

Mean of normalized counts

log2

fold

cha

nge

(HS

C/M

PP

1)

202

277

ProteinΔ Protein FDR=0.1

RNA, protein codingΔ RNA FDR=0.1

Δ RNA & protein FDR=0.1

RNA

Protein

RNA & proteinRNA &Protein

19,219

357

47+9

4,007

6315

32

435

Igf2bp2

Gda

Pygm

Hmga2Hmga1

Cmas

H2afy2

Gbp10

Ifitm1

Gstm1 Myof

Cdk6Tyms

Top2aSerpinb1a

Cdk1Kif4

Anxa2

Smc2Stm1

Gstm5Nagk

Hist1h1aHmgb2

H2afzDnajc6Ncf4Steap3Mtmr9 R

NA

log2

fold

cha

nge

(HSC

/MPP

1)

FaahSptlc2 Mllt1

Mis12Zc3h10

Protein log2 fold change (HSC/MPP1)

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 quantifiedprotein-codinggenes.Bars show the number of geneswithin each functional class, rankedbasedon theprotein 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

Molecular Landscape of Early Hematopoiesis

Cell Stem Cell 15, 507–522, October 2, 2014 ª2014 Elsevier Inc. 511

Weeks

B

C

1 3 6 9 12 160.01

0.10

1

10

100MPP2

1 3 6 9 12 160.01

0.10

1

10

100MPP3 MPP4

3 9 126 16 3 9 126 161 1

MPP4

MPP2MPP3

B-cellsMy-cells

T-cells

B-cellsMy-cells

T-cells

1 3 6 9 12 160.01

0.10

1

10

100

Don

orC

D45

.2(%

)

D

CD34

CD48

CD150

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48 77 313 52 63 536 77 24 48 17 45 32 16 10 19 15 1053 70 7 19

49 38 47 36 157 149 53 235 76 35 38 10 12 18 12 7 22 21 8 3 39 10 1346 4 3

18 16 76 73 118 39 18 6 27 7 22 29 65

18 18 6 13 917 13 16 7 6

56 38 45 22 135 136 48 201 67 32 35 15 9 24 11 18 12 8 21 37 5 1012 7

103 62 51 301 338 113 414 152 70 24 8 82 87 4714 5 335 84 86 115 40 5 4

1adjusted p-value

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

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Brc a1

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STRING

Edge color Node size

561Degrees

Node color

MPP1 MPP2 MPP3 MPP4Highest expression

M

CD

45.1

Read-out

PB, BM, SPL & THY

MY-cells LY-cells CD45.2

CD45.1/2 Supportive

CD45.2 B6J Donor

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HSCMPP1MPP2MPP3MPP4

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Enriched

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nes

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ster

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1 0 02 390 603 54 104 1484 2475 27 06 86 07 155 168 497 4319 36 0

10 57 011 20 012 22 013 204 5014 28 015 178 5716 17 417 84 018 340 26119 25 020 170 4721 39 022 14 023 42 024 3 025 709 32426 227 4127 58 028 4 029 1176 30630 11 031 330 30632 0 0

K

Regulation of metabolism

* *

**

*

** **

*

**

***

*

*

**

****

**

Lin28b*

Lin28a* Igf2*

Foxa3*

Igf2bp2*

Hmgb2* Hmga2*

Hmga1

Insr Igf1* Hif1a

Insrr* Hif3a*

Irs2* Igf2r*

Retinoic acid metabolism

* **

* **

*

* *

* **

**

Retinoic acid metabolic process

RAR / PPAR signaling

*

*

* *

** *

* ** * *

*

*

*

Aldh1a1* Bco2* Rbp1* Cyp26b1*

Rdh10* Rdh5* Rxra

Aldh1a7* Aldh1a3 Crabp2 Cyp26a1

Rara* Rarb* Rarg*

Adh1 Aldh1a2*

Pparg Ppard* Ppargc1a*

Mea

nex

pres

sion

HSC

MPP

1M

PP2

MPP

3M

PP4

* RNA DEG HSC-MPPs FDR=0.1

* * * RNA DEG HSC/MPP1,2,3 or 4

Proteome DEP HSC/MPP1

Ldhb: RNA mean expression >700Ldhc: RNA mean expression <700

Ldhb*

Lin28b

Let7

Igf2bp2; Hmga1; Hmga2 Insr; Igf1; Igf2; Igf2r

AnaerobicGlycolysis

OxPhos; TCAProliferation

FDR=0.1

FDR=0.1

Fancc

Kat5

lh1M

(legend on next page)

Cell Stem Cell

Molecular Landscape of Early Hematopoiesis

512 Cell Stem Cell 15, 507–522, October 2, 2014 ª2014 Elsevier Inc.

Cell Stem Cell

Molecular Landscape of Early Hematopoiesis

bias toward mostly myeloid (MPP3) or lymphoid (MPP4) cell

types.

Clustering Analyses Identify Global MolecularDifferences during HSC DifferentiationWe sequenced the transcriptomes of the MPP2, MPP3, and

MPP4 populations in biological triplicates (Figures 4D–4M; Fig-

ures 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 expres-

sion of a set of genes was confirmed by quantitative real-time

PCR (Figure S3F), validating the robustness of the data. Unsu-

pervised clustering of gene expression profiles grouped together

HSC andMPP1 as well as MPP3 andMPP4 (Figure 4E). This was

verified by principal component analysis (Figure 4F) and by the

numbers of differentially expressed genes in each pairwise com-

parison (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 SuggestsProcesses Operating in HSC/MPP1-MPP4 CellsThe 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; Sup-

plemental 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 re-

vealed an enrichment for MPP3 and MPP4 populations, respec-

tively (Mansson et al., 2007; Figure S4E). In addition, genes of a

Figure 4. Lineage Potential and Whole Transcriptome Molecular Signa

(A) Experimental workflow for investigation of the in vivo reconstitution potential. M

myeloid (MY) and lymphoid (LY) outcomes were monitored over time by flow cyt

(B and C) Monitoring the myeloid and lymphoid outcomes in peripheral blood afte

contribution (C) of donor myeloid (blue), B cells (gray), and T cells (purple) were m

animals at 1–16 weeks. n = 8-12 per group. The sizes of the circles in the right p

(D) Differential RNA expression of the surface markers used for FACS. Average R

(E) Clustered heat map. The colors represent the normalized average read co

(FDR = 0.1).

(F) Principal component analysis.

(G) Relative distances are shown based on numbers of differentially expressed g

(H) GO enrichment analysis of 6,387 genes with differential expression. Three rep

shown.

(I) Expression of megakaryocytic/erythrocytic genes.

(J) Transcriptome and proteome signatures of metabolism. Average RNA exp

peroxisome proliferator-activated receptor; OxPhos, oxidative phosphorylation;

(K) Gene expression clusters. 6,487 differentially expressed genes were grouped

population(s) compared with mean expression level across all cell populations.

(L) Representative overrepresented GO terms of gene expression clusters. The nu

(FDR = 0.1).

(M) Network of HSC-suppressed DNA repair genes. Genes with low expressio

interactions are shown. The colors depict the MPP population with the highest e

See also Figure S4.

Ce

megakaryocytic erythroid signature described previously

(Mansson 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 en-

zymes 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

(Figures4Hand4J), 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 Lin28awas spe-

cifically 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 suggestLin28b asa candidate for anupstreamposttranscrip-

tional regulator of glycolytic enzymes inHSCs (Figures3Fand3G).

Next we assigned each of the 6,487 differentially expressed

genes to one of the 32 possible relative expression patterns (Fig-

ure 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 expres-

sion 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

tures of HSC-MPP1-4 Populations

PP2, MPP3, and MPP4 were transplanted into lethally irradiated mice, and the

ometry. SPL, spleen; THY, thymus.

r transplantation of MPP2, MPP3, and MPP4. The relative percentage (B) and

onitored from the peripheral blood of MPP2-, MPP3-, and MPP4-transplanted

lot represent fold change relative to HSC contribution after 3 weeks.

NA expression ± SD is shown.

unt in each of the five cell populations for all differentially expressed genes

enes in pairwise comparisons.

resentative examples of significantly enriched GO terms out of 1,001 terms are

ression ± SD is shown (arbitrary units). RAR, retinoic acid receptor; PPAR,

DEG, differentially expressed genes.

into 32 clusters based on higher expression (enriched) in one or several cell

mber of genes within each cluster is shown for significantly enriched GO terms

n in HSCs (Z-score < �1.5) and with known and predicted protein-protein

xpression. Note that most genes are most highly expressed in MPP2.

ll Stem Cell 15, 507–522, October 2, 2014 ª2014 Elsevier Inc. 513

A B

D

HSC

HSC-MPP1

42

423

32

342

0 1 2 3

Nuclear ReceptorsMonoamine GPCRs

TGF Beta Signaling PathwayRetinol metabolism

GPCRs, OtherDiurnally Regulated Genes with Circadian Orthologs

Ovarian Infertility GenesExercise-induced Circadian Regulation

Senescence and AutophagySplicing factor NOVA regulated synpatic proteins

-log10(p value)

206

49

1352431419145

381510

0 2 4

Cytoplasmic Ribosomal ProteinsRetinol metabolism

Matrix MetalloproteinasesEndochondral Ossif ication

G Protein Signaling PathwaysOvarian Infertility Genes

ACE Inhibitor PathwayNuclear receptors in lipid metabolism and toxicity

Dopminergic NeurogenesisAdipogenesis

Focal AdhesionRegulation of Actin Cytoskeleton

Nuclear ReceptorsGlutathione metabolismWnt Signaling Pathway

Purine metabolismWnt Signaling Pathway and Pluripotency

-log10(p value)

Fzd1 Fzd2* Fzd3* Fzd4* Fzd5* Fzd6 Fzd7 Fzd8* Fzd9*

Wnt2a Wnt2b Wnt3 Wnt3a Wnt4 Wnt5a Wnt5b

Wnt6* Wnt7a Wnt7b Wnt10a* Wnt10b Wnt11 Wnt16

Frizzled ligands

Wnt receptors

Ldlr

Prkca* Prkcb*

Prkcd*

Prkce* Prkci

Prkch* Prkd1*

Prkcq Prkcz

Dvl1 Dvl2 Dvl3Protein Kinase C

Cytoskeleton

Apoptosis

Rhoa Racgap1*

Mapk9 Mapk10

Axin1

Ppp2r5c Ppp2r5e

Gsk3b Apc

Ctnnb1

Fbxw2 Pafah1b1

Tcf1

Nucleus

Myc* Ccnd1* Ccnd2* Ccnd3* Fosl1 Jun* Plau*

Transcriptional activation

Fzd10

Frat1Csnk1e

Phosphorylated

B-catenin

Ubiquitin tagged B-catenin

26S Proteosome degradation

P

*

Prkcc

HSCMPP1MPP2MPP3MPP4

* DEG HSC/MPP1 FDR=0.1

Myc* DEG HSC-MPPs FDR=0.1

Wnt1 RNA mean expression <700

Mycn*

Wnt1

differential exon usage

1

2

3

4

5

6

7

8

9

10

Foxj3 (ENSMUSG00000032998+)

Fitte

d ex

pres

sion

119537004 119557474 119577944 119598414 119618884

HSC

MPP1

exons

Genomic region

10

100

10002000

5000

Fzd7 RNA mean expression >700

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.

0 50 100 Counts C

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, neuro-

oncological 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).

Cell Stem Cell

Molecular Landscape of Early Hematopoiesis

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.

514 Cell Stem Cell 15, 507–522, 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 REACTOMEwithin 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

Cell Stem Cell

Molecular Landscape of Early Hematopoiesis

enrichments (Figure 4L), Wnt signaling was prominent in self-

renewing 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 inter-

actions 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 overrepre-

sented 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 ofWnt ligands sup-

port 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 expres-

sion in humanCD34+ 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 un-

derscores the relevance of Wnt signaling for HSCs but also out-

lines the complex isoform-specific enzyme utilization operational

in HSCs and the different MPPs. This likely contributes to the dy-

namic regulation of this pathway, which is critical for most stem/

progenitor cell types.

Transcription Factor and Transcript Isoform RegulatoryLandscapeAmong the genes expressed differentially across the five popu-

lations, 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 (Lopez, 1995; Sebastian et al., 2013) and alterna-

tive splicing has been shown to trigger switches between acti-

vating 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 vari-

ants across HSC MPPs (FDR = 0.1), we identified 46 TFs (Fig-

ure 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 hematopoi-

esis 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/MPPsIdentifies Candidate Regions for Epigenetic RegulationTo 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

Ce

81% and 83% and were not significantly different across popu-

lations, 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 repre-

sentation 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%, HSC-

MPP1). In contrast, transitions betweenmore differentiatedMPP

populations showed higher numbers of DMRs (1,874, MPP2-

MPP3/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 (Stama-

toyannopoulos et al., 2012; Shen et al., 2012) demonstrated a

significant overrepresentation of DNaseI hypersensitivity sites

(DHSs), promoters, enhancers, and/or transcription factor bind-

ing 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-regula-

tory elements (Figure 6E). Together, these data support the

hypothesis that many DMRs represent cis-regulatory elements

in HSC-MPPs.

Gene Expression Anticorrelates Globally with Changesin DNA MethylationGiven 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 signifi-

cantly 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

ll Stem Cell 15, 507–522, October 2, 2014 ª2014 Elsevier Inc. 515

A

HSC MPP1n=3

MPP2 MPP3/4

10-30 ng DNA/replicate

Tagmentation-based whole-genome bisulfite sequencing

(TWGBS)100bp paired-end reads

6.7 - 19.9 x 108 aligned reads

B

HSC MPP1 MPP2 MPP3/4

7,411

1,121 1,412

4,712

1,874

12,013gain of methylation

loss of methylation

C HSC MPP1 MPP2 MPP3/4R2 R3 R1 R2 R3 R1 R2 R3 R1 R2 R3R1

DMRs (HSC – MPP1)

HSC MPP1 MPP2 MPP3/4R2 R3 R1 R2 R3 R1 R2 R3 R1 R2 R3R1

DMRs (MPP2 – MPP3/4)

D 57.0%

34.3%

8.7%

HSC MPP1 MPP2 MPP3/4

IntergenicPromoterIntragenic

111

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99

778

258

1313

7

23

3730

12961553 2215

51

162

3474

DHSn=14,617(91.4%)

TFBSn=8,798(55.0%)

Promotern=4,745(29.7%)

Enhancern=4,561(28.5%)

E

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2M

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Hoxd13Hoxd12Hoxd11Hoxd10Hoxd9Hoxd8Hoxd3Hoxd1Hoxb13Hoxb9*Hoxb8Hoxb7*Hoxb6*Hoxb5*Hoxb4*Hoxb3*Hoxb2*Hoxb1*

Hoxb

Hoxd

−2 0 2Z-score

*Hoxb1

DEG HSC-MPPs FDR=0.1with DMRs

Hoxb7 no DMRs

0.0

0.2

0.4

0.6

0.8

1.0

HoxB Chr11: 96,270,000

Rel

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n

GenePromoter

DMREnhancer

b9 b8 b7 b4 b3b5Mir10a

b2 b1b6

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0.6

0.8

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Chr2:74,660,000HoxD

Rel

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Generomoter

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I

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DNAmethylation

G

F

Il4LsrSlc9a3r2PerpDoc2bPde1bEfna1PdgfcRorcTinagl1Gfi1St8sia1

EomesTenc1Plekhg6Clec14aFlrt1NdnfCpne8Kcnc1Cttnbp2nlSox7Hoxb2Dpy19l2

HaaoMrvi1Mc5rCd163Tex22Alx3Anp32eCd34Slc16a7Wif1Ltc4sMrc2

Spns3Wdr16F2rl2ZfatSept3EndouRfc2Poc1aZscan10Gfra1TymsRabgap1l

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 DNAmethylation 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 DNAmethylation 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.

Cell Stem Cell

Molecular Landscape of Early Hematopoiesis

516 Cell Stem Cell 15, 507–522, October 2, 2014 ª2014 Elsevier Inc.

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 re-

ceptor orphan receptor g(Rorc), become methylated and down-

regulated during differentiation toward MPP1.

Hox TF clusters are critical during normal and malignant he-

matopoiesis (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; Fig-

ures 6H and 6I). Notably, HSC differentiation showed a contin-

uous 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 DNAmethyl-

ation and that are possibly involved in the hard wiring of the hier-

archical organization of the HSC/progenitor populations.

Differential lncRNA Expression and the Imprinted GeneNetwork in Stem/ProgenitorsLong noncoding RNAs (lncRNAs) have been implicated in guid-

ing 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, respec-

tively (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 ex-

pressed in HSC-MPPs, 79 of which were differentially expressed

across all five populations (FDR = 0.1; Figure 7A; Table S2). Un-

supervised 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 clus-

tered 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 coex-

pressed in HSC-MPP1 (cluster 4), representing additional candi-

dates 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). AlthoughNeat1 is essential for the integrity of the nuclear

substructure, it has also been linked to the immune response af-

ter 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

Ce

and/or have unknown functions. The lncRNAs H19 and Meg3

that are highly expressed in HSCs compared with MPPs (Fig-

ure 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 (Var-

rault 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 (Fig-

ure 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 transcrip-

tion 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 toMPPs correlates with decreasing expression during dif-

ferentiation (Figure 7F). Overall, these data suggest that differen-

tial 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 addition-

ally 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 to-

ward slowly cycling multipotent MPP1, followed bymultipotently

cyclingMPP2. The steady increase in the activity of the cell cycle

and proliferation machinery is paralleled by the robust upregula-

tion of the entire DNA repair machinery. This raises the possibility

that physiological DNA replication in proliferating early progeni-

tors 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 DNAmethylation through early HSC differentiation. More-

over, we observed a global anticorrelation between DNAmethyl-

ation 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 pro-

tein 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 func-

tions in the stem/progenitor compartment may be regulated in

ll Stem Cell 15, 507–522, October 2, 2014 ª2014 Elsevier Inc. 517

A

B

C D

E

GF

Mus musculus

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.

Cell Stem Cell

Molecular Landscape of Early Hematopoiesis

518 Cell Stem Cell 15, 507–522, October 2, 2014 ª2014 Elsevier Inc.

Cell Stem Cell

Molecular Landscape of Early Hematopoiesis

this way. For example, our results point to a potential posttran-

scriptional 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 Lin28b-

let7-Hmga-Igfbp2 axis, the IGN regulatory network, and HSC-

enriched pathways such as Wnt and retinoic acid (RA) signaling.

Although some of these pathways are also implicated in embry-

onic HSC emergence from the hemogenic endothelium (Varrault

et al., 2006; Chanda et al., 2013; Copley et al., 2013), the individ-

ual 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 differen-

tially 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 Let7miRNAs and has

been suggested tomediate 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 self-

renewal, 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 embry-

onic 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 iso-

form regulation. Although the underlying regulatory mechanism

remains unknown, the lncRNAMalat1, 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 (Martın-Subero et al., 2013) andmye-

lodysplastic 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 prog-

eny. In addition to Malat1, we identified more than 70 differen-

tially 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, differ-

entiation, and self-renewal (Fatica and Bozzoni, 2014).

Ce

In summary, the global signatures for stemness and multipo-

tency generated in this study represent not only a compre-

hensive reference but also suggest distinct areas of stem cell

regulation (progressive DNA methylation, alternative splicing,

and lncRNAs). This study significantly extends the current under-

standing of HSC progenitor biology at the global level and

provides a solid basis for functional studies exploring the net-

works 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 pur-

chased 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 FACS-

sorted 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 retrans-

planted 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 ex-

tracted, 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 sepa-

rated by nanoflow ultra-high-performance liquid chromatography on a

120 min gradient and analyzed by electrospray ionization-tandemmass spec-

trometry (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 expres-

sion 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 isola-

tion kit (Life Technologies, Invitrogen) according to the manufacturer’s instruc-

tions. 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 indica-

tions. 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 Short-

Read 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

ll Stem Cell 15, 507–522, October 2, 2014 ª2014 Elsevier Inc. 519

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 (Regierungsprasidium Karlsruhe [no. Z110/02, DKFZ nos.

261, G175-12, and G140-13]).

ACCESSION NUMBERS

Proteome data have been deposited to the ProteomeXchange Consortium

(http://proteomecentral.proteomexchange.org) via the Proteomics Identifica-

tions Database (PRIDE) partner repository (Vizcaıno 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 E-

MTAB-2262. TWGBS data can be accessed under Gene Expression Omnibus

under accession no. GSE52709.

SUPPLEMENTAL INFORMATION

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.

AUTHOR CONTRIBUTIONS

N.C.-W., D.K., and A.T. designed and coordinated the study. A.T., J.K. (prote-

ome), C.P. (methylome), M.D.M. (methylome), and W.H. (bioinformatics) de-

signed 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 themethylome experiments, bioinformatic

analysis, and/or data interpretation. D.K., N.C.-W., and A.L. (methylome) per-

formed FACS. N.C.-W. and D.K. coordinated the animal experiments and per-

formed reconstitution experiments, FACS analysis, qPCR, and CFU assays.

L.v.P., S.R., P.W., and P.Z. assisted with FACS analysis, reconstitution exper-

iments, 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 theCentral Animal Laboratory; S. Schmidt, U. Ernst,

A. Hotz-Wagenblatt, C. Previti, and S.Wolf from theGenomics and Proteomics

Core Facility; S. Schmitt and G. de la Cruz from the Flow Cytometry Core Fa-

cility at the DKFZ; and the EMBL Proteomics Core Facility for expert assis-

tance. We also thank M. Bahr 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 fur 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 For-

schungsgemeinschaft (to A.T.), by a Vidi grant from the Netherlands Organiza-

tion 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 (Epigene-

tics@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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