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Single-cell transcriptional networks in differentiating preadipocytes suggest drivers associated with tissue heterogeneity

  • Alfred K. Ramirez
  • , Simon N. Dankel
  • , Bashir Rastegarpanah
  • , Weikang Cai
  • , Ruidan Xue
  • , Mark Crovella
  • , Yu Hua Tseng
  • , C. Ronald Kahn
  • , Simon Kasif

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

White adipose tissue plays an important role in physiological homeostasis and metabolic disease. Different fat depots have distinct metabolic and inflammatory profiles and are differentially associated with disease risk. It is unclear whether these differences are intrinsic to the pre-differentiated stage. Using single-cell RNA sequencing, a unique network methodology and a data integration technique, we predict metabolic phenotypes in differentiating cells. Single-cell RNA-seq profiles of human preadipocytes during adipogenesis in vitro identifies at least two distinct classes of subcutaneous white adipocytes. These differences in gene expression are separate from the process of browning and beiging. Using a systems biology approach, we identify a new network of zinc-finger proteins that are expressed in one class of preadipocytes and is potentially involved in regulating adipogenesis. Our findings gain a deeper understanding of both the heterogeneity of white adipocytes and their link to normal metabolism and disease.

Original languageEnglish
Article number2117
JournalNature Communications
Volume11
Issue number1
DOIs
StatePublished - Dec 1 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).

Funding

A.K.R., C.R.K. and S.K. designed and provided the overall oversight of the study. A.K.R., S.N.D., B.R., M.C., W.C., C.R.K. and S.K. wrote the paper. A.K.R., R.X. and S.N.D. performed the experiments. A.K.R. analyzed gene expression data. A.K.R., B.R., M.C. and S.K. contributed to the design of the single-cell analysis methods. A.K.R., Y.H.T., C.R.K. and S.K. analyzed and discussed the data and biological results. B.R. and M.C. wrote the code for the subnetwork detection. This work was supported in part by NIH grants P30DK036836, R01DK0835659 and R37031036. The authors would like to thank Evimaria Terzi for contributions to the development of the network algorithm; Drs Jonathan M. Dreyfuss and Hui Pan from Joslin Diabetes Center DRC Genomics and Bioinformatics Core for assistance and advice regarding data analysis; Mary Ellen Fitzpatrick for her assistance with the Green Cluster Computing Environment; and Guoxiao (Grace) Wang for her assistance with CRISPR knockouts. A. K.R. was supported by NIH grant T32 DK007260-37. B.R. and M.C. were supported by NSF IIS-1421759 and NSF CNS-1618207. This work was supported in part by NIH grant (R01DK082659 to C.R.K.). We also received support from the Joslin NIH-funded DRC (P30DK036836).

FundersFunder number
National Science Foundation Arctic Social Science ProgramR01DK082659, CNS-1618207, 1421759, IIS-1421759
National Institute of Diabetes and Digestive and Kidney DiseasesK01DK120740, R01DK082659, T32DK007260, P30DK036836
National Institutes of Health (NIH)R37031036, R01DK0835659

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    ASJC Scopus subject areas

    • General Chemistry
    • General Biochemistry, Genetics and Molecular Biology
    • General
    • General Physics and Astronomy

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