Mol. Cells 2022; 45(10): 673-684
Published online September 25, 2022
https://doi.org/10.14348/molcells.2022.0092
© The Korean Society for Molecular and Cellular Biology
Correspondence to : hannahhui@cuhk.edu.hk
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/.
The past two decades have witnessed an upsurge in the appreciation of adipose tissue (AT) as an immuno-metabolic hub harbouring heterogeneous cell populations that collectively fine-tune systemic metabolic homeostasis. Technological advancements, especially single-cell transcriptomics, have offered an unprecedented opportunity for dissecting the sophisticated cellular networks and compositional dynamics underpinning AT remodelling. The “re-discovery” of functional brown adipose tissue dissipating heat energy in human adults has aroused tremendous interest in exploiting the mechanisms underpinning the engagement of AT thermogenesis for combating human obesity. In this review, we aim to summarise and evaluate the use of single-cell transcriptomics that contribute to a better appreciation of the cellular plasticity and intercellular crosstalk in thermogenic AT.
Keywords adipose tissue, metabolism, obesity, single-cell sequencing, single-nucleus sequencing, thermogenesis
The brown adipose tissue (BAT) is the metabolically active organ specialised in heat generation (thermogenesis). The presence of BAT in mammals is believed to confer survival advantages that allow organisms to survive and to be active during low ambient temperatures without hibernation and hypothermia (Cannon and Nedergaard, 2004). Compared with their energy-storing lipid-laden white counterparts, the brown adipocytes are characterised by smaller multilocular lipid droplet and high amounts of mitochondria packed with cristae and iron, where the latter gives BAT its brownish appearance. Similarly, the beige adipocytes, despite having distinct cellular progenitors and developmental lineages, are inducible thermogenic adipocytes arising from the white adipose tissue (WAT) and possess brown-like morphology and transcriptional landscapes upon cold exposure or adrenergic stimulation. Remarkably, these beige adipocytes can be readily interconverted between whitening and browning when exposing to warm and cold stimuli respectively (Nanduri, 2021; Roh et al., 2018). Once activated, the high potential of mitochondrial respiration of beige and brown adipocytes facilitates uncoupling of substrate oxidation and ATP production, dissipating energy in the form of heat through uncoupling protein 1 (UCP1) and contributing to up to 60% of total energy expenditure in small mammals (Carpentier et al., 2018; Cohen and Kajimura, 2021). Alternatively, UCP1-indepedent futile cycling of lipids and amino acids where ATP are consumed in the process of synthesis and degradation of triglycerides and peptides can also fuel heat production (Onogi and Ussar, 2022). Other futile circuits identified in beige adipocytes include the phosphorylation and dephosphorylation of creatine and the exchange of calcium ions between endoplasmic reticulum and cytosol (Ikeda et al., 2017; Kazak et al., 2015). In addition to be praised as an energy-burning furnace, the BAT also serves as the macronutrient sink for buffering circulating lipids, glucose and amino acids and helps regulate systemic metabolic homeostasis by secreting endocrine molecules known as the batokines (Chondronikola et al., 2016; Hankir and Klingenspor, 2018; Villarroya et al., 2017; Yoneshiro et al., 2019). Moreover, the fact that BAT confers cardiometabolic benefits independent of increasing energy expenditure alludes to its health-promoting potential and functional complexity (Becher et al., 2021; Gu et al., 2021; Kajimura et al., 2015; Mills et al., 2021). In addition to the efforts on leveraging the functionality of thermogenic adipocytes, insights are being made into the interplay and interconversion between the distinct cell types within the thermogenic adipose tissue (AT), which are benefited from the appreciation of cellular diversity and the identification of new but underrepresented cell type(s).
In fact, early attempts in isolation, cloning and sorting of brown/beige adipocytes have hinted at the intrinsic cellular heterogeneity of AT, but such understanding can only scratch the surface due to the probable overrepresentation of highly-proliferating cells and the lack of characterisation into inter-cellular communication (Hagberg et al., 2018; Jespersen et al., 2019; Shinoda et al., 2015; Spaethling et al., 2016). Such challenges have been largely overcome in the era of single-cell genomics, where developments in single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) have allowed high-throughput profiling of transcriptomic signatures one cell at a time, leading to the appreciation on the diversity of adipose cell populations and accelerating advancements in the field of thermogenic AT research. The field of single-cell genomics has been rapidly evolved from manual picking of a few embryos to liquid-handling robotics by MARS (massively parallel single-cell)-seq enabling thousands of cells to be sampled (Jaitin et al., 2014; Tang et al., 2009). An exponential leap in the magnitude of cell numbers is achieved with the introduction of droplet-based platforms (10× Genomics Chromium, inDrop and Drop-seq) and the most recent
Due to the unique physical characteristics of AT, scRNA-seq is exclusively applied for investigating the relatively dense stromal-vascular fraction (SVF) of freshly harvested AT, as the floating fraction of buoyant adipocytes with various sizes cannot be dissociated into uniform single-cell suspension. Conversely, snRNA-seq, which bypasses harsh enzymatic dissociation, permits the recovery of major adipose cell types from either frozen or fresh tissues, and helps shed light on the exquisite networking between adipocytes and SVF components, especially the immune milieu. However, snRNA-seq can suffer from significant loss of reads, which may compromise the accuracy in distinguishing different cell types when based on transcripts only. Nevertheless, both technologies can face the challenge of contamination from ambient RNA (McLaughlin et al., 2022). A systematic comparison between different single-cell/nucleus sequencing platforms has been made elsewhere (Ding et al., 2020). With a focus on the single-cell toolbox, this review is aimed to offer the readers with the updated understanding on the heterogeneity of thermogenic adipocytes at single-cell resolution. Although prior attempts have been made to summarise the adipose single-cell atlas, specifically the adipose progenitors (Duerre and Galmozzi, 2022; Sun et al., 2021; Wang et al., 2022), efforts into delineating the crossroad between immunity and metabolism are lacking. A comprehensive discussion on each immune cell type is beyond our scope and has been covered by Trim and Lynch (2022), nonetheless, our review is aimed to highlight the pro-thermogenic inter-cellular crosstalk between immune cells and brown/beige adipocytes and their respective dynamics as revealed by single-cell transcriptomics (Tables 1 and 2). Future promises of single-cell technology and considerations when applying single-cell genomics data for human metabolic diseases will be discussed.
Perhaps one of the most prominent features of WAT plasticity is the dynamic interconversion of beige adipocytes by browning or whitening in response to environmental cues, but questions may be raised on: Are there multiple inputs that are capable of provoking beiging independently? Do different inputs signal different types/functionality of beige adipocytes? What are the modalities, in addition to histone modifications, that permit the beige adipocytes to swiftly adapt to the constantly evolving metabolic landscape? To answer these, Wang et al. (2016) analysed the morphologies and transcription signatures of
The realisation that not all brown adipocytes are created equal but have varying degrees of
With the subsequent boom in scRNA-seq research and databases, several lines of evidence begin to peel back the layers of heterogeneity within brown adipocytes. The coexistence of distinct subpopulations of brown adipocytes is first alluded by Song et al. (2020) showing that brown adipocytes with high adiponectin expression constitute less than 40% of the adipocyte pool in BAT. scRNA-seq of isolated adipocytes revealed that the
Instead of being thermogenic professional, the notion that some brown adipocytes could be specialised regulatory cells is elaborated by Sun et al. (2020) using snRNA-seq of brown adipocytes. They identify a rare adipocyte population in mice interscapular BAT (iBAT) clustered by marker genes
In fact, while thermogenic adipocytes may take the lead on stage, there is increasing appreciation on the regulatory actions of adipose immune cells during thermogenesis through intercellular crosstalk. Several thermogenesis-supportive candidates have been identified, including the γδT-cells, type 2 innate lymphoid cells (ILC2), invariant natural killer T-cells (iNKT), eosinophils and the Slit3-expressing M2-like macrophages (Brestoff et al., 2015; Kohlgruber et al., 2018; Lee et al., 2015a; Lynch et al., 2016; Rao et al., 2014; Wang et al., 2021). The pro-thermogenic actions of these immune cells are often significant and incontrovertible in the context of beigeing, while they are found to have little or no impact on BAT thermogenesis (Brestoff et al., 2015). On the other hand, more efforts are required to help understand the depot-specific difference in immunometabolism. Nevertheless, previous studies were predominantly relying on flow cytometry and cell sorting, which might have offered a relatively myopic perspective on the AT immune dynamics. Instead, by combining single-cell and single-nucleus sequencing on mice iWAT collected after cold or adrenergic stimuli, Rajbhandari et al. (2019) elegantly depicted the immune-adipose crosstalk as a regulatory mechanism of beige thermogenesis. They demonstrated that a distinct population of adipocytes characterised by gene enrichment in fatty acid metabolism and norepinephrine signalling (
Another fascinating piece of work comes from Rosina et al. (2022), who demonstrated that ATM and monocytes are key BAT housekeepers for removing damaged mitochondrial components released by the cold-stressed brown adipocytes. In their study, scRNA-seq combined with MacSpectrum analysis, which is an algorithm designed to resolve macrophage activation states and functional profile (Li et al., 2019), demonstrates an increased expansion of ATM with less-inflammatory pre-activation phenotype following cold exposure, which are differentiated from
In addition to potentiating thermogenesis of existing brown adipocytes, cold or adrenergic stimulation of BAT also facilitates local proliferation and adipogenic commitment of thermogenic precursor and progenitors (APCs) that licenses greater heat-generating capacity (Bukowiecki et al., 1986; Nedergaard et al., 2019). Lineage-tracing study has shown that BAT de novo adipogenesis is achieved mainly through recruiting the
Conversely, the modalities of cold-induced beige fat recruitment in mice are somewhat controversial. Beiging has been proposed to occur either through de novo adipogenesis from beige progenitors, trans-differentiation of pre-existing adipocytes or a combination of both (Shao et al., 2016; Wang et al., 2013). Although ADRB3 agonist treatment preferably activates trans-differentiation of white adipocytes (Himms-Hagen et al., 2000; Lee et al., 2015b), the presence of diverse beige populations as discussed previously may favour the argument supporting beige adipogenesis, and findings from scRNA-seq further elaborate on the heterogeneity of beige APCs arising to different stimuli. Through scRNA-seq of non-immune SVF fraction, Oguri et al. (2020) identified
The era of high-dimensional single-cell genomics has revolutionised our understanding of the heterogeneity of thermogenic AT and its elegant adipocyte-immune network (Fig. 1). Nonetheless, great promises of sc(n)RNA-seq come with many pitfalls. Specifically, statistical robustness and accuracy can be afflicted by the ever-expanding but inconsistent single-cell datasets. Technical and statistical challenges faced by single-cell genomics has been eloquently discussed in recent reviews (Lähnemann et al., 2020; Sun et al., 2021). Additionally, it is critical for studies to apply consensus markers for cell type annotation. Computational platforms, like SingleR, have enabled the automatic definition of cell subcluster, but such tools are often dependent on data availability of specific cell markers (Aran et al., 2019). Concurrently, stringent quality control should be applied to minimise noise-to-signal ratio and cell-free RNA contamination, while allowing high-throughput and the identification of rare populations. Distinguishing between acquired cell states responding to certain stimuli and cell populations with different ancestors using benchmark dataset is critical for developing cell-targeted therapy. Importantly, one should not miss the forests for the trees, as single-cell transcriptomic is often the tip of the iceberg, where inference of cell trajectory, cell-cell communication and their functionality dynamics requires integration from multiple types of data, including proteomics, metabolomics, and multi-omics, where the latter has been recently shown to hold great potential in teasing apart the epigenetic regulatory network licencing specific cell fates (Argelaguet et al., 2019). Likewise, the computer algorithm MEBOCOST combining scRNA-seq transcriptome with the Human Metabolome Database has recently shown to be a promising tool in interrogating intercellular metabolite-sensor communication in BAT (Zheng et al., 2022). Furthermore, despite the relatively scant discussion on BAT immunometabolism, recent report has strikingly laid out the divergence between thermogenesis and metabolic health, where the inflamed BAT promotes systemic insulin sensitivity through enhancing glucose uptake by other metabolic organs, while protecting against lipotoxicity and DIO at the expense of its energy-burning capacity (Huang et al., 2022). Additionally, there is emerging recognition on the central involvement in BAT thermogenesis, where the oestrogen receptor-expressing or heat-sensing neurons has shown to modulate BAT activity and whole-body metabolic rates (Makwana et al., 2021; Ye et al., 2022). Conversely, the BAT has sensory nerve outflow projecting the hindbrain, midbrain and forebrain regions and is also proposed to mediate the appetite-suppressing actions of secretin via the BAT-brain crosstalk (Ryu et al., 2015; Sun et al., 2022). In consideration of these, a more holistic approach resolving and integrating spatial single-cell multi-omics in brain and AT may help generate new insights into cell-cell interactions, functionality compartmentalisation and interorgan crosstalk governing the functional diversity of thermogenic AT.
This work is supported by the National Natural Science Foundation of China (NSFC) - Excellent Young Scientists Fund (Hong Kong and Macau) (81922079) and General Research Fund (17121520) (to X.H.H.).
Y.Q. wrote the original draft and contributed to the tables and figure. X.H.H. revised and edited the manuscript.
The authors have no potential conflicts of interest to disclose.
Single-cell studies on cellular heterogeneity of BAT
Species | Tissue depot, fraction | Techniques | Main findings | Reference | ||
---|---|---|---|---|---|---|
Adipocytes | APC/preadipocytes | Immune cells | ||||
E17.5 CD-1 mice | iBAT, mature | In vitro clonal and RNA-seq | Considerable variations in tde expressions of brown marker genes (Ucp1, Adrb3, Cidea, Ppargc1a) between nine brown adipocytes. | Spaetdling et al., 2016 | ||
10-week-old C57BL/6J male mice | iBAT, mature | scRNA-seq | AdipoqlowUcp1low low-thermogenic adipocytes marked by Fabp4/5, Cd36, Cldn5, Cav1/2. | Song et al., 2020 | ||
7-week-old AdipoCre-NucRed transgenic mice | iBAT, mature | snRNA-seq | 10 adipocyte subpopulations identified in CE, RT, and TN. Thermogenesis-regulatory adipocytes marked by Cyp2e1, Aldh1a1, Nrip1, Auts2. | Sun et al., 2020 | ||
16 patients (4 males, 12 females), aged 49.2 ± 19.0 y, BMI 24.8 ± 4.7 kg/m2 | Deep-neck BAT, whole | snRNA-seq | Eight adipocyte subpopulations identified with a greater enrichment of CYP2E1+ ALDH1A1+ adipocytes compared to mice. | Sun et al., 2020 | ||
8-week-old, male C57BL/6J mice | iBAT, SVF | scRNA-seq and in vitro clonal | Eif5, Tcf25, Bin1 each mark three subtypes of brown adipocytes with varying expressions of UCP1 and different degrees of adrenergic sensitivity. | Karlina et al., 2020 | ||
C57BL/6N male mice | Thoracic aorta PVAT, SVF | scRNA-seq | Adipogenic progenitors (fibroblast) marked by Pdgfra and Pparg in neonates. Adipogenic progenitors (SMCs) marked by Myh11, Trpv1 in adults. | Angueira et al., 2021 | ||
3 male patients, aged 64 y, BMI 28.2 kg/m2 | Peri-aortic PVAT, whole | snRNA-seq | Fibroblastic preadipocytes marked by PPARg, COL15A1/COL4A4. Adipogenic SMC-like cells marked by PPARg, PDGFRb. | Angueira et al., 2021 | ||
9-week-old male C57BL/6J mice | iBAT, SVF | scRNA-seq | VSM-derived adipogenic progenitors marked by Trpv1 are recruited by cold exposure. | Shamsi et al., 2021 | ||
C57BL/6 J male mice | iBAT, SVF | scRNA-seq and MacSpectrum | Increased recruitment of Ly6low patrolling monocytes and expansion of ATM witd pre-activation states. | Rosina et al., 2022 | ||
C57BL/6 mice | iBAT, SVF | scRNA-seq | tdree clusters of monocytes marked by different levels of Ly6c expressions and four clusters of macrophages involved in lipid handling and matrix remodelling. Adipocyte-specific ATGL deletion triggers increased monocyte recruitment and proportion of ATM witd lipid-handling phenotype. | Gallerand et al., 2021 |
APC, adipose progenitor cells; iBAT, interscapular brown adipose tissue; CE, cold exposure; RT, room temperature; TN, thermoneutrality; BMI, body mass index; SVF, stromal vascular fraction; PVAT, perivascular adipose tissue; SMC, smooth muscle cell; VSM, vascular smooth muscle; ATM, adipose macrophages; ATGL, adipose triglyceride lipase.
Single-cell studies on cellular heterogeneity of beige adipose tissue/adipocytes
Species | Tissue depot, fraction | Techniques | Main findings | Reference | ||
---|---|---|---|---|---|---|
Adipocytes | APC/preadipocytes | Immune cells | ||||
13 healtdy patients, 10 females, 3 males | SAT and VAT, whole | snRNA-seq | Seven adipocyte subpopulations witd depot-specific enrichment and correlations witd BMI. tdermogenic adipocyte subpopulation marked by EBF2, PPARGC1A, ESRRG is found to be exclusively enriched in VAT. | Emont et al., 2022 | ||
19-week-old C57BL/6J female and male mice | iWAT and eWAT, whole | snRNA-seq | Six adipocyte subpopulations witd diet-dependent enrichment. Two subclusters witdin mAd1 identified as tdermogenic and marked by Prdm16, Ppargc1a, Ucp1, Cidea. | Emont et al., 2022 | ||
C57BL/6J male mice | iWAT, SVF | scRNA-seq | SMC-like APC marked by Cd81, Pdgfra, Sca1 give rises to beige adipocytes independent of stimuli. Number of CD81+ APC is inversely associated witd metabolic syndrome in humans. | Oguri et al., 2020 | ||
Human | Abdominal SAT preadipocytes | In vitro clonal and scRNA-seq | Two-cluster separation of preadipocytes after 7- and 14-day differentiation driven by genes in protein syntdesis, ECM remodelling and metabolism. | Ramirez et al..2020 | ||
8- to 10-week-old C57BL/6 mice | iWAT, mature and SVF | snRNA-seq and scRNA-seq | 14 clusters of adipocytes. Adipocyte cluster nine characterised by tdermogenic markers Adrb3, Lipe, Plin1 at baseline and increased expression of Ucp1, Ppargc1a and Cidea following cold/CL treatment. | Adipose-resident T- and B-cells produce IL10 antagonising IL10Ra+ tdermogenic adipocytes. | Rajbhandari et al., 2019 | |
8-week-old C57BL/6 mice | iWAT, SVF | scRNA-seq | Mesenchymal stem cell cluster two may represent adipogenic progenitor marked by Fabp4, Pdgfra. | 13 immune cell clusters. Cold exposure and CL treatment favour tde expansion of lymphoid- and myeloid-derived immune cells respectively, where cold and CL influences type I interferon response differently. | Rabhi et al., 2020 | |
8-week-old male C57BL/6J WT and iAdFASNKO mice | iWAT, SVF | scRNA-seq | Increased M2 polarisation of ATM marked by Mgl2, Cd163 and Lyve1 and increased ratio of M2/M1 ATM. | Henriques et al., 2020 |
APC, adipose progenitor cells; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; BMI, body mass index; iWAT, inguinal white adipose tissue; eWAT, epididymal white adipose tissue; ECM, extracellular matrix; IL, interleukin; CL, beta-3 adrenergic agonist; WT, wild type; ATM, adipose macrophages.
Mol. Cells 2022; 45(10): 673-684
Published online October 31, 2022 https://doi.org/10.14348/molcells.2022.0092
Copyright © The Korean Society for Molecular and Cellular Biology.
Yue Qi and Xiaoyan Hannah Hui*
School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
Correspondence to:hannahhui@cuhk.edu.hk
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/.
The past two decades have witnessed an upsurge in the appreciation of adipose tissue (AT) as an immuno-metabolic hub harbouring heterogeneous cell populations that collectively fine-tune systemic metabolic homeostasis. Technological advancements, especially single-cell transcriptomics, have offered an unprecedented opportunity for dissecting the sophisticated cellular networks and compositional dynamics underpinning AT remodelling. The “re-discovery” of functional brown adipose tissue dissipating heat energy in human adults has aroused tremendous interest in exploiting the mechanisms underpinning the engagement of AT thermogenesis for combating human obesity. In this review, we aim to summarise and evaluate the use of single-cell transcriptomics that contribute to a better appreciation of the cellular plasticity and intercellular crosstalk in thermogenic AT.
Keywords: adipose tissue, metabolism, obesity, single-cell sequencing, single-nucleus sequencing, thermogenesis
The brown adipose tissue (BAT) is the metabolically active organ specialised in heat generation (thermogenesis). The presence of BAT in mammals is believed to confer survival advantages that allow organisms to survive and to be active during low ambient temperatures without hibernation and hypothermia (Cannon and Nedergaard, 2004). Compared with their energy-storing lipid-laden white counterparts, the brown adipocytes are characterised by smaller multilocular lipid droplet and high amounts of mitochondria packed with cristae and iron, where the latter gives BAT its brownish appearance. Similarly, the beige adipocytes, despite having distinct cellular progenitors and developmental lineages, are inducible thermogenic adipocytes arising from the white adipose tissue (WAT) and possess brown-like morphology and transcriptional landscapes upon cold exposure or adrenergic stimulation. Remarkably, these beige adipocytes can be readily interconverted between whitening and browning when exposing to warm and cold stimuli respectively (Nanduri, 2021; Roh et al., 2018). Once activated, the high potential of mitochondrial respiration of beige and brown adipocytes facilitates uncoupling of substrate oxidation and ATP production, dissipating energy in the form of heat through uncoupling protein 1 (UCP1) and contributing to up to 60% of total energy expenditure in small mammals (Carpentier et al., 2018; Cohen and Kajimura, 2021). Alternatively, UCP1-indepedent futile cycling of lipids and amino acids where ATP are consumed in the process of synthesis and degradation of triglycerides and peptides can also fuel heat production (Onogi and Ussar, 2022). Other futile circuits identified in beige adipocytes include the phosphorylation and dephosphorylation of creatine and the exchange of calcium ions between endoplasmic reticulum and cytosol (Ikeda et al., 2017; Kazak et al., 2015). In addition to be praised as an energy-burning furnace, the BAT also serves as the macronutrient sink for buffering circulating lipids, glucose and amino acids and helps regulate systemic metabolic homeostasis by secreting endocrine molecules known as the batokines (Chondronikola et al., 2016; Hankir and Klingenspor, 2018; Villarroya et al., 2017; Yoneshiro et al., 2019). Moreover, the fact that BAT confers cardiometabolic benefits independent of increasing energy expenditure alludes to its health-promoting potential and functional complexity (Becher et al., 2021; Gu et al., 2021; Kajimura et al., 2015; Mills et al., 2021). In addition to the efforts on leveraging the functionality of thermogenic adipocytes, insights are being made into the interplay and interconversion between the distinct cell types within the thermogenic adipose tissue (AT), which are benefited from the appreciation of cellular diversity and the identification of new but underrepresented cell type(s).
In fact, early attempts in isolation, cloning and sorting of brown/beige adipocytes have hinted at the intrinsic cellular heterogeneity of AT, but such understanding can only scratch the surface due to the probable overrepresentation of highly-proliferating cells and the lack of characterisation into inter-cellular communication (Hagberg et al., 2018; Jespersen et al., 2019; Shinoda et al., 2015; Spaethling et al., 2016). Such challenges have been largely overcome in the era of single-cell genomics, where developments in single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) have allowed high-throughput profiling of transcriptomic signatures one cell at a time, leading to the appreciation on the diversity of adipose cell populations and accelerating advancements in the field of thermogenic AT research. The field of single-cell genomics has been rapidly evolved from manual picking of a few embryos to liquid-handling robotics by MARS (massively parallel single-cell)-seq enabling thousands of cells to be sampled (Jaitin et al., 2014; Tang et al., 2009). An exponential leap in the magnitude of cell numbers is achieved with the introduction of droplet-based platforms (10× Genomics Chromium, inDrop and Drop-seq) and the most recent
Due to the unique physical characteristics of AT, scRNA-seq is exclusively applied for investigating the relatively dense stromal-vascular fraction (SVF) of freshly harvested AT, as the floating fraction of buoyant adipocytes with various sizes cannot be dissociated into uniform single-cell suspension. Conversely, snRNA-seq, which bypasses harsh enzymatic dissociation, permits the recovery of major adipose cell types from either frozen or fresh tissues, and helps shed light on the exquisite networking between adipocytes and SVF components, especially the immune milieu. However, snRNA-seq can suffer from significant loss of reads, which may compromise the accuracy in distinguishing different cell types when based on transcripts only. Nevertheless, both technologies can face the challenge of contamination from ambient RNA (McLaughlin et al., 2022). A systematic comparison between different single-cell/nucleus sequencing platforms has been made elsewhere (Ding et al., 2020). With a focus on the single-cell toolbox, this review is aimed to offer the readers with the updated understanding on the heterogeneity of thermogenic adipocytes at single-cell resolution. Although prior attempts have been made to summarise the adipose single-cell atlas, specifically the adipose progenitors (Duerre and Galmozzi, 2022; Sun et al., 2021; Wang et al., 2022), efforts into delineating the crossroad between immunity and metabolism are lacking. A comprehensive discussion on each immune cell type is beyond our scope and has been covered by Trim and Lynch (2022), nonetheless, our review is aimed to highlight the pro-thermogenic inter-cellular crosstalk between immune cells and brown/beige adipocytes and their respective dynamics as revealed by single-cell transcriptomics (Tables 1 and 2). Future promises of single-cell technology and considerations when applying single-cell genomics data for human metabolic diseases will be discussed.
Perhaps one of the most prominent features of WAT plasticity is the dynamic interconversion of beige adipocytes by browning or whitening in response to environmental cues, but questions may be raised on: Are there multiple inputs that are capable of provoking beiging independently? Do different inputs signal different types/functionality of beige adipocytes? What are the modalities, in addition to histone modifications, that permit the beige adipocytes to swiftly adapt to the constantly evolving metabolic landscape? To answer these, Wang et al. (2016) analysed the morphologies and transcription signatures of
The realisation that not all brown adipocytes are created equal but have varying degrees of
With the subsequent boom in scRNA-seq research and databases, several lines of evidence begin to peel back the layers of heterogeneity within brown adipocytes. The coexistence of distinct subpopulations of brown adipocytes is first alluded by Song et al. (2020) showing that brown adipocytes with high adiponectin expression constitute less than 40% of the adipocyte pool in BAT. scRNA-seq of isolated adipocytes revealed that the
Instead of being thermogenic professional, the notion that some brown adipocytes could be specialised regulatory cells is elaborated by Sun et al. (2020) using snRNA-seq of brown adipocytes. They identify a rare adipocyte population in mice interscapular BAT (iBAT) clustered by marker genes
In fact, while thermogenic adipocytes may take the lead on stage, there is increasing appreciation on the regulatory actions of adipose immune cells during thermogenesis through intercellular crosstalk. Several thermogenesis-supportive candidates have been identified, including the γδT-cells, type 2 innate lymphoid cells (ILC2), invariant natural killer T-cells (iNKT), eosinophils and the Slit3-expressing M2-like macrophages (Brestoff et al., 2015; Kohlgruber et al., 2018; Lee et al., 2015a; Lynch et al., 2016; Rao et al., 2014; Wang et al., 2021). The pro-thermogenic actions of these immune cells are often significant and incontrovertible in the context of beigeing, while they are found to have little or no impact on BAT thermogenesis (Brestoff et al., 2015). On the other hand, more efforts are required to help understand the depot-specific difference in immunometabolism. Nevertheless, previous studies were predominantly relying on flow cytometry and cell sorting, which might have offered a relatively myopic perspective on the AT immune dynamics. Instead, by combining single-cell and single-nucleus sequencing on mice iWAT collected after cold or adrenergic stimuli, Rajbhandari et al. (2019) elegantly depicted the immune-adipose crosstalk as a regulatory mechanism of beige thermogenesis. They demonstrated that a distinct population of adipocytes characterised by gene enrichment in fatty acid metabolism and norepinephrine signalling (
Another fascinating piece of work comes from Rosina et al. (2022), who demonstrated that ATM and monocytes are key BAT housekeepers for removing damaged mitochondrial components released by the cold-stressed brown adipocytes. In their study, scRNA-seq combined with MacSpectrum analysis, which is an algorithm designed to resolve macrophage activation states and functional profile (Li et al., 2019), demonstrates an increased expansion of ATM with less-inflammatory pre-activation phenotype following cold exposure, which are differentiated from
In addition to potentiating thermogenesis of existing brown adipocytes, cold or adrenergic stimulation of BAT also facilitates local proliferation and adipogenic commitment of thermogenic precursor and progenitors (APCs) that licenses greater heat-generating capacity (Bukowiecki et al., 1986; Nedergaard et al., 2019). Lineage-tracing study has shown that BAT de novo adipogenesis is achieved mainly through recruiting the
Conversely, the modalities of cold-induced beige fat recruitment in mice are somewhat controversial. Beiging has been proposed to occur either through de novo adipogenesis from beige progenitors, trans-differentiation of pre-existing adipocytes or a combination of both (Shao et al., 2016; Wang et al., 2013). Although ADRB3 agonist treatment preferably activates trans-differentiation of white adipocytes (Himms-Hagen et al., 2000; Lee et al., 2015b), the presence of diverse beige populations as discussed previously may favour the argument supporting beige adipogenesis, and findings from scRNA-seq further elaborate on the heterogeneity of beige APCs arising to different stimuli. Through scRNA-seq of non-immune SVF fraction, Oguri et al. (2020) identified
The era of high-dimensional single-cell genomics has revolutionised our understanding of the heterogeneity of thermogenic AT and its elegant adipocyte-immune network (Fig. 1). Nonetheless, great promises of sc(n)RNA-seq come with many pitfalls. Specifically, statistical robustness and accuracy can be afflicted by the ever-expanding but inconsistent single-cell datasets. Technical and statistical challenges faced by single-cell genomics has been eloquently discussed in recent reviews (Lähnemann et al., 2020; Sun et al., 2021). Additionally, it is critical for studies to apply consensus markers for cell type annotation. Computational platforms, like SingleR, have enabled the automatic definition of cell subcluster, but such tools are often dependent on data availability of specific cell markers (Aran et al., 2019). Concurrently, stringent quality control should be applied to minimise noise-to-signal ratio and cell-free RNA contamination, while allowing high-throughput and the identification of rare populations. Distinguishing between acquired cell states responding to certain stimuli and cell populations with different ancestors using benchmark dataset is critical for developing cell-targeted therapy. Importantly, one should not miss the forests for the trees, as single-cell transcriptomic is often the tip of the iceberg, where inference of cell trajectory, cell-cell communication and their functionality dynamics requires integration from multiple types of data, including proteomics, metabolomics, and multi-omics, where the latter has been recently shown to hold great potential in teasing apart the epigenetic regulatory network licencing specific cell fates (Argelaguet et al., 2019). Likewise, the computer algorithm MEBOCOST combining scRNA-seq transcriptome with the Human Metabolome Database has recently shown to be a promising tool in interrogating intercellular metabolite-sensor communication in BAT (Zheng et al., 2022). Furthermore, despite the relatively scant discussion on BAT immunometabolism, recent report has strikingly laid out the divergence between thermogenesis and metabolic health, where the inflamed BAT promotes systemic insulin sensitivity through enhancing glucose uptake by other metabolic organs, while protecting against lipotoxicity and DIO at the expense of its energy-burning capacity (Huang et al., 2022). Additionally, there is emerging recognition on the central involvement in BAT thermogenesis, where the oestrogen receptor-expressing or heat-sensing neurons has shown to modulate BAT activity and whole-body metabolic rates (Makwana et al., 2021; Ye et al., 2022). Conversely, the BAT has sensory nerve outflow projecting the hindbrain, midbrain and forebrain regions and is also proposed to mediate the appetite-suppressing actions of secretin via the BAT-brain crosstalk (Ryu et al., 2015; Sun et al., 2022). In consideration of these, a more holistic approach resolving and integrating spatial single-cell multi-omics in brain and AT may help generate new insights into cell-cell interactions, functionality compartmentalisation and interorgan crosstalk governing the functional diversity of thermogenic AT.
This work is supported by the National Natural Science Foundation of China (NSFC) - Excellent Young Scientists Fund (Hong Kong and Macau) (81922079) and General Research Fund (17121520) (to X.H.H.).
Y.Q. wrote the original draft and contributed to the tables and figure. X.H.H. revised and edited the manuscript.
The authors have no potential conflicts of interest to disclose.
Single-cell studies on cellular heterogeneity of BAT
Species | Tissue depot, fraction | Techniques | Main findings | Reference | ||
---|---|---|---|---|---|---|
Adipocytes | APC/preadipocytes | Immune cells | ||||
E17.5 CD-1 mice | iBAT, mature | In vitro clonal and RNA-seq | Considerable variations in tde expressions of brown marker genes (Ucp1, Adrb3, Cidea, Ppargc1a) between nine brown adipocytes. | Spaetdling et al., 2016 | ||
10-week-old C57BL/6J male mice | iBAT, mature | scRNA-seq | AdipoqlowUcp1low low-thermogenic adipocytes marked by Fabp4/5, Cd36, Cldn5, Cav1/2. | Song et al., 2020 | ||
7-week-old AdipoCre-NucRed transgenic mice | iBAT, mature | snRNA-seq | 10 adipocyte subpopulations identified in CE, RT, and TN. Thermogenesis-regulatory adipocytes marked by Cyp2e1, Aldh1a1, Nrip1, Auts2. | Sun et al., 2020 | ||
16 patients (4 males, 12 females), aged 49.2 ± 19.0 y, BMI 24.8 ± 4.7 kg/m2 | Deep-neck BAT, whole | snRNA-seq | Eight adipocyte subpopulations identified with a greater enrichment of CYP2E1+ ALDH1A1+ adipocytes compared to mice. | Sun et al., 2020 | ||
8-week-old, male C57BL/6J mice | iBAT, SVF | scRNA-seq and in vitro clonal | Eif5, Tcf25, Bin1 each mark three subtypes of brown adipocytes with varying expressions of UCP1 and different degrees of adrenergic sensitivity. | Karlina et al., 2020 | ||
C57BL/6N male mice | Thoracic aorta PVAT, SVF | scRNA-seq | Adipogenic progenitors (fibroblast) marked by Pdgfra and Pparg in neonates. Adipogenic progenitors (SMCs) marked by Myh11, Trpv1 in adults. |
Angueira et al., 2021 | ||
3 male patients, aged 64 y, BMI 28.2 kg/m2 | Peri-aortic PVAT, whole | snRNA-seq | Fibroblastic preadipocytes marked by PPARg, COL15A1/COL4A4. Adipogenic SMC-like cells marked by PPARg, PDGFRb. |
Angueira et al., 2021 | ||
9-week-old male C57BL/6J mice | iBAT, SVF | scRNA-seq | VSM-derived adipogenic progenitors marked by Trpv1 are recruited by cold exposure. | Shamsi et al., 2021 | ||
C57BL/6 J male mice | iBAT, SVF | scRNA-seq and MacSpectrum | Increased recruitment of Ly6low patrolling monocytes and expansion of ATM witd pre-activation states. | Rosina et al., 2022 | ||
C57BL/6 mice | iBAT, SVF | scRNA-seq | tdree clusters of monocytes marked by different levels of Ly6c expressions and four clusters of macrophages involved in lipid handling and matrix remodelling. Adipocyte-specific ATGL deletion triggers increased monocyte recruitment and proportion of ATM witd lipid-handling phenotype. |
Gallerand et al., 2021 |
APC, adipose progenitor cells; iBAT, interscapular brown adipose tissue; CE, cold exposure; RT, room temperature; TN, thermoneutrality; BMI, body mass index; SVF, stromal vascular fraction; PVAT, perivascular adipose tissue; SMC, smooth muscle cell; VSM, vascular smooth muscle; ATM, adipose macrophages; ATGL, adipose triglyceride lipase.
Single-cell studies on cellular heterogeneity of beige adipose tissue/adipocytes
Species | Tissue depot, fraction | Techniques | Main findings | Reference | ||
---|---|---|---|---|---|---|
Adipocytes | APC/preadipocytes | Immune cells | ||||
13 healtdy patients, 10 females, 3 males | SAT and VAT, whole | snRNA-seq | Seven adipocyte subpopulations witd depot-specific enrichment and correlations witd BMI. tdermogenic adipocyte subpopulation marked by EBF2, PPARGC1A, ESRRG is found to be exclusively enriched in VAT. |
Emont et al., 2022 | ||
19-week-old C57BL/6J female and male mice | iWAT and eWAT, whole | snRNA-seq | Six adipocyte subpopulations witd diet-dependent enrichment. Two subclusters witdin mAd1 identified as tdermogenic and marked by Prdm16, Ppargc1a, Ucp1, Cidea. | Emont et al., 2022 | ||
C57BL/6J male mice | iWAT, SVF | scRNA-seq | SMC-like APC marked by Cd81, Pdgfra, Sca1 give rises to beige adipocytes independent of stimuli. Number of CD81+ APC is inversely associated witd metabolic syndrome in humans. |
Oguri et al., 2020 | ||
Human | Abdominal SAT preadipocytes | In vitro clonal and scRNA-seq | Two-cluster separation of preadipocytes after 7- and 14-day differentiation driven by genes in protein syntdesis, ECM remodelling and metabolism. | Ramirez et al..2020 | ||
8- to 10-week-old C57BL/6 mice | iWAT, mature and SVF | snRNA-seq and scRNA-seq | 14 clusters of adipocytes. Adipocyte cluster nine characterised by tdermogenic markers Adrb3, Lipe, Plin1 at baseline and increased expression of Ucp1, Ppargc1a and Cidea following cold/CL treatment. |
Adipose-resident T- and B-cells produce IL10 antagonising IL10Ra+ tdermogenic adipocytes. | Rajbhandari et al., 2019 | |
8-week-old C57BL/6 mice | iWAT, SVF | scRNA-seq | Mesenchymal stem cell cluster two may represent adipogenic progenitor marked by Fabp4, Pdgfra. | 13 immune cell clusters. Cold exposure and CL treatment favour tde expansion of lymphoid- and myeloid-derived immune cells respectively, where cold and CL influences type I interferon response differently. |
Rabhi et al., 2020 | |
8-week-old male C57BL/6J WT and iAdFASNKO mice | iWAT, SVF | scRNA-seq | Increased M2 polarisation of ATM marked by Mgl2, Cd163 and Lyve1 and increased ratio of M2/M1 ATM. | Henriques et al., 2020 |
APC, adipose progenitor cells; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; BMI, body mass index; iWAT, inguinal white adipose tissue; eWAT, epididymal white adipose tissue; ECM, extracellular matrix; IL, interleukin; CL, beta-3 adrenergic agonist; WT, wild type; ATM, adipose macrophages.
. Single-cell studies on cellular heterogeneity of BAT.
Species | Tissue depot, fraction | Techniques | Main findings | Reference | ||
---|---|---|---|---|---|---|
Adipocytes | APC/preadipocytes | Immune cells | ||||
E17.5 CD-1 mice | iBAT, mature | In vitro clonal and RNA-seq | Considerable variations in tde expressions of brown marker genes (Ucp1, Adrb3, Cidea, Ppargc1a) between nine brown adipocytes. | Spaetdling et al., 2016 | ||
10-week-old C57BL/6J male mice | iBAT, mature | scRNA-seq | AdipoqlowUcp1low low-thermogenic adipocytes marked by Fabp4/5, Cd36, Cldn5, Cav1/2. | Song et al., 2020 | ||
7-week-old AdipoCre-NucRed transgenic mice | iBAT, mature | snRNA-seq | 10 adipocyte subpopulations identified in CE, RT, and TN. Thermogenesis-regulatory adipocytes marked by Cyp2e1, Aldh1a1, Nrip1, Auts2. | Sun et al., 2020 | ||
16 patients (4 males, 12 females), aged 49.2 ± 19.0 y, BMI 24.8 ± 4.7 kg/m2 | Deep-neck BAT, whole | snRNA-seq | Eight adipocyte subpopulations identified with a greater enrichment of CYP2E1+ ALDH1A1+ adipocytes compared to mice. | Sun et al., 2020 | ||
8-week-old, male C57BL/6J mice | iBAT, SVF | scRNA-seq and in vitro clonal | Eif5, Tcf25, Bin1 each mark three subtypes of brown adipocytes with varying expressions of UCP1 and different degrees of adrenergic sensitivity. | Karlina et al., 2020 | ||
C57BL/6N male mice | Thoracic aorta PVAT, SVF | scRNA-seq | Adipogenic progenitors (fibroblast) marked by Pdgfra and Pparg in neonates. Adipogenic progenitors (SMCs) marked by Myh11, Trpv1 in adults. | Angueira et al., 2021 | ||
3 male patients, aged 64 y, BMI 28.2 kg/m2 | Peri-aortic PVAT, whole | snRNA-seq | Fibroblastic preadipocytes marked by PPARg, COL15A1/COL4A4. Adipogenic SMC-like cells marked by PPARg, PDGFRb. | Angueira et al., 2021 | ||
9-week-old male C57BL/6J mice | iBAT, SVF | scRNA-seq | VSM-derived adipogenic progenitors marked by Trpv1 are recruited by cold exposure. | Shamsi et al., 2021 | ||
C57BL/6 J male mice | iBAT, SVF | scRNA-seq and MacSpectrum | Increased recruitment of Ly6low patrolling monocytes and expansion of ATM witd pre-activation states. | Rosina et al., 2022 | ||
C57BL/6 mice | iBAT, SVF | scRNA-seq | tdree clusters of monocytes marked by different levels of Ly6c expressions and four clusters of macrophages involved in lipid handling and matrix remodelling. Adipocyte-specific ATGL deletion triggers increased monocyte recruitment and proportion of ATM witd lipid-handling phenotype. | Gallerand et al., 2021 |
APC, adipose progenitor cells; iBAT, interscapular brown adipose tissue; CE, cold exposure; RT, room temperature; TN, thermoneutrality; BMI, body mass index; SVF, stromal vascular fraction; PVAT, perivascular adipose tissue; SMC, smooth muscle cell; VSM, vascular smooth muscle; ATM, adipose macrophages; ATGL, adipose triglyceride lipase..
. Single-cell studies on cellular heterogeneity of beige adipose tissue/adipocytes.
Species | Tissue depot, fraction | Techniques | Main findings | Reference | ||
---|---|---|---|---|---|---|
Adipocytes | APC/preadipocytes | Immune cells | ||||
13 healtdy patients, 10 females, 3 males | SAT and VAT, whole | snRNA-seq | Seven adipocyte subpopulations witd depot-specific enrichment and correlations witd BMI. tdermogenic adipocyte subpopulation marked by EBF2, PPARGC1A, ESRRG is found to be exclusively enriched in VAT. | Emont et al., 2022 | ||
19-week-old C57BL/6J female and male mice | iWAT and eWAT, whole | snRNA-seq | Six adipocyte subpopulations witd diet-dependent enrichment. Two subclusters witdin mAd1 identified as tdermogenic and marked by Prdm16, Ppargc1a, Ucp1, Cidea. | Emont et al., 2022 | ||
C57BL/6J male mice | iWAT, SVF | scRNA-seq | SMC-like APC marked by Cd81, Pdgfra, Sca1 give rises to beige adipocytes independent of stimuli. Number of CD81+ APC is inversely associated witd metabolic syndrome in humans. | Oguri et al., 2020 | ||
Human | Abdominal SAT preadipocytes | In vitro clonal and scRNA-seq | Two-cluster separation of preadipocytes after 7- and 14-day differentiation driven by genes in protein syntdesis, ECM remodelling and metabolism. | Ramirez et al..2020 | ||
8- to 10-week-old C57BL/6 mice | iWAT, mature and SVF | snRNA-seq and scRNA-seq | 14 clusters of adipocytes. Adipocyte cluster nine characterised by tdermogenic markers Adrb3, Lipe, Plin1 at baseline and increased expression of Ucp1, Ppargc1a and Cidea following cold/CL treatment. | Adipose-resident T- and B-cells produce IL10 antagonising IL10Ra+ tdermogenic adipocytes. | Rajbhandari et al., 2019 | |
8-week-old C57BL/6 mice | iWAT, SVF | scRNA-seq | Mesenchymal stem cell cluster two may represent adipogenic progenitor marked by Fabp4, Pdgfra. | 13 immune cell clusters. Cold exposure and CL treatment favour tde expansion of lymphoid- and myeloid-derived immune cells respectively, where cold and CL influences type I interferon response differently. | Rabhi et al., 2020 | |
8-week-old male C57BL/6J WT and iAdFASNKO mice | iWAT, SVF | scRNA-seq | Increased M2 polarisation of ATM marked by Mgl2, Cd163 and Lyve1 and increased ratio of M2/M1 ATM. | Henriques et al., 2020 |
APC, adipose progenitor cells; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; BMI, body mass index; iWAT, inguinal white adipose tissue; eWAT, epididymal white adipose tissue; ECM, extracellular matrix; IL, interleukin; CL, beta-3 adrenergic agonist; WT, wild type; ATM, adipose macrophages..
Yadanar Than Naing and Lei Sun
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Mol. Cells 2020; 43(9): 763-773 https://doi.org/10.14348/molcells.2020.0118Sim Namkoong, Chun-Seok Cho, Ian Semple, and Jun Hee Lee
Mol. Cells 2018; 41(1): 3-10 https://doi.org/10.14348/molcells.2018.2213