Abstract
Human mesenchymal stem cells (hMSCs) can be differentiated into adipocytes and osteoblasts. The processes are driven by the rewiring of chromatin architectures and transcriptomic/epigenomic changes. Here, we induced hMSCs to adipogenic and osteogenic differentiation, and performed 2 kb resolution Hi-C experiments for chromatin loops detection. We also generated matched RNA-seq, ChIP-seq and ATAC-seq data for integrative analysis. After comprehensively comparing adipogenesis and osteogenesis, we quantitatively identified lineage-specific loops and screened out lineage-specific enhancers and open chromatin. We reveal that lineage-specific loops can activate gene expression and facilitate cell commitment through combining enhancers and accessible chromatin in a lineage-specific manner. We finally proposed loop-mediated regulatory networks and identified the controlling factors for adipocytes and osteoblasts determination. Functional experiments validated the lineage-specific regulation networks towards IRS2 and RUNX2 that are associated with adipogenesis and osteogenesis, respectively. These results are expected to help better understand the chromatin conformation determinants of hMSCs fate commitment.
Similar content being viewed by others
Log in or create a free account to read this content
Gain free access to this article, as well as selected content from this journal and more on nature.com
or
Data availability
All data needed to evaluate the conclusions in the paper are present in the paper and the Supplementary data files. The raw and processed data generated in this study are deposited in the Gene Expression Omnibus under accession number GSE151324.
References
Brunmeir R, Wu J, Peng X, Kim SY, Julien SG, Zhang Q, et al. Comparative transcriptomic and epigenomic analyses reveal new regulators of murine brown adipogenesis. PLoS Genet. 2016;12:e1006474.
Piek E, Sleumer LS, van Someren EP, Heuver L, de Haan JR, de Grijs I, et al. Osteo-transcriptomics of human mesenchymal stem cells: accelerated gene expression and osteoblast differentiation induced by vitamin D reveals c-MYC as an enhancer of BMP2-induced osteogenesis. Bone 2010;46:613–27.
Lehrke M, Lazar MA. The many faces of PPARγ. Cell 2005;123:993–9.
Rosen ED, Hsu CH, Wang X, Sakai S, Freeman MW, Gonzalez FJ, et al. C/EBPα induces adipogenesis through PPARγ: a unified pathway. Genes Dev. 2002;16:22–6.
Ducy P, Zhang R, Geoffroy V, Ridall AL, Karsenty G. Osf2/Cbfa1: a transcriptional activator of osteoblast differentiation. Cell 1997;89:747–54.
Nakashima K, Zhou X, Kunkel G, Zhang Z, Deng JM, Behringer RR, et al. The novel zinc finger-containing transcription factor osterix is required for osteoblast differentiation and bone formation. Cell 2002;108:17–29.
Meyer MB, Benkusky NA, Sen B, Rubin J, Pike JW. Epigenetic plasticity drives adipogenic and osteogenic differentiation of marrow-derived mesenchymal stem cells. J Biol Chem. 2016;291:17829–47.
Wang L, Niu N, Li L, Shao R, Ouyang H, Zou W. H3K36 trimethylation mediated by SETD2 regulates the fate of bone marrow mesenchymal stem cells. PLoS Biol. 2018;16:e2006522.
Rauch A, Haakonsson AK, Madsen JGS, Larsen M, Forss I, Madsen MR, et al. Osteogenesis depends on commissioning of a network of stem cell transcription factors that act as repressors of adipogenesis. Nat Genet. 2019;51:716–27.
Sexton T, Cavalli G. The role of chromosome domains in shaping the functional genome. Cell 2015;160:1049–59.
Greenwald WW, Chiou J, Yan J, Qiu Y, Dai N, Wang A, et al. Pancreatic islet chromatin accessibility and conformation reveals distal enhancer networks of type 2 diabetes risk. Nat Commun. 2019;10:2078.
Bonev B, Mendelson Cohen N, Szabo Q, Fritsch L, Papadopoulos GL, Lubling Y, et al. Multiscale 3D genome rewiring during mouse neural development. Cell 2017;171:557–72.e24.
Greenwald WW, Li H, Benaglio P, Jakubosky D, Matsui H, Schmitt A, et al. Subtle changes in chromatin loop contact propensity are associated with differential gene regulation and expression. Nat Commun. 2019;10:1054.
Madsen JGS, Madsen MS, Rauch A, Traynor S, Van Hauwaert EL, Haakonsson AK, et al. Highly interconnected enhancer communities control lineage-determining genes in human mesenchymal stem cells. Nat Genet. 2020;52:1227–38.
Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 2009;326:289–93.
Rao SS, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 2014;159:1665–80.
Bult CJ, Blake JA, Smith CL, Kadin JA, Richardson JE. Mouse Genome Database G. Mouse Genome Database (MGD) 2019. Nucleic Acids Res. 2019;47:D801–6.
The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 2012;489:57–74.
Mikkelsen TS, Xu Z, Zhang X, Wang L, Gimble JM, Lander ES, et al. Comparative epigenomic analysis of murine and human adipogenesis. Cell 2010;143:156–69.
Siersbaek R, Madsen JGS, Javierre BM, Nielsen R, Bagge EK, Cairns J, et al. Dynamic rewiring of promoter-anchored chromatin loops during adipocyte differentiation. Mol Cell. 2017;66:420–35.e5.
Rubin AJ, Barajas BC, Furlan-Magaril M, Lopez-Pajares V, Mumbach MR, Howard I, et al. Lineage-specific dynamic and pre-established enhancer-promoter contacts cooperate in terminal differentiation. Nat Genet. 2017;49:1522–8.
Wu H, Whitfield TW, Gordon JA, Dobson JR, Tai PW, van Wijnen AJ, et al. Genomic occupancy of Runx2 with global expression profiling identifies a novel dimension to control of osteoblastogenesis. Genome Biol. 2014;15:R52.
Meyer MB, Benkusky NA, Pike JW. The RUNX2 cistrome in osteoblasts: characterization, down-regulation following differentiation, and relationship to gene expression. J Biol Chem. 2014;289:16016–31.
Cadoudal T, Distel E, Durant S, Fouque F, Blouin JM, Collinet M, et al. Pyruvate dehydrogenase kinase 4: regulation by thiazolidinediones and implication in glyceroneogenesis in adipose tissue. Diabetes 2008;57:2272–9.
Miki H, Yamauchi T, Suzuki R, Komeda K, Tsuchida A, Kubota N, et al. Essential role of insulin receptor substrate 1 (IRS-1) and IRS-2 in adipocyte differentiation. Mol Cell Biol. 2001;21:2521–32.
Komori T. Molecular mechanism of Runx2-dependent bone development. Mol Cells. 2020;43:168–75.
Hiruma Y, Tsuda E, Maeda N, Okada A, Kabasawa N, Miyamoto M, et al. Impaired osteoclast differentiation and function and mild osteopetrosis development in Siglec-15-deficient mice. Bone 2013;53:87–93.
Zhou Q, Yu M, Tirado-Magallanes R, Li B, Kong L, Guo M, et al. ZNF143 mediates CTCF-bound promoter-enhancer loops required for murine hematopoietic stem and progenitor cell function. Nat Commun. 2021;12:43.
Itoh S, Udagawa N, Takahashi N, Yoshitake F, Narita H, Ebisu S, et al. A critical role for interleukin-6 family-mediated Stat3 activation in osteoblast differentiation and bone formation. Bone 2006;39:505–12.
Najafova Z, Tirado-Magallanes R, Subramaniam M, Hossan T, Schmidt G, Nagarajan S, et al. BRD4 localization to lineage-specific enhancers is associated with a distinct transcription factor repertoire. Nucleic Acids Res. 2017;45:127–41.
Chesi A, Wagley Y, Johnson ME, Manduchi E, Su C, Lu S, et al. Genome-scale Capture C promoter interactions implicate effector genes at GWAS loci for bone mineral density. Nat Commun. 2019;10:1260.
Kwan T, Grundberg E, Koka V, Ge B, Lam KC, Dias C, et al. Tissue effect on genetic control of transcript isoform variation. PLoS Genet. 2009;5:e1000608.
Hsu YH, Zillikens MC, Wilson SG, Farber CR, Demissie S, Soranzo N, et al. An integration of genome-wide association study and gene expression profiling to prioritize the discovery of novel susceptibility loci for osteoporosis-related traits. PLoS Genet. 2010;6:e1000977.
Barutcu AR, Tai PW, Wu H, Gordon JA, Whitfield TW, Dobson JR, et al. The bone-specific Runx2-P1 promoter displays conserved three-dimensional chromatin structure with the syntenic Supt3h promoter. Nucleic Acids Res. 2014;42:10360–72.
Sancisi V, Manzotti G, Gugnoni M, Rossi T, Gandolfi G, Gobbi G, et al. RUNX2 expression in thyroid and breast cancer requires the cooperation of three non-redundant enhancers under the control of BRD4 and c-JUN. Nucleic Acids Res. 2017;45:11249–67.
Kawane T, Komori H, Liu W, Moriishi T, Miyazaki T, Mori M, et al. Dlx5 and mef2 regulate a novel runx2 enhancer for osteoblast-specific expression. J Bone Min Res. 2014;29:1960–9.
Kurozumi A, Nakano K, Yamagata K, Okada Y, Nakayamada S, Tanaka Y. IL-6 and sIL-6R induces STAT3-dependent differentiation of human VSMCs into osteoblast-like cells through JMJD2B-mediated histone demethylation of RUNX2. Bone 2019;124:53–61.
Li P, Mitra S, Spolski R, Oh J, Liao W, Tang Z, et al. STAT5-mediated chromatin interactions in superenhancers activate IL-2 highly inducible genes: Functional dissection of the Il2ra gene locus. Proc Natl Acad Sci USA. 2017;114:12111–9.
Wingelhofer B, Neubauer HA, Valent P, Han X, Constantinescu SN, Gunning PT, et al. Implications of STAT3 and STAT5 signaling on gene regulation and chromatin remodeling in hematopoietic cancer. Leukemia 2018;32:1713–26.
Dubois-Chevalier J, Oger F, Dehondt H, Firmin FF, Gheeraert C, Staels B, et al. A dynamic CTCF chromatin binding landscape promotes DNA hydroxymethylation and transcriptional induction of adipocyte differentiation. Nucleic Acids Res. 2014;42:10943–59.
Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671–5.
Li D, Ning C, Zhang J, Wang Y, Tang Q, Kui H, et al. Dynamic transcriptome and chromatin architecture in granulosa cells during chicken folliculogenesis. Nat Commun. 2022;13:131.
Gayral P, Weinert L, Chiari Y, Tsagkogeorga G, Ballenghien M, Galtier N. Next-generation sequencing of transcriptomes: a guide to RNA isolation in nonmodel animals. Mol Ecol Resour. 2011;11:650–61.
Chen XF, Zhu DL, Yang M, Hu WX, Duan YY, Lu BJ, et al. An osteoporosis risk SNP at 1p36.12 acts as an allele-specific enhancer to modulate LINC00339 expression via long-range loop formation. Am J Hum Genet. 2018;102:776–93.
Chen X, Loo JX, Shi X, Xiong W, Guo Y, Ke H, et al. E6 protein expressed by high-risk HPV activates super-enhancers of the EGFR and c-MET oncogenes by destabilizing the histone demethylase KDM5C. Cancer Res. 2018;78:1418–30.
Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods. 2013;10:1213–8.
Wingett S, Ewels P, Furlan-Magaril M, Nagano T, Schoenfelder S, Fraser P, et al. HiCUP: pipeline for mapping and processing Hi-C data. F1000Res. 2015;4:1310.
Imakaev M, Fudenberg G, McCord RP, Naumova N, Goloborodko A, Lajoie BR, et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat Methods. 2012;9:999–1003.
Yang T, Zhang F, Yardimci GG, Song F, Hardison RC, Noble WS, et al. HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Genome Res. 2017;27:1939–49.
Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 2012;485:376–80.
Ke Y, Xu Y, Chen X, Feng S, Liu Z, Sun Y, et al. 3D chromatin structures of mature gametes and structural reprogramming during mammalian embryogenesis. Cell 2017;170:367–81.e20.
Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38:576–89.
Barutcu AR, Lajoie BR, McCord RP, Tye CE, Hong D, Messier TL, et al. Chromatin interaction analysis reveals changes in small chromosome and telomere clustering between epithelial and breast cancer cells. Genome Biol. 2015;16:214.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139–40.
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.
Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9:R137.
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010;26:841–2.
Fang H, Knezevic B, Burnham KL, Knight JC. XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits. Genome Med. 2016;8:129.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013;29:15–21.
DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43:491–8.
Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinforma. 2011;12:323.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.
The GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 2015;348:648–60.
Grundberg E, Kwan T, Ge B, Lam KC, Koka V, Kindmark A, et al. Population genomics in a disease targeted primary cell model. Genome Res. 2009;19:1942–52.
Futschik ME, Carlisle B. Noise-robust soft clustering of gene expression time-course data. J Bioinform Comput Biol. 2005;3:965–88.
Ramirez F, Ryan DP, Gruning B, Bhardwaj V, Kilpert F, Richter AS, et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 2016;44:W160–5.
Yevshin I, Sharipov R, Kolmykov S, Kondrakhin Y, Kolpakov F. GTRD: a database on gene transcription regulation-2019 update. Nucleic Acids Res. 2019;47:D100–5.
Thynn HN, Chen XF, Hu WX, Duan YY, Zhu DL, Chen H, et al. An allele-specific functional SNP associated with two systemic autoimmune diseases modulates IRF5 expression by long-range chromatin loop formation. J Invest Dermatol. 2020;140:348–60.e11.
Cremer M, Muller S, Kohler D, Brero A, Solovei I. Cell preparation and multicolor FISH in 3D preserved cultured mammalian cells. CSH Protoc 2007;2007:pdb prot4723.
Ott CJ, Federation AJ, Schwartz LS, Kasar S, Klitgaard JL, Lenci R, et al. Enhancer architecture and essential core regulatory circuitry of chronic lymphocytic leukemia. Cancer Cell. 2018;34:982–95.e7.
Acknowledgements
We would like to thank the GTEx Consortium. We obtained GTEx data through dbGaP authorized access at https://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?page=login with the accession number of phs000424.v8.p2. This study is also supported by the High-Performance Computing Platform and Instrument Analysis Center of Xi’an Jiaotong University. This work was supported by grants from the National Natural Science Foundation of China (82170896, 32170616, 31970569, 32100416, 31871264), the Natural Science Basic Research Program of Shaanxi Province (2021JC-02), Innovation Capability Support Program of Shaanxi Province (2022TD-44), China Postdoctoral Science Foundation (2021M702618), and the Fundamental Research Funds for the Central Universities.
Author information
Authors and Affiliations
Contributions
TLY and YG conceived and supervised this project. RHH conducted the computational work. CW, CXD and FC performed the functional experiments. JG, RHH and QLC performed the cell culture experiments. YR performed visualization. SSD built the pipeline for Hi-C data analysis. HC and DLZ carried out the library construction experiments. SY and YXC participated in data analysis. RHH and YG wrote the manuscript with the assistance of other authors.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethical approval
The research was approved by the Ethics Committee of Xi’an Jiaotong University (Shaanxi, China). This study was performed in accordance with the Declaration of Helsinki.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Edited by D Aberdam
Rights and permissions
About this article
Cite this article
Hao, RH., Guo, Y., Wang, C. et al. Lineage-specific rearrangement of chromatin loops and epigenomic features during adipocytes and osteoblasts commitment. Cell Death Differ 29, 2503–2518 (2022). https://doi.org/10.1038/s41418-022-01035-7
Received:
Revised:
Accepted:
Published:
Issue date:
DOI: https://doi.org/10.1038/s41418-022-01035-7
This article is cited by
-
Extrachromosomal circular DNAs in the differentiation of human bone marrow mesenchymal stem cells
Stem Cell Research & Therapy (2025)
-
Epigenetic Control of Osteogenesis: Pathways Toward Improved Bone Regeneration
Current Osteoporosis Reports (2025)
-
Dynamic alternations of three-dimensional chromatin architecture contribute to phenotypic characteristics of breast muscle in chicken
Communications Biology (2024)
-
Single-cell sequencing advances in research on mesenchymal stem/stromal cells
Human Cell (2024)
-
Systematic evaluation of vertebral bone quality score as an opportunistic screening method for BMD in spine surgery patients
European Spine Journal (2024)