The energy-burning capability of beige adipose tissue is a potential therapeutic tool for reducing obesity and metabolic disease, but this capacity is decreased by aging. Here, we evaluate the impact of aging on the profile and activity of adipocyte stem and progenitor cells (ASPCs) and adipocytes during the beiging process. We found that aging increases the expression of Cd9 and other fibrogenic genes in fibroblastic ASPCs and blocks their differentiation into beige adipocytes. Fibroblastic ASPC populations from young and aged mice were equally competent for beige differentiation in vitro, suggesting that environmental factors suppress adipogenesis in vivo. Examination of adipocytes by single nucleus RNA-sequencing identified compositional and transcriptional differences in adipocyte populations with age and cold exposure. Notably, cold exposure induced an adipocyte population expressing high levels of de novo lipogenesis (DNL) genes, and this response was severely blunted in aged animals. We further identified natriuretic peptide clearance receptor Npr3, a beige fat repressor, as a marker gene for a subset of white adipocytes and an aging-upregulated gene in adipocytes. In summary, this study indicates that aging blocks beige adipogenesis and dysregulates adipocyte responses to cold exposure and provides a unique resource for identifying cold and/or aging-regulated pathways in adipose tissue.
This fundamental study provides evidence that de novo beige adipogenesis from Pdgfra+ adipocyte progenitor cells is blocked during early aging in subcutaneous fat. The depth of the data at early ages is compelling, with rigorous cell tracing methodology employed. The study will aid in identifying new approaches to switch dormant adipocytes into an active thermogenic phenotype, and should be of interest to cell biologists at large.
Brown and beige fat cells are specialized to burn calories for heat production in response to certain stimuli and have the capacity to reduce obesity and metabolic disease. Brown adipocytes are localized in dedicated brown adipose tissue (BAT) depots, whereas beige adipocytes develop in white adipose tissue (WAT) in response to cold exposure, and other stimuli (W. Wang & Seale, 2016). Adult humans possess thermogenic adipose depots that resemble rodent beige adipose tissue (Jespersen et al., 2013; Wu et al., 2012). Brown and beige adipocytes share similar cellular features such as abundant mitochondria, multilocular lipid droplets, and expression of thermogenic genes like Uncoupling Protein-1 (UCP1). UCP1, when activated, dissipates the mitochondrial proton gradient, leading to high levels of substrate oxidation and heat production (Cannon & Nedergaard, 2004). Brown and beige adipocytes can also produce heat via several other UCP1-independent futile cycles (Chouchani, Kazak, & Spiegelman, 2019).
Increasing beige fat development in mice reduces obesity and improves insulin sensitivity, whereas ablation of beige fat in mice causes metabolic dysfunction (Cederberg et al., 2001; Cohen et al., 2014; Seale et al., 2011; Shao et al., 2016; Stine et al., 2016). Furthermore, transplantation of human beige adipocytes into obese mice reduces liver steatosis and improves metabolic health (Min et al 2016). Beige adipocytes develop via the de novo differentiation of adipocyte precursor cells (ASPCs) or through induction of the thermogenic program in adipocytes (Ferrero, Rainer, & Deplancke, 2020; Sakers, De Siqueira, Seale, & Villanueva, 2022; Shao et al., 2019).
Human and mouse thermogenic adipose tissue activity declines with aging, predisposing to cardiometabolic disease and limiting the potential of brown/beige fat targeted therapies (Becher et al., 2021; Berry et al., 2017; Cypess et al., 2012; Rogers, Landa, Park, & Smith, 2012; W. Wang et al., 2019; Yoneshiro et al., 2011). In mice, beige adipose tissue is reduced by ‘middle-age’ (i.e., 1-year-old), preceding many of the damaging effects of old age on organ function (Berry et al., 2017; Goncalves et al., 2017; Rogers et al., 2012). The aging-associated decline in beige fat activity can occur independently of increases in body weight (Rogers et al., 2012; St-Onge, 2005). A variety of processes and pathways have been linked to the aging-induced deficit in beige fat formation, including diminished proliferation and cellular senescence of ASPCs (Berry et al., 2017), increased fibrosis (W. Wang et al., 2019), increased inflammation (Amiya Kumar Ghosh, 2019), accumulation of anti-adipogenic regulatory cells (Nguyen et al., 2021), and reduced adrenergic tone (Rogers et al., 2012). However, a detailed understanding of how cold exposure and aging affect ASPC identity, adipogenesis, and adipocyte phenotypic switching remains elusive.
We applied ASPC lineage tracing, along with unbiased single-cell and single-nucleus RNA sequencing (scRNA-seq; snRNA-seq) to comprehensively profile the beiging process and evaluated the impact of aging on this process. We found that aging modulates the gene program of multiple fibroblastic ASPC populations and blocks the differentiation of these cells into beige adipocytes in vivo. snRNA-seq analysis revealed four types of adipocytes defined by different responses to cold exposure and aging: beige, Npr3-high, de novo lipogenesis (DNL)-low, and DNL-high. Notably, DNL-high adipocytes were defined by the marked induction of DNL genes during cold exposure in young compared to aged animals. A white adipocyte subpopulation in young mice were marked by expression of Natriuretic peptide receptor-3 (Npr3), which was also increased in multiple adipocyte populations of aged mice. Altogether, this study shows that aging blocks cold-stimulated adipocyte reprogramming and ASPC adipogenesis and implicates suppression of natriuretic peptide signaling and DNL in contributing to the aging-mediated decline in beige fat formation.
Aging impairs iWAT beiging
To study the impact of aging on beige adipose tissue development, we exposed young (9-week-old) and middle aged (57-week-old) C57BL/6 mice to 6°C for either 3 or 14 days. All mouse groups were first acclimated to 30°C (thermoneutrality [TN]) for 3 weeks to reduce beige adipose tissue to baseline (low) levels. Following acclimation, TN-housed mice remained at 30°C; acute cold mice (3D) were transitioned to 6°C after 11 days for the final 3 days; and chronic cold mice (14D) were moved to 6°C for two weeks (Figure 1A). As expected, the aged mice weighed more and had larger iWAT depots than the young mice (Figure S1A,B). Cold exposure greatly and progressively increased the expression levels of thermogenic genes Ucp1, Cidea, Dio2 and Ppargc1a in young iWAT, but the activation of these genes was significantly blunted in aged mice, especially at the 3D timepoint (Figure 1B). Immunofluorescence staining showed a robust induction of UCP1 protein in multilocular adipocytes of young iWAT at 3D of cold exposure, which was further increased at 14D. The induction of UCP1+ beige adipocytes was severely reduced in aged animals, with strikingly few UCP1+ adipocytes detected at. At 14D, the beige adipocytes in young and aged look morphologically similar, although there are fewer in aged. (Figure 1C). Beige adipocytes in young and aged were most prominent in the inguinal region (versus dorsolumbar) of iWAT, consistent with other reports (Barreau et al., 2016; Chi et al., 2018; Dichamp et al., 2019) and beiging was largely absent in the dorsolumbar region of aged mice (Figure S1C-D). To determine if the beiging response is delayed in aged mice, we exposed young and aged mice at 6°C for 6 weeks. At this time point, the iWAT of aged mice exhibited a larger deficit in thermogenic gene expression compared to young animals (Figure 1D). Thermogenic gene levels in interscapular BAT were similar between young and aged mice, at TN and after cold exposure, indicating that the inhibitory effects of aging were selective to WAT (Figure S1E).
Next, we examined beige fat formation in young and aged animals upon treatment with the β3-selective adrenergic agonist CL-316,243 (CL). CL acts in an adipose tissue autonomous manner to stimulate beige fat biogenesis, bypassing the central nervous system pathways that mediate the response to cold exposure. Acute CL treatment for only 1 hour increased iWAT Ucp1 expression in young mice to a much greater extent than in aged mice (Figure 1E). Chronic CL exposure for 5 days also induced much higher expression levels of Ucp1 and Cidea in iWAT of young compared to aged mice (Figure 1F). Taken together, these results demonstrate that beige adipose tissue induction is severely impaired in middle aged mice.
Aging blocks beige adipogenesis from Pdgfra+ ASPCs
To determine the contribution of fibroblastic ASPCs to beige adipocytes during cold exposure, we performed lineage tracing using Pdgfra-CreERT2;R26RtdTomato reporter mice. Pdgfra expression marks multiple ASPC populations, including preadipocytes (Merrick et al., 2019; Sakers et al., 2022). Young and aged reporter mice were treated with tamoxifen for 5 days at TN (30°C; “pulse”) to activate Cre and induce tdTomato expression in Pdgfra+ cells. Following a 9 day washout period, mice were transferred to 6°C (cold) for two weeks (“chase”) (Figure 2A). We observed near complete and specific labeling of ASPCs during the pulse period, with ∼95% of PDGFRα+ cells in iWAT from young and aged mice displaying tdTomato expression (Figures 2B, S2A). No tdTomato-expressing adipocytes were observed after the pulse (Figure S2B). After 14 days of cold exposure, we detected many newly developed beige adipocytes from ASPCs in young mice (visible as tdTomato+/UCP1+ multilocular adipocytes). By contrast, very few ASPC-derived (tdTomato+) were detected in the beige fat areas of aged iWAT at day 14 (Figures 2C). Quantifying across the entire length of iWAT pads revealed that most beige adipogenesis occurred in the inguinal region and was ∼12-fold lower in aged than in young (Figure 2D,E). However, the overall contribution of Pdgfra+ ASPCs to beige adipocytes was relatively low, even in young animals, with <20% of beige adipocytes expressing tdTomato.
Single cell expression profiling of ASPCs
We previously identified three main fibroblastic ASPC populations in iWAT: DPP4+ interstitial cells, ICAM1+ preadipocytes, and CD142+ cells. All these cell types express Pdgfra and have the capacity to undergo adipogenic differentiation (Merrick et al., 2019). We hypothesized that the aging-related impairment of beige adipogenesis was caused by dysregulation of these ASPC types. To investigate this, we performed scRNA-seq on stromal vascular cells from iWAT of young and aged animals, maintained at TN, or following transition to cold for 3 or 14 days (Figure 1A). ASPCs were enriched by removing immune (CD45+) cells using fluorescence activated cell sorting (FACS). We integrated the datasets from all conditions together and performed clustering analysis. The following cell populations were annotated based on their expression of cell type-specific marker genes: four fibroblast populations (Dpp4+; Icam1+ preadipocytes; Cd142+, Spp1+), two populations of endothelial cells (Pecam1+); smooth muscle cells/pericytes (Myh11+, Pdgfrb+); Schwann cells (Mpz+); and residual immune cells (Ptprc+) (Figures 3A-C). Aging or cold exposure did not promote the emergence of any specific cell populations. In this regard, we did not identify ‘aging-dependent regulatory cells (ARCs)’, which were previously defined as ASPCs expressing Lgals3 and other inflammatory genes (Figure S3A) (Nguyen et al., 2021). The expression levels of identity markers of the ASPC populations were not modulated during cold exposure or aging (Figure S3B).
Differential gene expression analyses identified aging-modulated genes in the ASPC populations (Figure 3D). Notably, expression of Cd9, previously identified as a fibrogenic marker, was upregulated with age in Dpp4+ cells and preadipocytes (Marcelin et al., 2017). Pltp and Gpnmb were also elevated by aging across all ASPC populations and temperature conditions. Genes downregulated by aging in all ASPC populations included Meg3, Itm2a and Gpc3 and Postn. Of note, Postn encodes an extracellular matrix protein that was previously reported to regulate adipose tissue expansion and decrease in expression during aging (Graja et al., 2018).
ASPCs from aged mice are competent for beige adipogenesis ex vivo
We next evaluated if ASPCs from young and aged animals exhibit cell-autonomous differences in adipogenic differentiation capacity. We FACS-purified the three ASPC populations, DPP4+, ICAM1+ and CD142+ cells, from the iWAT of young and aged mice, plated them in culture and induced adipocyte differentiation. Using a minimal differentiation stimulus consisting of insulin only (MIN), ICAM1+ and CD142+ cells underwent more efficient differentiation into lipid droplet-containing adipocytes, and expressed higher levels of adipocyte genes (Adipoq and Fabp4) than DPP4+ cells, consistent with prior work (Figures 4A,B) (Merrick et al., 2019). DPP4+ and CD142+ cells from young and aged mice underwent adipocyte differentiation and induced adipocyte genes with equivalent efficiency. Unexpectedly, aged ICAM1+ cells exhibited greater differentiation capacity than young ICAM1+ cells, as evidenced by higher expression levels of Adipoq and Fabp4 (Figures 4A,B). Maximal stimulation with a full cocktail of adipogenic inducers (MAX), produced similar and robust differentiation in all ASPC populations from young or aged mice (Figures 4C,D). To assess whether young and aged precursor cells behave differently when cultured as a mixed heterogeneous population, we isolated the stromal vascular fraction (SVF) for adipogenesis assays. Again, SVF cell cultures from both young and aged mice displayed similar adipogenic differentiation capacity with either MIN or MAX stimulation (Figures 4E,F). Finally, we stimulated cell cultures with the pan-adrenergic agonist isoproterenol for 4 hours to evaluate thermogenic gene activation (i.e., beiging). Basal levels of Ucp1 expression appear to be lower in DPP4+ cells compared to other ASPC types, but all three ASPC populations activated Ucp1 expression to high and similar levels in response to stimulation and did not differ by age (Figure 4G). We also did not observe an aging-related difference in the levels of Ucp1 induction in SVF-derived adipocyte cultures stimulated with either MAX or MIN cocktail, and as expected, MAX differentiated cells demonstrated greater stimulated capacity (Figure 4H). Together, these data suggest that the beige adipogenic capacity of ASPCs is not intrinsically compromised in aged mice, and therefore the in vivo deficit in beige adipogenesis could be due to non-ASPC-autonomous effects.
Single nucleus RNA sequencing uncovers adipocyte heterogeneity
To determine the effects of aging and cold exposure on adipocyte gene profiles, we performed snRNA-seq analyses of iWAT samples using the same experimental paradigm described above (Figure 1A). We integrated all the conditions together for analyses from two separate runs. Similar cell types were captured as with scRNA-seq (Figure 3A), but with the addition of mature adipocyte populations (Figure 5A). This dataset has increased representation from immune cells since there was no negative selection against CD45+ cells. As with the single-cell data set, we did not identify any aging-specific cell populations (Figure S4A). However, we observed striking differences in the adipocyte cluster across age and temperature. Most obvious was the emergence and expansion of a distinct beige adipocyte population, marked by expression of Ucp1 and other thermogenic genes, during cold exposure (Figure 5B).
To focus on adipocyte responses, we reintegrated the snRNA-seq data using only the adipocytes, which revealed four main clusters (Figures 5D-F). Beige adipocytes, marked by high expression of many thermogenic genes (i.e., Ppargc1a, Esrrg, Cidea, Gk and Ucp1), were the most distinctive cluster and were largely absent at TN in young and aged mice, but they began to appear in young mice after 3 days of cold exposure, and were further increased at 14 days. By contrast, in aged mice, beige cells were barely detectable at 3 days of cold exposure and were present at greatly reduced numbers than in young mice at 14 days (Figure 5E). This analysis also revealed three sub-populations of ‘white’ adipocytes. ‘Npr3-high’ adipocytes were enriched for expression of Npr3, Synpo2, Prr16, and Tshr, expressed higher levels of canonical white fat marker genes Leptin (Lep) and Nnat, and exhibited the lowest expression levels of thermogenic (beige) genes. Two additional white adipocyte clusters were designated as ‘de novo lipogenesis (DNL)-low’ and ‘DNL-high’ cells, both of which expressed lower levels of Npr3 and shared selective expression of Fgf14. DNL-high cells uniquely expressed Ces1f and Gsta3, and activated high levels of DNL pathway genes (i.e., Fasn, Acss2 and Acly) upon cold exposure (Figure 5F). Quantification of adipocyte nuclei from this data set showed that the proportions of Npr3-high and DNL-high adipocytes remain stable across temperature, with aged mice having more Npr3-high adipocytes. The proportion of beige adipocytes increased dramatically during cold exposure selectively in young animals, as expected, while DNL-low adipocytes decreased with cold exposure in both young and aged mice (Figure 5G).
Aging dysregulates gene programming in adipocyte populations
To evaluate the global effects of cold exposure and aging on adipocytes, we performed differential gene expression analysis between young and aged adipocytes within each cluster. DNL-high and beige adipocytes exhibited the most dramatic expression changes between young and aged animals (Figures 6A-B, S4B-C). At TN, DNL-high cells from aged animals expressed lower levels of several genes, including Fkbp5, Spon1 and Adam12. Interestingly, Npr3, in addition to marking Npr3-high cells, was increased by aging in DNL-high adipocytes and to a lesser extent in other adipocyte populations (Figure 6C,D). In young animals, Npr3 expression was downregulated by cold exposure in the three white adipocyte populations, and this downregulation was blunted in aged animals (Figure 6D). Gene expression analysis of whole iWAT pads confirmed that Npr3 mRNA levels are progressively decreased by cold exposure and elevated in aged versus young mice under all temperature conditions (Figure 6E). Npr3 expression levels were also increased in isolated primary adipocytes from aged relative to young mice (Figure 6F). Expression levels of the G-protein coupled NP receptors Npr1 or Npr2 were not modulated by cold or aging in iWAT or iWAT adipocytes (Figure S4D,E).
We also observed a striking activation of the DNL and related gene programs (Acly, Fasn, Acaca, Scd1, etc.) in DNL-high and beige adipocytes during cold exposure (Figures 6G,H). The induction of these genes during cold exposure, exemplified by Acly expression, was a cluster-defining attribute of DNL-high cells, which did not express beige genes like Ucp1 even after 14 days of cold exposure. Of note, we found two types of beige (UCP1+) adipocytes, distinguished by the presence vs. absence of high DNL gene levels (i.e., UCP1+; DNL+ and UCP1+;DNL(-)), with the latter arising first during cold exposure (3D vs. 14D) (Figures 6G, S4F,G). Importantly, the induction of DNL genes was nearly completely blocked in DNL-high cells and reduced in beige cells of aged animals (Figure 6G). Indeed, the top aging downregulated genes in adipocytes from cold exposed mice correspond to DNL and related pathways, especially in DNL-high cells (Figure S4I). Lastly, at the whole tissue level, we observed robust induction of Acly in iWAT of young relative to aged mice with increasing duration of cold exposure (Figure S4H). Taken together, these results implicate the suppression of natriuretic peptide signaling and DNL in contributing to the aging-related impairment of beige fat formation.
Thermogenic adipose tissue activity declines during aging of mice and humans, correlating with increases in fat mass and susceptibility to cardiometabolic diseases (Berry et al., 2017; Cypess et al., 2009; Pfannenberg et al., 2010; Rogers et al., 2012; Saito et al., 2009; W. Wang et al., 2019; Yoneshiro et al., 2011). Our study provides a comprehensive unbiased profile of the adipose tissue beiging process and reveals pathways dysregulated by aging in ASPCs and adipocytes during this process.
Beige adipocytes develop via the de novo differentiation of ASPCs or through activation of the thermogenic gene program in mature adipocytes. Previous studies defined three populations of fibroblastic ASPCs in iWAT, namely Dpp4+ interstitial cells, Icam1+ preadipocytes, and Cd142+ cells (Burl et al., 2018; Merrick et al., 2019). Aging or cold exposure did not induce dramatic shifts in either the proportions, or gene expression signatures of any of these ASPC types, suggesting that these cell populations are stably maintained across a range of conditions. In support of this, aging did not diminish the cell-intrinsic adipogenic capacities of these ASPC populations, when isolated and subjected to adipogenesis assays ex vivo. Notably, we did not observe the emergence of aging-dependent regulatory cells (ARCs), previously described as modulated ASPCs co-expressing ASPC and immune marker genes, which have the capacity to suppress adipocyte differentiation (Nguyen et al., 2021). However, we did observe the induction of ARC-selective gene markers (i.e., Lgals3, Cd36) specifically in immune cells (Ptprc+, Adgre1+) from aged mice in both our scRNA-seq and snRNA-seq datasets. This Lgals3/Cd36 gene signature has also been described in Lin+ macrophages and CD45+ lipid-associated (LAM) macrophages (Burl et al., 2018; Jaitin et al., 2019). Overall, our results suggest that aging-induced alterations to the systemic milieu or adipose tissue environment are responsible for the block in beige adipogenesis.
Gene expression analyses identified several genes that were altered by aging across multiple ASPC types and temperature conditions. The top aging-upregulated gene was Cd9, which was previously identified as a marker of fibrogenic (fibrosis-generating) progenitor cells (Marcelin et al., 2017). Cd9 encodes for a tetraspanin protein implicated in various processes that could affect adipogenesis, including extracellular vesicle production, cell adhesion, inflammation, and platelet activation (Brosseau, Colas, Magnan, & Brouard, 2018). Aging also upregulated the expression of Pltp and Gpnmb, which are both linked to the regulation of inflammation and fibrosis (Prabata, Ikeda, Rahardini, Hirata, & Emoto, 2021; Saade, Araujo de Souza, Scavone, & Kinoshita, 2021). Conversely, Meg3, Itm2a and Postn were consistently downregulated across all ASPC populations from aged versus young mice. Of note, Periostin (Postn) is an extracellular matrix protein that regulates adipose tissue lipid storage, and its levels were previously shown to decrease in several adipose tissue depots during aging (Graja et al., 2018).
We were surprised by the limited (<20%) contribution of fibroblastic (Pdgfra+) ASPCs, (which includes Pparg-expressing preadipocytes), to beige adipocytes during cold exposure. Previous studies in mice using an adipocyte fate tracking system show that a high proportion of beige adipocytes arise via the de novo differentiation of ASPCs (Q. A. Wang, Tao, Gupta, & Scherer, 2013). However, the relative contribution from ASPC differentiation and direct adipocyte conversion to the formation of beige adipocytes depends highly on the experimental conditions, especially cold exposure history (Shao et al., 2019). Mice housed at TN from birth undergo high rates of de novo beige adipogenesis upon first cold exposure, whereas mice reared at room temperature acquire many ‘dormant’ beige adipocytes that can be re-activated by cold exposure (Rosenwald, Perdikari, Rulicke, & Wolfrum, 2013; Shao et al., 2019). Based on these findings, we presume that mature (dormant beige) adipocytes serve as the major source of beige adipocytes in our cold-exposure paradigm. However, long-term cold exposure also recruits smooth muscle cells to differentiate into beige adipocytes; a process that we did not investigate here (Berry, Jiang, & Graff, 2016; Long et al., 2014; McDonald et al., 2015; Shamsi et al., 2021).
The beiging process is associated with a dramatic remodeling of adipose tissue structure and metabolic function. We applied snRNA-seq analysis to investigate the cold response of iWAT adipocytes in young and aged animals, leading us to identify four adipocyte clusters: beige adipocytes and three “white” subsets: Npr3-high, DNL-low and DNL-high adipocytes. Npr3-high adipocytes were enriched for expression of white fat-selective genes and exhibit the lowest levels of thermogenic genes (Rosell et al., 2014; Ussar et al., 2014). Interestingly, Npr3 also upregulated by aging in all white adipocytes. Previous studies show that obesity also increases Npr3 levels in adipose tissue of mice and humans (Gentili et al., 2017; Kovacova et al., 2016). NPR3 represses beige fat development and adipocyte thermogenesis by functioning as a clearance receptor for natriuretic peptides (NPs), thereby reducing their lipolytic and thermogenic effects (Bordicchia et al., 2012; Coue et al., 2018; Moro et al., 2004; Sengenès, Berlan, Glisezinski, Lafontan, & Galitzky, 2000; Sengenes et al., 2003). Together, these results suggest that Npr3-high adipocytes may impede beige fat development in a cell non-autonomous manner by reducing NP signaling. Moreover, high NPR3 levels in aged animals could contribute to the block in beige fat development, and targeting this pathway may be a promising avenue to elevate beige fat activity.
We were also intrigued by the dramatic induction of lipogenesis genes in both beige adipocytes and DNL-high cells during cold exposure. Previous work established that cold stimulates opposing pathways of lipid oxidation and lipogenesis in thermogenic fat tissue (Mottillo et al., 2014; Sanchez-Gurmaches et al., 2018; Yu, Lewin, Forrest, & Adams, 2002). The co-occurrence of these two processes is unusual and may provide a mechanism to ensure the continued availability of fatty acids to fuel thermogenesis and/or provide critical metabolic intermediates, such as acetyl-CoA. The Granneman lab demonstrated that high expression of the lipid catabolic enzyme MCAD and lipogenic enzyme FAS occurred in separate populations of iWAT adipocytes upon stimulation with a β3-adrenergic agonist for 3-7 days (Lee, Kim, Kwon, & Granneman, 2017). We identified two subsets of UCP1+ beige adipocytes, distinguished by the presence vs. absence of high levels of DNL genes (i.e., UCP1+; DNL-high and UCP1+; DNL-low). Interestingly, the UCP1+; DNL-high cells accumulated later during cold exposure (14D), suggesting that fully cold-adapted beige adipocytes express both pathways simultaneously. Of note, the induction of Acly and other lipogenic genes was very severely impaired in aged animals. Related to this point, Martinez Calejman and colleagues showed that Acly deficiency in brown adipocytes caused a whitened phenotype, coupled with an unexpected and unexplained reduction in Ucp1 expression (Martinez Calejman et al., 2020). We speculate that high levels of ACLY may be required to support thermogenic gene transcription by supplying and efficiently shuttling acetyl-CoA for acetylation of histones or other factors.
In summary, this work shows that aging impairs beige adipogenesis through non-cell-autonomous effects on adipose tissue precursors and by disrupting adipocyte responses to environmental cold exposure. Expression profiling at the single-cell level reveals adipocyte heterogeneity, including two different types of UCP1+ beige adipocytes. Finally, aging-dysregulated pathways, including natriuretic peptide signaling and lipogenesis, may provide promising targets for unlocking beige adipocyte development.
We thank members of the Seale lab for helpful advice and discussions.
NIH grants DK120982 and DK121801 to P.S.; T32 HD083185 to C.D.H; T32 DK007314 to E.F.; RC2 DK116691 to E.D.R.
C.D.H, A.P.S, R.C. and P.S. were responsible for conceptualization and data analysis. C.D.H and P.S were responsible for writing of the manuscript. C.D.H and A.P.S conducted the majority of the experiments. R.C. and E.F. performed bioinformatics analyses. L.C. processed tissue sections for histology and performed immunostaining. C.J., L.T., and E.D.R. performed and processed the snRNA-seq experiment.
The authors declare no competing interests.
Data and materials availability
scRNA-seq and snRNA-seq datasets are deposited in the Gene Expression Omnibus (GEO) under the super series accession number GSE227441. Data analysis pipelines used for processing of raw sequencing data, integration and clustering can be obtained from: https://github.com/calhounr/Aging-impairs-cold-induced-beige-adipogenesis-and-adipocyte-metabolic-reprogramming
Materials and Methods
Key Resources Table
All animal procedures were approved and performed under the guidance of the University of Pennsylvania Institutional Animal Care and Use Committee. Young (4 weeks) and aged (52 weeks) C57BL/6 male mice were obtained from the National Institute of Aging (C57BL/6JN) or Jackson Laboratories (C57BL/6J, stock number 000664). Mice were housed at 30°C for 3 weeks, then were either: maintained at 30°C for 2 weeks (TN); kept at 30°C for 11 more days before moving to 6°C for 3 days (3D cold) or moved to 6°C for 14 days (14D cold). Mice were single housed during the final two week temperature treatment and provided with a nestlet and shepherd shack. For experiments with CL316,243 (CL, Sigma-C5976), mice were housed at 30°C for 5 weeks, followed by intraperitoneal (IP) injection of 1 mg/kg/d CL either 1 hour prior to tissue harvest or for 5 days. PdgfraCreERT2 mice were obtained from Dr. Brigid Hogan (Duke University) (Chung, Bujnis, Barkauskas, Kobayashi, & Hogan, 2018) and crossed with Rosa26tdTomato (strain: B6.Cg-Gt(ROSA)26Sortm14(CAG-tdTomato)Hze/J, stock no. 007914). To induce Cre activity, tamoxifen (Sigma, T5648) dissolved in corn oil (Sigma, C8267) was injected intraperitonially (IP) into mice at a dose of 100 mg/kg/d for 5 days. For all iWAT processing other than histology, the inguinal lymph node was removed.
Histology and Immunofluorescence
Tissues were fixed overnight in 4% paraformaldehyde, washed with PBS, dehydrated in ethanol, paraffin-embedded and sectioned. Following deparaffinization, slides were subjected to heat antigen retrieval in a pressure cooker with Bulls Eye Decloaking buffer (Biocare), unless otherwise noted. Slides were incubated in primary antibody overnight and secondary antibody conjugated to peroxidase and then developed using Tyramide Signal Amplification (TSA, Akoya Biosciences). Samples were stained with the following antibodies: anti-red fluorescent protein (RFP) (rabbit; 1:500; Rockland #600-401-379), anti-UCP1 (rabbit, 1:2000, AstraZeneca), and anti-PLIN1 (rabbit, 1:200 Cell Signaling #3470). Slides were imaged on an inverted fluorescence microscope (Keyence BZ-X710). For quantification of tdTomato-expressing adipocytes, full-length iWAT slices were tile imaged, stitched, exported as a BigTiff, and quantified using the Count Tool in Photoshop (Adobe).
Isolation of stromal vascular cells (SCVs) and adipocytes
As previously described (Merrick et al 2019, Wang et al 2019), iWAT tissue was dissected, minced gently and digested with Collagenase Type I (1.5 units/ml; Worthington) and Dispase II (2.4 units/ml; Roche) in DMEM/F12 containing 1% fatty acid-free bovine serum albumin (Gold Biotechnology) in a gentleMACS dissociator (Miltenyi Biotec) on program “37 MR ATDK-1.” The digestion was quenched with DMEM/F12 containing 10% FBS, and the dissociated cells were passed through a 100 μm filter and spun at 400 x g for 4 mins. The pellet was resuspended in red blood cell lysis buffer (BioLegend), incubated for 4 mins at RT, then quenched with DMEM/F12 containing 10% serum. Cells were passed through a 70 μm filter, spun, resuspended, then passed through a final 40 μm filter, spun at 400 x g for 4 minutes and plated or underwent further processing for FACS. Mice were not pooled unless indicated.
Tissue went through the same process as above, except after digestion and quenching, adipocyte/SVF slurry was filtered through a 200 μm filter and centrifuged at 50 x g for 3 mins at RT. Using a 20 mL syringe and 1.5-inch, 25G needle, media containing the SVCs was removed from below the adipocytes (and saved if concurrently isolating SVCs), leaving only the adipocytes in the tube. Adipocytes were washed twice with the same media as quenching, transferred to 2 mL tubes, spun a final time, media was removed from below the adipocytes again, and TRIzol was added for RNA extraction. Mice were not pooled.
DPP4+, ICAM1+, and CD142+ cells were isolated as previously described (Merrick et al 2019). Briefly, SVCs from the subcutaneous adipose of mice (n= 2-5) were pooled and resuspended in FACS buffer (HBSS containing 3% FBS; Fisher), then incubated for 1 hr at 4°C with the following antibodies: CD26 (DPP4)-fluorescein isothiocyanate (FITC) (Biolegend, 137806; 1:200), anti-mouse ICAM1-phycoerythrin (PE)/Cy7 (Biolegend, 116122; 1:100), anti-mouse CD45-allophycocyanin (APC)/Cy7 (Biolegend, 103116; 1:1000), anti-mouse CD31-APC-Fire (Biolegend, 102528; 1:1000), and anti-mouse CD142 (Sino Biological, 50413-R001, 1:100; or R&D Systems, AF3178, 1:50). Anti-mouse CD142 antibodies were conjugated with Biotium Mix-n-Stain CF647 (Sigma, MX647S100). For lineage tracing pulse analysis, SVCs were isolated from individual mice without pooling. SVCs were stained with anti-mouse CD31, anti-mouse CD45, and anti-mouse CD140a (PDGFRΑ) (PE/Cy7) (Biolegend, 135912; 1:100). In all FACS experiments, cells were stained with 4′,6-diamidino-2-phenylindole (DAPI) (Roche, 10236276001; 1:10,000) for 5 minutes, then washed three times with FACS buffer to remove unbound antibodies. Cells were sorted with a BD FACS Aria cell sorter (BD Biosciences) equipped with a 100 μm nozzle and the following lasers and filters: DAPI, 405 and 450/50 nm; FITC, 488 and 515/20 nm; mTomato, 532 and 610/20 nm; PE/Cy7, 532 and 780/60 nm; CF647, 640 and 660/20 nm; and APC/Cy7 and APC-Fire, 640 and 780/60 nm. All compensation was performed at the time of acquisition in Diva software by using compensation beads (BioLegend, A10497) for single-color staining and SVCs for negative staining and fluorescence (DAPI and tdTomato).
Cell culture and differentiation
Adipocyte precursor cells
All cells were cultured in DMEM/F12 containing 10% FBS and Primocin (50 ng/ml) (InvivoGen, ant-pm-1). DPP4+, ICAM1+, and CD142+ populations were FACS purified, plated on CellBind 384-well plates (Corning) at 15-25K cells/well, and incubated for 48 (25K cells) to 72 hours (15K cells) to facilitate attachment before the induction of adipogenic differentiation. For whole SVF, SVCs were isolated and plated in a 48 well CellBind plate (Corning) at a high confluency of one mouse per 18 wells. No cells were passaged after plating to maintain adipogenic competency. Differentiation was carried out with either maximum adipogenic cocktail, max: 500 μM isobutylmethylxanthine (Sigma, I7018), 10 μM dexamethasone (Sigma, D4902), 125 μM indomethacin (Sigma, I8280), 1 μM rosiglitazone (Cayman Chemical, 11884), 1 nM T3 (Sigma, T6397), and 20 nM insulin (Novolin) or a minimal adipogenic cocktail, min: 20 nM insulin. For the max adipogenic cocktail induction, cells were incubated with cocktail for 2 days and then transferred to adipogenic maintenance medium for the remaining 6 days (1 μM rosiglitazone, 1 nM T3, and 20 nM insulin). For all conditions, medium was changed every 2 days, and cells were harvested on day 8 of differentiation. For drug treatments, cells were treated for 4 hrs on day 8 with 1 μM isoproterenol (Sigma, I6504). Adipogenesis was assessed by staining with Biodipy 493/503 (Invitrogen, D3922) for lipid droplet accumulation and Hoechst 33342 (Thermo Fisher, 62249) for nuclei number. The cells were imaged on a Keyence inverted fluorescence microscope (BZ-X710) by using DAPI (excitation, 360/40 nm; emission, 460/50 nm) and green fluorescent protein (excitation, 470/40 nm; emission, 525/50 nm) filters. Individual wells were imaged in their entirety at 4x magnification, and at 20x to see morphology. 384-well plates were not stained and imaged in brightfield due to low cell number recovery from FACS prior to RNA extraction.
RNA Extraction, qRT-PCR and RNA Sequencing
Total RNA was extracted using TRIzol (Invitrogen) combined with PureLink RNA Mini columns (Thermo Fisher, 12183025) for tissue and SVC cells or by PicoPure RNA Isolation Kit (Applied Biosystems, KIT0204) for 384-well plate populations and adipocytes. Prior to the addition of chloroform, all tissue and primary adipocytes in TRIzol included an extra spin at max speed for 10 minutes at RT, then TRIzol was removed from below the lipid layer to avoid lipid contamination disrupting the subsequent phase separation with chloroform. Chloroform was added to the lipid-free TRIzol, spun for 15 mins at 12,000 x g and the aqueous layer was removed and added to columns. mRNA was quantified using a Nanodrop and reverse transcribed to cDNA using the ABI High-Capacity cDNA Synthesis kit (ABI, 4368813). Real-time PCR was performed on a QuantStudio5 qPCR machine using SYBR green fluorescent dye (Applied Biosystems). Fold changes were calculated using the ddCT method, with TATA binding Protein (Tbp) mRNA serving as a normalization control.
Single Cell RNA-seq Samples
Cells were flow sorted to isolate live (DAPI-) cells and remove debris. We enriched non-immune cells by sorting out CD45+ cells. Next-generation sequencing libraries were prepared using the Chromium Next GEM Single Cell 3’ Reagent kit v3.1 (10x Genomics, 1000121) per manufacturer’s instructions. Libraries were uniquely indexed using the Chromium Single Index Kit T Set A, pooled, and sequenced on an Illumina NovaSeq 6000 sequencer in a paired-end, dual indexing run by the CHOP Center for Applied Genomics at the University of Pennsylvania. Sequencing for each library targeted 20,000 mean reads per cell.
Single Nucleus RNA-seq Samples
Nuclei were isolated from frozen mouse iWAT samples as previously described, with the following modifications to integrate hash multiplexing and FANS-assisted nuclear quality thresholding and sample pooling (Drokhlyansky et al., 2020; Slyper et al., 2020). Briefly, 300 mg of flash-frozen adipose samples were held on dry ice until immediately before nuclei isolation, and all sample handling steps were performed on ice. Each sample was placed into a gentleMACS C tube (Miltenyi Biotec, 130-093-237) with 2 mL freshly prepared TST buffer (0.03% Tween 20 (Bio-Rad), 0.01% Molecular Grade BSA (New England Biolabs), 146 mM NaCl (ThermoFisher Scientific), 1 mM CaCl2 (VWR International), 21 mM MgCl2 (Sigma Aldrich), and 10 mM Tris-HCl pH 7.5 (ThermoFisher Scientific) in ultrapure water (ThermoFisher Scientific)) with 0.2 U/μL of Protector RNase Inhibitor (Sigma Aldrich, RNAINH-RO). gentleMACS C tubes were then placed on the gentleMACS Dissociator (Miltenyi Biotec) and tissue was dissociated by running the program “mr_adipose_01” three times, and then incubated on ice for 10 minutes. Lysate was passed through a 40 μm nylon filter (CellTreat) and collected into a 50 mL conical tube (Corning). Filter was rinsed with 3 mL of freshly prepared ST buffer (146 mM NaCl, 1 mM CaCl2, 21 mM MgCl2; 10 mM Tris-HCl pH 7.5) with 0.2 U/μL RNase Inhibitor, and collected into the same tube. Flow-through was passed through a 20 μm pre-separation filter (Miltenyi Biotec) set on top of a 5 mL FACS tube (Corning) and collected into the same tube. Suspension was centrifuged in a swinging-bucket centrifuge (Eppendorf) at 500 × g for 5 minutes at 4°C with brake set to low. Following centrifugation, supernatant was removed and 5 mL of PBS pH 7.4 (ThermoFisher Scientific) with 0.02% BSA and 0.2 U/μL RNase Inhibitor was added without resuspending the nuclear pellet. Sample was centrifuged again at 500 × g for 5 minutes at 4°C with brake set to low. Following centrifugation, supernatant was removed, and the nuclear pellet was resuspended in 1 mL PBS-0.02% BSA with 0.2 U/μL RNase Inhibitor. Each sample was split into two 500 μL aliquots and transferred to new 5 mL FACS tubes for subsequent hashing. Each aliquot of resuspended nuclei was stained with NucBlue (ThermoFisher, R37605), labeled with 1 μg of a unique TotalSeq anti-Nuclear Pore Complex Proteins Hashtag Antibody (Biolegend), and then incubated on ice for 30 minutes. Suspension was centrifuged at 500 × g for 5 minutes at 4°C with brake set to low. Following centrifugation, 450 μL of supernatant was removed and the nuclear pellet was resuspended in 450 μL PBS-0.02% BSA with 0.2 U/μL RNase Inhibitor. For nuclear quality thresholding, fluorescence-activated nuclear sorting (FANS) was implemented to collect 4,000-4,300 nuclei from hashtagged aliquots directly into a shared well of a 96-well PCR plate (Thermo Scientific) containing 24.6 μL of 10X RT Reagent B with 1U/uL RNase Inhibitor on a Beckman Coulter MoFlo AstriosEQ fitted with a 70 μm nozzle. High-quality nuclei were selected by initial gating at 360 nm with laser filter 405-448/59 followed by SSC-H and FSC-H to remove doublets and unlysed cells. Once all sample aliquots were FANS-sorted, the pool of 43,000 nuclei was loaded on the 10x Chromium controller (10x Genomics) according to the manufacturer’s protocol. cDNA and gene expression libraries were generated according to the manufacturer’s instructions (10x Genomics). Libraries of hashtag oligo fractions were generated according to the manufacturer’s instructions (Biolegend). cDNA and gene expression library fragment sizes were assessed with a DNA High Sensitivity Bioanalyzer Chip (Agilent). cDNA and gene expression libraries were quantified using the Qubit dsDNA High Sensitivity assay kit (ThermoFisher, Q32854). Gene expression libraries were multiplexed and sequenced on the Nextseq 500 (Illumina) using a 75-cycle kit and the following read structure: Read 1: 28 cycles, Read 2: 55 cycles, Index Read 1: 8 cycles.
Single Cell RNA Sequencing
Data was processed using the Cell Ranger pipeline (10x Genomics, v.3.1.0) for demultiplexing and alignment of sequencing reads to the mm10 transcriptome and creation of feature-barcode matrices. The cell ranger output files were read into R (version 4.1.1) and processed utilizing the standard Seurat CCA integrated workflow (version 4.3.0). Each of the six samples went through a first phase of filtering, where only cells that recorded more than 200 features and only features present in a minimum of 3 cells were kept. Each sample was filtered prior to downstream analysis on nCount_RNA, nFeature_RNA, and mitochondrial percentages. Samples were then normalized using a LogNormalization method with a scaling factor of 10000 followed by FindVariableFeatures using Variance Stabilization Transformation with the top 6000 features to be returned. The samples were scored on their cell cycle phases which would be used in the regression later. The FindIntegrationAnchors function using the CCA reduction method and IntegrateData was utilized to integrate the data together. The integrated data-set was then scaled in which mitochondrial percentage and cell cycle state was regressed out. A principal component analysis was performed and the top 15 dimensions were kept. Uniform Manifold and Projection (UMAP) was run on the dataset, in addition to FindNeighbors and FindClusters. Differential gene expression between clusters was performed using the FindMarkers function with the Wilocox test in Seurat. Violin plots and individual UMAP plots were all generated using the Seurat toolkit VlnPlot and FeaturePlot functions, respectively. Heatmaps were generated utilizing the pheatmap package (version 1.0.12).
Single Nucleus RNA Sequencing
Raw sequencing reads were demultiplexed to FASTQ format files using bcl2fastq (Illumina; version 2.20.0). Digital expression matrices were generated from the FASTQ files using Cell Ranger (Zheng et al., 2017)(version 6.1.2) with the option to include intronic reads (--include-introns). Reads were aligned against the GRCm38 mouse genome assembly and gene counts were obtained, per-droplet, by summarizing exonic and intronic UMIs that overlapped with the GENCODE mouse annotation (release 24) for each gene symbol. In order to adjust for downstream effects of ambient RNA expression within mouse nuclei, we used the “remove-background” module from CellBender (Fleming et al., 2022) (version 0.2.0) to remove counts due to ambient RNA molecules from the count matrices and to estimate the true cells. Genes were subsequently filtered such that only genes detected in two or more cells and with at least 6 total counts (across all cells) were retained. Sample demultiplexing via hashtag oligonucleotide sequences (HTOs) was performed with the Cumulus sc/snRNA-Seq processing pipeline (Li et al., 2020). Specifically, HTO quantification was performed with the Cumulus Tool on Feature Barcoding, which provided a cell-by-HTO count matrix. This HTO count matrix, along with the gene count matrices generated via Cell Ranger (above) were used to assign each cell to their respective sample(s) with the demuxEM program. Only cells that were identified as singlets were retained (i.e. no cells identified as a multiplet or unassignable) in the per-sample CellBender-ed gene count matrices.
Cellbender output files were read into R (version 4.1.1) and processed utilizing the standard Seurat CCA and later RPCA integration workflows (version 4.3.0). Each of the hashed samples (24 in total) were merged with their respective pair to have a total of twelve samples consisting of six different groups. Each sample was filtered prior to downstream analysis based on their nCount_RNA, nFeature_RNA, and mitochondrial percentages. Samples were then normalized using a LogNormalization method with a scaling factor of 10000 followed by FindVariableFeatures using a Variance-Stabilizing Transformation as the method with the top 2000 features to be returned. The FindIntegrationAnchors function using the CCA reduction method and IntegrateData was utilized to integrate the data together. The integrated data-set was then scaled on which mitochondrial percentage was regressed. A principal component analysis was performed in which only the top 18 dimensions were retained. Uniform Manifold and Projection (UMAP), FindNeighbors, and FindClusters with a resolution of 0.4 was performed on the dataset. To remove doublets in the dataset, we used the package scDblFinder (1.8.0) and their function scDblFinder with the parameters of samples set to our twelve samples, dbr set to NULL, dbr.sd set to 1, clusters set to FALSE, and multiSampleMode set to split. The object was then subsetted to only contain expected singlets. Differential gene expression between clusters was performed using the FindMarkers function with the Wilocox test in Seurat. Violin plots and individual UMAP plots were all generated using the Seurat toolkit VlnPlot and FeaturePlot functions, respectively. Heatmaps were generated utilizing the dittoSeq package (1.9.1) and pheatmap package (version 1.0.12).
After identifying the adipocyte population, we subsetted our object on that population, extracting the raw RNA counts on the cells for each of the six samples (YTN, OTN, Y3D, O3D, Y14D, O14D) (Y is young, O is “Old” or as referred to in this paper, Aged). These samples were then integrated together using the standard RPCA integration workflow. There was no further filtering done on the reintegrated adipocyte population. Samples were normalized using a LogNormalization method with a scaling factor of 10000 followed by FindvariableFeatures using a Variance-Stabilizing Transformation as the method with the top 2000 features to be returned. The function SelectIntegrationFeatures was performed on the dataset where it was then scaled on which mitochondrial percentage was regressed, and principal components were found using the ScaleData and RunPCA functions. The FindIntegrationAnchors function using the ROCA reduction method and a k.anchors of 20 and IntegrateData was utilized to integrate the data together. After integration, the dataset was then scaled in which mitochondrial percentage was regressed on again. A principal component analysis was performed in which only the top 18 dimensions were retained. Uniform Manifold and Projection (UMAP), FindNeighbors, and FindClusters with a resolution of 0.2 was performed on the dataset. Differential gene expression between clusters was performed using the FindMarkers function with a Wilcoxon signed-rank test as the method in Seurat. Violin plots and individual UMAP plots were all generated using the Seurat toolkit VlnPlot and FeaturePlot functions, respectively. Heatmaps were generated utilizing the dittoSeq package (1.9.1) and pheatmap package (version 1.0.12).
Enrichment analysis was performed on the positively expressed genes with a log2 fold change (LFC) > 0.25 and a P adjusted value < 0.01 on comparison of the young 14 days cold and old 14 days cold groups in the DNL high cluster. The generated gene list, which was in order of significance, was fed into g:Profiler (version 0.2.1) using default parameters except with modifications to query as an ordered query against the ‘mmusculus’ database, a gSCS correction method for multiple testing, with domain scope set to annotated, and sources set to the Reactome database. The top six enriched pathways yielded from the database were taken and displayed in order of P adjusted value.
All bar graphs represent the mean ± SE. A Student’s t-test was used when 2 groups were compared. Where multiple conditions were compared, we applied a Holm-Šidák correction for multiple comparisons. p values are indicated by asterisks and defined as *p < 0.05, **p < 0.01 and ***p < 0.001. All statistics were calculated with GraphPad Prism Version 9.5.0.
- Adipose Tissue Senescence and Inflammation in Aging is Reversed by the Young MilieuJ Gerontol A Biol Sci Med Sci 74:1709–1715https://doi.org/10.1093/gerona/gly290
- Regionalization of browning revealed by whole subcutaneous adipose tissue imagingObesity (Silver Spring 24:1081–1089https://doi.org/10.1002/oby.21455
- Brown adipose tissue is associated with cardiometabolic healthNat Med 27:58–65https://doi.org/10.1038/s41591-020-1126-7
- Cellular Aging Contributes to Failure of Cold-Induced Beige Adipocyte Formation in Old Mice and HumansCell Metab 25:166–181https://doi.org/10.1016/j.cmet.2016.10.023
- Mouse strains to study cold-inducible beige progenitors and beige adipocyte formation and functionNat Commun 7https://doi.org/10.1038/ncomms10184
- Cardiac natriuretic peptides act via p38 MAPK to induce the brown fat thermogenic program in mouse and human adipocytesJ Clin Invest 122:1022–1036https://doi.org/10.1172/JCI59701
- CD9 Tetraspanin: A New Pathway for the Regulation of Inflammation?Front Immunol 9https://doi.org/10.3389/fimmu.2018.02316
- Deconstructing Adipogenesis Induced by beta3-Adrenergic Receptor Activation with Single-Cell Expression ProfilingCell Metab 28:300–309https://doi.org/10.1016/j.cmet.2018.05.025
- Brown Adipose Tissue: Function and Physiological SignificancePhysiology Review 84:277–359https://doi.org/10.1152/physrev.00015.2003
- FOXC2 is a winged helix gene that counteracts obesity, hypertriglyceridemia, and diet-induced insulin resistanceCell 106:563–573https://doi.org/10.1016/s0092-8674(01)00474-3
- Three-Dimensional Adipose Tissue Imaging Reveals Regional Variation in Beige Fat Biogenesis and PRDM16-Dependent Sympathetic Neurite DensityCell Metab 27:226–236https://doi.org/10.1016/j.cmet.2017.12.011
- New Advances in Adaptive Thermogenesis: UCP1 and BeyondCell Metab 29:27–37https://doi.org/10.1016/j.cmet.2018.11.002
- Niche-mediated BMP/SMAD signaling regulates lung alveolar stem cell proliferation and differentiationDevelopment 145https://doi.org/10.1242/dev.163014
- Ablation of PRDM16 and beige adipose causes metabolic dysfunction and a subcutaneous to visceral fat switchCell 156:304–316https://doi.org/10.1016/j.cell.2013.12.021
- Natriuretic peptides promote glucose uptake in a cGMP-dependent manner in human adipocytesSci Rep 8https://doi.org/10.1038/s41598-018-19619-0
- Cold but not sympathomimetics activates human brown adipose tissue in vivoProc Natl Acad Sci U S A 109:10001–10005https://doi.org/10.1073/pnas.1207911109
- Identification and importance of brown adipose tissue in adult humansN Engl J Med 360:1509–1517https://doi.org/10.1056/NEJMoa0810780
- 3D analysis of the whole subcutaneous adipose tissue reveals a complex spatial network of interconnected lobules with heterogeneous browning abilitySci Rep 9https://doi.org/10.1038/s41598-019-43130-9
- The Human and Mouse Enteric Nervous System at Single-Cell ResolutionCell 182:1606–1622https://doi.org/10.1016/j.cell.2020.08.003
- Toward a Consensus View of Mammalian Adipocyte Stem and Progenitor Cell HeterogeneityTrends Cell Biol 30:937–950https://doi.org/10.1016/j.tcb.2020.09.007
- CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasetsbioRxiv https://doi.org/10.1101/791699
- Modulation of natriuretic peptide receptors in human adipose tissue: molecular mechanisms behind the “natriuretic handicap” in morbidly obese patientsTransl Res 186:52–61https://doi.org/10.1016/j.trsl.2017.06.001
- Ageing is associated with brown adipose tissue remodelling and loss of white fat browning in female C57BL/6 miceInt J Exp Pathol 98:100–108https://doi.org/10.1111/iep.12228
- Loss of periostin occurs in aging adipose tissue of mice and its genetic ablation impairs adipose tissue lipid metabolismAging Cell 17https://doi.org/10.1111/acel.12810
- Lipid-Associated Macrophages Control Metabolic Homeostasis in a Trem2-Dependent MannerCell 178:686–698https://doi.org/10.1016/j.cell.2019.05.054
- A classical brown adipose tissue mRNA signature partly overlaps with brite in the supraclavicular region of adult humansCell Metab 17:798–805https://doi.org/10.1016/j.cmet.2013.04.011
- Adipose tissue natriuretic peptide receptor expression is related to insulin sensitivity in obesity and diabetesObesity (Silver Spring 24:820–828https://doi.org/10.1002/oby.21418
- Metabolic heterogeneity of activated beige/brite adipocytes in inguinal adipose tissueSci Rep 7https://doi.org/10.1038/srep39794
- Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seqNat Methods 17:793–798https://doi.org/10.1038/s41592-020-0905-x
- A smooth muscle-like origin for beige adipocytesCell Metab 19:810–820https://doi.org/10.1016/j.cmet.2014.03.025
- A PDGFRalpha-Mediated Switch toward CD9(high) Adipocyte Progenitors Controls Obesity-Induced Adipose Tissue FibrosisCell Metab 25:673–685https://doi.org/10.1016/j.cmet.2017.01.010
- mTORC2-AKT signaling to ATP-citrate lyase drives brown adipogenesis and de novo lipogenesisNat Commun 11https://doi.org/10.1038/s41467-020-14430-w
- Myocardin-related transcription factor A regulates conversion of progenitors to beige adipocytesCell 160:105–118https://doi.org/10.1016/j.cell.2014.12.005
- Identification of a mesenchymal progenitor cell hierarchy in adipose tissueScience 364https://doi.org/10.1126/science.aav2501
- Functional and pharmacological characterization of the natriuretic peptide-dependent lipolytic pathway in human fat cellsJ Pharmacol Exp Ther 308:984–992https://doi.org/10.1124/jpet.103.060913
- Coupling of lipolysis and de novo lipogenesis in brown, beige, and white adipose tissues during chronic beta3-adrenergic receptor activationJ Lipid Res 55:2276–2286https://doi.org/10.1194/jlr.M050005
- Aging-dependent regulatory cells emerge in subcutaneous fat to inhibit adipogenesisDev Cell 56:1437–1451https://doi.org/10.1016/j.devcel.2021.03.026
- Impact of age on the relationships of brown adipose tissue with sex and adiposity in humansDiabetes 59:1789–1793https://doi.org/10.2337/db10-0004
- GPNMB plays a protective role against obesity-related metabolic disorders by reducing macrophage inflammatory capacityJ Biol Chem 297https://doi.org/10.1016/j.jbc.2021.101232
- Aging leads to a programmed loss of brown adipocytes in murine subcutaneous white adipose tissueAging Cell 11:1074–1083https://doi.org/10.1111/acel.12010
- Brown and white adipose tissues: intrinsic differences in gene expression and response to cold exposure in miceAm J Physiol Endocrinol Metab 306:E945–964https://doi.org/10.1152/ajpendo.00473.2013
- Bi-directional interconversion of brite and white adipocytesNat Cell Biol 15:659–667https://doi.org/10.1038/ncb2740
- The Role of GPNMB in InflammationFront Immunol 12https://doi.org/10.3389/fimmu.2021.674739
- High incidence of metabolically active brown adipose tissue in healthy adult humans: effects of cold exposure and adiposityDiabetes 58:1526–1531https://doi.org/10.2337/db09-0530
- Adipose-tissue plasticity in health and diseaseCell 185:419–446https://doi.org/10.1016/j.cell.2021.12.016
- Brown Fat AKT2 Is a Cold-Induced Kinase that Stimulates ChREBP-Mediated De Novo Lipogenesis to Optimize Fuel Storage and ThermogenesisCell Metab 27:195–209https://doi.org/10.1016/j.cmet.2017.10.008
- Prdm16 determines the thermogenic program of subcutaneous white adipose tissue in miceJ Clin Invest 121:96–105https://doi.org/10.1172/JCI44271
- Natriuretic peptides: a new lipolytic pathway in human adipocytesThe FASEB Journal 14:1345–1351https://doi.org/10.1096/fasebj.14.10.1345
- Involvement of a cGMP-dependent pathway in the natriuretic peptide-mediated hormone-sensitive lipase phosphorylation in human adipocytesJ Biol Chem 278:48617–48626https://doi.org/10.1074/jbc.M303713200
- Vascular smooth muscle-derived Trpv1(+) progenitors are a source of cold-induced thermogenic adipocytesNat Metab 3:485–495https://doi.org/10.1038/s42255-021-00373-z
- Zfp423 Maintains White Adipocyte Identity through Suppression of the Beige Cell Thermogenic Gene ProgramCell Metab 23:1167–1184https://doi.org/10.1016/j.cmet.2016.04.023
- Cellular Origins of Beige Fat Cells RevisitedDiabetes 68:1874–1885https://doi.org/10.2337/db19-0308
- A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumorsNat Med 26:792–802https://doi.org/10.1038/s41591-020-0844-1
- Relationship between body composition changes and changes in physical function and metabolic risk factors in agingCurrent Opinion in Clinical Nutrition and Metabolic Care 8:523–528
- EBF2 promotes the recruitment of beige adipocytes in white adipose tissueMol Metab 5:57–65https://doi.org/10.1016/j.molmet.2015.11.001
- ASC-1, PAT2, and P2RX5 are cell surface markers for white, beige, and brown adipocytesSci Transl Med 6https://doi.org/10.1126/scitranslmed.3008490
- Tracking adipogenesis during white adipose tissue development, expansion and regenerationNat Med 19:1338–1344https://doi.org/10.1038/nm.3324
- A PRDM16-Driven Metabolic Signal from Adipocytes Regulates Precursor Cell FateCell Metab 30:174–189https://doi.org/10.1016/j.cmet.2019.05.005
- Control of brown and beige fat developmentNat Rev Mol Cell Biol 17:691–702https://doi.org/10.1038/nrm.2016.96
- Beige adipocytes are a distinct type of thermogenic fat cell in mouse and humanCell 150:366–376https://doi.org/10.1016/j.cell.2012.05.016
- Age-related decrease in cold-activated brown adipose tissue and accumulation of body fat in healthy humansObesity (Silver Spring 19:1755–1760https://doi.org/10.1038/oby.2011.125
- Cold elicits the simultaneous induction of fatty acid synthesis and beta-oxidation in murine brown adipose tissue: prediction from differential gene expression and confirmation in vivoFASEB J 16:155–168https://doi.org/10.1096/fj.01-0568com
- Massively parallel digital transcriptional profiling of single cellsNat Commun 8https://doi.org/10.1038/ncomms14049