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A subpopulation of lipogenic brown adipocytes drives thermogenic memory

Nature Metabolism volume 5, pages 1691–1705 (2023)Cite this article

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Abstract

Sustained responses to transient environmental stimuli are important for survival. The mechanisms underlying long-term adaptations to temporary shifts in abiotic factors remain incompletely understood. Here, we find that transient cold exposure leads to sustained transcriptional and metabolic adaptations in brown adipose tissue, which improve thermogenic responses to secondary cold encounter. Primary thermogenic challenge triggers the delayed induction of a lipid biosynthesis programme even after cessation of the original stimulus, which protects from subsequent exposures. Single-nucleus RNA sequencing and spatial transcriptomics reveal that this response is driven by a lipogenic subpopulation of brown adipocytes localized along the perimeter of Ucp1hi adipocytes. This lipogenic programme is associated with the production of acylcarnitines, and supplementation of acylcarnitines is sufficient to recapitulate improved secondary cold responses. Overall, our data highlight the importance of heterogenous brown adipocyte populations for ‘thermogenic memory’, which may have therapeutic implications for leveraging short-term thermogenesis to counteract obesity.

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Fig. 1: Enhanced secondary thermogenic response to acute cold.
Fig. 2: A primary thermogenic response leads to delayed induction of a lipid biosynthesis programme in brown adipose tissue independent of sustained cold exposure.
Fig. 3: Lipogenic brown adipocytes promote the improved secondary thermogenic response to acute cold.
Fig. 4: A primary thermogenic response induces a subpopulation of lipogenic brown adipocytes.
Fig. 5: Lipogenic brown adipocytes show distinct spatial tissue patterns in brown adipose tissue following a primary thermogenic response.
Fig. 6: A primary thermogenic response results in lipogenesis-driven sustained elevation of acylcarnitines in brown adipose tissue.

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Data availability

All snRNA-seq and spatial transcriptomics data reported in here have been deposited in the Gene Expression Omnibus under accession number GSE218711. RNA-seq data are available at the sequence read archive under BioProject number PRJNA866352. The snRNA-seq data of BAT from human come from a published study31 with accession number E-MTAB-8564 as described in the snRNA-seq section in the methods. Source data are provided with this paper. All other data are available in the main text or the Supplementary Information.

Code availability

Representative code to reproduce the spatial transcriptomics analysis are available at https://github.com/kpcoleman/BAT-SpaDecon/. Any additional information required to analyse the data reported in this paper is available from C.A.T. upon request.

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Acknowledgements

We thank all members of the laboratory of C.A.T. for valuable discussions and input, as well as members of the P.V.S. laboratory (J. Ishibashi and A. Angueira), the J. Wherry laboratory (Z. Chen) and D. Allman laboratory (B. Gaudette) at UPenn for their scientific and technical advice. We thank the Penn Metabolomics Core in the Penn Cardiovascular Institute for LC–MS quantification of acylcarnitines. We acknowledge L. Micha (UPenn) for excellent mouse husbandry. We further thank M. Lazar (UPenn) and C. Wolfrum (ETH Zurich) for Scapfl/fl Ucp1-CreER mice. We thank C. Semenkovich (WashU) for sharing Fasnfl/fl mice. We gratefully acknowledge M. Lazar, J. Henao-Mejia, R. Faryabi and N. Betley (all at UPenn) for scientific advice throughout this study. C.A.T. is a Pew Biomedical Scholar and a Kathryn W. Davis Aging Brain Scholar, and is supported by a National Institutes of Health Director’s New Innovator Award (DP2AG067492), the Edward Mallinckrodt, Jr. Foundation, the Global Probiotics Council, the Mouse Microbiome Metabolic Research Program of the National Mouse Metabolic Phenotyping Centers, and grants by the IDSA Foundation, the Thyssen Foundation, the PennCHOP Microbiome Program, the Penn Institute for Immunology, the Penn Center for Molecular Studies in Digestive and Liver Diseases (P30-DK-050306), the Penn Skin Biology and Diseases Resource-based Center (P30-AR-069589), the Penn Diabetes Research Center (P30-DK-019525) and the Penn Institute on Aging. This work was further supported by a National Institutes of Health Training Grant T32AI141393 (to P.L.), Fellowship Grant F31HL160065 (to P.L.), Medical Scientist Training Program T32 GM07170 (to P.V.S. and L.L.), Training Grant in Computational Biology 5-T32-HG-000046-21 (to L.L.), Boehringer Ingelheim Fonds MD Fellowship (to S.K.), and University of Pennsylvania Center for Undergraduate Research Fellowship (to J.C.). Graphical images in Figs. 1a and Fig. 2a and Extended Data Fig. 3a were created with BioRender.com.

Author information

Authors and Affiliations

  1. Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Patrick Lundgren, Prateek V. Sharma, Lenka Dohnalová, Giulia T. Uhr, Susanna Kircher, Lev Litichevskiy, Klaas Bahnsen, Hélène C. Descamps, Jacqueline Chan, Timothy O. Cox, Maayan Levy & Christoph A. Thaiss

  2. Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Patrick Lundgren, Prateek V. Sharma, Lenka Dohnalová, Giulia T. Uhr, Susanna Kircher, Lev Litichevskiy, Klaas Bahnsen, Hélène C. Descamps, Jacqueline Chan, Timothy O. Cox, Maayan Levy & Christoph A. Thaiss

  3. Institute for Obesity, Diabetes and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Patrick Lundgren, Prateek V. Sharma, Lenka Dohnalová, Giulia T. Uhr, Susanna Kircher, Lev Litichevskiy, Klaas Bahnsen, Hélène C. Descamps, Jacqueline Chan, Karthikeyani Chellappa, Timothy O. Cox, Joseph A. Baur, Patrick Seale & Christoph A. Thaiss

  4. Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA, USA

    Prateek V. Sharma, Christina Demetriadou & Kathryn E. Wellen

  5. Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA, USA

    Prateek V. Sharma, Christina Demetriadou & Kathryn E. Wellen

  6. Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA, USA

    Prateek V. Sharma, Christina Demetriadou & Kathryn E. Wellen

  7. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Kyle Coleman, Lev Litichevskiy & Mingyao Li

  8. Center for Metabolic Disease Research, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA

    Christina Demetriadou & Nathaniel W. Snyder

  9. Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Karthikeyani Chellappa & Joseph A. Baur

  10. Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Yael Heyman & Arjun Raj

  11. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Yael Heyman, Sarshan R. Pather, Ophir Shalem & Arjun Raj

  12. Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Sarshan R. Pather & Ophir Shalem

  13. Penn Metabolomics Core, Penn Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA, USA

    Clarissa Shoffler & Christopher Petucci

  14. Department of Cell and Development Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Patrick Seale

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Contributions

P.L. conceived the study, designed and performed the experiments, interpreted the results, and wrote the manuscript. P.V.S., L.D., G.T.U., S.K., H.C.D., C.D., J.C., K. Chellappa, T.O.C., Y.H., S.R.P., C.S. and C.P. performed experiments. K. Coleman, L.L., P.V.S., K.B. and M. Li performed computational and statistical analyses. P.S., M. Levy, M. Li, K.E.W., N.W.S., J.A.B., A.R. and O.S. provided essential tools and insights. C.A.T. conceived the study, designed the experiments, interpreted the results and wrote the manuscript.

Corresponding author

Correspondence to Christoph A. Thaiss.

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Nature Metabolism thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Isabella Samuelson, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 Whole-body energy expenditure is increased in a secondary cold challenge 4 days after a primary cold challenge.

a, Whole-body energy expenditure over time in the primary cold challenge compared to secondary cold challenge of the same mice over time (n = 6 independent animals). b, Area under the curve (AUC) analysis of energy expenditure over time in primary and secondary cold challenge (9am-5pm). Error bars indicate means ± s.e.m. *, P < 0.05. Exact P values are presented in the source data file for Extended Data Fig. 1.

Source data

Extended Data Fig. 2 The transcriptional induction of lipid biosynthesis following a primary thermogenic response is specific to BAT.

a, Relative gene expression of Ucp1 in iWAT and BAT following transient cold exposure (n = 5 independent animals per condition, except iWAT Day 1 n = 4, iWAT Day 2 n = 2, and iWAT Day 4 n = 4). b, Relative gene expression of Fasn in iWAT and BAT following transient cold exposure (n = 5 independent animals per condition, except iWAT Day 1 n = 4, iWAT Day 2 n = 3, and iWAT Day 4 n = 4). c, Relative gene expression of Fasn in BAT, liver, and muscle from mice in their primary thermogenic response (1cyc) and secondary thermogenic response (2cyc) (n = 5 independent animals per condition). d, Western blot of FASN protein in BAT from cold-naïve mice compared to cold-experienced mice, and relative expression analysis compared to housekeeping gene (Vinculin) (n = 4 independent animals per condition). e, Western blot of UCP1 protein in BAT from cold-naïve mice compared to cold-experienced mice, and relative expression analysis compared to housekeeping gene (Tubulin) (n = 4 independent animals per condition). f, Representative H&E histology images of BAT from cold-naïve mice and cold-experienced mice 4 days after a transient cold exposure (n = 4 independent animals per condition), scale bar = 50μm. Error bars indicate means ± s.e.m. ns, not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001. Exact P values are presented in the source data file for Extended Data Fig. 2.

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Extended Data Fig. 3 Workflow and validation of nuclei isolation from BAT.

a, Workflow schematic. b, Nuclei sorting strategy using DAPI positivity, including a re-sort validation experiment. c, Inspection and counting of nuclei quality on haemocytometer.

Extended Data Fig. 4 Single-nucleus RNA-sequencing of brown adipose tissue resolves cell types and adipocyte subpopulations.

a, Expression of canonical marker genes of cell types identified by BAT snRNAseq. b-c, UMAP plots of cell types separated by cold-naïve BAT (b) and cold-experienced BAT (4 days following transient acute cold) (c). d, Cell type distributions separated by condition. e, Feature plots for Fasn, Ucp1, and Cpt2 expression within the adipocyte population. f, Dot plot for Slc7a10, Ucp1, and Fasn expression across five identified adipocyte subpopulations (A1-A5).

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Extended Data Fig. 5 Cell type distributions in spatial transcriptomics data and snRNAseq data.

a, b, Cell type distributions quantified in spatial transcriptomics data (a) and snRNAseq data (b). c, Violin plots for the expression of indicated genes across all spots in each condition from BAT spatial transcriptomics data.

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Extended Data Fig. 6 Total free fatty acids, glycerolipids, or phospholipids & sphingolipids in brown adipose tissue following a primary thermogenic response.

a-d, Relative abundance and distribution of free fatty acids (a), glycerolipids (b), and phospholipid & sphingolipid (c) species in BAT in different experimental conditions. Data presented in panels a-d is based on metabolomics from n = 5 independent animals per condition. Each data point represents the average value across replicates for each species. d, Comparison of indicated lipid metabolite species in BAT between TN, day 4, and day 4 with Fasn inhibition (Fasni). Each data point represents the average value across replicates for each species. Error bars indicate means ± s.e.m. ns, not significant; *, P < 0.05; ***, P < 0.001; ****, P < 0.0001. Exact P values are presented in the source data file for Extended Data Fig. 6.

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Extended Data Fig. 7 Scap knockout in brown adipocytes does not affect the expression of genes involved in acylcarnitine transport or fatty acid oxidation.

a-c, Relative gene expression of Cact (a), Lcad (b), and Mcad (c) in brown adipose tissue from ScapΔUcp1 (n = 7 independent animals) and Scapflox mice (n = 7 independent animals) 4 days after primary cold exposure. Error bars indicate means ± s.e.m. ns, not significant. Exact P values are presented in the source data file for Extended Data Fig. 7.

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Lundgren, P., Sharma, P.V., Dohnalová, L. et al. A subpopulation of lipogenic brown adipocytes drives thermogenic memory. Nat Metab 5, 1691–1705 (2023). https://doi.org/10.1038/s42255-023-00893-w

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