Chromatin reconstruction during mouse terminal erythropoiesis

Honghao Bi; Ye Hou; Juan Wang; Zongjun Xia; Dongmei Wang; Yijie Liu; Haiyan Bao; Xu Han; Kehan Ren; Ermin Li; Feng Yue; Peng Ji

doi:10.1016/j.isci.2022.105554

. 2022 Nov 11;25(12):105554. doi: 10.1016/j.isci.2022.105554

Chromatin reconstruction during mouse terminal erythropoiesis

Honghao Bi ^1,², Ye Hou ^2,³, Juan Wang ^2,³, Zongjun Xia ^1,², Dongmei Wang ^1,², Yijie Liu ^1,², Haiyan Bao ^1,⁴, Xu Han ^1,², Kehan Ren ^1,², Ermin Li ^1,², Feng Yue ^2,^3,^∗, Peng Ji ^1,^2,^5,^∗∗

PMCID: PMC9709226 PMID: 36465116

Summary

Mammalian terminal erythropoiesis involves chromatin and nuclear condensation followed by enucleation. Late-stage erythroblasts undergo caspase-mediated nuclear opening that is important for nuclear condensation through partial histone release. It remains unknown the dynamic changes of three-dimensional (3D) genomic organization during terminal erythropoiesis. Here, we used Hi-C to determine the chromatin structural change during primary mouse erythroblast terminal differentiation. We also performed RNA-sequencing and ATAC-sequencing under the same experimental setting to further reveal the genome accessibility and gene expression changes during this process. We found that late-stage terminal erythropoiesis involves global loss of topologically associating domains and establishment of inter-chromosomal interactions of the heterochromatin regions, which are associated with globally increased chromatin accessibility and upregulation of erythroid-related genes.

Subject areas: Cell biology, Structural biology

Graphical abstract

Highlights

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3D genomic organization changes during mouse terminal erythropoiesis are revealed
•
Late-stage terminal erythropoiesis involves global loss of TADs
•
Decreased intra- and increased inter-chromosomal interactions during erythropoiesis

Cell biology; Structural biology

Introduction

Under constant replenishment, the development of mammalian erythrocytes can be broadly divided into two stages, commitment of hematopoietic stem and progenitor cells to erythroid lineage and terminal differentiation of erythroblasts to enucleated red blood cells. During late stages of terminal erythropoiesis, erythroblasts undergo a series of unique changes including nuclear condensation, enucleation, and loss of major organelles.¹^,²^,³^,⁴^,⁵^,⁶

Besides terminal erythropoiesis, nuclear and chromatin condensation also occurs during apoptosis. Apoptotic mechanisms including caspase activation are known to play an important role in erythroid nuclear condensation.⁷^,⁸ We recently reported that late-stage erythroblasts undergo caspase-mediated transient nuclear opening that mediates partial histone release to the cytoplasm and nuclear condensation.⁷^,⁹^,¹⁰^,¹¹^,¹²^,¹³ Mechanistically, inactivation of caspases inhibits caspase-mediated lamin B cleavage and compromises nuclear condensation and enucleation. Loss of caspase-3 or ectopic expression of a caspase-3 non-cleavable lamin B mutant also inhibits nuclear opening and condensation. Same as mouse erythroblasts, disruption of caspase functions in human CD34⁺ cells leads to compromised nuclear opening and histone release.¹⁰

Histone cytoplasmic release during terminal erythropoiesis was also reported to be mediated by exportin 7, a highly erythroid specific exportin. Loss of exportin 7 inhibits histone and DNA release, as well as nuclear condensation.¹¹ Moreover, a recent report showed that loss of Wdr, a gene essential for lamin B degradation, could induce anemia in zebrafish and disrupt erythroid nuclear condensation in mouse.¹² Together, these studies demonstrate that histone release is essential in nuclear condensation and enucleation. However, the chromatin structural change during histone release and nuclear condensation remains unclear.

To address this question, we used a murine fetal liver primary erythroid culture system to investigate the changes in the three-dimensional (3D) genomic landscape during terminal erythropoiesis. Hi-C, ATAC-seq and RNA-seq experiments were performed to integrate the study of chromatin structural changes during terminal erythropoiesis. We found that terminal erythropoiesis involves compaction and establishment of inter-chromosomal interactions of the heterochromatin regions, which is associated with globally increased accessibility and upregulation of erythroid-related genes.

Results

Loss of topologically associating domains during mouse terminal erythropoiesis

To study the 3D chromatin architectural changes during terminal erythropoiesis, we used a well-established murine primary erythroid in vitro differentiation system in which Ter119 negative fetal liver erythroid precursors from E13.5 fetus were cultured in erythropoietin (EPO)-containing medium for 48 h.¹⁴ These cells undergo gradual chromatin and nuclear condensation, as well as 4 to 5 rounds of nuclear opening formation with associated histone cytoplasmic release, before the start of enucleation at around 30 h in culture⁹ (Figure 1A).

Our previous study demonstrated that mouse fetal liver erythroid cells cultured at 30 h exhibit dramatic chromatin condensation compared to the cells at the beginning of culture.¹⁵ Thus, to compare the dynamic change of chromatin 3D structures during mouse terminal erythropoiesis, we performed Hi-C with cells at 0 and 30 h in culture. By comparing the difference of Hi-C contact map, we observed increased contacts in 30 h group at a higher resolution on certain chromosome regions (Figure 1B) and no detectable changes in others (data not shown), compared with 0 h group. Further quantitative analysis in the whole genome level revealed that cells at 30 h group show detectable shift of contact distance with less long-range (>10 million base pairs) interactions compared with those at 0 h group. Short (<1 million base pairs) and medium-range (1-10 million base pairs) interactions are indiscernible (Figure 1C).

To further investigate the chromatin structural change, we performed a Topologically Associating Domains (TADs) analysis. We found that TADs are decreased in the late stage of erythroid terminal differentiation (Figure 1D). The TAD lengths are predominantly less than 2 Mb in both groups (Figure 1E). The average profile of insulation score on TADs demonstrated weakened boundaries in late-stage cells (30 h) compared with those at 0 h (Figure 1F). We further aggregated the Hi-C contact map on the TADs and the surrounding regions. We observed decreased interactions within the TADs in 30 h group cells compared to cells at 0 h (Figure 1G). We also observed multiple regions with disrupted TADs in cells at 30 h group, exemplified at chr3:41-51Mb and chr4:74-83Mb regions (Figure 1H).

Increased inter-chromosomal interactions during erythroid chromatin condensation

Chromosome can be arranged into A and B compartments on a large scale in Hi-C analyses. A and B compartments are associated with open/active and closed/inactive chromatins, respectively. To further characterize the DNA structure during terminal erythropoiesis, we investigated the A/B compartment and compartment switches in these cells. Comparing cells at 0 versus 30 h, 2.73% of B compartments in cells at 0 h switch to A compartment at 30 h whereas 2.24% A switch to B. The rest of the compartments remain unchanged (Figure 2A).

To compare the genome-wide compartment interactions, we performed a saddle plots analysis. We determined and fixed genes’ compartment identity at 0 h and applied for all group analysis for better picturing of the dynamic compartmental changes during development. We then generated saddle plots for cis and trans interactions, representing relative intra-chromosomal and inter-chromosomal interactions, respectively (Figure 2B). The analysis demonstrates that the interaction patterns change significantly during development. The cis interactions show a slight decrease in AA, BB and AB interactions in cells at 30 h whereas the trans interactions increase in BB and AB interactions in these cells (Figure 2C). These results indicate increased inter-chromosomal interactions during chromatin condensation.

Changes of chromatin accessibility during terminal erythropoiesis

We next performed ATAC-seq analyses on these cells to determine the chromatin accessibility changes during terminal erythropoiesis. Cells from the same experimental conditions as above were used (0 and 30 h). In total, we identified 17,941 peaks, among which 1,518 peaks show differential accessibility (>2-fold change, adjusted pvalue < 0.00001). Compared to cells at 0 h, 394 peaks were upregulated and 1,124 were downregulated in cells at 30 h group (Figure 3A).

We then grouped peaks in ATAC-seq into 12 clusters using seqMINER. Among these clusters, cluster 9 and 10 show decreased peak reads at 30 h, suggesting reduced accessibility (Figures 3B and3C). Of interest, Gene Ontology (GO) analysis reveals that many genes in these clusters are involved in immune responses (Figure S1). Cluster 11 and 12 show increased accessibility at 30 h (Figures 3B and3C). GO analysis shows that genes in these two clusters are closely related to terminal erythropoiesis including porphyrin metabolism and biosynthesis, heme metabolism and biosynthesis, erythrocyte differentiation, iron hemostasis, etc. (Figure S1).

Gene expression changes during terminal erythropoiesis

Under the same experimental settings, we then performed RNA-sequencing analyses to further determine the changes in gene expression during terminal erythropoiesis. In total, 23,564 genes were identified, among which 3,076 genes were found differentially expressed between 0 and 30 h groups (fold change>4, p<0.01). Among the differentially expressed genes, 813 genes were upregulated in cells at 30 h compared to those at 0 h, whereas 2,263 were downregulated (Figure 4A). We then generated a heatmap with most significantly differentially expressed genes. As expected, we observed that erythroid-related genes are among those that are most significantly upregulated (Figure 4B). GO enrichment analysis showed that the upregulated genes are closely related to erythropoiesis, as well as cell cycle and autophagy that are also well known to be involved in terminal erythropoiesis (Figure S2).

Cross dataset analyses reveal diverse ways of gene expression regulation during terminal erythropoiesis

The A and B compartments in Hi-C are generally related to active and inactive areas in chromatin, respectively. Chromatin accessibility is also positively associated with genes’ expression and activity. To test whether these are applicable during terminal erythropoiesis, we cross-analyzed datasets in Hi-C, ATAT-seq, and RNA-seq. We identified 3,787 genes switched from compartment B to A, which represents increased gene activity. When compared to the RNA-seq data, 121 genes switched from B to A showed increased expression, whereas 612 genes with significantly increased expression did not show compartment change in PCA analysis (Fold change>4, p<0.01). Among these 121 genes, we identified 25 genes with increased chromatin accessibility in ATAC-seq (Figure 5A). Notably, most of the genes in these datasets do not overlap, which indicates that gene expression is regulated in diverse ways during terminal erythropoiesis.

Cross analyses of Hi-C, ATAC-seq, and RNA-seq

(A) Green: genes switched from compartment B to A in Hi-C; Pink: Genes with increased peaks in ATAC-seq; Blue: Genes with increased expression in RNA-seq. Representative genome browser view of genes with increased expression and chromatin accessibility is on the right.

(B) Gene Ontology analysis of the 25 overlapped genes in three datasets.

GO analysis shows that these 25 genes are closely related to terminal erythropoiesis (Figure 5B). We reviewed literature and listed related studies on these genes in Table 1. Within these 25 genes, Kel, Rhag and Abcg2 are well known red blood cell markers. Genes like Arl4a, Hpf1, Snca, and Trim10 have been reported to be involved in terminal erythropoiesis. The roles of other genes in erythropoiesis remain unclear.

Table 1.

Genes that are upregulated during terminal erythropoiesis with increased chromatin accessibility and compartment switch from B to A

OFFICIAL GENE SYMBOL	Gene Name	Alias	Function	Reference
1110034G24Rik	RIKEN cDNA 1110034G24 gene(1110034G24Rik)	Shld1
Abcg2	ATP-binding cassette, sub-family G (WHITE), member 2(Abcg2)	ABC15, ABCP, BCRP, BCRP1, BMDP, CD338, CDw338, EST157481, GOUT1, MRX, MXR, MXR1, UAQTL1, MXR-1	Abcg2 null mice show similar phenotype as erythropoietic protoporphyria	Jonker et al.¹⁶
Arl4a	ADP-ribosylation factor-like 4A(Arl4a)		Knockdown of arl4a in Zebrafish induces reduced definitive hematopoiesis by interfering with Notch Receptor maturation	Guo et al.¹⁷
Cdc42ep3	CDC42 effector protein (Rho GTPase binding) 3(Cdc42ep3)	BORG2, CEP3, UB1
Cpeb4	cytoplasmic polyadenylation element binding protein 4(Cpeb4)	KIAA1673	Knockdown of Cpeb4 causes inhibition of terminal erythropoiesis	Hu et al.¹⁸
Gda	guanine deaminase(Gda)	GAH, Cytoplasmic PSD-95 Interactor, P51-Nedasin, EC 3.5.4.3
Ghitm	growth hormone inducible transmembraneprotein(Ghitm)	TMBIM5, DERP2, HSPC282, PTD010, My021
Hace1	HECT domain and ankyrin repeat containing, E3 ubiquitin protein ligase 1(Hace1)	KIAA1320, EC 2.3.2.26, EC 6.3.2, SPPRS
Hpf1	histone PARylation factor 1(Hpf1)	C4orf27, FLJ20534, Chromosome 4 Open Reading Frame 27, UPF0609 Protein C4orf27	Modulation of PARP2, which sustains erythropoiesis	Farres et al.¹⁹ Kurgina et al.²⁰
Ifit1bl1	interferon induced protein with tetratricpeptide repeats 1B like 1(Ifit1bl1)	IFNAl1, G10P1, IFI56, GARG-16, IFI-56K
Kel	Kell blood group(Kel)	CD238, ECE3	Endopeptidase that cleaves big endothelin-3.	Lee et al.²¹
Lrrc1	leucine rich repeat containing 1(Lrrc1)	LANO, DJ523E19.1
Mllt3	myeloid/lymphoid or mixed-lineage leukemia; translocated to, 3(Mllt3)	YEATS3, AF9	Regulate Erythrocyte/Megakaryocyte fate decision	Pina et al.²²
Ncoa7	nuclear receptor coactivator 7(Ncoa7)	ERAP140, DJ187J11.3. TLDC4
Pkhd1l1	polycystic kidney and hepatic disease 1-like 1(Pkhd1l1)	Fibrocystin-L
Pla2g16	phospholipase A2, group XVI(Pla2g16)	HREV107-3, HREV107, AdPLA, PLAAT-3
Rhag	Rhesus blood group-associated A glycoprotein(Rhag)	SLC42A1, CD241, RH50A	Amonia transport	Stewart et al.²³Genetet et al.²⁴
Scai	suppressor of cancer cell invasion(Scai)	NET40, FLJ36664, C9orf126
Sec61g	SEC61, gamma subunit(Sec61g)	SSS1
Smc2os	structural maintenance of chromosomes 2, opposite strand(Smc2os)
Snca	synuclein, alpha(Snca)	NACP, PARK1. PD1, PARK4	Regulate oxidative stress	Renella et al.²⁵
Trim10	tripartite motif-containing 10(Trim10)	RFB30, HERF1, RNF9	regulating splicing of exon16 in 4.1R and required for terminal erythropoiesis	Blaybel et al.²⁶
Tspan8	tetraspanin 8(Tspan8)	CO-029, TM4SF3		Zhao et al.²⁷
Zfand6	zinc finger, AN1-type domain 6(Zfand6)	AWP1, ZFAND5B, ZA20D3
Zfp760	zinc finger protein 760(Zfp760)

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Discussion

In this study, we report for the first time the genomewide chromatin 3D architectural changes during murine terminal erythroid differentiation. Our study reveals decreased long-range chromosome interactions and TADs during terminal erythropoiesis. Similar to the decreased TADs in the late-stage murine terminal erythroblasts, it was reported that chicken mature erythrocytes lack typical TADs found in mammals and chicken embryonic fibroblasts.²⁸ A more recent study comparing erythrocytes 3D genome organization across various vertebrates further showed lack of TADs in vertebrate erythrocytes, including late-stage erythroblasts in mouse.²⁹ Because chicken mature erythrocytes also undergo nuclear condensation despite retaining their nuclei, these findings suggest that the terminal erythropoiesis is evolutionally conserved among vertebrates. It is possible that immature chicken erythroblasts in the bone marrow could have typical TADs as those found in the murine erythroblasts and gradually decrease. It would be interesting to explore from the perspective of comparative biology.

Besides compartment switching, we also found increased trans interactions in BB and AB compartments during development, suggesting increased inter-chromosomal interactions. Overall, our study reveals a dynamic process during chromatin condensation in terminal erythropoiesis. In this process, a small portion of genome DNA switches between active and inactive states. Chromatin penetrates the compartmental boundary and builds new interactions, which is featured by decreased intra-chromosomal and increased inter-chromosomal interactions (Figure 6).

Schematic overview of chromatin 3D structural changes during terminal erythropoiesis

Blue and red strings represent independent chromosomes. Blue and red dots represent intra-chromosomal interactions. Purple dots represent inter-chromosomal interactions. During differentiation, chromosomal interactions within the same chromosome decrease, whereas the interactions between chromosomes increase.

Another key feature in terminal erythropoiesis is nuclear opening that is mediated by partial histone release and facilitates nuclear condensation.⁹^,³⁰^,³¹ This process occurs more frequently during the polychromatic to orthochromatic stage. We previously reported that caspases play critical roles in this process by cleaving lamin B to generate transient nuclear openings for histone release. Histone cytoplasmic release during terminal erythropoiesis was also reported to be mediated by exportin 7, a highly erythroid-specific exportin. Loss of exportin 7 inhibits histone and DNA release, as well as nuclear condensation.¹¹ Moreover, a recent report showed that loss of Wdr, a gene essential for lamin B degradation, could induce anemia in zebrafish and disrupt erythroid nuclear condensation in mouse.¹² Together, these studies demonstrate that histone release is essential in nuclear condensation and enucleation. It would be interesting to investigate whether inhibition or downregulation of these proteins would affect chromatin 3D structural changes. In this respect, erythroid specific and inducible knockout models will be essential to reveal the detailed mechanisms.

Limitations of the study

The current study used cultured mouse fetal liver cells. The culture artifact could be a potential concern. In addition, the rapid differentiation process of mouse fetal liver cells within 48 h makes it difficult to dissect the chromatin reconstruction at each stage of terminal erythropoiesis.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies

APC Rat Anti-Mouse TER-119	BD	Cat# 557909; RRID:AB_398635
FITC Rat Anti-Mouse CD71	BD	Cat# 567260; RRID:AB_2916522

Deposited data

HiC-seq data for erythrocyte differentiation	This paper	GSE184974
ATAC-seq and RNA-seq data for erythrocyte differentiation	This paper	GSE185470

Experimental models: Organisms/strains

C57BL/6j mice	Jax Lab	strain #: 000,664

Software and algorithms

runHiC python package	Wang³²	https://pypi.org/project/runHiC/
cooltools	Venev et al.³³	https://github.com/open2c/cooltools
cworld matrix2insulation and insulation2tads	NA	https://github.com/dekkerlab/cworld-dekker
juicebox	NA	https://github.com/aidenlab/Juicebox
MACS2	Zhang et al.³⁴	NA
seqMINER	Ye et al.³⁵	NA
bedtools	Quinlan et al.³⁶	NA
TopHat2	Kim et al.³⁷	NA

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Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Peng Ji (peng-ji@fsm.northwestern.edu).

Materials availability

This study did not generate new unique reagents.

Experimental model and subject details

E13.5 fetal liver cells were acquired by breeding male and female C57BL/6J mice purchased from the Jackson laboratory. The mice were maintained at animal facility in center for comparative medicine, Feinberg School of Medicine at Northwestern University. After 1 night of breeding (day 1), female mice were separated and observed for 2 weeks. At day 14 after breeding, the pregnant female mice were sacrificed, and fetal livers were then retrieved. The fetal livers were then homogenized by repeated pipetting and filtered through 40 μm cell strainer to obtain single cells. The filtered cells were incubated with biotin-antimouse Ter119 antibody for 15 min and washed with PBS. After washing, the cells were resuspended and Ter119 positive cells were pulled down with streptavidin magnetic beads. The remained Ter119 negative cells were then used for the following experiments.

The Ter119 negative cells were either processed for sequencing directly or cultured in erythropoietin (EPO)-containing medium (15% FBS, 1% BSA, 10μg/ml Insulin, 200μg/ml Holo-transferrin, 10⁻⁴M β-Mercaptoethanol, 1% Penicillin-Streptomycin, 2mM Glutamine, 2U/ml EPO in IMDM) for up to 48 h. The cells were harvested at 30 h for the follow up studies.¹⁵^,³⁸

Method details

Mice

C57BL/6J mice were purchased from the Jackson laboratory (strain #: 000,664). Both male and females were used. All animal studies were performed in accordance with the Guidelines for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committees at Northwestern University.

Cell culture

Purification of mouse fetal liver erythroblast precursors and in vitro culture were performed as previously described.³¹ Briefly, fetal liver erythroid precursors were isolated from the E13.5 fetal liver by depletion of Ter119 positive cells using a biotinylated anti-Ter119 antibody. The Ter119 negative cells were then incubated in EPO-containing medium (15% FBS, 1% BSA, 10μg/ml Insulin, 200 μg/mL Holo-transferrin, 10⁻⁴M β-Mercaptoethanol, 1% Penicillin-Streptomycin, 2mM Glutamine, 2U/ml EPO in IMDM) for differentiation and enucleation for up to 48 h. The cells were harvested at 30 h for further analysis.

Cell surface marker staining

Cells were washed in PBS and resuspended in 100 μL PBS with a concentration of 1 million cells/ml. 0.5 μg APC-Ter119 antibody (BD, Cat# 557909) and FITC-CD71(BD, Cat# 567260) were added to the cell suspension. In turn, Hoechst 33,342 was added to the cell suspension to a final concentration of 20 μg/mL. The Cells were incubated in dark under room temperature for 10 min and washed with PBS. The washed cells were then resuspended with PBS containing 1 μg/mL propidium iodide to exclude dead cells. The resuspended cells will then be analyzed by flow cytometry within 1 h.

Total RNA extraction

Cells were harvested and washed for 3 times with PBS. Qiagen RNeasy kit was used to extract total RNA. Briefly, cells were lysed and homogenized in lysis buffer. Ethanol was then added to the lysate followed by centrifugation through silica membrane. After 2 times wash, RNAs were eluted by elution buffer and stocked for further analysis.

Hi-C data analysis

The adapters of the raw reads were trimmed and mapped against the mm10 reference genome using the runHiC python package (https://pypi.org/project/runHiC/). The contact matrix was binned at multiple resolutions: 5, 10, 25, 40, 50, 100, 250, 500kb, 1, 2.5, 5 and 10Mb using the run-cool2multirescool script from the 4DN consortium. The ICE normalization was performed for each resolution. For A/B compartment analysis, we first identified A/B compartment at 100kb resolution using the cooltools (https://github.com/open2c/cooltools) call-compartment function. To quantify the A and B compartment interactions, we used the cooltools compute-saddle function. The cworld matrix2insulation and insulation2tads (https://github.com/dekkerlab/cworld-dekker) were used for insulations scores calculation and TAD identification. We also generated the multi-resolution.hic files using the juicer tools for the convenience of example Hi-C matrices visualization in juicebox (https://github.com/aidenlab/Juicebox). The accession number for Hi-C is GSE184974.

RNA-seq analysis

RNA-seq reads were aligned to the mouse genome (NCBI37/mm10) using TopHat2.³⁷ Expression of each gene was calculated from the resulting alignment bam file by HTSeq-count.³⁹ Differentially expressed genes were determined using R package DESeq2⁴⁰ with 2-fold change or 4-fold change as cutoff. Gene ontology analysis for changed genes was performed using DAVID.⁴¹

ATAC-seq analysis

Paired-end ATAC-seq reads were aligned to the mouse genome (NCBI37/mm10) using Bowtie2⁴² with option ‘‘--very-sensitive-local’’. Mitochondrial reads were excluded from downstream analysis. Peak calling was performed on each individual sample by MACS2³⁴ with parameter ‘‘-BAMPE’’. Peaks from different ATAC-seq samples were merged using bedtools.³⁶ Merged peaks were used as genome coordinate for K-means clustering which was performed using seqMINER.³⁵ 10 million randomly sampled mapped reads from each sample were used as input. Signals were calculated in a 1 kb region (±500 bp) surrounding the center of the peak with 20 nt bin. Peaks assignment to the genes and subsequent ontology analysis were done with GREAT.⁴³ The accession number for ATAC-seq and RNA-seq is GSE185470.

Quantification and statistical analysis

Results are expressed as mean ± SEM unless otherwise indicated. Statistical comparisons between two groups were performed with a two-tailed unpaired Student’s t test using GraphPad Prism version 6.0 software. p <0.05 was considered to be significant.

Acknowledgments

We thank Dr. Ching Man Wai’s technical support during this study. This work was supported by National Institute of Diabetes and Digestive and Kidney Disease (NIDDK) grant R01-DK124220 (P.J.), National Heart, Lung, and Blood Institute (NHLBI) grant R01-HL148012 (P.J.), and R01-HL150729 (P.J.). P.J. is a scholar of the Leukemia and Lymphoma Society.

Author contributions

H.B., Y.H., J.W., A.M., D.H., Y.L., H.B., X.H., K.R., and E.L. performed the experiments and interpreted data. Y.H. performed the Hi-C experiment. J.W., Z.X., and D.W. analyzed the Hi-C data. H.B., Y.L., F.Y., and P.J. designed the experiments, interpreted data, and edited the manuscript. H.B. and P.J. wrote the manuscript.

Declaration of interests

The authors declare no competing interests.

Published: December 22, 2022

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2022.105554.

Contributor Information

Feng Yue, Email: yue@northwestern.edu.

Peng Ji, Email: peng-ji@fsm.northwestern.edu.

Supplemental information

Document S1. Figures S1 and S2

mmc1.pdf^{(1.8MB, pdf)}

Data and code availability

•
All data were deposited at NCBI GEO database. The accession number for Hi-C is GEO: GSE184974. The accession number for ATAC-seq and RNA-seq data is GEO: GSE185470.
•
This paper does not report original code.
•
Any additional information required to reanalyze the data reported in this paper is available from the lead contactupon request.

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39.Anders S., Pyl P.T., Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–169. doi: 10.1093/bioinformatics/btu638. [DOI] [PMC free article] [PubMed] [Google Scholar]
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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1 and S2

mmc1.pdf^{(1.8MB, pdf)}

Data Availability Statement

•
All data were deposited at NCBI GEO database. The accession number for Hi-C is GEO: GSE184974. The accession number for ATAC-seq and RNA-seq data is GEO: GSE185470.
•
This paper does not report original code.
•
Any additional information required to reanalyze the data reported in this paper is available from the lead contactupon request.

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PERMALINK

Chromatin reconstruction during mouse terminal erythropoiesis

Honghao Bi

Ye Hou

Juan Wang

Zongjun Xia

Dongmei Wang

Yijie Liu

Haiyan Bao

Xu Han

Kehan Ren

Ermin Li

Feng Yue

Peng Ji

Summary

Graphical abstract

Highlights

Introduction

Results

Loss of topologically associating domains during mouse terminal erythropoiesis

Figure 1.

Increased inter-chromosomal interactions during erythroid chromatin condensation

Figure 2.

Changes of chromatin accessibility during terminal erythropoiesis

Figure 3.

Gene expression changes during terminal erythropoiesis

Figure 4.

Cross dataset analyses reveal diverse ways of gene expression regulation during terminal erythropoiesis

Figure 5.

Table 1.

Discussion

Figure 6.

Limitations of the study

STAR★Methods

Key resources table

Resource availability

Lead contact

Materials availability

Experimental model and subject details

Method details

Mice

Cell culture

Cell surface marker staining

Total RNA extraction

Hi-C data analysis

RNA-seq analysis

ATAC-seq analysis

Quantification and statistical analysis

Acknowledgments

Author contributions

Declaration of interests

Footnotes

Contributor Information

Supplemental information

Data and code availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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