Skip to main content
Advertisement

Main menu

  • Home
  • Articles
    • Newest Articles
    • Current Issue
    • Methods & Resources
    • Archive
    • Subjects
  • Collections
  • Submit
    • Submit a Manuscript
    • Author Guidelines
    • License, Copyright, Fee
    • FAQ
    • Why Submit
  • About
    • About Us
    • Editors & Staff
    • Board Members
    • Licensing and Reuse
    • Reviewer Guidelines
    • Privacy Policy
    • Advertise
    • Contact Us
    • LSA LLC
  • Alerts
  • Other Publications
    • EMBO Press
    • The EMBO Journal
    • EMBO reports
    • EMBO Molecular Medicine
    • Molecular Systems Biology
    • Rockefeller University Press
    • Journal of Cell Biology
    • Journal of Experimental Medicine
    • Journal of General Physiology
    • Cold Spring Harbor Laboratory Press
    • Genes & Development
    • Genome Research

User menu

  • My alerts

Search

  • Advanced search
Life Science Alliance
  • Other Publications
    • EMBO Press
    • The EMBO Journal
    • EMBO reports
    • EMBO Molecular Medicine
    • Molecular Systems Biology
    • Rockefeller University Press
    • Journal of Cell Biology
    • Journal of Experimental Medicine
    • Journal of General Physiology
    • Cold Spring Harbor Laboratory Press
    • Genes & Development
    • Genome Research
  • My alerts
Life Science Alliance

Advanced Search

  • Home
  • Articles
    • Newest Articles
    • Current Issue
    • Methods & Resources
    • Archive
    • Subjects
  • Collections
  • Submit
    • Submit a Manuscript
    • Author Guidelines
    • License, Copyright, Fee
    • FAQ
    • Why Submit
  • About
    • About Us
    • Editors & Staff
    • Board Members
    • Licensing and Reuse
    • Reviewer Guidelines
    • Privacy Policy
    • Advertise
    • Contact Us
    • LSA LLC
  • Alerts
  • Follow lsa Template on Twitter
Resource
Source Data
Transparent Process
Open Access

Identification and characterization of distinct brown adipocyte subtypes in C57BL/6J mice

Ruth Karlina, Dominik Lutter, Viktorian Miok, View ORCID ProfileDavid Fischer, Irem Altun, Theresa Schöttl, Kenji Schorpp, Andreas Israel, Cheryl Cero, James W Johnson, Ingrid Kapser-Fischer, Anika Böttcher, View ORCID ProfileSusanne Keipert, Annette Feuchtinger, Elisabeth Graf, Tim Strom, Axel Walch, Heiko Lickert, Thomas Walzthoeni, Matthias Heinig, Fabian J Theis, Cristina García-Cáceres, View ORCID ProfileAaron M Cypess, View ORCID ProfileSiegfried Ussar
Ruth Karlina
1Research Group Adipocytes and Metabolism, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
2German Center for Diabetes Research (DZD), Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dominik Lutter
2German Center for Diabetes Research (DZD), Neuherberg, Germany
3Computational Discovery Research Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Viktorian Miok
2German Center for Diabetes Research (DZD), Neuherberg, Germany
3Computational Discovery Research Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
4Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Center Munich, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
David Fischer
5Institute for Computational Biology, Helmholtz Center Munich, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for David Fischer
Irem Altun
1Research Group Adipocytes and Metabolism, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
2German Center for Diabetes Research (DZD), Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Theresa Schöttl
1Research Group Adipocytes and Metabolism, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
2German Center for Diabetes Research (DZD), Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kenji Schorpp
6Assay Development and Screening Platform, Institute for Molecular Toxicology and Pharmacology, Helmholtz Zentrum München, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andreas Israel
1Research Group Adipocytes and Metabolism, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
2German Center for Diabetes Research (DZD), Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cheryl Cero
7Diabetes, Endocrinology and Obesity Branch, National Institutes of Health, Bethesda, MD, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
James W Johnson
7Diabetes, Endocrinology and Obesity Branch, National Institutes of Health, Bethesda, MD, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ingrid Kapser-Fischer
1Research Group Adipocytes and Metabolism, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
2German Center for Diabetes Research (DZD), Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anika Böttcher
2German Center for Diabetes Research (DZD), Neuherberg, Germany
8Institute for Diabetes and Regeneration Research, Helmholtz Center Munich, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Susanne Keipert
9Department of Molecular Biosciences, Stockholm University, Stockholm, Sweden
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Susanne Keipert
Annette Feuchtinger
10Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Elisabeth Graf
11Institute for Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tim Strom
11Institute for Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Axel Walch
10Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Heiko Lickert
2German Center for Diabetes Research (DZD), Neuherberg, Germany
8Institute for Diabetes and Regeneration Research, Helmholtz Center Munich, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Thomas Walzthoeni
5Institute for Computational Biology, Helmholtz Center Munich, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Matthias Heinig
5Institute for Computational Biology, Helmholtz Center Munich, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Fabian J Theis
5Institute for Computational Biology, Helmholtz Center Munich, Neuherberg, Germany
12Department of Mathematics and School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cristina García-Cáceres
2German Center for Diabetes Research (DZD), Neuherberg, Germany
4Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Center Munich, Neuherberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aaron M Cypess
7Diabetes, Endocrinology and Obesity Branch, National Institutes of Health, Bethesda, MD, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Aaron M Cypess
Siegfried Ussar
1Research Group Adipocytes and Metabolism, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
2German Center for Diabetes Research (DZD), Neuherberg, Germany
13Department of Medicine, Technische Universität München, Munich, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Siegfried Ussar
Published 30 November 2020. DOI: 10.26508/lsa.202000924
  • Article
  • Figures & Data
  • Info
  • Metrics
  • Reviewer Comments
  • PDF
Loading

Article Figures & Data

Figures

  • Supplementary Materials
  • Figure S1.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure S1. Expression of Ucp1 in adipose tissues and preadipocyte markers in the scRNAseq clusters of the SVF of BAT.

    (A) Quantitative PCR analysis of Ucp1 expression in interscapular brown adipose tissue, subscapular BAT, interscapular white adipose tissue, subcutaneous fat (SC), and perigonadal fat (PG) from 6-wk-old C57BL/6 mice (n = 3; ***P < 0.001). Data are mean expressions normalized to Tbp ± SEM. (B) Representative immunofluorescence staining of UCP1 on BAT used for quantifications in Fig 1B (Dapi: blue, UCP1: green, lipid droplets: red, F-actin: gray). (C) Violin plots showing the distribution of selected marker genes (Pdgfra, Pparg, and Fabp4) across louvain cluster computed on all cell types identified in the single-cell RNA-seq data set.

    Source data are available for this figure.

    Source Data for Figure S1[LSA-2020-00924_SdataFS1.xlsx]

  • Figure 1.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 1. scRNAseq identifies distinct stages of brown adipocyte differentiation.

    (A) Immunofluorescence staining of UCP1 (green), F-actin (white), lipid droplets (red), and DAPI (blue) from WT and UCP1 knockout mice. (B) Quantifications of UCP1 content in individual brown adipocytes in brown adipose tissue normalized to total area (right panel, n = 11). Lipid droplets are included into area measured, and F-actin was used to distinguish each cell. (C) UMAP computed on full processed single-cell RNA-seq data set with annotated louvain clusters superimposed. (D) UMAP computed on set of preadipocytes with louvain subclustering. (E) Network of KEGG-enriched pathways of preadipocyte clusters. Significantly enriched pathways are connected to the respective cluster nodes. Size of the pathway nodes and proportion of the node-pie-chart refer to −log10 of enrichment P-value. (D) Node colors refer to single-cell cluster identified in (D). (F) Diffusion map dimensionality reduction of preadipocytes colored by Louvain clusters (top panel) and pseudotime (lower panel). (D, G) Expression of Pdgfra, Cd34, Cebpd, Cebpb, Cebpa, Pparg, Fabp4, and Lpl in preadipocytes shown in (D). The expression values are size factor normalized and log-transformed.

    Source data are available for this figure.

    Source Data for Figure 1[LSA-2020-00924_SdataF1.1.pdf][LSA-2020-00924_SdataF1.2.xlsx]

  • Figure 2.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 2. Heterogeneity in differentiation capacity and lipid accumulation of brown adipocyte clones.

    (A) Quantification of relative lipid accumulation measured by Oil Red O staining at day 8 of differentiation from 67 immortalized brown preadipocyte clones (n = 4–6; mean OD normalized to DAPI ± SEM). (B) Representative images of differentiated cell lines, stained with F-actin (red), lipid droplets (green), and DAPI (blue). (C) Correlation between mean lipid droplet size and lipid area normalized by the number of nuclei per clone (n > 200), calculated from pictures shown in Supplemental Data 2. Values are mean of different area scanned per clones (n = 9). (D) Correlation analysis of Pparg expression and lipid accumulation. The values were mean ± SEM (n = 5).

    Source data are available for this figure.

    Source Data for Figure 2[LSA-2020-00924_SdataF2.1.xlsx][LSA-2020-00924_SdataF2.2.xlsx]

  • Figure 3.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 3. Brown adipocyte clones heterogeneously express brown, beige, and white fat markers.

    (A) mRNA expressions of Ucp1 at day 8 of differentiation (n = 2–5). (B) Heat map of mRNA expression for different adipocyte markers in all 67 clones, and in vitro differentiated white, beige and brown adipocytes at day 8 of differentiation (n = 3–8). Median expression for each cell line was transformed to log scale and gene wise z-scores were computed independently for each cell line. Dendrogram colors denote identified adipocyte clone cluster. (C) Correlation of Ucp1 with Pparg (left panel), Prdm16 (middle panel), and Ppargc1a (right panel). The values were mean ± SEM, and log transformed for Ucp1, Prdm16, and Ppargc1a (n = 4–5). Gene expression was normalized to Tbp. (D) Ucp1 expression of controls and cells treated for 3 h with 0.5 μM CL-316,243 (n = 2–5).

    Source data are available for this figure.

    Source Data for Figure 3[LSA-2020-00924_SdataF3.xlsx]

  • Figure S2.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure S2. mRNA expression of brown and beige fat-specific markers in brown, beige and white adipocytes.

    (A, B) qPCR analysis of (A) Pparg, Prdm16, Ppargc1a, Ucp1, P2rx5, Pat2, Cd137, and Tmem26 expression in brown, beige and white adipocytes (n = 3–4) and (B) Adiponectin, Cd36, Fabp4 (log), Fasn, Glut4, and Hsl expression in brown adipose tissue clones (n = 2–3). All qPCR data were normalized to Tbp and shown as mean ± SEM. (B) *P < 0.05, **, ##P < 0.01, ***P < 0.001, ****P < 0.0001.

    Source data are available for this figure.

    Source Data for Figure S2[LSA-2020-00924_SdataFS2.xlsx]

  • Figure S3.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure S3. MTT assay of 20 preadipocyte clones for 4 d (left to right bar, n = 3–5).

    Source data are available for this figure.

    Source Data for Figure S3[LSA-2020-00924_SdataFS3.xlsx]

  • Figure 4.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 4.

    Transcriptional profiling of selected BAT clones. (A) Heat map of all 9,483 expressed genes in preadipocyte. Gene expression in rows was z-score normalized, columns refer to cell lines (left panel). Hierarchical clustering heat map of all 10,363 expressed genes in differentiated adipocytes (right panel). (B) PCA plot of the first two PC of preadipocytes expression data (upper panel) and differentiated adipocytes (lower panel). (C) Scatterplot comparison of the estimated brown adipose tissueness from pre- and differentiated adipocyte cell lines. Dots, triangles, and square indicate the three clusters identified by k-means clustering.

  • Figure 5.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 5. Laplacian eigenmap–based extraction of expression modules.

    (A) Brown preadipocyte gene expression in Laplacian eigenmap–derived low dimensional space. Expression of the three eigenvectors ϕ2, ϕ4, and ϕ6. Distance from estimated center is color-coded (left panel). After removal of genes with low variance module membership is coded in colors (right panel). (B) Heat map of hierarchical clustered module genes in rows. Gene expression was z-score normalized. Module membership is indicated by the right color bar. (C) Distribution of correlation coefficients of the gene-wise comparison of expression to estimated brown adipose tissue (BAT)ness for each module. Upper line: preadipocyte gene expressions versus estimated BATness of preadipocytes from ProFat database. Middle line: preadipocyte expressions versus differentiated BATness from ProFat database. Lower line: differentiated adipocyte expressions versus differentiated BATness from ProFat database. (D) Network of KEGG-enriched pathways. Significantly enriched pathways are connected to the respective module nodes. Size of the pathway nodes and proportion of the node-pie-chart refer to −log10 of enrichment P-value. Color of the nodes refers to module membership.

    Source data are available for this figure.

    Source Data for Figure 5[LSA-2020-00924_SdataF5.xlsx]

  • Figure S4.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure S4. PCA plot for the first two PCs of gene expression module genes from preadipocytes (left panel) and differentiated brown adipocytes (right panel).
  • Figure 6.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 6. EIF5, TCF25, and BIN1 mark subsets of brown adipocytes.

    (A) Heat maps of stably expressed genes of preadipocytes (left panel) and differentiated brown adipocytes (right panel). Module membership is indicated by the right color bar. (B) Correlation coefficients for stable expressed genes compared with estimated brown adipose tissue (BAT)ness. Color of bars indicates module membership. (C) Correlation plot for pairwise comparison of selected markers to selected stably expressed genes. Red plots denote a significant correlation. (D) Expression of Ucp1, Eif5, Tcf25, and Bin1 in mice kept in cold or thermoneutrality (n = 6–8, data are mean expressions normalized to B2m ± SEM). (E) Expression analysis of Ucp1, Eif5, Tcf25, and Bin1 in chow- and high-fat diet-fed mice (n = 4–6, data were mean expressions normalized to B2m ± SEM). (F) BAT co-staining for EIF5, TCF25 or BIN1 (red) and UCP1 (green), with F-actin (gray) and Dapi (blue) from wild-type C57BL/6J mice. Arrows indicate nuclear staining. (G) Representative color gradient picture of EIF5 staining on BAT (left panel), and percentage area of different EIF5 intensity (high, medium, low, and very low) normalized to total area (second panel, n = 8 sections). Quantification of TCF25- and BIN1-positive and negative cells in percentage of total cells (n = 9–18 sections). (H) Heat map of mRNA expression in human periadrenal (ADR), supraclavicular (SCLV), and subcutaneous (SCT) adipose tissue from six different donors for ADR and SCLV, and five donors for SCT. (A, B) Based on RNAseq data and remaining analysis based on RT-qPCR data. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

    Source data are available for this figure.

    Source Data for Figure 6[LSA-2020-00924_SdataF6.1.xlsx][LSA-2020-00924_SdataF6.2.pdf][LSA-2020-00924_SdataF6.3.xlsx]

  • Figure S5.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure S5. EIF5, TCF25 and BIN1 mark subpopulation in BAT and are differentially expressed in human fat depots.

    (A, B) PCA plots of pre- (left panel) and differentiated (right panel) adipocytes (A) from stable expressed gene expression module genes and (B) color-coded clones based on the Oil Red O quantification. (C) Scatter plots of stable expressed genes with regards to their maximum expression in differentiated adipocytes and maximal log fold change (log2) between differentiated adipocyte clones. (D) Correlation coefficients for stably expressed genes compared to lipid content as measured by Oil Red O. Color of bars indicates module membership. (E) UMAP of all preadipocytes with Eif5, Tcf25, and Bin1 expression superimposed. The expression values shown are size factor normalized and log transformed. (F) Representative immunofluorescence staining of EIF5 on brown adipose tissue (BAT) sections (DAPI: blue, EIF5: green, lipid droplets: red, F-actin: gray). (G, H) mRNA expression of P2rx5, Eif5, Tcf25, and Bin1 in (G) interscapular BAT, subscapular BAT, interscapular white adipose tissue, SC, and PG of wild-type C57BL/6J mice as assessed by qPCR (n = 3, data are mean expression normalized to B2m ± SEM) *P < 0.05, **P < 0.01, ***P < 0.001 and (H) in supraclavicular, periadrenal, and subcutaneous adipose tissue biopsies from six human donors for ADR and SCLV, and five donors for SCT as assessed by RNAseq.

    Source data are available for this figure.

    Source Data for Figure S5[LSA-2020-00924_SdataFS5.xlsx]

  • Figure 7.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 7. Loss of Bin1 increases basal respiration and UCP1 protein content.

    (A) Heat map and hierarchical clustering of cell lines with regard to the mRNA expressions of Eif5, Tcf25 and Bin1. (B) Expression of Eif5, Tcf25, and Bin1 in control cells (shScr) of different clones (B1, D1, and D5) and the respective knockdown (shEif5 B1, shTcf25 D1, and shBin1 D5); Preads (d0 of differentiation) and Ads (d8 of differentiation) (n = 5). (C) Oxygen consumption rate of shScr B1 and shEif5 B1 (left panel), shScr D1, and shTcf25 D1 (middle panel) and shScr D5 and shBin1 D5 (right panel) at day 8 of differentiation measured by Seahorse (n = 4–5). (D) Expression of Eif5, Tcf25, and Bin1 in control cells (shScr) and respective knock-down (shEif5, shTcf25, and shBin1); Preads (d0 of differentiation) and Ads (d8 of differentiation) (n = 5). (E) Immunofluorescence staining of EIF5/TCF25/BIN1 (green), F-actin (red), and DAPI (blue) on shScr/shEif5/shTcf25/shBin1 preadipocytes. (F) Representative images of differentiated cell lines (shScr, shEif5, shTcf25, and shBin1) stained with F-actin (white), lipid droplets (red), and DAPI (blue). (G) Quantification of relative lipid accumulation measured by Oil Red O staining at day 8 of differentiation from knockdown cell lines (n = 4, mean OD normalized to DAPI ± SEM). (H) mRNA expressions of Pparg (left panel) and Ucp1 (right panel) on shScr, shEif5, shTcf25, and shBin1 adipocytes and CL-316,243-treated adipocytes (0.5 μM CL-316,243 for 3 h; n = 6). (I) Quantification of Western blots for UCP1 (Cat. no. 10983; Abcam) in shScr, shEif5, shTcf25, and shBin1 adipocytes and adipocytes treated with 0.5 μM CL-316,243 for 6 h (n = 6–7) normalized to β-actin. Values are fold changes to the respective shScr adipocytes (ads). (J) Oxygen consumption rate of control and knockdown cell lines at day 8 of differentiation measured by Seahorse (n = 3); data were normalized by non-mitochondrial respiration. (K) Schematic illustration of brown adipose tissue heterogeneity: Murine brown adipose tissue is composed of functionally distinct brown adipocytes. We identify EIF5 expressing brown adipocytes as “classical” brown adipocytes, with high UCP1 content and mitochondrial uncoupling. TCF25 expressing brown adipocytes are similar, albeit with lower UCP1 expression. In contrast, BIN1-expressing brown adipocytes appear in a dormant state, expressing low UCP1 levels with little response to beta adrenergic stimulation. However, loss of BIN1 or chemical mitochondrial uncoupling reveals high thermogenic capacity of these cells. Subpopulations of brown adipocytes are color coded regarding to their thermogenic capacity, with the highest shown in dark brown (Eif5high), followed by brown (Tcf25high) and light brown (Bin1high) (=indicates unchanged expression). All RT-qPCR data were normalized to Tbp and shown as mean ± SEM. *P < 0.05, **, ##P < 0.01, ***P < 0.001, ****P < 0.0001.

    Source data are available for this figure.

    Source Data for Figure 7[LSA-2020-00924_SdataF7.1.xlsx][LSA-2020-00924_SdataF7.2.xlsx][LSA-2020-00924_SdataF7.3.pdf]

  • Figure S6.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure S6. mRNA expression of brown, beige and white fat-specific markers in Eif5, Tcf25 and Bin1 knockdown clones and brown preadipocytes.

    (A, B) Semi-quantitative PCR analysis of Pparg, Ucp1, Prdm16, Ppargc1a, P2rx5, Cd137, and Asc1 expression at day 8 of differentiation and 3 h treatment with 0.5 μM CL-316,243 in (A) shScr B1 and shEif5 B1 (left panel), shScr D1 and shTcf25 D1 (middle panel), shScr D5 and shBin1 D5 (right panel) cells (n = 5), and (B) shScr, shEif5, shTcf25, and shBin1 on mixed brown adipocytes cells (n = 6). Data are shown as mean expression normalized to Tbp ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

    Source data are available for this figure.

    Source Data for Figure S6[LSA-2020-00924_SdataFS6.xlsx]

Supplementary Materials

  • Figures
  • Table S1 qPCR primers used in quantitative RT-PCR.

  • Supplemental Data 1.

    Full computational analysis of the scRNAseq.[LSA-2020-00924_Supplemental_Data_1.pdf]

  • Table S2 Individual gene expression in gene expression modules.

  • Table S3 KEGG pathways of gene expression modules.

  • Table S4 RNAseq sample overview.

  • Supplemental Data 2.

    RIN value determined by automated electrophoresis (Bioanalyzer).[LSA-2020-00924_Supplemental_Data_2.pdf]

PreviousNext
Back to top
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on Life Science Alliance.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Identification and characterization of distinct brown adipocyte subtypes in C57BL/6J mice
(Your Name) has sent you a message from Life Science Alliance
(Your Name) thought you would like to see the Life Science Alliance web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Brown preadipocyte heterogeneity
Ruth Karlina, Dominik Lutter, Viktorian Miok, David Fischer, Irem Altun, Theresa Schöttl, Kenji Schorpp, Andreas Israel, Cheryl Cero, James W Johnson, Ingrid Kapser-Fischer, Anika Böttcher, Susanne Keipert, Annette Feuchtinger, Elisabeth Graf, Tim Strom, Axel Walch, Heiko Lickert, Thomas Walzthoeni, Matthias Heinig, Fabian J Theis, Cristina García-Cáceres, Aaron M Cypess, Siegfried Ussar
Life Science Alliance Nov 2020, 4 (1) e202000924; DOI: 10.26508/lsa.202000924

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Brown preadipocyte heterogeneity
Ruth Karlina, Dominik Lutter, Viktorian Miok, David Fischer, Irem Altun, Theresa Schöttl, Kenji Schorpp, Andreas Israel, Cheryl Cero, James W Johnson, Ingrid Kapser-Fischer, Anika Böttcher, Susanne Keipert, Annette Feuchtinger, Elisabeth Graf, Tim Strom, Axel Walch, Heiko Lickert, Thomas Walzthoeni, Matthias Heinig, Fabian J Theis, Cristina García-Cáceres, Aaron M Cypess, Siegfried Ussar
Life Science Alliance Nov 2020, 4 (1) e202000924; DOI: 10.26508/lsa.202000924
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
Issue Cover

In this Issue

Volume 4, No. 1
January 2021
  • Table of Contents
  • Cover (PDF)
  • About the Cover
  • Masthead (PDF)
Advertisement

Jump to section

  • Article
    • Abstract
    • Introduction
    • Results
    • Discussion
    • Materials and Methods
    • Data Availability
    • Acknowledgements
    • References
  • Figures & Data
  • Info
  • Metrics
  • Reviewer Comments
  • PDF

Subjects

  • Metabolism

Related Articles

  • No related articles found.

Cited By...

  • No citing articles found.
  • Google Scholar

More in this TOC Section

  • γδ T-cell single-cell RNA-seq
  • Leishmania infection alters extracellular vesicles
Show more Resource

Similar Articles

EMBO Press LogoRockefeller University Press LogoCold Spring Harbor Logo

Content

  • Home
  • Newest Articles
  • Current Issue
  • Archive
  • Subject Collections

For Authors

  • Submit a Manuscript
  • Author Guidelines
  • License, copyright, Fee

Other Services

  • Alerts
  • Twitter
  • RSS Feeds

More Information

  • Editors & Staff
  • Reviewer Guidelines
  • Feedback
  • Licensing and Reuse
  • Privacy Policy

ISSN: 2575-1077
© 2022 Life Science Alliance LLC

Life Science Alliance is registered as a trademark in the U.S. Patent and Trade Mark Office and in the European Union Intellectual Property Office.