Evidence map›Paper›PMID 40835891›Full record

ArticleNature genetics2025

Single-cell DNA methylome and 3D genome atlas of human subcutaneous adipose tissue.

Zeyuan Johnson Chen, Sankha Subhra Das, Asha Kar, Seung Hyuk T Lee, Kevin D Abuhanna, Marcus Alvarez, Mihir G Sukhatme, Zitian Wang, Kyla Z Gelev, Matthew G Heffel and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
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  5. Review
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  10. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

21 authors.

Zeyuan Johnson Chen *Department of Computer Science, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-9564-6271
Sankha Subhra Das *Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.
Asha KarDepartment of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0009-0000-1717-1744
Seung Hyuk T LeeDepartment of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-0943-6076
Kevin D AbuhannaDepartment of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-0994-7658
Marcus AlvarezDepartment of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.
Mihir G SukhatmeDepartment of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0001-5127-6136
Zitian WangDepartment of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.
Kyla Z GelevDepartment of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-3980-7189
Matthew G HeffelDepartment of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.
Yi ZhangDepartment of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.
Oren AvramDepartment of Computer Science, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-1984-2139
Elior RahmaniDepartment of Computational Medicine, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-9017-2070
Sriram SankararamanDepartment of Computer Science, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-1586-9641
Markku LaaksoDepartment of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.ORCID http://orcid.org/0000-0002-3394-7749
Sini HeinonenObesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Hilkka PeltoniemiEira Hospital, Helsinki, Finland.
Eran HalperinDepartment of Computational Medicine, University of California, Los Angeles, CA, USA.
Kirsi H PietiläinenObesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.ORCID http://orcid.org/0000-0002-8522-1288
Chongyuan LuoDepartment of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-8541-0695
Päivi PajukantaDepartment of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA. ppajukanta@mednet.ucla.edu.ORCID http://orcid.org/0000-0002-6423-8056

Funding

Multimodal omics approach to identify health to cardiometabolic disease transitionsR01HL170604 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Paivi Pajukanta · 2023 to 2026
$2.8M
Methods for Genomic Analysis in Heterogeneous TissuesR01HG010505 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HALPERIN, ERAN · 2019 to 2022
$2.6M
Genetics of adipose cell-type expression and cardiometabolic traitsR01DK132775 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI MOHLKE, KAREN L., PAJUKANTA, PAIVI · 2022 to 2025
$2.4M
NHGRI NIH HHS R01 HG010505NHLBI NIH HHS R01 HL170604NIDDK NIH HHS R01 DK132775Novo Nordisk Fonden (Novo Nordisk Foundation) NNF10OC1013354Novo Nordisk Fonden (Novo Nordisk Foundation) NNF17OC0027232Novo Nordisk Fonden (Novo Nordisk Foundation) NNF20OC0060547U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI) R01HL170604U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) R01HG010505U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) R01DK132775
6 · The paper itself

Abstract

The cell-type-level epigenomic landscape of human subcutaneous adipose tissue (SAT) is not well characterized. Here, we elucidate the epigenomic landscape across SAT cell types using snm3C-seq. We find that SAT CG methylation (mCG) displays pronounced hypermethylation in myeloid cells and hypomethylation in adipocytes and adipose stem and progenitor cells, driving nearly half of the 705,063 differentially methylated regions (DMRs). Moreover, TET1 and DNMT3A are identified as plausible regulators of the cell-type-level mCG profiles. Both global mCG profiles and chromosomal compartmentalization reflect SAT cell-type lineage. Notably, adipocytes display more short-range chromosomal interactions, forming complex local 3D genomic structures that regulate transcriptional functions, including adipogenesis. Furthermore, adipocyte DMRs and A compartments are enriched for abdominal obesity genome-wide association study (GWAS) variants and polygenic risk, while myeloid A compartments are enriched for inflammation. Together, we characterize the SAT single-cell-level epigenomic landscape and link GWAS variants and partitioned polygenic risk of abdominal obesity and inflammation to the SAT epigenome.

Indexed as

DNA MethylationEpigenomeSubcutaneous FatAdipocytesDNA Methyltransferase 3AEpigenesis, GeneticEpigenomicsGenome, HumanGenome-Wide Association StudyHumansSingle-Cell AnalysisDNA Methyltransferase 3ADNMT3A protein, human

Identifiers

PMID40835891
PMCPMC12373012

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.