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Single cell multiomic landscape reveals gene programs driving lipid droplet heterogeneity in hepatic steatosis
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  • Published: 23 February 2026

Single cell multiomic landscape reveals gene programs driving lipid droplet heterogeneity in hepatic steatosis

  • Tejasav S. Sehrawat1,13 na1,
  • Shawna A. Cooper1,2 na1,
  • Amaia Navarro-Corcuera1,
  • Ryan J. Schulze1,3,
  • Mengfei Liu1,3,
  • Usman Yaqoob1,
  • Yubin Yeon4,
  • Sangwoong Yoon5,
  • Yung-Kyun Noh6,7,
  • Chady Meroueh8,
  • Joseph C. Ahn1,
  • Josepmaria Argemi9,14,
  • Ramon A. Bataller9,
  • Mark A. McNiven1,3,
  • Mrunal K. Dehankar10,
  • Ying Li10,
  • Jeong-Heon Lee11,
  • Carol A. Casey12,
  • William A. Faubion1,
  • Tamas Ordog1,11,
  • Patrick S. Kamath1,
  • Douglas A. Simonetto1,
  • Harmeet Malhi1,
  • Sheng Cao1 &
  • …
  • Vijay H. Shah1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Computational biology and bioinformatics
  • Diseases
  • Gastroenterology
  • Genetics
  • Molecular biology

Abstract

Alcohol-associated liver disease (ALD) in its earliest form is evidenced as hepatic steatosis which may progress to liver cirrhosis. The mechanisms behind this are poorly understood and therapeutics limited. Liver is a specialized organ exhibiting heterogeneity along the porto-central axis. Periportal preponderance of lipid droplet accumulation was noted in human ALD livers compared to other causes of hepatic steatosis. Using single cell multiomics, we studied transcriptional mechanisms across the hepatic lobule that could account for zonation of lipid droplets in a murine ALD model. Alcohol led to periportal zonation of lipogenesis-associated genes in mice, including Hsd17b13 and Fasn. Chromatin landscape studies demonstrated zonation of master transcription factors that led to these changes in the transcriptome. We utilized these data to provide novel insight into zone-specific HNF4α and PPARα regulation of HSD17B13. We conclude novel mechanisms underlying ALD leading to spatially distinct establishment of hepatic steatosis and provide insight into disease pathogenesis.

Data availability

The scRNA-seq and scATAC-seq data generated in this publication are available on the GEO database [https://www.ncbi.nlm.nih.gov/geo/] under accession (GSE199064, reviewer token: mxgtksespxetxuh). The RNA-seq data are available on the GEO database (GSE155926, GSE155907) and the dbGAP of the National Center for Biotechnology Information, US National Library of Medicine, Bethesda, MD (accession number phs001807.v1.p1). The ChIP-seq data are available on the GEO database (GSE166564). JASPAR database is accessible via weblink [http://jaspar.genereg.net/].

Code availability

Analysis utilized publicly available and previously published code.

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Acknowledgements

The authors thank Constantine Tzouanas, M.S. and Alex K. Shalek, Ph.D. from MIT/Harvard University, USA for their intellectual input and important discussions regarding the scATAC-sequencing analyses.

Funding

This work is supported by funding provided by the National Institutes of Health (NIH), USA grants R01 AA21171 (V.H.S.), R01 DK59615 (V.H.S.), and U01 AA21788 (H.M. and V.H.S.) and MSIT, Korea grants 2018R1A5A7059549 (Y.K.N) and 2020–0-01373 (Y.Y., Y.K.N). Mayo Clinic Center for Cell Signaling in Gastroenterology (C-SiG) provided support through the NIH, USA funding (P30DK084567). S.A.C. is a member of the Biochemistry and Molecular Biology Ph.D. Graduate Program and is supported by Mayo Clinic Graduate School of Biomedical Sciences.

Author information

Author notes
  1. Tejasav S. Sehrawat and Shawna A. Cooper contributed equally.

Authors and Affiliations

  1. Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA

    Tejasav S. Sehrawat, Shawna A. Cooper, Amaia Navarro-Corcuera, Ryan J. Schulze, Mengfei Liu, Usman Yaqoob, Joseph C. Ahn, Mark A. McNiven, William A. Faubion, Tamas Ordog, Patrick S. Kamath, Douglas A. Simonetto, Harmeet Malhi, Sheng Cao & Vijay H. Shah

  2. Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN, USA

    Shawna A. Cooper

  3. Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA

    Ryan J. Schulze, Mengfei Liu & Mark A. McNiven

  4. Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea

    Yubin Yeon

  5. University College London, London, UK

    Sangwoong Yoon

  6. School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Republic of Korea

    Yung-Kyun Noh

  7. Department of Computer Science, Hanyang University, Seoul, Republic of Korea

    Yung-Kyun Noh

  8. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

    Chady Meroueh

  9. Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh, Pittsburgh, PA, USA

    Josepmaria Argemi & Ramon A. Bataller

  10. Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA

    Mrunal K. Dehankar & Ying Li

  11. Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA

    Jeong-Heon Lee & Tamas Ordog

  12. Department of Internal Medicine, University of Nebraska Medical Center, Omaha, NE, USA

    Carol A. Casey

  13. Digestive Diseases, Yale University School of Medicine, New Haven, CT, USA

    Tejasav S. Sehrawat

  14. Centro de Investigacion Biomedica en Red (CIBER-EHD), Madrid, Spain

    Josepmaria Argemi

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  1. Tejasav S. Sehrawat
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Contributions

T.S.S. and S.A.C. contributed to study conception and design; acquisition, analysis, and interpretation of most of the data; and drafting of the original and finalizing the manuscript. S.A.C., Y.L., and M.K.D. contributed to the analysis and interpretation of the scRNA and scATAC-sequencing data. A.N.C. contributed to acquisition, analysis, and interpretation of data and drafting of the manuscript. R.J.S. and M.A.M contributed to acquisition of cell biology experiments related to lipid droplets. C.A.S. contributed to proteomics data. M.L. and UY contributed to animal experiments. Y.Y., S.Y., Y.K.N., and J.C.A. contributed to the machine-learning model used for lipid-droplet assessment. C.M. contributed to the machine-learning model used for lipid-droplet assessment and provided expert pathologist review and annotation of human biopsy slides. J.A. and R.A.B. contributed to bulk-RNA-sequencing studies and intellectual input. J.H.L. and T.O. contributed to scATAC-sequencing and ChIP-sequencing experiments. W.A.F. and P.S.K. contributed to interpretation of data, intellectual input, and editing of the manuscript. D.A.S. contributed to interpretation of data, intellectual input, and the machine-learning model development supervision. H.M. contributed to data analysis, interpretation, intellectual input, supervision, funding, and drafting of the manuscript. S.C. contributed to study conception and design, analysis and interpretation of data, intellectual input, and editing of the manuscript. V.H.S. contributed to study conception and design; analysis and interpretation of data; resources; funding support; drafting and editing of the manuscript; and overall study supervision.

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Correspondence to Sheng Cao or Vijay H. Shah.

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Sehrawat, T.S., Cooper, S.A., Navarro-Corcuera, A. et al. Single cell multiomic landscape reveals gene programs driving lipid droplet heterogeneity in hepatic steatosis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39913-6

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  • Received: 04 August 2025

  • Accepted: 09 February 2026

  • Published: 23 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-39913-6

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