Single Cell Approaches to Address Adipose Tissue Stromal Cell Heterogeneity

Abstract

INTRODUCTION

We are in the midst of an epidemic of obesity-related diseases including diabetes, cardiovascular disease, and cancer (1–4). It is now clear that obesity itself, i.e., the excessive accumulation of fat tissue, is not the specific cause of obesity-related disease. Rather, obesity-related diseases occur when the environmental demands to store excess energy disrupts the integrative metabolic, endocrine and immune functions of adipose tissue (reviewed in (5–9)). Viewed from this perspective, an important therapeutic goal is to improve functioning of adipose tissue by augmenting its energy buffering capacity, improving the mixture of released adipokines, and/or reducing levels of persistent inflammation (7, 10–17). Thus, central to the ability to therapeutically target adipose tissue is a more detailed understanding of the cellular composition (i.e., adipocytes, stromal cells and immune cells) among tissue depots and how this composition changes in response to physiological, nutritional, and environmental challenges.

Adipocyte cell number is thought to be determined by early adulthood, with homeostatic turnover estimated at ~8% per year in humans (18) and ~18% per month in mice (19). However, white adipose tissue (WAT) is highly plastic and can undergo extensive remodeling in response to metabolic, nutritional, and pharmacological challenges (reviewed in (20–23)). During periods of excess energy intake, WAT expands by both hypertrophy (increase in cell size) and/or hyperplasia (increase in cell number) (24–26), and differences in the anatomical localization and type of adipose tissue expansion has profound effects on metabolic health (27–31). In general, hypertrophy in visceral adipose tissue (VAT) is linked more strongly to inflammation and insulin resistance than that in subcutaneous depots (32–36). In rodent models, high fat feeding leads to adipocyte hypertrophy in both depots, whereas adipogenesis is thought to occur primarily in VAT (24–26, 37). The ability of adipose tissue to expand by hyperplasia confers a protective advantage on insulin sensitivity and metabolic disease risk (33–36, 38). During chronic overnutrition, adipocyte death coupled with accumulation of pro-inflammatory macrophages and fibrotic changes eventually leads to ectopic fat accumulation in muscle and liver with systemic insulin resistance (reviewed in (7, 20, 39, 40)). In addition to remodeling during overnutrition, exposure to cold temperatures, β-adrenergic receptor agonists, and a number of hormones and metabolites stimulate the appearance of thermogenic brown adipocytes (“beige” or brown in white, “brite”) within WAT (41–54). Brown adipocytes are thought to exert a protective role on metabolism through increased fatty acid and glucose utilization (55). Brown adipocytes can arise from either existing white adipocytes in which the brown adipocyte program is reinstated (43, 44, 47, 48, 54), or via differentiation of new brown/beige adipocytes from progenitors, depending on the location, duration, and type of inductive stimuli (26, 46, 49, 54, 56).

Given its central role in metabolism, there is substantial interest in better defining cellular subtypes involved in adipose tissue homeostasis and the mechanisms that regulate in vivo adipogenesis, plasticity, and inflammation for therapeutic strategies to treat metabolic disease (22, 57). While adipocytes are the major functional cell type in adipose tissue and occupy the greatest mass and volume of the tissue (owing to stored triglyceride), these cells comprise fewer than ~20% of total cells (58). The remaining ~80% of cells is a complex mixture of poorly defined stromal cells (fibroblasts, vascular cells, adipocyte stem cells) and immune cells (58–61). Importantly, these stromal and immune cell types play critical roles in the establishment and maintenance of parenchymal cell function. The composition of stromal cells varies across fat depots (24, 59), likely reflecting tissue specialization and differences in energy storage, vascularization, innervation, and metabolism (62). Traditional methods of addressing heterogeneity such as antibody-based cell sorting approaches (e.g., FACS) have provided useful information for some cellular populations (46, 63); however, this approach is limited by the number of antibodies available to characterize cell populations. Additionally, surface marker expression can be altered during distinct biological processes, as is the case with Pdgfra, which decreases in cells undergoing active adipogenesis (64). Similar challenges exist for immune cells that are dynamic, and whose marker expression can change depending on differentiation and activation states (65, 66). Other methods, such as bulk RNA-seq analysis of whole tissue and/or sorted cells provides a greater depth in gene expression profiles. However, this technology does not completely discriminate differences in gene expression due to shifts in cellular composition versus changes in cellular states (67, 68). Additionally, relatively minor or undefined cell types can be underrepresented or missed entirely (65, 67, 69). Therefore, an important barrier to understanding adipose tissue biology is the ability to define and characterize cellular subtypes and expression profiles in a comprehensive and unbiased fashion. In this regard, single cell RNA sequencing (scRNAseq) technology has recently emerged as a powerful tool to address tissue heterogeneity (65). This review highlights recent findings on the use of scRNAseq to address adipose tissue stromal and immune cell complexity in WAT, with a focus on studies in mouse models.

OVERVIEW OF scRNASEQ

High throughput single cell RNA technology has evolved as a powerful tool to deconvolve tissue heterogeneity (65, 68, 70–74). The concept of scRNAseq is to characterize individual cells based on the expression profile of thousands of genes compared to only a handful of surface markers commonly used in FACS-based sorting (68, 75). One of the benefits of this technique is that no prior knowledge of tissue composition is required. Although subject to some technical noise, scRNAseq provides crucial information about cellular populations, gene expression profiles, as well as changes in cellular states that may arise under different physiological conditions (65, 75). Available scRNAseq platforms differ slightly in design (droplet vs plate-based), throughput, and information acquired (e.g., full length mRNA vs 5′ or 3′ digital end sequencing); for recent reviews see (65, 70, 75–77). Droplet-based microfluidic technology, such as inDrop (71, 72), Drop-seq (73), and 10X Genomics (74) are among the most widely used scRNAseq platforms due to increased throughput and more automated protocols (77, 78). The technical aspects of droplet generation are similar among methods (78). Briefly, cells and barcoded beads are partitioned into separate channels of a microfluidic device, and each bead/cell complex is then mixed with oil to generate an emulsion containing nanoliter-sized single cell droplets (Fig. 1) (71–74, 78). Each bead contains primers, cellular barcodes, and a unique molecular identifier (UMI) to provide transcript counts per cell (71–74, 78–80). Reverse transcription either takes place within the droplets (inDrop and 10X Genomics) or following demulsification and cell lysis (Drop-seq) (71–74, 78). cDNA libraries are then prepared for sequencing through preamplification, tagmentation/fragmentation, and PCR indexing (Drop-seq, 10X Genomics) or by linear amplification (in vitro transcription, RNA fragmentation, and RT-PCR) for inDrop (71–74, 78). A comparison among the different droplet-based technologies has recently been presented (78).

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Figure 1.

Schematic Diagram of Basic Steps in Droplet-Based Microfluidic scRNAseq.

Cells and DNA-barcoded beads are partitioned into a microfluidic device and mixed with oil to generate nanoliter-sized single cell droplets. Each bead contains primers, cellular barcodes, and a unique molecular identifier (UMI). Cells are lysed within droplets, and reverse transcription either takes place within the droplets or following primer annealing and demulsification. Downstream steps differ slightly between platforms (not shown) to generate libraries for sequencing. Following sequencing, reads are demultiplexed, aligned, and then UMI counts per cell are quantified. Modified from (78).

Numerous bioinformatic tools are available and continuously emerging for analyzing scRNAseq output (77, 79, 81). Standardized pipelines involve initial preprocessing of reads (demultiplexing, alignment, and quantification), normalization as well as filtering to remove technical noise, such as cells containing high mitochondrial counts and doublets (75, 77, 79, 82). This is followed by dimensionality reduction, visualization, and identification of cell clusters based on differential gene expression (75, 77, 79, 82). In addition to identifying major and minor cell types within complex tissues, advanced bioinformatic programs are available for analysis of cellular transitions/hierarchies (83, 84), inference of gene regulatory networks (85, 86), predictions of cell-cell interactions (87), and integrative analyses of diverse datasets (88, 89).

ADIPOSE STEM CELL POPULATIONS IN MAJOR WAT DEPOTS AS IDENTIFIED BY scRNASEQ

There is a general consensus that adipocyte progenitors within adult WAT express the mesenchymal surface markers Pdgfrα, CD34, and Sca-1 (Ly6a) (46, 63, 90–92). Based on previous work, these progenitors are located in the interstitial and perivascular space and have a dendritic morphology with long processes that extend and contact several cells (46, 92). Lineage tracing experiments with constitutive and inducible Pdgfrα-Cre drivers indicates that most adipocytes arising in adult tissues are derived from Pdgfrα progenitors (90) and contribute to de novo adipogenesis both following high fat diets (HFD) and during catabolic tissue remodeling (46, 92). However, additional populations of adipocyte progenitors have also been described (38, 49, 93–97). For example, adipogenesis is recognized to be tightly linked to vasculature development (93, 98–101) and some, but not all, studies indicate adipocytes can be derived from endothelial or mural cells that also express Pparγ or Pdgfrβ (38, 49, 90, 93–95). Hematopoietic-derived progenitor cells have also been shown to contribute to neogenesis as well as the heterogeneity of adipocytes within adipose tissue (96, 97).

More recent studies have used scRNAseq to deconvolve progenitor heterogeneity in adipose tissue during development and in adults, under basal conditions (64, 102–104) and during catabolic remodeling induced by β3 adrenergic receptor (ADRB3) activation (64). A summary of the basic designs, including mouse strain, sex, and depots examined are presented in Table 1. In order to characterize the major cell populations present within non-immune stromal cells across studies, we integrated these diverse datasets using canonical correlation analysis (CCA) (89, 105). CCA is designed to reduce variability that can arise under different experimental conditions (e.g., cell isolation, mice strains, sex, and treatment) and clusters cell populations based on common or shared characteristics (89, 105). Approximately 11, 000 cells from eWAT and 28, 000 from iWAT were included in the integration (64, 102–104). Where appropriate, selection markers that were used to further characterize ASC subtypes with cell sorting along with the original cluster identity from each study are also presented (Table 1). A proposed nomenclature for adipocyte stem cells (ASC) based on marker gene expression is also included to facilitate comparisons between studies (Table 1). Additionally, individual findings from each study are summarized and discussed separately below.

Across studies, a majority of the non-immune stromal cell population detected by scRNAseq from each depot (>70%) were identified as ASC cells and express genes for previously identified surface markers including Pdgfrα (Pdgfra), CD29 (Itgb1), CD34 (Cd34), and Sca-1 (Ly6a) (Figs. 2B, ​,3B)3B) (64, 102–104). Two main populations of ASC (ASC1a and 2) are further distinguishable by scRNAseq, with an additional subpopulation (ASC1b) present in iWAT (64, 102–104). Some of the ASC2 genes commonly expressed within both adipose depots include those associated with inflammation/prostaglandin synthesis (Ptgs2), angiogenesis/endothelial cell migration (Anxa3), proteolysis (Pi16, Dpp4), as well as immune signaling (Cd55) (Supplemental Figs. 2,3). Genes commonly expressed within the ASC1a population include those involved in lipid uptake/metabolism (Plpp1, G0S2, and Apoe) and the metalloreductase Steap4 (Supplemental Figs. 2,3). Several genes encoding extracellular matrix (ECM) components are also strongly expressed in both ASC2 and ACS1, however the type of ECM differs between populations (Supplemental Figs. 2,3). For example, Fn1 and Col14a1 are more enriched in ASC2 whereas Col15a1 and Col4a2 are enriched in ASC1 progenitors (Supplemental Figs. 2, 3) (102–104). The relative proportion of total ACS1 to ASC2 in adult animals is also higher in eWAT than that observed in iWAT (Figs. 2A, ​,3A),3A), and functional assays suggest that the ASC1a population is more poised for adipogenesis (102–104).

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Figure 2.

Integrated scRNAseq of Immune-Depleted Stromal Cells Isolated from eWAT of Adult Mice.

Publicly available scRNAseq datasets (64, 104) from immune-depleted stromal cells isolated from eWAT were integrated using Seurat (105). (A) tSNE plots displaying the overlay of integrated datasets (left) and cell cluster identification (right). (B) tSNE plots displaying the log2 expression levels for select genes commonly used to purify adipose stem cells by FACS and/or used in lineage tracing experiments. (C) Violin plots displaying relative log2 expression levels of select marker genes in individual clusters. Abbr: Ly6a (stem cell antigen-1 (Sca-1)), Itgb1 (CD29).

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Figure 3.

Integrated scRNAseq of Immune-Depleted Stromal Cells Isolated from iWAT of Adult Mice.

Publicly available scRNAseq datasets (64, 102, 103) from immune-depleted stromal cells isolated from iWAT were integrated using Seurat (105). (A) tSNE plots displaying the overlay of integrated datasets (left) and cell cluster identification (right). (B) tSNE plots displaying the log2 expression levels for select genes commonly used to purify adipose stem cells by FACS and/or used in lineage tracing experiments. (C) Violin plots displaying relative log2 expression levels of select marker genes in individual clusters. Abbr: Ly6a (stem cell antigen-1 (Sca-1)), Itgb1 (CD29).

As expected, very few actively differentiating preadipocytes were detected in either depot (Figs. 2, ​,3).3). In the study by Burl et al. (64), the proportion of proliferating (Birc5, Cdca8, Top2a) and differentiating preadipocytes (Cebpa, Adig, Plin1, Scd1) was drastically increased in response to ADRB3 stimulation in eWAT, and readily resolved as a separate cluster (Figs. 2A, ​,2C)2C) (64). A population of mesothelial-like cells present only in eWAT was also distinguished by scRNAseq (Fig. 2A) (64, 104). The mesothelium is a thin membrane containing a layer of epithelial-like cells that surrounds some internal organs, including visceral adipose depots, and has been proposed to be a source of adipocyte progenitors during development (106–109). This cluster shares some similarity with marker genes for ASC2 (Dpp4, Ackr3, Smpd3), expresses low levels of Pdgfra/Pdgfrb, and highly express genes characteristic of mesothelial/epithelial cells (Msln, Krt8, and Krt14) (104). Wt1, a developmental marker (109), is also strongly expressed in these cells, as well as the other ASC clusters in eWAT, but not in iWAT (Fig. 2B). Other subpopulations present to varying degrees in each depot were identified based on marker gene expression, and included endothelial cells (Plvap, Flt1, Tie1), smooth muscle cells (SMC; Tagln, Acta2, Myh11), and subpopulations of contaminating immune cells tentatively identified as macrophages, NKT cells, B lymphocytes, and dendritic cells (DC) that were somewhat variable across studies (Figs. 2A, ​,3A3A).

A number of different genetic tracers have been used to determine progenitor fate both during development and in adult tissue (46, 49, 90–92, 94, 109–112). During development, adipocytes that arise in eWAT have a history of Wt1 and Pax3 (in males) expression, whereas subcutaneous depots are thought to be derived from cells that once expressed Prx1 (91, 109, 111, 112). Among these, only Wt1 was detectable in ASC from adults and expression was restricted to eWAT (Fig. 2B). Dlk1 (Pref-1) also marks mesenchymal ASC progenitors (110) and was largely absent in ASC from adults, but present earlier in development and more strongly in ASC1a (unpublished observations). In addition to Pdgfra, which was broadly expressed in both ASC populations, other markers that have been used to trace the adipocyte lineage in adult tissue including Pdgfrb and Pparg are more strongly expressed in ASC1a. However, these genes are also co-expressed in endothelial/SMC clusters that are positive for Cdh5 and Acta2, but generally negative for Pdgfra (Figs. 2B, ​,3B)3B) (64).

Immunohistochemical Localization of ASC Subpopulations in iWAT and eWAT

Cluster analysis indicates the major cell populations in eWAT or iWAT exhibit similar marker gene profiles, as well as other functional characteristics. For example, a pro-adipogenic phenotype was observed for ASC1a from both WAT depots (102–104), and an inflammatory, fibrotic, or mesenchymal phenotype for ASC2 (102, 104, 119). Merrick et al. (102) evaluated the distribution of ASC progenitors during perinatal adipose tissue development and found ASC subtypes to be compartmentally localized in iWAT. ASC2 (Pref-1- DPP4+) cells were mainly confined to the collagen-rich fascia (“reticular interstitium”), whereas ASC1a progenitors (Pref-1+DPP4-) were located within the parenchyma body, closely associated with adipocytes (102). To determine whether these subtypes share a similar distribution in eWAT, we evaluated total ASC (Pdgfrα) and ASC2 (DPP4, Pi16) markers in both depots from adult mice by immunohistochemistry (Fig. 4). Similar to findings from Merrick et al. (102), ASC2 (Pdgfrα+DPP4+/Pi16+) cells were restricted mainly to the fascia (extracellular connective tissue) in iWAT, which encapsulates the adipose depot and is several cell layers thick (Fig. 4B). Double positive cells also could be found in a thin connective layer lining individual fat lobules (Fig. 4B). By comparison, ASC1 cells (Pdgfrα+DPP4-) were found throughout the parenchyma body and in areas surrounding vessels, as previously observed (46).

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Figure 4.

Immunohistochemical Staining of Total ASC (Pdgfrα) and ASC2 (DPP4, Pi16) in eWAT and iWAT of Adult Male Mice.

(A, B) Immunofluorescence for Pdgfrα (green) and either DPP4 (red, top panel) or Pi16 (red, bottom panel) was evaluated in formaldehyde-fixed tissue sections (10 μM) from (A) eWAT and (B) iWAT. (A) ASC2 cells (Pdgfrα+DPP4+ or PI16+) cells are localized in areas underlying the mesothelium in eWAT. Note the strong immunoreactivity of DPP4 surrounding eWAT, which is also expressed in mesothelial cells from scRNAseq (Fig. 2C). (B) ASC2 progenitors are localized primarily within the fascia that encapsulates the inguinal adipose depot and is also present in a thin layer of connective tissue lining individual fat lobules. ASC1 cells (Pdgfrα+DPP4-) are localized within the parenchyma body in association with mature adipocytes and surrounding vessels. DAPI was used for nuclear staining as indicated in blue. Orange arrows denote some areas immunoreactive for both Pdgfrα and either DPP4 or Pi16. Abbr: Mesothelium (Mesoth.).

In eWAT, a layer of cells that stained strongly for DPP4 surrounded the entire adipose depot (Fig. 4A). This distribution is consistent with the visceral mesothelium previously described (Fig. 4A) (106, 109). Some double-labeled cells (ASC2; Pdgfrα+DPP4+) were observed underlying the mesothelial layer but were mostly absent within fat lobules (Fig. 4A). To better distinguish cells, we additionally evaluated distribution of Pi16, a marker more specific for this ASC cell type in eWAT (Fig. 2C). Doubly labeled cells (ASC2; Pdgfrα+Pi16+) were localized to areas directly underlying the mesothelium but were generally fewer in number compared to ASC1a cells (Pdgfrα+Pi16-) (Fig. 4A). Pi16+ cells were also found in areas surrounding large vessels in eWAT (not shown). In comparison, ASC1a cells were located near the mesothelium and dispersed throughout the fat lobules. Mesothelial cells are thought to serve as a protective barrier, with additional roles including immunomodulatory functions as well as tissue repair (107, 108, 120–123). It is interesting that ASC2 cells were closely associated with this cell type in eWAT and may be related to the higher pro-inflammatory and fibrotic phenotypes observed in vitro and in vivo (104, 119). Whether ASC2 can transition into ASC1a as suggested for iWAT (102) or if there are distinct progenitor lineages for each population is currently under investigation.

ADIPOSE STEM CELL POPULATIONS IN DERMAL WAT (dWAT) AND THE LACTATING MAMMARY GLAND AS IDENTIFIED BY scRNASEQ

In addition to ASC identified within the major WAT depots, Scherer and colleagues (124) recently used scRNAseq to address plasticity of dermal white adipose tissue (dWAT) during hair follicle cycling and in the mammary gland during pregnancy and lactation (125). The dermal adipocyte layer expands by both hypertrophy and hyperplasia during active periods of hair follicle growth (anagen) and then diminishes in thickness during the regression of follicles and rest (catagen/telogen) (124, 126). Using the “AdipoChaser-mT/mG” model, whereby mGFP is expressed on the membrane of cells with a history of adiponectin expression at the time of pulsing (second anagen), dermal adipocytes (GFP+) were found to dedifferentiate into Pdgfrα/GFP+ progenitor cells following hair follicle regression/rest (second telogen) (124). FACS-isolated Pdgfrα (CD45-/CD31-) progenitors were then profiled with scRNAseq during the second telogen to evaluate transcriptomic differences between dedifferentiated (GFP+) and unlabeled (GFP-) progenitors (124). Using this model, two main populations of ASC were identified that share some similarity in marker gene profiles with those observed in the other major WAT depots (ASC2, Ptgs2; ASC1a, Apoe) (64, 102–104, 124). Although few transcriptional changes were observed between dedifferentiated (GFP+) and GFP- ASC, dedifferentiated cells were slightly more adipogenic in vitro (124). Similar to the above, the fate of adipocytes during lactation and recurrence at involution were evaluated in the “Adipochaser-mT/mG” model (125). Adipocytes (GFP+) labeled prior to pregnancy were found to dedifferentiate into Pdgfrα+/GFP+ SVC during lactation and re-differentiate into adipocytes upon weaning of pups (125). Using scRNAseq, the Pdgfrα+/GFP+ (CD31-/CD45-) progenitors were found to have a similar expression profile to Pdgfrα progenitors isolated from virgin females (125). However, due to limitations in sample size, it is unknown if the dedifferentiated cells represent more than one ASC subtype (125). Nonetheless, these findings highlight the cellular plasticity of adipose tissue and further suggest that adipocytes can fully dedifferentiate into ASC under certain physiological conditions (124, 125).

IMMUNE CELL POPULATIONS IN eWAT DETECTED WITH scRNASEQ

Visceral adipose tissue is known to have differential susceptibility to inflammation and neogenesis compared to subcutaneous fat (24–26, 37, 46, 92, 127). Infiltration and local proliferation of adipose tissue macrophages (ATMs) is frequently observed in obesity and contributes to insulin resistance and other metabolic derangements through local secretion of pro-inflammatory mediators (16, 37, 128–131). In obese states, a large proportion of ATMs in the visceral depot surround dead adipocytes, forming “crown-like structures” (CLS) (37, 118, 127), that are thought to be an important source of these mediators (37). The recruitment and proliferation of macrophages is also tightly linked with de novo adipogenesis and is important for initiating WAT remodeling in response to energy excess, β-adrenergic stimulation, and following tissue injury (45, 92, 117). Recruited macrophages also exhibit heterogeneity in phenotypes depending on the type and duration of the stimulus (37, 45, 92, 130), although the distinct populations involved and activation states have not been thoroughly characterized. Several studies have used scRNAseq to characterize immune cell populations during WAT remodeling, either following ADRB3 stimulation or during diet-induced obesity (64, 132–134). However, due to the extent of data evaluated, results are only briefly summarized below.

SUMMARY OF scRNASEQ STUDIES IN WAT

Collectively, studies exploring adipose tissue composition using scRNAseq have provided relatively consistent data regarding WAT progenitor populations, expression profiles, and insight into immune cell phenotypes during catabolic and anabolic tissue remodeling. The adipocyte progenitors identified from subcutaneous and visceral depots express several of the previously identified genes for stem cells including Pdgfra, Itgb1, Cd34, and Ly6a; however, 2–3 distinct subpopulations could be further resolved by scRNAseq. Based on both in vitro characterization (102–104) and in vivo trajectories (64), ASC1a appears to represent an immediate adipocyte progenitor. Further, immunochemical evidence indicates this cell type is localized in the parenchyma body in association with adipocytes (102).

Some inconsistencies exist regarding ASC1b in iWAT, as both pro-adipogenic and anti-adipogenic roles have been reported (102, 103). The reason for this discrepancy is currently unknown but may be due to the different selection markers used to purify cell populations, as suggested (102). ASC2 appears to be a relatively new defined progenitor subset, and available data suggest a pro-inflammatory role (104, 119), at least in eWAT. This subpopulation is detected by immunohistochemistry in the collagen-rich fascia encapsulating iWAT (102) and in close contact with mesothelial cells in eWAT. ASC2 are further characterized by a higher expression of Wnt genes generally associated with a less committed, multi-lineage mesenchymal stem cell, and have been hypothesized to be a direct precursor for both ASC1a and b in iWAT (102). Lineage tracing using cell-selective markers identified through these studies will help further delineate a potential hierarchal role for this subtype in vivo (102).

In visceral depots, the most prominent immune cell population is monocyte/macrophages. In separate models of tissue remodeling, scRNAseq was used to detect population and phenotype changes in response to either HFD or ARDB3 stimuli (64, 132–134). A shift in macrophage populations as well as gene expression patterns were resolved using scRNAseq. Further, a subset of macrophages associated with “crown-like structures” were distinguishable based on gene profiling and further confirmed with histochemical analyses (64, 132, 133). Cd9, Spp1, and Trem2 were identified as genes enriched within this subset. Additionally, Trem2 signaling appears to drive the transcriptional and functional responses of this subgroup in obesity, which is suggested to promote formation of CLS to control adipocyte size (133). These findings highlight an important role for CLS-associated macrophages in initiating adipose tissue remodeling and provide new insights into the complexity of immune activation states in adipose tissue.

Although the current review aimed to summarize findings available from preclinical mouse models, a few comparable studies addressing heterogeneity of stromal cells with scRNAseq in humans have also been reported (102, 132, 133). For example, Merrick et al. (102) found similar populations of the two major ASC (ASC1a, ASC2), in human abdominal subcutaneous adipose tissue as those identified in mice, with a proportionally higher number of ASC1a cells. Conversely, ASC1b markers were more diffusely expressed (102). FACS-sorted ASC populations also exhibited similar functional properties in adipogenic assays, with ASC1a being more poised for adipogenesis than the ASC2 subtype (102). The presence of an analogous, lipid-laden CD9/TREM2+ macrophage population associated with CLS in obese human visceral fat was also identified using scRNAseq (133) and by histological and FACS-based analyses (132). Importantly, a positive association between the accumulation of CD9+ macrophages in VAT and obesity was reported by both Jaiten et al. (133) and Hill et al. (132). These studies suggest at least some conserved cell types and cellular responses between species. Nonetheless, further integration and interrogation of single cell datasets will lead to a better understanding of species-dependent differences among adipose tissue depots and potentially aid in more directed treatments for obesity-related metabolic dysfunction.

FUTURE DIRECTIONS

It is evident that scRNAseq differentiates stromal cell populations and cellular states with high resolution, providing a powerful tool for addressing adipose tissue heterogeneity. One limitation from the available studies is that all have enriched for lineage selection, therefore the relative composition of stromal cells within the different depots and proportional changes in cell types under different conditions are not well defined. Cellular input and capture efficiencies differ to some degree among different scRNAseq platforms (65, 78). As scRNAseq becomes increasingly streamlined, the capacity to detect wider range of cells is feasible and should be considered in future studies.

Although the focus of this review was on stromal cell heterogeneity, adipocytes arise from distinct developmental lineages and depot specific differences in adipokine release, lipolysis, and inflammation are well established (reviewed in (60, 136, 137)). Heterogeneity among adipocytes within the same depot have also been reported by both genetic and histological criteria (138, 139). For example, adjacent brown adipocytes within the interscapular brown adipose tissue (iBAT) of mice often express substantially different levels of uncoupling protein 1 (UCP1), yielding a ‘harlequin’ type appearance upon histological staining (138). Due to their fragility and natural buoyancy in suspension, and for brown adipocytes, high mitochondrial content, examining isolated adipocytes by scRNAseq is technically challenging (64). Methods for single nucleus RNA-seq (snRNAseq) are available (140, 141) and results from other tissues suggest good correlation to profiles generated with scRNAseq that are less affected by cell isolation methods (142, 143). Two recent studies have begun to address adipocyte heterogeneity, either in isolated brown adipocytes or from inguinal white adipocyte nuclei using sc/snRNAseq, respectively (139, 144). These studies reported some evidence of heterogeneity within adipocyte gene-expressing clusters (139, 144). Nonetheless, both studies also identified additional clusters of putative adipocytes that co-express markers prominent in other cell types, including endothelial cells (e.g., Flt1, Pecam1) (139, 144) as well as ASC subtypes (e.g., Pi16, Pdgfra, Gdf10) (144). Indeed, the profile of putative brown adipocytes expressing low levels of UCP1 reported by Song et al. (139) is more similar to capillary endothelial cells than brown adipocytes when integrated with multiple independent scRNAseq data sets. Whether these cells represent non-classical adipocytes or contaminating cells present in the floating adipocyte fraction is worthy of future validation.

While scRNAseq provides deep information regarding cell types and states, it does not provide information on localization of cellular populations within adipose tissue. Nonetheless, scRNAseq data provide additive and informative markers that can be used to localize cells within the tissue microenvironment and confirm potential cell-cell interactions. In this way, ASC2 has been localized to the fascia or “reticular interstitium” (102) in iWAT, and Spp1/Trem2 macrophages and activated ASC progenitors to the CLS of visceral depots (46, 64, 92, 117, 132, 133). We anticipate that additional exploration of markers genes identified through current scRNAseq studies as well as future developments in spatial transcriptomics (145) will provide a more complete picture of adipose tissue heterogeneity, cellular interactions, and help dissect mechanisms of tissue plasticity.

Supplementary Material

Acknowledgements

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Footnotes

REFERENCES