Directional DNA Methylation Changes and Complex Intermediate States Accompany Lineage Specificity in the Adult Hematopoietic Compartment

ArticleinMolecular cell 44(1):17-28 · September 2011with46 Reads
Impact Factor: 14.02 · DOI: 10.1016/j.molcel.2011.08.026 · Source: PubMed
Abstract

DNA methylation has been implicated as an epigenetic component of mechanisms that stabilize cell-fate decisions. Here, we have characterized the methylomes of human female hematopoietic stem/progenitor cells (HSPCs) and mature cells from the myeloid and lymphoid lineages. Hypomethylated regions (HMRs) associated with lineage-specific genes were often methylated in the opposing lineage. In HSPCs, these sites tended to show intermediate, complex patterns that resolve to uniformity upon differentiation, by increased or decreased methylation. Promoter HMRs shared across diverse cell types typically display a constitutive core that expands and contracts in a lineage-specific manner to fine-tune the expression of associated genes. Many newly identified intergenic HMRs, both constitutive and lineage specific, were enriched for factor binding sites with an implied role in genome organization and regulation of gene expression, respectively. Overall, our studies represent an important reference data set and provide insights into directional changes in DNA methylation as cells adopt terminal fates.

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Molecular Cell
Article
Directional DNA Methylation Changes and Complex
Intermediate States Accompany Lineage Specificity
in the Adult Hematopoietic Compartment
Emily Hodges,
1,2
Antoine Molaro,
1,2
Camila O. Dos Santos,
1,2
Pramod Thekkat,
1,2
Qiang Song,
3
Philip J. Uren,
3
Jin Park,
3
Jason Butler,
2,4
Shahin Rafii,
2,4
W. Richard McCombie,
1
Andrew D. Smith,
3,
*
and Gregory J. Hannon
1,2,
*
1
Watson School of Biological Sciences, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA
2
Howard Hughes Medical Institute
3
Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
4
Department of Genetic Medicine and Ansary Stem Cell Institute, Weill Cornell Medical College, New York, NY 10065, USA
*Correspondence: andrewds@usc.edu (A.D.S.), hannon@cshl.edu (G.J.H.)
DOI 10.1016/j.molcel.2011.08.026
SUMMARY
DNA methylation has been implicated as an epige-
netic component of mechanisms that stabilize cell-
fate decisions. Here, we have characterized the
methylomes of human female hematop oietic stem /
progenitor cells (HSPCs) and mature cells from the
myeloid and lymphoid lineages. Hypomethylated
regions (HMRs) associated with lineage-specific
genes were often methylated in the opposing
lineage. In HSPCs, these sites tended to show inter-
mediate, complex patterns that resolve to uniformity
upon differentiation, by increased or decreased
methylation. Promoter HMRs shared across diverse
cell types typically display a constitutive core that
expands and contracts in a lineage-specific manner
to fine-tune the expression of associated genes.
Many newly identified intergenic HMRs, both consti-
tutive and lineage specific, were enriched for factor
binding sites with an implied role in genome organi-
zation and regulation of gene expression, respec-
tively. Overall, our studies represent an important
reference data set and provide insights into direc-
tional changes in DNA methylation as cells adopt
terminal fates.
INTRODUCTION
Development and tissue homeostasis rely on the balance
between faithful stem-cell self-renewal and the ordered, sequen-
tial execution of programs essential for lineage commitment.
Under normal circumstances, commitment is thought to be
unidirectional with repressive epigenetic marks stabilizing loss
of plasticity (De Carvalho et al., 2010). However, certain differen-
tiated mammalian cells can be reverted to an induced pluripotent
state (iPSCs) through exogenous transduction of specific tran-
scription factors (Takahashi and Yamanaka, 2006). Yet, even
these reprogrammed cells retain a residual ‘memory’ of their
former fate, displaying DNA methylation signatures specific to
their tissue of origin (Kim et al., 2010).
DNA methylation is critical for the self-renewal and normal
differentiation of somatic stem cells. For example, within the
hematopoietic compartment, impaired DNA methyltransferase
function disrupts stem cell maintenance (Maunakea et al., 2010;
Trowbridge and Orkin, 2010), and loss of DNMT1 leads to defec-
tive differentiation and unbalanced commitment to the myeloid
and lymphoid lineages (Bro
¨
ske et al., 2009; Trowbridge et al.,
2009). These studies highlight the well-characterized hematopoi-
etic compartment as a context in which to study the link between
DNA methylation patterns and cell-fate specification.
Toward this end, DNA methylation profiles of murine hemato-
poietic progenitors through early stages of lineage commitment
were recently compared with CHARM (Irizarry et al., 2008; Ji
et al., 2010), which profiles a predefined set of CpG-dense inter-
vals. Overall, CHARM revealed that early lymphopoeisis involves
more global acquisition of DNA methylation than myelopoiesis
and that DNMT1 inhibition skews progenitors toward the
myeloid state. These data support earlier reports that DNMT1
hypomorphic hematopoietic stem and progenitor cells (HSPCs)
show reduced lymphoid differentiation potential (Bro
¨
ske et al.,
2009). Importantly, regions identified to have differential methyl-
ation through sequential stages of differentiation most often did
not correspond to CpG islands (CGIs) but instead lay adjacent in
areas referred to as ‘shores.’
Higher-resolution maps of DNA methylation with shotgun
bisulfite sequencing have mainly been produced from cultured
cells (Laurent et al., 2010; Lister et al., 2009) or mixed cell types
(Li et al., 2010). Several unexpected findings emerged from these
early studies including significant frequencies of cytosines meth-
ylated in a non-CpG context in human embryonic stem cells
(ESCs), a characteristic previously thought to be restricted to
plants. Other genome-wide studies have implicated DNA meth-
ylation in the regulation of alternative promoters and even RNA
splicing patterns (Maunakea et al., 2010). These observations
emphasize the need for complete, unbiased, and quantitative
assessment of cytosine methylation and the establishment of
reference methylomes from purified populations of primary cells.
Here, we performed whole-genome shotgun bisulfite se-
quencing on female human HSPCs, B cells, and neutrophils to
Molecular Cell 44, 1–12, October 7, 2011 ª2011 Elsevier Inc. 1
Please cite this article in press as: Hodges et al., Directional DNA Methylation Changes and Complex Intermediate States Accompany Lineage Spec-
ificity in the Adult Hematopoietic Compartment, Molecular Cell (2011), doi:10.1016/j.molcel.2011.08.026
Page 1
Scale
chr16:
10 kb
28850000
28855000
28860000
RefSeq Genes
UCSC CpG Islands
CGIs (HMM-based)
ESC HMRs
HSPC HMRs
CD133 HMRs
BCell HMRs
Neut HMRs
Sperm HMRs
CD19
ESCs
1
0
HSPCs
CD133+
B Cells
Neutrophils
Sperm
1
0
1
0
1
0
1
0
1
0
Methylation Level
Individual CpG Sites
Scale
chr19:
10 kb
38475000
38480000
38485000
RefSeq Genes
UCSC CpG Islands
CGIs (HMM-based)
ESC HMRs
HSPC HMRs
CD133 HMRs
BCell HMRs
Neut HMRs
Sperm HMRs
CEBPA
LOC80054
ESCs
1
0
HSPCs
CD133+
B Cells
Neutrophils
Sperm
1
0
1
0
1
0
1
0
1
0
Methylation Level
Individual CpG Sites
A
B
UCSC CGI
Sperm
Neutrophil
32444
54137
9330
7800
405
1606
18415
HSPC
Neutrophil
B cell
9244
7345
1384
2633
1486
10145
41720
C
Sperm
ESC
BCell
CD133
HSPC
Neut
0.0 0.4 0.8 1.2
Height
D
Figure 1. Features of Methylomes in Hematopoietic Cells
(A and B) Genome browser tracks depict methylation profiles across a lymphoid (A) and myeloid (B) specific locus in blood cells, ESCs, and sperm. Methylation
frequencies, ranging between 0 and 1, of unique reads covering individual CpG sites are shown in gray with identified hypomethylated regions (HMRs) indicated
Molecular Cell
Human Hematopoietic Methylomes
2 Molecular Cell 44, 1–12, October 7, 2011 ª2011 Elsevier Inc.
Please cite this article in press as: Hodges et al., Directional DNA Methylation Changes and Complex Intermediate States Accompany Lineage Spec-
ificity in the Adult Hematopoietic Compartment, Molecular Cell (2011), doi:10.1016/j.molcel.2011.08.026
Page 2
examine the relationships between the methylation states of mul-
tipotent blood-forming stem cells and two divergent derived line-
ages. This enabled us to probe directional changes in DNA meth-
ylation associated with cell-fate specification. Comparison of the
three reference methylomes revealed a number of important prin-
ciples of epigenetic regulation, in addition to providing insights
into the dynamics of epigenetic changes during development.
RESULTS AND DISCUSSION
Lineage-Specific Hypomethylated Regions Extend
beyond Annotated CGIs
We sought to generate reference, single nucleotide-resolution
methylation profiles for several nodes within the human hemato-
poietic lineage using whole-genome bisulfite sequencing (see
the Experimental Procedures). Therefore, we examined CD34+
CD38–Lin– HSPCs, CD19+ B cells, and granulocytic neutrophils
from peripheral blood of pooled human female donors. These cell
types represent one of the earliest self-renewing, multipotent
populations, and two derived, mature cell types from the
lymphoid and myeloid lineages, respectively. For comparison,
we generated methylomes from HSPCs from male umbilical
cord blood (CD133+CD34+CD38–Lin–) and compared to data
sets created from primate sperm (Molaro et al., 2011) and embry-
onic stem cells (Laurent et al., 2010). In all cases, we achieved
a median of 103 independent sequence coverage, sufficient to
interrogate 96% of genomic CpG sites (Figure S1A and Table
S1A available online). While this level of coverage is still subject
to sampling error at individual sites (see discussion in Hodges
et al., 2009), features such as transitions from high to low levels
of methylation can still be identified with a resolution of the
boundaries to within a few CpG sites.
In the genome as a whole, CpG dinucleotides have a strong
tendency to be methylated (70%–80%) (Lister et al., 2009). Coin-
cidently, CpGs are also underrepresented, perhaps because
of their vulnerability to methylation-induced deamination and
consequent loss over evolutionary time (Cooper and Krawczak,
1989; Gardiner-Garden and Frommer, 1987). Areas of increased
CpG density, called CpG islands (CGIs) have a lower probability
of being methylated and these or their adjacent regions (CGI
shores) have been implicated as potential regulatory domains
(Gardiner-Garden and Frommer, 1987; Irizarry et al., 2009a;
Wu et al., 2010). Though CGIs have been defined computation-
ally (Irizarry et al., 2009b), we developed an algorithm to identify
hypomethylated regions (HMRs) empirically in bisulfite
sequencing data sets, based on their methylation state alone
(see Figures 1A and 1B).
Between 50,000 and 60,000 HMRs were identified from each
hematopoietic profile (Table S1B), with neutrophils displaying
the greatest number (60,000), followed by HSPCs (55,000)
and B lymphocytes (53,000) (Figure 1C). Interestingly, this
was lower than the number in male germ cells (80,000),
perhaps because of the extensive repeat hypomethylation
observed in sperm as compared to somatic cells.
Certainly, many annotated CGIs were contained within our set
of functionally defined HMRs; however, CGIs appeared to fall
short as a benchmark by which to define all HMRs with probable
regulatory significance. Annotated CGIs accounted for fewer
than half of the HMRs identified in any cell type (Figure 1C and
Figure S1B). Moreover, many HMRs whose biological relevance
is supported by lineage-specific methylation failed to meet the
conservative CGI criteria.
Sequence tracks showing methylation levels for a lymphoid-
(Figure 1A) or myeloid- (Figure 1B) specific gene illustrate several
characteristics of HMRs. The locus for the B cell marker CD19
displays a broad, cell type-specific HMR at its transcriptional start
site (TSS), which does not overlap a predicted CGI. In contrast,
‘tidal’ methylation at CGI shores characterizes several HMRs
surrounding the myeloid transcription factor, CEBPA. The cores
of these HMRs are shared among blood forming cells, but their
widths differ, with neutrophils demonstrating the most expansive
hypomethylation. Infact, shared HMRs oftenshow variable widths,
suggesting that the boundaries of HMRs fluctuate in a cell type-
dependent manner. Due to the dynamic behavior of the HMRs,
we were motivated to seek further validation of these characteris-
tics as biological phenomena, rather than as technical artifacts of
the methodology. Therefore, we focused on an independent data-
set derived from chimpanzee. We reasoned that genic relation-
ships to methylation dynamics should be preserved in closely
related species. Indeed, HMRs show significant overlap between
human and chimp, with chimp HMRs following very similar
patterns of boundary fluctuations (Table S1CandFigure S2).
While a high proportion of identified HMRs (R70%) inter-
sected all blood cell types studied, 10-fold more HMRs were
shared only between HSPCs and neutrophils than exclusively
between HSPCs and B cells (Figure 1C). In contrast, 45%–
50% of HMRs identified in blood cells overlap sperm HMRs.
Interestingly, the diversity of differentially expressed genes
within the hematopoietic lineage has been reported to be similar
to the complexity observed across human tissues (Novershtern
et al., 2011). However, at the epigenetic level, HMR profiles
easily distinguished closely related cell types (blood forming)
from distantly related ones (Figure 1D), indicating that patterns
of DNA methylation are strongly correlated within a lineage.
HMR Expansion Correlates with Differential Expression
Differentially methylated regions (DMRs) at promoters have been
ascribed regulatory roles, with differential methylation being
by orange bars. UCSC predicted/annotated CpG islands (green bars) and HMM-based CpG islands (blue bars) (Irizarry et al., 2009b) are also displayed. Numbers
(top) indicate base position along the chromosome.
(C) Venn diagrams depict the intersection between HMRs identified in blood as well as the overlap between blood-derived cells, sperm, and UCSC CpG islands.
The size of the circles and the proportion of circle overlap reflect the relative number of HMRs identified as well as the degree of intersection between each set of
HMRs.
(D) Dendrogram clusters cell-types according to their pearson correlations of individual CpG methylation levels within HMRs, both overlapping and nonover-
lapping, across all tissues examined.
See also Figures S1 and S2 and Table S1.
Molecular Cell
Human Hematopoietic Methylomes
Molecular Cell 44, 1–12, October 7, 2011 ª2011 Elsevier Inc. 3
Please cite this article in press as: Hodges et al., Directional DNA Methylation Changes and Complex Intermediate States Accompany Lineage Spec-
ificity in the Adult Hematopoietic Compartment, Molecular Cell (2011), doi:10.1016/j.molcel.2011.08.026
Page 3
−4000 −2000 0 2000 4000
0.00 0.02 0.04 0.06 0.08
Position relative to TSS (100 base bins)
Correlation: differential methylation vs.
differential expression (Neutrophil/B cells)
Promoters near N<B DMRs
B-cell Neutrophil SpermESC
Frequency of coverage by DMRs (50bp windows)
Promoters near B<N DMRs
-4000 -2000 0 2000 4000
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
600
500
400
300
200
100
0
600
500
400
300
200
100
0
Scale
chr19:
10 kb
40505000
40510000 40515000 40520000
40525000 40530000
40535000 40540000
UCSC CpG Islands
CGIs (HMM-based)
HSPC RNA-seq
HSPC HMRs
B cell RNA-seq
BCell HMRs
Neutrophil RNA-seq
Neut HMRs
CD22
CD22
FFAR1 FFAR3
300
_
0
_
HSPC Methylation
1
_
0
_
300
_
0
_
B cell Methylation
1
_
0
_
Neutrophil Methylation
300
_
0
_
1
_
0
_
HSPC RNA-seq Reads
B cell RNA-seq Reads
Neutrophil
RNA-seq Reads
RefSeq Genes
A
B
D
C
0.05
-0.05 -0.15 -0.25
Position relative to TSS (100 base bins)
Correlation: methylation and RPKM
Neutrophil
B cell
HSPC
-4000 -2000 0 2000 4000
n=3237
n=2522
Position relative to TSS
Methylation levelMethylation level
DMR coverage DMR coverage
Individual CpG Sites
Figure 2. Promoter Differential Methylation and Gene Expression
(A) Average methylation levels across promoters of genes having a DMR within 4 kb of the TSS are shown. Two separate graphs display neutrophil hypo-
methylated promoter DMRs relative to B cells (N < B, top) and B cell hypomethylated promoter DMRs relative to neutrophils (B < N, bottom). The number of DMRs
covering nonoverlapping 50 bp windows across the promoter is also shown.
(B) Correlations between differential methylation and differential expression between neutrophils and B cells as a function of position relative to the TSS are
shown. The correlations were obtained by comparing log odds of differential methylation and log of RPKM. The probability for differential methylation at a given
CpG is described in the Supplemental Experimental Procedures. The gray area displays the smoothed 95% confidence interval. The closed circles indicate
correlation coefficients that are significantly different from 0.
Molecular Cell
Human Hematopoietic Methylomes
4 Molecular Cell 44, 1–12, October 7, 2011 ª2011 Elsevier Inc.
Please cite this article in press as: Hodges et al., Directional DNA Methylation Changes and Complex Intermediate States Accompany Lineage Spec-
ificity in the Adult Hematopoietic Compartment, Molecular Cell (2011), doi:10.1016/j.molcel.2011.08.026
Page 4
linked to tissue-specific expression. Yet, HSPCs, B cells, and
neutrophils mainly share promoter-associated HMRs at differen-
tially expressed genes. Prior studies have associated changes in
gene expression with changes in methylation states adjacent to
constitutively hypomethylated CGIs, in so-called ‘CGI shores’
(Irizarry et al., 2009a). Therefore, we examined correlations
between the geography of promoter HMRs and changes in
lineage-specific expression, focusing on a comparison of B cells
and neutrophils.
Differential methylation often manifested as a broadening of
TSS-associated HMRs in a specific lineage (Table S2A). The
changes were asymmetric, with the greatest loss of methylation
on the gene-ward side (Wilcoxon ranks sum: p < 5e-60, both
DMR sets). Globally, these HMRs were broadest in sperm and
constricted in ESCs (Figure 2A) (see also Molaro et al., 2011),
widening again in a tissue-specific fashion. Thus, our analyses
provide global support for ‘tidal’ methylation changes at CGI
shores.
For deeper analysis of these tidal patterns, we measured
differential methylation in 50 base windows surrounding TSSs
(Figure 2A). Moving 3
0
toward B cell hypomethylated promoters
(B < N), coverage by DMRs peaked between 1.5 Kbp and 2 Kbp
downstream of the TSS. A slightly different pattern was observed
for neutrophil hypomethylated promoters (N < B), with DMRs
rising to a peak directly at the TSS. In both data sets, the greatest
concentration of differential methylation occurred 1–2 Kb
downstream of the TSS, consistent with overall methylation
being selectively reduced in the transcribed regions of genes
with tissue-specific DMRs.
We next asked whether any element of DMR geography corre-
lated with tissue-specific gene expression. We carried out
RNA-seq and computed RPKM values for each cell type (Table
S2B). We then computed the correlation between differential
expression and differential methylation in 100 base windows
surrounding the TSS (see the Experimental Procedures). This
correlation was strongly asymmetric, peaking 1,000 bases
downstream of the TSS. Notably, this corresponded with the
expansion of HMRs that contributes to tissue-specific promoter
hypomethylation (Figure 2B).
CD22 provides a specific example of the general phenomena
that we observed (Figure 2C). CD22 is expressed in B cells, but
not neutrophils. In each cell type its TSS is covered by an HMR,
which in HSPCs and neutrophils extends 500 bp and centered
on the TSS. In B cells, the HMR begins at the same position
upstream of the CD22 TSS, but extends more than 4,300 bp
into the transcribed region.
The properties noted for differentially expressed genes were
extensible to the entire set of REFSEQ genes. Though hypome-
thylation was largely symmetric around REFSEQ TSSs, a strong
correlation could be seen between RPKM and lower methylation
levels peaking 1.0 Kb downstream of the TSS (Figure 2D). This
was true of all cell types examined, though the magnitude of the
effect was lowest in HSPCs.
Our results are in accord with a recent study that revealed
a unique chromatin signature surrounding the TSS of tissue-
specific loci. Spreading of H3K4me2 into the 5
0
untranslated
region (UTR) was observed at tissue-specific genes, whereas it
remained as a discrete peak at the TSS of ubiquitously ex-
pressed genes (Pekowska et al., 2010). To look for similar rela-
tionships between histone profiles and expanding promoter
HMRs, we analyzed chromatin immunoprecipitation sequencing
(ChIP-seq) data for H3K4me3, H3K4me1, and H3K27ac enrich-
ment across eight different ENCODE cell lines (Bernstein et al.,
2005; Birney et al., 2007 ). The ENCODE cell lines are derived
from a variety of tissues and include GM12878, which is a lym-
phoblastoid cell line. First, we observe a strong enrichment for
these histone marks at B cell promoters containing expanded
HMRs. In addition, the greatest difference between the lymphoid
cell line and the other cell lines appears upstream and down-
stream of the TSS compared to all promoters. Interestingly, the
H3K4me3 differential enrichment is biased on the 3
0
side of the
TSS (Figure 3).
It has also been noted that for a subset of CGI-associated
promoters, high CpG density extends downstream of the TSS
and hypomethylation of the extended region is required for
RNA polymerase II binding (Appanah et al., 2007). In fact, anal-
ysis of existing lymphoid ChIP-seq data of RNA polymerase II
revealed a 33 enrichment in B cell expanded HMR regions
compared to neutrophil-expanded regions ( Table S2C) (Barski
et al., 2010). This suggests that while core CGI promoters remain
hypomethylated by default, expansion downstream of the TSS
may be important for productive transcription.
Features of Shared and Lineage-Specific
Intergenic HMRs
While REFSEQ gene promoters were often associated with an
HMR, the majority of HMRs were not found at promoters (Fig-
ure S3). Nearly half of all identified HMRs were located in gene
bodies. An additional quarter lay >10 Kb from the nearest anno-
tated genes, and we defined this class as ‘intergenic HMRs.’
Like promoter-associated HMRs, intergenic HMRs showed
sequence conservation, suggesting that these are functional
elements (Figure 4A). In fact, genome-wide comparisons of
methylation states of orthologous sites in the corresponding
cell types of chimpanzee supported concomitant conservation
of constitutive and cell type-specific patterns of intergenic meth-
ylation (data not shown). Intergenic HMRs tended to be narrower
than those found at promoters and were less likely to be shared
among cell types. When they were shared, they displayed
patterns of expansion and contraction very similar to what was
observed for promoter-associated regions (Figure 4A), with their
overall extent being widest in sperm.
(C) The browser image shows gene expression for CD22 in the form of mapped read profiles from RNA-seq data. Methylation profiles are also shown (as in
Figure 1A) along with HMRs.
(D) Correlations between methylation levels and expres sion levels represented by RPKM values are shown as a function of position relative to the TSS. Correlation
coefficients were averaged in 100 bp bins across regions between 4 kb upstream and downstream of the TSS. Y axis labels were reversed.
See also Figure S3 and Table S2.
Molecular Cell
Human Hematopoietic Methylomes
Molecular Cell 44, 1–12, October 7, 2011 ª2011 Elsevier Inc. 5
Please cite this article in press as: Hodges et al., Directional DNA Methylation Changes and Complex Intermediate States Accompany Lineage Spec-
ificity in the Adult Hematopoietic Compartment, Molecular Cell (2011), doi:10.1016/j.molcel.2011.08.026
Page 5
An early, pervasive view of DNA methylation proposed that
germ cell profiles should represent a default state of hypomethy-
lation in all potential regulatory regions (Gardiner-Garden and
Frommer, 1987). This was based on the idea that hypomethyla-
tion in germ cells would prevent CpG erosion over evolutionary
time spans. The high number of nonoverlapping HMRs in the
adult somatic cell strongly argues against both of these notions
(Figure 1C). However, the width of both genic and intergenic
HMRs in sperm compared to somatic cells suggests that germ
cells can define the ultimate boundaries of somatic HMRs.
Guided by the strong general enrichment for potential tran-
scription factor binding sites in all HMRs (see Table 1), we
searched for motifs in intergenic DMRs specific to neutrophils
or B cells (Figure 4B). The strongest scoring motifs in the neutro-
phil-specific intergenic DMRs included those associated with
C/EBP and ETS families, along with HLF and STAT motifs. This
striking enrichment for C/EBP and ETS family binding sites is
consistent with the functions of ETS factor PU.1 and several
C/EBP factors as multipotent progenitors commit to become
myeloblasts, which ultimately give rise to neutrophils (Nerlov
and Graf, 1998 ). Because the ETS family contains a large number
of transcription factors, we sought experimental support for their
binding at HMRs. Therefore we probed existing ChIP-seq data of
PU.1 from human HSPCs (Novershtern et al., 2011). We find
numerous examples PU.1 enrichment in HMRs, several of which
are provided in Figure S4. In contrast, the strongest scoring
motifs in B cell-specific intergenic DMRs included the EBF motif,
POU family motifs, E-boxes, a PAX motif, and those associated
with NFkB and IRF. The simultaneous enrichment of EBF, E-box,
and PAX motifs is consistent with the interacting roles of EBF,
E2A (which binds E-boxes) and PAX5 as common lymphoid
progenitors progress along the B cell lineage (Lin et al., 2010;
Medina et al., 2004; Sigvardsson et al., 2002). The enrichment
of NFkB and IRF motifs is consistent with the known roles for
these factors in both activation and differentiation of lympho-
cytes (Hayden et al., 2006). Considered together, these analyses
strongly suggest that at least a subset of intergenic DMRs can
be engaged by tissue-specific transcription factors, leading to
changes in chromatin organization that might have long-
distance impacts on annotated genes or more local impacts on
as yet unidentified ncRNAs. In fact, we do find evidence of tran-
scriptional activity surrounding intergenic DMRs in our RNA-seq
data sets, but we have not yet pursued this observation further
(data not shown). Irrespective of the model, our results strongly
support the biological relevance of tissue-specific intergenic
HMRs.
We also probed the possible functions of shared intergenic
HMRs. Prior studies had experimentally identified binding sites
for the insulator protein, CTCF, by chromatin immunoprecipita-
tion (Kim et al., 2007). These sites are strongly enriched (155-
fold) in nonrepeat intergenic HMRs that are common to all cell
types examined. In fact, 90% (>500) of the nonrepeat, shared
intergenic HMRs contain a CTCF site. This correlates with the
known propensity of CTCF to bind unmethylated regions and
suggests that many of the shared intergenic HMRs that we
detect may function in the structural organization of chromo-
somes and nuclear domains.
Myeloid-Biased, Poised Methylation States
Characterize HSPC Methylomes
For loci whose differential expression characterizes the lym-
phoid and myeloid lineages, we set out with a simple general
expectation. Low methylation levels in stem and progenitor cells
would be permissive for expression in either lineage, and an
accumulation of methylation during differentiation would corre-
late with silencing of loci in the lineage in which they are not
expressed.
To test this hypothesis, we selected lineage-specific HMRs
arising from a comparison of neutrophils and B cells and exam-
ined their status in HSPCs. Both at the level of individual CpGs
(Figure 5A) and at the level of overall methylation (Figure 5B),
HSPCs showed intermediate methylation states at sites where
B cells and neutrophils show opposing methylation patterns.
−10
−8 −6
−4
−2 0 2 4 6 8 10
1.0 1.5 2.0
Position relative to TSS (kb)
Enrichment ratio
A
−10
−8 −6
−4
−2 0 2 4 6 8 10
Position relative to TSS (kb)
1.0 1.5 2.0 2.5 3.0
−10
−8
−6
−4 −2 0 2 4 6 8 10
Position relative to TSS (kb)
Enrichment ratio
B
C
1.0 1.2 1.4 1.6 1.8 2.0 2.2
H3K4me3
H3K4me1
H3K27ac
Enrichment ratio
Figure 3. Histone Enrichment across Expanded HMRs
Read count enrichment ratios per 25 bp bins located 10 kb upstream and 10 kb
downstream of the TSS were calculated for promoters overlapping HMRs
included in Figure 2A for B cell HMRs (red lines) or neutrophil HMRs (blue lines)
for H3K4me3 (A), H3K4me1 (B), and H3K27ac (C) by comparison of read
counts across all REFSEQ annotated promoters. Data were obtained from
ENCODE and include histone profiles for eight different cell lines. The lym-
phoblastoid cell line GM12878 is highlighted in darker shaded colors.
Molecular Cell
Human Hematopoietic Methylomes
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ificity in the Adult Hematopoietic Compartment, Molecular Cell (2011), doi:10.1016/j.molcel.2011.08.026
Page 6
Figure 4. Features of Intergenic HMRs and DMRs
(A) Composite methylation profiles are plotted for individual CpG sites within HMRs. The x axes of the plots indicate genomic position centered on the midpoint of
HMRs in the reference cell type labeled for each plot. Methylation profiles are given for the reference cell and sperm, separately for regions where the reference
Molecular Cell
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Page 7
This suggests that differentiation involves both gains and losses
of DNA methylation at lineage-specific HMRs, an observation
consistent with recent studies using other methodologies (At-
tema et al., 2007; Claus et al., 2005; Ji et al., 2010).
At the level of individual CpGs, HSPC patterns correlated
better with those seen in neutrophils at myeloid HMRs than
they did with B cell methylation patterns at nonoverlapping
lymphoid HMRs (Figure 5A). Moreover, the median methylation
level for B cells at B cell DMRs was more than twice as high as
the median level at neutrophil specific DMRs ( Figure 5B). This
finding, along with the fact that B cells exhibited fewer total
HMRs than either HSPCs or neutrophils, supported an earlier
observation that lymphoid commitment in mice involves globally
increased DNA methylation (Ji et al., 2010). As a whole, our
results indicate that the HSPC methylome has more myeloid
than lymphoid character. Many fewer DMRs were identified in
comparisons of HSPC and neutrophil methylation profiles than
of HSPCs and B cells ( Figure S3). Such a myeloid bias is also
consistent with prior studies, which point to the myeloid lineage
as a default differentiation path for HSPCs ( Ma
˚
nsson et al.,
2007).
Regions that exhibit intermediate methylation occurred in two
forms. The well-documented mode is allelic methylation that is
characteristic of dosage compensated and imprinted genes.
We detected such loci abundantly in our data sets, and these
encompassed both known monoallelic genes and new candi-
dates for monoallelic expression (data not shown). More
prevalent were regions of intermediate methylation wherein
each chromosome displayed different patterns of CpG modifica-
tion with little correlation between the states of adjacent CpGs.
Partially methylated regions were previously noted in ESCs
(Lister et al., 2009), though they did not investigate whether these
presented allelic versus stochastic and complex patterns.
To discriminate between allelic and complex patterns, we per-
formed targeted conventional bisulfite PCR sequencing of indi-
vidual clones from HSPCs across a selected set of myeloid loci
and a known locus with allele-specific methylation (Figure 5C,
Figure S5, and Table S3). This allowed detailed analysis of adja-
cent CpG methylation on individual molecules. As expected, for
the allelic XIST locus on chromosome X, we observed uniform
methylation profiles of adjacent CpG sites within individual
clones representing two states that contributed nearly equally
to the partial methylation observed. In contrast, the myeloid
AZU1 locus exemplified a stochastic pattern of methylation in
HSPC. We cannot determine whether the complex states that
we observed were in dynamic equilibrium or whether they were
fixed in each chromosome that contributed to our analysis.
While the mechanisms underlying complex, partial methyla-
tion patterns in HSPCs are unclear, they are reminiscent of biva-
lent promoters that contain both repressive and active histone
marks (Bernstein et al., 2006). Both during embryonic develop-
ment and during stem cell differentiation, such poised promoters
are converted to a determinate chromatin state by shifting the
balance of histone marks. This has already been noted for
lineage-specific genes in HSPCs (Attema et al., 2007), and our
data indicate that this well-established property of chromatin
may also extend to DNA methylation patterns.
Alternative explanations for our results must also be consid-
ered. Since we have used pooled individuals, each of the
observed patterns could be specific to one donor, giving rise
to a complex pool of clones; however, this seems unlikely as
we also detect lower correlations between neighboring CpGs
within single clones. Alternatively, complex states could repre-
sent heterogeneity within the isolated HSPC population (see Fig-
ure S6), with our data coming from a mixture of self-renewing
and more committed cell types. To investigate this possibility,
we searched within our RNA-seq data for expression patterns
characteristic of each purified cell population. Transcriptional
profiles revealed the top differentially expressed genes within
the HSPC compartment to be highly enriched for signature
gene markers associated with self-renewing hematopoietic
stem cells (Figure 5D) and depleted for genes associated with
committed progenitors. Collectively, these data suggest that
the observed methylation patterns are likely derived from a highly
enriched stem cell population, and indicate that those popula-
tions may naturally adopt complex, potentially dynamic, methyl-
ation patterns at lineage-specific HMRs.
Both the general trends of methylation loss along a lineage and
the possibility of dynamic poised methylation states imply that
demethylation, either passive or active, is a common event. In
mammals, factors capable of promoting active demethylation
have remained somewhat elusive (Ooi and Bestor, 2008).
In vitro studies have demonstrated that MBD2, a methyl-CpG
binding protein, can specifically demethylate cytosines, and
components of the elongator complex and the cytidine deami-
nase, AID, have been implicated in demethylation during early
development (Bhattacharya et al., 1999; Okada et al., 2010;
Popp et al., 2010). Furthermore, in zebrafish, the coordinated
activities of glycosylases, deaminases, and DNA repair proteins
have been reported to cause differentiation defects when disrup-
ted, and this has been posited as an effect of improper DNA
methylation (Rai et al., 2010). Alternatively, demethylation could
potentially be achieved through the action of hydroxymethylases
(e.g., TET1-3), which have been proposed to execute an interme-
diate step toward methylation loss (Ito et al., 2010; Tahiliani et al.,
2009; Zhang et al., 2010). Additional information will be neces-
sary to resolve the relevance of any of these pathways to the
transition in methylation states between HSPCs and mature
neutrophils and B cells.
As a whole, our data not only provide insights into the global
behavior of DNA methylation, both in individual cell types and
along a well-characterized lineage, but also provide a critical
cell HMR spans a TSS and intergenic region (>10 Kbp from any RefSeq transcript; not overlapping a repeat). Average cross-species conservation scores from
PhyloP probabilities derived from 44-way multiple alignments are plotted separately for promoter and intergenic HMRs.
(B) Transcription factor binding site motifs enriched in DMRs between neutrophils and B cells are shown. The top 20 most enriched motifs are shown separately
for N < B and B < N DMRs, based on the motifclass tool in the CREAD package. See the Supplemental Experimental Procedures for details of enrichment
calculations.
See also Figures S3 and S4.
Molecular Cell
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Please cite this article in press as: Hodges et al., Directional DNA Methylation Changes and Complex Intermediate States Accompany Lineage Spec-
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Page 8
reference data set to enable detailed future studies of both the
mechanisms that set somatic DNA methylation patterns and
the consequences of those patterns for gene expression and
genome organization.
EXPERIMENTAL PROCEDURES
Flow Cytometry and DNA Extraction
Peripheral blood was collected from six healthy female donors ages 25–35 and
pooled. After isolation by Ficoll gradient, mononuclear cells were fixed in 1%
paraformaldehyde (PFA) and stained with antibodies against the following
human cell surface markers (eBiosciences): anti-CD34 (mucosialin) conju-
gated to PE-Cy7, anti-CD38 conjugated to APC, anti-CD45 conjugated to
PE, anti-CD19 conjugated to PE, and anti-CD235a (Glycophorin) conjugated
to PE. For lineage depletion, either a combination of PE-conjugated antibodies
against CD45, CD19, and CD235a or a commercially available human hema-
topoietic lineage cocktail was used. CD34+CD38–Lin– hematopoietic stem
cells and CD19+ B cells were purified with the FACSAriaII (Becton Dickinson).
Neutrophils were purified according to their forward and side-scatter profile.
FACS profiles are provided in Figure S6. Umbilical cord blood was collected
from a single donor, and CD133+ cells were selected via magnetic separation
on CD133+ microbeads (Milteny Biotec) according to instructions supplied by
the manufacturer. Two column separations were performed for additional
purity. All cells were collected in cell lysis buffer (50 mM Tris, 10 mM EDTA
and 1% SDS), and PFA induced crosslinks were reversed with RNase A and
a65
C incubation overnight, after which residual proteins were digested
with Proteinase K for 3 hr at 42
C. DNA was extracted with an equal volume
of phenol:chloroform, followed by a single extraction with chloroform and
ethanol precipitation. Human sperm was purified and sequenced according
to methods described in Molaro et al. (2011).
Illumina Library Preparation for Bisulfite Sequencing
Bisulfite sequencing libraries were generated by previously described
methods (Hodges et al., 2009) and on the manufacturer’s instructions (Illumina)
but with several additional modifications. In brief, after each enzymatic
step, genomic DNA was recovered by phenol:chloroform extraction and
ethanol precipitation. Adenylated fragments were ligated to Illumina-compat-
ible paired-end adaptors synthesized with 5
0
-methyl-cytosine, and, when
necessary, adaptors were diluted 1003–10003 to compensate for low-input
libraries and maintain an approximate 10-fold excess of adaptor oligonucleo-
tides. After ligation, DNA fragments were purified and concentrated on
MinElute columns (QIAGEN). The standard gel purification step for size selec-
tion was excluded from the protocol. Fragments were denatured and treated
with sodium bisulfite with the EZ DNA Methylation Gold kit according to the
manufacturer’s instructions (Zymo). Lastly, the sample was desulfonated
and the converted, adaptor-ligated fragments were PCR enriched with
paired-end adaptor-compatible primers 1.0 and 2.0 (Illumina) and the Expand
High Fidelity Plus PCR system (Roche). Paired-end Illumina sequencing was
performed on bisulfite converted libraries for 76–100 cycles each end.
RNA-Seq
For isolation of RNA from target cell populations, unfixed (live) cells were
sorted as described above into Trizol-LS (Invitrogen), and RNA was purified
Table 1. TFBS Enrichment in HMRs across Intergenic and
Promoter Regions
Cell Region CGI? HMR
a
TFBS Expected
Enrich-
ment
N/A promoter 34,257 244,998 91,570.8 2.7
promoter cgi 24,601 191,452 65,760.9 2.9
promoter nocgi 9,656 53,852 25,810 2.1
intergenic cgi 10,630 13,608 4,603.76 3.0
B Cell all 53,834 339,943 76,196.1 4.5
intergenic 5,849 16,150 3,779 4.3
intergenic cgi 1,670 4,802 1,194.97 4.0
intergenic nocgi 4,179 11,348 2,584.01 4.4
promoter 13,650 212,644 36,548.3 5.8
promoter cgi 12,828 206,556 35,080 5.9
promoter nocgi 822 6,088 1,468.27 4.1
CD133 all 49,593 339,191 67,778.2 5.0
intergenic 6,494 17,708 3,816.73 4.6
intergenic cgi 1,630 4,817 1,207.45 4.0
intergenic nocgi 4,864 12,891 2,609.26 4.9
promoter 13,745 224,955 37,395.1 6.0
promoter cgi 12,965 219,407 36,309.9 6.0
promoter nocgi 780 5,548 1,085.18 5.1
ESC all 40,476 318,377 65,062.3 4.9
intergenic 3,768 11,220 2,404.28 4.7
intergenic cgi 1,151 3,295 882.802 3.7
intergenic nocgi 2,617 7,925 1,521.45 5.2
promoter 13,098 222,654 36,332.4 6.1
promoter cgi 12,661 218,765 35,769.4 6.1
promoter nocgi 437 3,889 562.951 6.9
HSPC all 55,984 352,574 77,671.2 4.5
intergenic 6,154 17,619 3,972.1 4.4
intergenic cgi 1,663 4,775 1,222.27 3.9
intergenic nocgi 4,491 12,844 2,749.81 4.7
promoter 13,820 222,635 37,830.8 5.9
promoter cgi 12,948 216,433 36,461.3 5.9
promoter nocgi 872 6,202 1,369.4 4.5
Neut. all 60,594 362,074 82,427.7 4.4
intergenic 6,422 18,515 4,212.75 4.4
intergenic cgi 1,626 4,760 1,243.88 3.8
intergenic nocgi 4,796 13,755 2,968.85 4.6
promoter 13,862 224,621 38,503.6 5.8
promoter cgi 12,950 218,281 37,060.6 5.9
promoter nocgi 912 6,340 1,442.93 4.4
Sperm all 81,446 440,856 201,006 2.2
intergenic 2,616 14,903 3,158.15 4.7
intergenic cgi 865 6,181 1,307.11 4.7
intergenic nocgi 1,751 8,722 1,851.02 4.7
promoter 14,051 270,798 63,641.3 4.3
promoter cgi 13,588 266,658 62,357.8 4.3
promoter nocgi 463 4,140 1,283.49 3.2
Enrichment of predicted transcription factor binding sites (TFBSs) in in-
tergenic HMRs and HMRs that overlap promoters. For each set of
HMRs, corresponding to a cell type, the TFBS enrichment (observed/
expected site counts) is given for all HMRs, those overlapping promoters,
those that are intergenic, separately according to whether the HMRs
overlap CGIs. Data are presented for each of the following cell types: B
cells, CD133 cord blood, HSPCs, ESCs, neutrophils, and sperm. For
comparison, the TFBS enrichment in the full set of promoters (including
those overlapping CGIs) is given, along with enrichment in the full set of
intergenic CGIs.
a
For the ‘N/A’ group, the HMRs are simply the number of promoters
or CGIs.
Molecular Cell
Human Hematopoietic Methylomes
Molecular Cell 44, 1–12, October 7, 2011 ª2011 Elsevier Inc. 9
Please cite this article in press as: Hodges et al., Directional DNA Methylation Changes and Complex Intermediate States Accompany Lineage Spec-
ificity in the Adult Hematopoietic Compartment, Molecular Cell (2011), doi:10.1016/j.molcel.2011.08.026
Page 9
Figure 5. Methylation Dynamics during Lineage Selection
(A) Smoothed scatter plot heat maps showing the correlat ion between individual CpG methylation levels in HSPCs versus B cells (left) and HSPCs
versus neutrophils (right) within B cell- and neutrophil-specific HMRs, respectively. Darker shading (red) indicates greater density of data points, while
lighter (yellow) shading reflects low er density. Positive correlations between HSPCs and both B cells and neutrophils indicate an intermediate state for
HSPCs.
Molecular Cell
Human Hematopoietic Methylomes
10 Molecular Cell 44, 1–12, October 7, 2011 ª2011 Elsevier Inc.
Please cite this article in press as: Hodges et al., Directional DNA Methylation Changes and Complex Intermediate States Accompany Lineage Spec-
ificity in the Adult Hematopoietic Compartment, Molecular Cell (2011), doi:10.1016/j.molcel.2011.08.026
Page 10
according to the manufacture r’s recommendations. Double-stranded comple-
mentary DNA (cDNA) libraries were generated with the Ovation RNA-seq
system (Nugen). After reverse transcription and cDNA amplification, double-
stranded cDNA fragments were phosphorylated, adenylated, and ligated to
Illumina paired-end adaptors followed by 15 cycles of PCR amplification
with Phusion HF PCR master mix (Finnzymes) according to the standard
Illumina protocol for genomic libraries. Single-end sequencing was performed
for 36 cycles.
Conventional Bisulfite Cloning and Sanger Sequencing
Genomic DNA isolated from pooled human HSPCs was bisulfite converted
with the EZ DNA Methylation Gold kit (Zymo). For selection of specific regions
for amplification, forward and reverse primers were designed with Methprimer
(Li and Dahiya, 2002). Primer sequences are provided in the Table S3. The
following PCR reaction components were combined in a total volume of
25 ml: 5 ml53 Expand High Fidelity Plus buffer without MgCl
2
,1ml10mM
dNTPs, 1 ml 10 mM each forward and reverse primers, 2.5 ml 25 mM MgCl
2
,
2 ml DNA template, and 11.5 ml nuclease-free water. Thermal cycling was per-
formed as follows: 35 cycles each of denaturation at 94
C for 2 min, annealing
at 60
Cor53
C for 1 min, and extension at 72
C for 30 s followed by 7 min at
72
C. The PCR products were purified on columns with a PCR purification kit
(QIAGEN). PCR products were adenylated with Klenow exo– and purified.
Purified amplicons were cloned and sequenced according to previously
described methods (Hodges et al., 2009).
Computational Methods Summary
The Supplemental Experimental Procedures contain a detailed description of
computational methods. Mapping bisulfite treated reads was done with
methods described by Smith et al. (2009) with tools from the RMAP package
(Smith et al., 2009). Hypomethylated regions (HMRs) were identified with
a hidden Markov model as described in Molaro et al. (2011). DMRs were iden-
tified by (1) computation of probabilities of differential methylation at individual
CpGs based on number of reads and frequencies of methylation, and (2) iden-
tification of peaks in these profiles after kernel smoothing. Cross-species
conservation information was taken from UCSC MULTIZ 44-way vertebrate
alignments and PhyloP profiles from these alignments.
ACCESSION NUMBERS
Data analyzed herein have been deposited in GEO with accession number
GSE31971.
SUPPLEMENTAL INFORMATION
Supplemental Information includes Supplemental Experimental Procedures,
six figures, and three tables and can be found with this article online at
doi:10.1016/j.molcel.2011.08.026.
ACKNOWLEDGMENTS
We thank members of the McCombie lab and Michelle Rooks for help with
experimental procedures, and Assaf Gordon, Luigi Manna, and the Cold
Spring Harbor Laboratory and University of Southern California High Perfor-
mance Computing Centers for computational support. Chimp blood was
supplied by the New Iberia Research Center and the Southwest National
Primate Center. This work was supported in part by grants from the National
Institutes of Health and by a kind gift from Kathryn W. Davis (A.S., G.J.H.).
The ENCODE ChIP-seq data were generated at the Broad Institute and in
the Bradley E. Bernstein lab at the Massachusetts General Hospital/Harvard
Medical School. Data generation and analysis was supported by funds from
the National Human Genome Research Institute, the Burroughs Wellcome
Fund, Massachusetts General Hospital, and the Broad Institute.
Received: May 20, 2011
Revised: July 19, 2011
Accepted: August 26, 2011
Published online: September 15, 2011
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Human Hematopoietic Methylomes
12 Molecular Cell 44, 1–12, October 7, 2011 ª2011 Elsevier Inc.
Please cite this article in press as: Hodges et al., Directional DNA Methylation Changes and Complex Intermediate States Accompany Lineage Spec-
ificity in the Adult Hematopoietic Compartment, Molecular Cell (2011), doi:10.1016/j.molcel.2011.08.026
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