程序代写代做代考 DNA Bioinformatics assembly scheme Agda Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation

Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation

Resource

Dynamic Gene Regulatory Networks Drive

Hematopoietic Specification and Differentiation
Graphical Abstract
Highlights
d Comprehensive genome-scale resource for studying

embryonic blood cell specification

d Genome-scale definition of cis elements driving differential

gene expression

d A gene regulatory network model for hematopoiesis aiding

reprogramming experiments

d Analysis suggests a role for TEAD factors in hematopoietic

specification
Goode et al., 2016, Developmental Cell 36, 1–16
March 7, 2016 ª2016 The Authors
http://dx.doi.org/10.1016/j.devcel.2016.01.024
Authors

Debbie K. Goode, Nadine Obier,

M.S. Vijayabaskar, …, Valerie Kouskoff,

Berthold Göttgens, Constanze Bonifer

Correspondence
c.bonifer@bham.ac.uk

In Brief

Goode, Obier, Vijayabaskar et al. isolate

cells at six different stages of

hematopoietic differentiation, starting

from embryonic stem cells, and perform a

comprehensive multi-omics analysis of

this developmental pathway. The data

identify regulators of hematopoietic

specification and highlight the minimum

requirements for the reprogramming of

non-blood cells to blood.
Accession Numbers
GSE69101

mailto:c.bonifer@bham.ac.uk
http://dx.doi.org/10.1016/j.devcel.2016.01.024

Please cite this article in press as: Goode et al., Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation, Develop-
mental Cell (2016), http://dx.doi.org/10.1016/j.devcel.2016.01.024
Developmental Cell

Resource
Dynamic Gene Regulatory Networks Drive
Hematopoietic Specification and Differentiation
Debbie K. Goode,1,5 Nadine Obier,2,5 M.S. Vijayabaskar,4,5 Michael Lie-A-Ling,3 Andrew J. Lilly,3 Rebecca Hannah,1

Monika Lichtinger,2 Kiran Batta,3 Magdalena Florowska,3 Rahima Patel,3Mairi Challinor,3 KirstieWallace,3 JaneGilmour,2

Salam A. Assi,2 Pierre Cauchy,2 Maarten Hoogenkamp,2 David R. Westhead,4,6 Georges Lacaud,3,6 Valerie Kouskoff,3,6

Berthold Göttgens,1,6 and Constanze Bonifer2,6,*
1Department of Haematology, Cambridge Institute for Medical Research and Wellcome Trust and MRC Cambridge Stem Cell Institute,

Cambridge CB2 0XY, UK
2Institute of Cancer end Genomic Sciences, College of Medicine and Dentistry, University of Birmingham, Birmingham B152TT, UK
3CRUK Manchester Institute, University of Manchester, Manchester M20 4BX, UK
4School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK
5Co-first author
6Co-senior author

*Correspondence: c.bonifer@bham.ac.uk
http://dx.doi.org/10.1016/j.devcel.2016.01.024

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
SUMMARY

Metazoan development involves the successive acti-
vation and silencing of specific gene expression pro-
grams and is driven by tissue-specific transcription
factors programming the chromatin landscape. To
understand how this process executes an entire
developmental pathway, we generated global gene
expression, chromatin accessibility, histone modifi-
cation, and transcription factor binding data from pu-
rified embryonic stem cell-derived cells representing
six sequential stages of hematopoietic specification
and differentiation. Our data reveal the nature of reg-
ulatory elements driving differential gene expression
and inform how transcription factor binding impacts
on promoter activity. We present a dynamic core reg-
ulatory network model for hematopoietic specifica-
tion and demonstrate its utility for the design of
reprogramming experiments. Functional studies
motivated by our genome-wide data uncovered a
stage-specific role for TEAD/YAP factors in mamma-
lian hematopoietic specification. Our study presents
a powerful resource for studying hematopoiesis and
demonstrates how such data advance our under-
standing of mammalian development.

INTRODUCTION

Cellular identities in multicellular organisms are defined by their

individual gene expression programs and are established in a se-

ries of cell fate changes starting from pluripotent cells of the em-

bryo. The information on the balanced and coordinated up- and

downregulation of gene expression is encoded in our genome

and is read by transcription factors (TFs), which interact with

the epigenetic regulatory machinery to program the chromatin

of lineage-specific genes into active and inactive states. To un-
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derstand the mechanisms by which TFs establish and maintain

specific transcriptional programs, it is essential to investigate

developing biological systems, as illustrated by studies in non-

vertebrate models (Van Nostrand and Kim, 2011; Zinzen et al.,

2009).

Embryonic blood cells arise from early mesodermal cells via

hemangioblast and hemogenic endothelial intermediates (Med-

vinsky et al., 2011). Studies of chromatin programming and

gene expression during the generation of mature blood cells

from hematopoietic stem cells were instrumental in defining the

concept that development at the level of chromatin is a gradual

and hierarchical process starting long before the overt transcrip-

tional activation of lineage-specific genes (Bonifer et al., 2008;

Hoogenkamp et al., 2009; Org et al., 2015; Wamstad et al.,

2012; Wang et al., 2015). This notion is illustrated by the regula-

tory circuit essential for macrophage differentiation, the gene

encoding TF PU.1 (Spi1), and its target, the Csf1r growth factor

receptor gene (reviewed in Bonifer et al., 2008). Both are targets

of RUNX1, but Spi1 expression is induced prior to Csf1r. Early

Spi1 induction follows an initial enhancer priming event by TFs

upstream of RUNX1 followed by upregulation via autoregulation

(Leddin et al., 2011; Lichtinger et al., 2012), whereas subsequent

full expression of Csf1r requires the concerted action of RUNX1,

PU.1, and PU.1-induced factors (Krysinska et al., 2007; Lich-

tinger et al., 2012). This example illustrates the complexity of

the molecular mechanisms underlying the establishment of

cell-type-specific expression profiles. However, the global tran-

scriptional control mechanisms underlying such dynamic pro-

gression events have remained largely obscure, because of a

lack of comprehensive information on TF binding and the dy-

namic nature of the chromatin template with which they interact.

We also know very little about how such transcriptional control

mechanisms are interlinked with outside signaling.

Thedevelopmental hierarchiesof early embryonic hematopoie-

sis are recapitulated indifferentiatingembryonic stemcells (ESCs)

(Lancrin et al., 2010), which provide a tractable system capable of

generating the cell numbers required for performing multiple

genome-wide assays on the same samples. Recent studies

have investigated the function of individual regulators at specific
Developmental Cell 36, 1–16, March 7, 2016 ª2016 The Authors 1

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mailto:c.bonifer@bham.ac.uk
http://dx.doi.org/10.1016/j.devcel.2016.01.024
http://creativecommons.org/licenses/by/4.0/

functions of the TF SCL/TAL1 and the RUNX1-controlled transi-

tion from hemogenic endothelium to hematopoietic progenitors

(HPs) (Lancrin et al., 2012; Lichtinger et al., 2012; Lie-A-Ling

et al., 2014; Liu et al., 2015; Tanaka et al., 2012). However, while

a number of studies have examined individual cell fate transitions

or investigated the differentiation of mature blood cells from he-

matopoietic stem cells (Garber et al., 2012; Lara-Astiaso et al.,

2014; Tsankov et al., 2015), no study to date has reported an inte-

grated genome-scale analysis of an entire developmental time

course from early ESCs to fully defined blood cells.

In this study, we surveyed the global transcriptional journey

from the ESC to the terminally differentiated state of macro-

phages via blood precursor cells by generating data for RNA

sequencing (RNA-seq), DNase sequencing (DNA-seq), and

chromatin immunoprecipitation sequencing (ChIP-seq) for his-

tone marks and 16 different TFs across six sequential develop-

mental stages. To facilitate access across the wider scientific

community, we have integrated all genome-scale datasets into

an online resource with advanced browse, search, and analysis

capabilities. We have exploited our datasets to assemble a core

regulatory network model that was able to inform the design of

TF-mediated reprogramming strategies for the production of

blood cells from fibroblasts. Furthermore, computational anal-

ysis of regulatory elements revealed the nature of TFs involved

in stage-specific priming of distal elements, and informed func-

tional validation experiments identifying TEAD/YAP interaction

as a stage-specific regulator of early murine blood specification

in vitro and in vivo. Finally, we identified TEAD target genes and

their associated pathways, thus significantly enhancing our un-

derstanding of the signaling processes driving embryonic blood

cell development.

RESULTS

Capturing a Complete Developmental Pathway using
Genome-Scale Technologies
To study the specification of hematopoietic cells and their further

differentiation, we employedmouse ESC in vitro differentiation to

purify well-defined intermediate cell populations en route from

pluripotent ESCs to adherent macrophages (Lancrin et al.,

2009;Sroczynskaet al., 2009),makinguseof aBrachyuryGFP re-

porter (Fehling et al., 2003) and surface marker expression. Full

details of this strategy are given in Figure S1A. In brief, pluripotent

ESCs differentiate to mesoderm (MES) cells (Bry:GFP+/Flk1�),
which then progress to the hemangioblast (HB) stage (Bry:GFP+/

Flk1+) with smooth muscle, endothelial, and hematopoietic po-

tential, followed by the hemogenic endothelium (HE) stage that

has both endothelial and hematopoietic potential (CD41�/Tie2+/
Kit+). HE cells then undergo the endothelial-hematopoietic transi-

tion (EHT) involving a shape change, after which they are fully

committed to blood (CD41+ cells). CD41+ cellswere further differ-

entiated to generate CD11b+macrophages (MAC). From purified

cells we determined global gene expression profiles by RNA-seq

andmapped the full set of cis-regulatory elements at each devel-

opmental stage by global DNaseI hypersensitive site (DHS) map-

ping (DNaseI-seq).We usedChIP-seq to generate globalmapsof

TF binding for key regulators across this entire developmental

pathway as well as global patterns of H3K4me3, H3K9ac,
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2 Developmental Cell 36, 1–16, March 7, 2016 ª2016 The Authors
H3K27ac, and H3K27me3 histone modifications to investigate

how TFs programmed the chromatin landscape. TFs were cho-

sen according to the cell type in which they were expressed (Fig-

ure S1B), and all integrative analysis of ChIP and DHS data was

focused on genomic regions found in at least two independent

biological experiments (Table S1). Our datasets were comple-

mented with published data for undifferentiated mouse ESCs

(Chen et al., 2008; Whyte et al., 2013). The quality of this data

resource is exemplified in a browser window depiction of

sequence tags aligning to the Tal1 locus (Figure 1A), which en-

codes a key regulator of early blood specification (Shivdasani

et al., 1995; Wilson et al., 2009).

Initially, we used RNA-seq to investigate the dynamic changes

of gene expression across the six differentiation stages and how

these changes were reflected in the simultaneous changes in

chromatin structure. To this end, we clustered RNA-seq (Fig-

ure 1B; Tables S2A and S2B) and DNaseI-seq data (Figure 1C).

For both features, samples clustered in line with the known

developmental progression, with an early cluster consisting of

the ESC, and the more closely related MES and HB and a later

cluster made up of HE and HP with the macrophage samples

clustering separately. We then performed a similar analysis using

TF binding data (Figure 1D). While cell-type-specific clustering of

specific TF binding events were evident in ESCs and for certain

TFs (e.g. FLI1) in HPs and MACs, others (such as C/EBPb)

showed patterns predominantly driven by the identity of the fac-

tor rather than the tissue type (Figure 1D).

To facilitate inspection of individual genes and generate a

resource for further data analysis, we developed a web interface

to allow streamlined access for the wider scientific community:

http://www.haemopoiesis.leeds.ac.uk/data_analysis/. The web

portal provides access to both raw and processed data as well

as user-driven analysis options. These include queries for spe-

cific genes and gene sets across our multi-omics datasets, as

well as the visualization of all our data through a custom installa-

tion of the UCSC genome browser. In the following sections, we

describe how our data can be explored to inform the functional

validation of potential mechanisms.

Identification of the Complete Set of Differentially
Active cis-Regulatory Elements Driving Hematopoietic
Specification
We next inspected the nature of genes changing expression at

each cellular transition. 9,627 transcripts from 8,986 genes

were dynamically expressed during the developmental time

course (Figures S1C–S1E; Tables S2A and S2B). Expression

changes between any two sequential developmental stages

(transitions T1 to T5, Figure 1E) showed specific enrichment for

functionality with the ensuing stage of development for upregu-

lated genes (e.g. T4 shows enrichment for hematopoiesis), and

alternative cell fates for downregulated genes (e.g. T4 angiogen-

esis, heart/muscle development; Table S2C).

To capture dynamic expression patterns across the entire

developmental pathway and correlate such changes with alter-

ations in chromatin structure and TF binding, we performed

unsupervised/k-means clustering, which identified 31 major

expression clusters E1 to E31 (Figure 1F and Table S3A) repre-

senting different gene ontology (GO) categories (Table S3B and

Figure S4A). For example, E17–E19 represent clusters with
6

http://www.haemopoiesis.leeds.ac.uk/data_analysis/

Figure 1. Integrated Global Data over a Whole Developmental Pathway

(A) UCSC browser screenshot depicting the Tal1 locus aligning RNA-seq, DNaseI-seq, and ChIP-seq data from the six stages of development depicted in the

left-hand flow chart. The stage-specific color scheme is used in all subsequent figures. Panels display ChIP-seq data for four histone modifications (left) and

16 different TFs (right) plus DHS data. The grayed-out regions indicate known regulatory regions: from left to right, promoters 1a and 1b, enhancers +19 and +40.

(B–D) Hierarchical clustering of cell populations based on the normalized expression values of the genes (B), normalized correlation among the DHS sites (C), and

correlation among the TF sites (D). The correlations were normalized between �1 and +1 to preserve the color scale. ESC, embryonic stem cell; HB,
hemangioblast; HE, hemogenic endothelium; HP, hematopoietic progenitors, MES, mesoderm.

(E) Functional enrichment for genes that are differentially regulated during developmental transitions (T1–T5) in the progression of hematopoietic commitment.

(F) The expression dynamics of the differentially expressed genes in the pathway given in (A) that are clustered into 31 patterns. The standardized expression

values (zij) of the differentially regulated genes in the developmental pathway (Figure S1E) were clustered into 31 expression patterns, and the plot shows the

expression profiles of these patterns. The methodology is detailed in Supplemental Experimental Procedures.

functions relating to the immune response. Similarly, pattern

E11with upregulation inHEanddownregulation inHP is enriched

for functions relating to vasculogenesis and adhesion, whereas

pattern E20with upregulation towardHP is enriched for functions

relating to hematopoiesis (Figure S4Ai–S4Aiv). Thus, our expres-

sion dataset defines distinct gene sets relevant for specific

developmental transitions during early blood specification.
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We next investigated the correlation between expression ki-

netics and dynamic changes of chromatin at the gene promoters

(Figures 2A and S2A) by using ChromHMM, which was reported

as an automated computational system for annotating chromatin

states (Ernst and Kellis, 2012). We modified this methodology to

integrate both histone modifications and DNaseI accessibility

data. The latter indicates regions of chromatin bound by TFs

(Cockerill, 2011) and allows for the distinction between inactive
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(legend on next page)

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4 Developmental Cell 36, 1–16, March 7, 2016 ª2016 The Authors

regions carrying H3K27me3. An initial number of 23 chromatin

states (see Figure S2B) was further compressed, providing a

simple four-state model of active (DHS marked with H3K4me3

and acetylated H3), repressed (marked with H3K27me3), poised

(DHS marked with H3K27me3 but also acetylated H3 and/or

marked with H3K4me3), or unmarked chromatin (Figure 2A). Ex-

amples (Nanog, Runx1) for dynamic alterations in promoter state

are shown in Figure S2C, demonstrating that such changes

occur gradually, both during the transition from the active to

the inactive state and during gene activation. This behavior is

also evident on a global scale with all differentially expressed

genes (Figure S2D). It was proposed that poised promoters of

key regulatory genes are held in this state until developmental

cues shift the balance from poised to active or repressed states

(as in the case of Runx1). The promoters of some genes highly

expressed in macrophages indeed transit through a poised

state, which in many cases is already evident in ESCs (Fig-

ure S2D). However, transitions from the unmarked or repressed

state are more frequent (Figure S2D, last row at the bottom).

A direct correlation between promoter state and gene expres-

sion is not seenwith all differentially expressed genes (Figure 2B).

Promoters of around one-third of differentially regulated genes

are persistently in the active state despite highly dynamic

gene expression (Figure 2B, highlighted). When this gene set

was investigated for GO term enrichment, we found that most

of their functions were ‘‘housekeeping’’ roles pertinent to regula-

tion of cell cycle, protein catabolism, transport, and localization

(Figures 2B and S2D; Table S4).

To link gene expression with the chromatin state of distal cis-

regulatory elements, we associated them with their nearest

genes and correlated changes in their chromatin state with the

31 gene expression patterns across the differentiation pathway

(Figures S3A and S3B depict the actual expression patterns

as heatmaps). This comparison demonstrates a strong correla-

tion between the dynamics of the chromatin state of distal

elements and gene expression, indicating that most of these

elements function as enhancers. We noted that the number of

distal elements that displayed a poised or repressed chromatin

state was small. These results add to the increasing evidence

that cell-type-specific spatiotemporal expression patterns are

largely driven by distal regulatory elements (Lara-Astiaso et al.,

2014) and in addition demonstrate that such elements are in

either the active or inactive chromatin state.

Chromatin Dynamics and TF Binding Determines the
Differential Activity of cis-Regulatory Elements
We next addressed the question of which TFs were responsible

for the cell-stage-specific opening of chromatin. We therefore

determined dynamic DHS patterns during the differentiation
Figure 2. Chromatin Programming during Progressive Lineage Comm

(A) Schematic diagram of the method used to coarse grain the 23-state chromat

(B) Clustering of promoters (1Kb up or downstream of the transcription start site

expression pattern of genes that are constitutively expressed (right).

(C and D) Integration of DHS pattern and TF binding. (C) Methodology of the inte

events and the p values denoting the significance of overlap are depicted as gray

(columns) patterns across the six stages with the population size of each DHS

patterns with a population size >100 were considered.

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time course and classified DHS patterns using a binary code

with six digits (Figures 2C and 2D). We then performed pairwise

comparisons between all our DHS patterns with each of the

32 TF ChIP-seq experiments (our own and publicly available

data). Linking a set of regulatory genomic regions to annotated

gene sets is sensitive to the varying sizes of the intergenic re-

gions. We therefore used gene-set control analysis (GSCA)

(Joshi et al., 2013), a tool designed to account for the differing

sizes of such regions, to calculate pairwise correlation between

TF ChIP-seq peak sets and expression gene sets, thus identi-

fying all significant overlaps between TF binding events and

DHS appearance. Figure 2D shows the most prevalent patterns

of open chromatin over the six stages of development overlaid

with the most significant TF binding events which in general,

but not always, correlate with DHS presence. The most frequent

DHS patterns are stage specific, over half of which involved DHS

present only in macrophages (000001, 15,443 DHSs) and a

quarter in ESC (100000, 7,302 DHSs). Notably, DHSs exclusively

open in the HE (000100, 4,732 DHSs) are already primed by TF

binding in HBs. The remaining patterns represent approximately

30% of all DHS whereby the majority of all patterns are contin-

uous over at least two developmental stages. A common DHS

pattern is 111111 (6,750 occurrences), the majority of which

are CpG island promoters (Figure S4B) with a constitutively

active chromatin state (Figure S4C). This class of DHS also con-

tains the majority of binding events for C/EBPb prior to the HP/

MAC stages, suggesting a more widespread role of this tran-

scription factor in development than previously thought.

Early binding of both LMO2 and TAL1 is highly significant in

regulatory elements whose chromatin is first opened in HBs,

HEs, or HPs, and include binding prior to the appearance of overt

DHS sites, which is indicative of TF-mediated enhancer priming

(see pattern DHS_000110). PU.1 binding shows significant over-

lap with DHS patterns in HPs but is also found at sites that only

become hypersensitive in MACs. This suggests that PU.1 can

prime MAC-specific regulatory regions already in early multi-

potent progenitors, lending weight to the finding that it is capable

of opening chromatin (Garber et al., 2012; Natoli et al., 2011; Bar-

ozzi et al., 2014; Heinz et al., 2010, 2015).

We next correlated the statistical significance of the dynamics

of distal DHS patterns with dynamic gene expression patterns

(Figure S3B). This again demonstrates that the dynamics of

chromatin accessibility at distal sites correlates well with the

dynamics of gene expression (Figure S3B).

The Complex Interplay between Chromatin Dynamics,
Gene Expression, and TF Binding Events
Our next analysis determined the combinatorial pattern of TF-

DNA interactions driving target gene expression at key stages

of blood development. We therefore interrogated the 31
itment

in model to four potential chromatin states.

, TSS) based on their chromatin state patterns (left) and the clustering of the

grative analysis of chromatin dynamics and TF binding events. (D) TF binding

-scale density plots, shown as dots. Integration of DHS (rows) and TF binding

pattern given on the right-hand side. For significance calculations only DHS

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Developmental Cell 36, 1–16, March 7, 2016 ª2016 The Authors 5

Figure 3. Integration of Chromatin Dynamics, TF Binding Events, and Gene Expression during Hematopoietic Specification

(Left) Flow diagram of data integration. The average expression values (log10(FPKM)) of genes in expression patterns E1 to E31 were calculated for each

developmental stage. The significance (p < 0.0001) of the overlap between the genes in each expression pattern and a given TF ChIP-seq peak set was obtained using gene-set control analysis. Z scores were obtained from the mean enrichment of H3K27ac in TF binding sites at these loci using bootstrapping. (Right) Average expression levels for each pattern (rows) are shown as a red-blue heatmap (see key), with columns for each cell type labeled at the top. The columns are further divided into TF ChIP-seq experiments, and the significant overlap between TF binding events and gene sets belonging to each expression pattern are depicted by gray-scale density plots shown as dots (see key). Significant overlap of these binding events and H3K27ac sites are also shown as a density plot depicted by yellow-green boxes (see key). Please cite this article in press as: Goode et al., Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation, Develop- mental Cell (2016), http://dx.doi.org/10.1016/j.devcel.2016.01.024 expression clusters (Figure 1F) to ascertain (1) whether expres- sion patterns correlated with enriched binding of any of our examined 32 TF datasets to these genes, and (2) how such bind- ing events correlated with histone H3K27 acetylation at this position. For visual inspection, the TF binding and histone acet- ylation data were then overlaid onto a heatmap summarizing gene expression for patterns E1–E31 (Figure 3). This analysis shows the overall correlation between dynamic transcription fac- tor binding, histone acetylation, and gene expression. The genes expressed in patterns E17–E20 are associated with increased gene expression during hematopoiesis, all showing early low- level induction prior to high-level expression (Table S3). This in- duction is associated with significant binding of hematopoietic regulators, but not MEIS1. Highly significant early binding of LMO2/TAL1 in HB and FLI1/LMO2/TAL1 in HE occurs in genes expressed in patterns E9–E11. All three patterns are associated with binding of the repressor GFI1 in HP and with the repression of gene expression inmacrophages. Patterns E9 and E11 involve upregulation of genes in the major HB-HE transition but then downregulation in HPs. Both sets of genes are enriched for func- DEVCEL 352 6 Developmental Cell 36, 1–16, March 7, 2016 ª2016 The Authors tions relating to vasculogenesis, heart development, and cell adhesion (Figure S4A). Our results therefore highlight GFI1 as a candidate regulator involved in downregulating genes involved in non-hematopoietic cell fates following the HE to HP transition. This is consistent with data in the mouse that demonstrate a fail- ure of EHT in the combined absence of GFI1 andGFIB in addition to the continued expression of endothelial genes (Lancrin et al., 2012; Lie-A-Ling et al., 2014; Thambyrajah et al., 2016). In sum- mary, our analysis provides a highly informative integrated view of the dynamic relationships between gene expression, chro- matin state, and TF binding. ADynamic CoreGeneRegulatory Network Driving Blood Development To uncover the hierarchy of transcription factors driving blood specification forward, we generated gene regulatory network (GRN) representations connecting all 16 TFs analyzed by ChIP- seq, with separate representations for all six stages of develop- ment. To visualize different features, we illustrated multiple different data types within a single GRN representation at each 6 Please cite this article in press as: Goode et al., Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation, Develop- mental Cell (2016), http://dx.doi.org/10.1016/j.devcel.2016.01.024 locus (Figure 4). Annotation for each of the six sequential devel- opmental stages provided effective representation of the dy- namics of cellular states, highlighted the chromatin features of the promoter of each gene locus, and indicated how interactions between a core set of key regulators drives developmental pro- gression and terminal differentiation. In ESCs all four pluripotency TFs participate in a highly con- nected core network circuit and already at this stage bind loci for hematopoietic TFs, including Cebpb, Elk4, Gata2, Lmo2, Meis1, Runx1, and Tal1, which display open or poised chromatin at their promoters, but also bind Gfi1b, Gata1, and Spi1, whose promoters are organized in closed-unmarked/repressed chro- matin. As early as the HB stage, several hematopoietic regulator genes are upregulated, including Tal1 and Lmo2, which exhibit autoregulation and co-regulate multiple genes. These include Fli1 and Meis1, both of which are upregulated upon differentia- tion into HE. This stage is characterized by the involvement of LMO2, TAL1, and FLI1 (and in some cases MEIS1) in co-regu- lating genes for

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