KEY FIGURE

Highlights

• Single-cell transcriptomic classification delineates 8 distinct ASPC subpopulations

• Adipogenic differentiation trajectories characterized 4 subtypes

• Subtype abundances are altered in metabolic diseases

• Functionally diverse ASPC subclusters associate with metabolic perturbations

• Sex, depot origin, age, and metabolic state define ASPC diversity

INTRODUCTION

Adipose tissue (AT) is a metabolically active and highly heterogeneous endocrine organ. In fact, AT may be regarded as a collection of distinct organs, as humans possess functionally specialized AT depots—most prominently brown adipose tissue (BAT), which drives thermogenesis [1], and white adipose tissue (WAT). Beige AT represents an intermediate state capable of ‘browning,’ i.e., acquiring thermogenic properties in response to specific stimuli [2]. This review will focus on human WAT.

Within WAT, individual depots can be considered distinct organs in their own right, given their unique developmental origins, cellular composition, and metabolic functions [3,4]. The major WAT depots, subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT), display divergent endocrine and metabolic profiles, with VAT-dysregulation associated with adverse clinical outcomes [5]. Despite these functional differences, AT across depots shares several key cellular components: (1) adipocytes, (2) immune cells, (3) endothelial cells (ECs), and (4) adipose stromal and progenitor cells (ASPCs). Mesothelial cells represent an additional cell-type found predominantly in VAT [6].

While adipocytes [7], ECs [8], and immune cells [9] have been extensively reviewed, ASPCs remain underexplored, despite their critical roles in tissue remodeling, adipogenesis, and metabolic regulation [10-12]. The terminology describing these cells remains inconsistent, with overlapping or context-derived designations including fibroblasts [13-17], preadipocytes [15,18-24], adipose-derived (mesenchymal) stem or stromal cells (AD-MSCs, ADSCs, alternatively adipose stem cells or mesenchymal stem cells) [15,16,20,25-28], adipogenic progenitor cells [4,16,21,23,29-32], and fibro-adipogenic progenitors [23,33-35] have been used to describe overlapping or distinct subsets within the ASPC population. Following the Human Cell Atlas (HCA) consensus [36], we adopt the term ASPCs to describe this heterogenous population.

This review summarizes state-of-the-art approaches for identifying ASPC-subpopulations, outlines recurrent ASPCsubtypes and their functional roles, and discusses the clinical relevance of ASPC dysregulation in metabolic disease.

METHODOLOGICAL APPROACHES FOR STUDYING ASPC

Identification and validation of ASPCs

Traditional methods for ASPC identification

ASPC-subpopulations have classically been studied using immunofluorescence (IF) and fluorescence-activated cell sorting (FACS). FACS allows subpopulation enrichment of primary human samples based on surface markers such as platelet-derived growth factor receptor α (PDGFRα) [37]. Proteomic and transcriptomic profiling enables the identification of subcluster- specific markers, facilitating the targeted isolation of distinct ASPC-subpopulations for functional assays. For example, CD34 has been applied in FACS-based ASPC isolation [25,38]. Although CD34 is typically considered a universal ASPC-marker, emerging research suggests the presence of CD34−-ASPCs with beige-like properties [38]. FACS-isolation of intercellular adhesion molecule-1 (ICAM1)⁺-ASPCs revealed two functionally distinct subsets (CD44^high/low), representing stages along the adipogenic trajectory, with declining CD44-levels towards mature adipocytes [39].

Additionally, immunohistochemical detection of subtype-specific biomarkers enables identification and localization of ASPC-subpopulations. Peroxisome proliferator-activated receptor γ (PPARγ) identifies adipogenic ASPCs via IF [14], while antifibrotic subpopulations were visualized by CD74-labeling [40]. Slit guidance ligand 2 (SLIT2) served as a marker for an EC-colocalized ASPC-cluster [34]. Other examples for subcluster-defining targets include proteoglycan 4 (PRG4), collagen triple helix repeat containing 1 (CTHRC1), CXC motif chemokine ligand 14 (CXCL14), and chitinase-3-like protein 1 (CHI3L1) [28]. Lipid-targeting stains such as Oil Red O and boron-dipyrromethene (BODIPY) were applied to label (pre-)mature adipocytes [41,42].

ASPC identification on single-cell scale

Despite their usefulness, classical approaches lack the capacity to map ASPC-heterogeneity comprehensively. Recent singlecell and spatial approaches revealed a new picture of ASPCs, highlighting them as a major subtype within WAT [24,43-45]. Evaluating transcriptional profiles of single cells permits high-resolution dissection of complex tissues and enables the definition of distinct subclusters within heterogenous tissues, uncovering novel subclusters and differentiation pathways [3,4,16,17,20-23,25,28,30,32,34,35,43,46-53].

While being untargeted, these methods have evident limitations. As adipocytes are too large, buoyant, and fragile for current single-cell approaches, most papers rely either on (1) single-cell RNA-sequencing (scRNAseq) of the stromal vascular fraction (SVF) after collagenase-digestion, or (2) nuclei-isolation and single-nuclei RNA-sequencing (snRNAseq) of the whole tissue. Both methods introduce distinct biases and yield results that do not align well [3,54]. Specifically, scRNAseq provides higher relative abundance and greater resolution of ASPC-subpopulations [34], whereas snRNAseq is preferred due to lower expression of stress-response genes (e.g., FOS, JUN, HSP) and to include adipocytes. However, snRNAseq lacks non-nuclear enriched transcripts, thereby capturing only a fraction of the transcriptome [55] and may underrepresent distinct genes [54]. For both approaches, sample sizes remain limited by cost, and many subpopulations lack validation using established methods and larger cohorts. Nonetheless, these methods provide a high-resolution classification, and recent efforts by the HCA AT-Bionetwork provide a standardized consensus framework [36].

Advanced methods for ASPC identification and validation

Spatial transcriptomics (STx), including spot-based sequencing (i.e., Visium [49]) and in situ methods (i.e., Multiplexed Error-Robust Fluorescence in situ Hybridization [MERFISH] [56], Xenium [32]), offers alternative approaches to resolve cellular heterogeneity without nuclei- or SVF-isolation. Modern platforms (i.e., Visium-HD, CosMx, Stereo-seq) now achieve near single-cell spatial resolution [57], however have not been applied to AT yet. Full-length sc/snRNAseq has also been applied to WAT, providing broader gene coverage at lower cell numbers than 3ʹ-based methods and enabling isoform-level analyses [54]. Complementary multi-omics approaches integrate transcriptomic, secretomic, proteomic and genomic data to unravel intercellular regulatory networks within AT [3].

Epigenomic profiling complements ASPC-characterization, focusing on three-dimensional genome mapping [45,58], chromatin conformation [43,45,59], DNA methylation [45, 60], histone modifications [60,61], and subsequent transcriptional regulation utilizing chromatin Immunoprecipitation (ChIP) [61], assay for transposase-accessible chromatin (ATAC)-RNAseq [58,60,62], single-nucleus methyl-3C sequencing (snm3C-seq) [45], cleavage under targets and tagmentation (CUT&RUN/TAG) [60,63], chromosome conformation techniques such as Hi-C [58], and estimation of cell age from single-cell ATAC data using EpiTrace [64].

Together, these technical advances have supported the identification of ASPCs in AT and supported the classification of ASPC-subpopulations with distinct functions.

Computational strategies for ASPC analysis

Computational guides summarize best-practice workflows for single-cell analysis [65,66] and AT-specific considerations [36]. Here, we focus only on aspects relevant to ASPCs.

Data integration minimizes technical variations unrelated to biology. Batch effects arise from differences in sample handling, library preparation, or sequencing platforms. While unnecessary when all data is processed uniformly, integration becomes critical when combining datasets from multiple laboratories. Widely adopted approaches for ASPCs [3,17,20,25,46,51] include Seurat-based methods [67] and Harmony [68]. Benchmarking different algorithms remains good practice to ensure removal of technical effects while preserving biological signal [69]. Reprocessing raw FASTQ-files with consistent pipelines further enhances cross-study comparability.

Given their differentiation potential, ASPC-trajectories can be reconstructed computationally. However, subtypes lacking adipogenic potential should be excluded to avoid misleading lineage inferences. Splicing-based trajectory tools, including Velocyto [70] and scVelo [71], are not applicable to snRNAseq, as it lacks sufficient unspliced transcripts. Velocity-based interpretations in AT-atlases should therefore be treated cautiously. Complementary lineage-tracing approaches, including genetic barcoding and mitochondrial variant tracking, can reveal clonal relationships but remain limited to model organisms for ethical reasons [13,72,73]. Time-resolved RNAseq and pseudotime analysis have delineated differentiation trajectories and progenitor hierarchies within SVF-populations [20,29,34,46].

Cell-cell communication inference in AT follows general frameworks, with CellPhoneDB [74] and CellChat [75] most commonly applied. ASPCs consistently emerged as central communication hubs, acting as both signal senders and receivers [3,21,34,51].

Subtype relevance can be evaluated by correlating proportional abundances with clinical parameters, although limited cohort sizes constrain statistical power. Integrating sc/snRNAseq with bulk transcriptomics data through deconvolution enhances power and allows meta-analytic validation across cohorts [34]. Similarly, deconvolution of low-resolution STx enables estimation of cell-type composition and spatial co-localization patterns [76].

Marker overlap among subclusters [77] complicates deconvolution. Aggregating transcriptionally similar subtypes, as outlined in Fig. 1A, Supplementary Table 1, reduces this bias.

Functional characterization of ASPCs

Appropriate and well-characterized model systems are essential for investigating ASPC-physiology. Murine and duck ASPC lines like 3T3-L1 [78] and CCL-141 [79] have been established for in vitro studies, but their non-human origin limits translational relevance. Human ASPC-generation protocols were published [80], and commercially available human models (human multipotent adipose-derived stem cells [hMADS] [81], Simpson-Golabi-Behmel syndrome [SGBS] cells [82], ASC-52telo [83]) exist, providing more physiologically relevant systems.

Differentiation and functional assays of ASPCs

Multipotent human ASPCs can be in vitro differentiated into mature adipocytes [41,84-86], smooth muscle cells (SMCs) [87], extracellular matrix (ECM)-producing fibroblast-like cells [88], immune-regulatory cells [4,89] or myofibroblasts [27], enabling systematic exploration of functional states and lineage flexibility. Further, osteogenic [90] and chondrogenic [91] differentiation pathways were confirmed in vivo. Standard gene editing tools (Clustered Regularly Interspaced Short Palindromic Repeats [CRISPR]-Cas9 [92,93], Cre-loxP [14,34,94]), have been applied to ASPCs for perturbation studies and lineage tracing, clarifying differentiation pathways in vivo and in vitro [13,72]. These models support mechanistic and high-throughput studies but cannot reproduce the native WAT-microenvironment. Patient-derived cells and FACS-isolated subpopulations remain the most physiologically relevant approach.

ASPC-subclusters can be interrogated using assays tailored to their putative, subtype-specific roles. Classical fibroblast-like states can be assessed by ECM secretion [10], fibrotic gene expression, immunomodulation [95], and wound healing capacity [96,97]. Adipogenic potential is evaluated by lipid accumulation and adipocyte marker expression (e.g., perilipin-1 [PLIN1]; adiponectin [ADIPOQ]) [85,98]. CD34-expression can further distinguish adipocyte subtypes with divergent lipid turnover and thermogenic capacities [38]. Additional assays capture myofibroblast differentiation [99], angiogenic potential [100,101], and paracrine immunomodulation [38,102]. Metabolic assays, focusing on thermogenesis [2] and mitochondrial activity [29,103,104], provide complementary insight into energetic states. Spatial metabolomics and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging have been adapted for WAT to visualize metabolic and lipidomic heterogeneity [105,106]. Together, these readouts allow functional annotation of subclusters and integration of molecular signatures with biological phenotypes.

Advanced ASPC model systems

These technological advances have expanded our understanding of AT-heterogeneity, yet models better recapitulating the native microenvironment are required. Coculture systems incorporating ECs [107,108], dermal fibroblasts [95] and macrophages [102] have improved functional characterization, revealing that ASPCs can promote angiogenesis [100,109] enhance ECM production [10,50] and reduce expression of classical fibroblast marker genes [95]. Murine AT-organoids derived from SVF, including immune populations, have enabled investigation of lipid metabolism and immune-adipocyte crosstalk [110] and pre-vascularized human beige AT-organoids have recently been established [111]. Embedding ASPCs into growth factor-reduced matrigel promotes differentiation into unilocular adipocytes with enlarged lipid droplets, facilitating studies of adaptive AT-expansion and precursor heterogeneity [112]. Human AT-derived organoids show substantial potential for tissue engineering and regenerative applications [113] and may be further improved by incorporating donor-specific variability and standardized functional readouts. Recent reviews have summarized these advances and their translational relevance [114]. Finally, transplantation of human ASPCs into immunocompromised mice provides an additional avenue to examine functional properties in vivo [115].

FUNCTIONALITY AND CELLULAR HETEROGENEITY OF HUMAN ASPC

ASPCs function as adipogenic precursors, regulate ECM production and remodeling, and modulate local inflammation. Dysregulation of these processes contributes to fibrosis, chronic inflammation, and maladaptive ECM remodeling [10,116]. While these general roles are established, the functional specificity of individual ASPC-subclusters remains unclear.

To address this, we integrated published sc/snRNA-seq datasets to derive consensus ASPC signatures (Supplementary Table 1), identifying eight recurrent subtypes (Fig. 1A). Below, we outline the rationale for this classification and highlight key subtype-specific functions.

Heterogeneity of ASPCs

General ASPC markers

To isolate ASPCs from other cell types, several general markers are commonly used, including platelet-derived growth factor receptor alpha (PDGFRA) [17,20,23,25,28,35,46,49,50,53,117], PDGFRB [21,25,30,34], decorin (DCN) [25,28,47,49,50,52,53], laminin subunit alpha 2 (LAMA2) [23,49], integrin subunit beta 1 (ITGB1; CD29) [21,28], THY1 (CD90) [28,50,51] and CD34 [4,17,22,28,34,50,51]. Most studies rely on PDGFRA and CD34, although both show subtype-specific variation: PDGFRA-expression increases during adipogenesis [20,24,32,46,48,54,118], while CD34 marks fibroblast-like ASPCs with higher stemness [17,21,28,48,52], and CD34^–-ASPCs tend to be more committed to adipogenic differentiation [38,46]. THY1 is used as a general ASPC-marker [28,50,51], yet other studies identified it as a subtype-specific marker across depots [25,30,35,46,49,54]. Similarly, DCN is sometimes applied broadly but appears enriched in ASPCs with high differentiation potential [24,25,32,48,54]. Overall, several markers historically treated as ‘general’ ASPC identifiers likely capture only subsets of the full ASPC compartment, most notably CD34 and DCN.

Classification of adipogenic ASPCs

The first well-defined cluster is identified by expressing dipeptidyl peptidase 4 (DPP4; CD26) [3,20,21,23,25,28,30,32,34,46,47,49,50,52-54], CD55 [3,17,20,23,25,28,30,32,34,35,46,49-52,54] and peptidase inhibitor 16 (PI16) [3,17,20,21,23,30,34,35,47,49,51,54], referring to early uncommitted, multipotent progenitor cells (UMP) (I). DPP4 has been widely used to isolate progenitors for downstream applications, i.e., differentiation [50,119,120]. ASPCs have also been identified by PRG4 [3,4,20,21,28,35,49,52,54,56,121], which is a major player in wound healing and thus might refer to anti-fibrotic functions, being not a suitable marker for UMPs. Recently, an ASPC-subcluster was defined by high expression of DPP4, ITGB1, THY1, and CD9 [46], likely referring to a profibrotic cluster with high differentiation potential. Further, the CD9⁺ subpopulation was identified as an intermediate state between UMPs and more differentiated cells [51]. We assume that CD9⁺ and PRG4⁺ clusters may overlap, displaying an early progenitor of pro-inflammatory mediators.

Another subcluster can be identified by ICAM1-expression [20,21,23,30,35,48,50-52]. This uncommitted, intermediate preadipocyte (IPA) cluster (II) can be further subdivided by DPP4-expression decreasing along adipogenesis [20,21]. More committed precursors can be defined by PPARG-expression [17,20,23,25,28,30,32,34,36,46,48,50] and more adipose-specific markers, such as CD38 [25,54], gamma-glutamyltransferase 5 (GGT5) [28,30], zinc finger protein 423 (ZNF423) [25,54], delta like non-canonical Notch ligand 1 (DLK1) [25,54], apolipoprotein C1 (APOC1) [20], APOD [3,4,12,16,20-22,24,28,34,35,48,49,52,54], APOE [4,16,20,28,30,35,50,52], fatty acid binding protein 4 (FABP4) [4,16,20,34,47-50], CCAAT enhancer binding protein beta (CEBPB) [4,35,56], matrix Gla protein (MGP) [4,16,22,24,28,34,49,52,54], CD36 [4,12,16,17,20,22,23,52,53], CXCL14 [3,4,12,16,21,22,24,28,34,35,49,52-54,56] and complement factor D (CFD) [4,16,22,34,48,49,52-54,56]. Those marker genes vary in their expression along adipogenic differentiation and further subtypes might be identified. Here, we define two ASPC-subtypes: (III) committed, intermediate preadipocytes (CPA) and (IV) premature adipocytes (PreAd). CPAs already exhibit adipocyte-related gene expression, e.g., PPARG, and are therefore considered ‘committed.’ But we assume that alternative differentiation pathways can be induced in CPAs, similar to UMPs and IPAs. In contrast, PreAds are more strictly committed to the adipogenic line and express specific genes, i.e., APOD, CXCL14, and CD36 or even ADIPOQ. Gene enrichment analysis defines main functions in triglyceride metabolism pointing to adipogenic commitment [28]. Furthermore, depot-dependent PreAd-markers were reported. For instance, APOE was enriched in APOD⁺ CXCL14⁺ PreAd in SAT [16,28,52], but found enriched in non-PreAd subclusters in VAT, i.e., in a fibro-progenitor/adipogenic regulatory cell (Areg)-like [35] and a retinol binding protein 5 (RBP5)⁺ ASPC-cluster [20]. CD36 was identified in SAT-PreAds across studies [4,23,52,53] and was reported in VAT-Aregs [23] and fibro-progenitor-like ASPCs in perivascular AT (PVAT) [34]. Enriched glutathione peroxidase 3 (GPX3)-expression was demonstrated in VAT-PreAds [4] and in a APOD⁺-cluster in creeping fat [48] but not in SAT. In addition, some identified CPA and PreAd markers, such as FABP4, CEBPA, and S100A4, appear to be very unspecific and are inappropriate for defining PreAds or other ASPC-subclusters.

Contrary, adipogenesis is inhibited by a specific ASPC-cluster being responsive to external stimuli: anti-Aregs (V) are identified by expressing F3 (CD142) and EPH receptor A3 (EPHA3) [3,20,23,25,32,50-53] and also regulate angiogenesis and immune responses [3,20,50,51,89].

Non-adipogenic ASPC-subtypes

Apart from adipogenesis, we define functionally diverse subtypes: profibrotic, inflammatory, and fibroblast-like. Profibrotic fibroblast-precursors (VI) are marked by the expression of CD9, versican (VCAN), and LY6C [26,28,32,35,46,49,51,54,56]. This alternative differentiation pathway is regulated by PDGFRα, acting as a molecular switch [88,94]. Intermediate states from UMPs to CD9⁺-ASPCs were confirmed [46,51]. Spatial mapping locates this subcluster in a profibrotic niche within WAT [34]. A pro-inflammatory subtype is marked by CD74, LY6E, and interferon alpha inducible protein 6 (IFI6) (VII) and induces immune responses via cytokine secretion (CXCL1, CXCL2) or distinct pathway signaling [3,4,34,52,54,56]. This cluster may compromise functionally distinct subsets [4,34,52], although evidence remains limited. Precisely, antifibrotic and immunomodulatory activity of CD74⁺-ASPCs has been reported by elevated secretion of fibroblast growth factor 2 (FGF) and hepatocyte growth factor (HGF) [40]. ECM-production and tissue remodeling is mainly exhibited by a CD34⁺- subcluster (VIII) that is additionally identified by expressing collagens (COL15A1, COL1A1, COL3A1, COL6A3) [4,17,20,25,32,34,47-49,54,56]. This subcluster strongly contributes to AT-remodeling during weight loss (WL) or gain, or after disease recovery. We assume that clusters (VI–VIII) can either differentiate from UMPs, IPAs, or CPAs or from each other. Only few studies, primarily in mice, showed non-adipogenic differentiation of ASPCs. In fact, transforming growth factor beta (TGFβ)-treatment of Pdgfra⁺-ASPCs in mice led to increased expression of fibrosis markers leading to ECM-synthesizing Cd9⁺-ASPCs, a combination of our clusters (VI) and (VIII) [88]. In human UMPs, TGFβ-treatment induced differentiation towards cluster (VIII) [10] and reduced cluster (VII) activation [40], along with adipogenesis inhibition [30,122]. Pdgfra enhances mechanistic target of rapamycin (mTOR) signaling and induces the differentiation into ECM-synthesizing cells, corresponding to our cluster (VIII) [94]. UMPs differentiate into DPP4−-myofibroblasts upon TGFβ stimulation and subsequent Hippo pathway activity, increasing AT fibrosis and being connected to cluster (VII) [120]. In humans, a reversible differentiation pathway towards ECM-expressing, progenitor-like cells (structural Wnt-regulated AT-resident [SWAT] cells, putative intermediate state towards (VIII)), is induced by transient Wnt-signaling [29,123]. Moreover, functionalities may overlap, e.g., inflammation and profibrotic signaling contribute to obesity-induced AT fibrosis [11,88,94,124].

Conclusion of proposed ASPC classification

Collectively, eight distinct ASPC-subtypes are defined: (I) UMP, (II) IPA, (III) CPA, (IV) PreAd, (V) Aregs, (VI) fibro-progenitors (profibrotic), (VII) immunomodulatory cells (inflammatory), and (VIII) ECM-producing cells with tissue remodeling capacity (Fibroblast-like). These subclusters are commonly identified by specific marker genes: (I) DPP4, CD55, PI16 [3,20,46,47,51], (II) ICAM1, ITGB1, CD44 [3,30,51], (III) PPARG, ZNF423, DLK1, CD38 [25,28,30], (IV) PPARG, CXCL14, APOD, CD36 [4,12,22,23,28,30,34,49], (V) F3, EPHA3 [3,30,46,51], (VI) CD9, VCAN, LY6C [26,32,35,49], (VII) CD74, CXCL1, LY6E [4,34], and (VIII) CD34, COL1A1, COL6A3, COL15A1 [4,32,47,51] (Fig. 1).

However, more subpopulations can be defined, depending on varying computational cutoffs, e.g., when setting the resolution to separate clusters. For the following subtypes, there is only evidence from isolated studies: ferroptosis suppressor protein 1 (FSP1)⁺-ASPCs support the maintenance of adipocyte differentiation potential of adjacent PreAds but are not capable of adipogenesis themselves [14]. Thioredoxin-interacting protein (TXNIP)⁺-ASPCs have been associated with strong metabolic pathway enrichment and high differentiation potential [16] and were identified across SAT, VAT, and PVAT [34]. STx-data indicated proximity of SLIT2⁺-ASPCs to blood vessels [34] and SLIT2 in ECs has been reported to be affected by obesity [109], suggesting a putative angiogenesis-regulating function of this ASPC-subtype. MTX-ASPCs show increased metallothionein-expression (MT1X, MT1A, MT2A, MTRNR1) and may reflect on a stress-responsive subtype [4,20,28,34,35,52]. Further meta-analyses across depots and diseases comparing data from scRNAseq and snRNAseq are required for a comprehensive overview.

Differentiation and functional characterization of ASPC-subtypes

ASPC with adipogenic differentiation potential

ASPCs are reported to be the prevailing adipocyte progenitor in both BAT and WAT [13]. Differentiation of human pluripotent stem cells into adipocytes has been described utilizing PPARγ, CEBPB, PR domain containing 16 (PRDM16) [82,84]. Recent scRNAseq-studies have revealed UMPs as a common progenitor of adipocytes and ASPC-subclusters, being Wnt-regulated and showing enrichment in ECM-production and developmental genes [29]. Other differentiation pathways may be exhibited in response to various stimuli.

During adipogenesis, UMPs differentiate into IPAs, CPAs, and PreAds, along multiple intermediate states, before reaching the state of mature adipocytes (Fig. 1) [125]. Other progenitors, such as pericytes [126], mesothelial cells (via insulin like growth factor binding protein 2 [IGFBP2]⁺-intermediate state) [20] and SMCs [87], can, however, likewise serve as progenitors for adipocytes. In contrast, other differentiation pathways may be followed by adipogenic ASPC-subtypes, leading to the formation of clusters (V–VIII) and thus inhibiting adipogenesis, i.e., via Areg activity [23,89]. Enhanced PDGFRα-activity may lead to the differentiation of adipogenic ASPCs (UMP, IPA, CPA) or to formation of clusters (VI–VIII) [94]. Further, Aregs and IPAs can arise from the profibrotic CD9⁺-population too [51]. UMPs [30,34,127] and profibrotic ASPCs [34,88] are sensitive to TGFβ-signaling and subsequent adipogenesis inhibition. IPA and CPA are associated with cellular responses to various stimuli, including mechanical stress, FGF and calcium signaling, regulating adipogenesis [51]. Activating the Wnt/β-catenin pathway inhibits adipogenic commitment [128] and distinct mutations have been shown to promote obesity [129] and type 2 diabetes mellitus (T2DM) [130]. Adipogenic commitment can be further regulated via obesity-induced senescence [131,132], gremlin 2, DAN family BMP antagonist (GREM2)-overexpression in aging [133], mitochondrial metabolism [134], genetic variants such as LDL receptor related protein 5/6 (LRP5/6; Wnt-coreceptors) [135], and anti-adipogenic substances, i.e., retinoic acid [136], microRNAs [137], and inflammatory cytokines [42].

Proposed functional roles of ASPC-subclusters

ASPCs are the major players of ECM remodeling and AT structure integrity [10,11,15]. This dual role of, either adipogenic differentiation capacity or structural integrity as main function, has been confirmed previously [15,29]. Specifically, UMPs, Aregs and profibrotic ASPCs have been implicated in ECM organization [51]. Further functions include WAT beiging among exposure to cold or adrenalin signals [13], regulation of lipid and cholesterol metabolism [25], cell-cell communication [11] and inflammation [138]. However, ASPC-subpopulations vary in ECM-related gene expression, adipogenic commitment metabolic signatures [25] as well as angiogenesis regulation [50]. To some extent, those functions may be associated with specific ASPC-clusters, e.g., immunomodulatory responses may be correlated with clusters (VI) and (VII) [4,54]. But, as subcluster classification is inconsequent across studies, functional annotations may only be speculated on, and further meta-analysis is required. Moreover, depot- and disease-specific shifts in ASPC-populations suggest functional relevance for metabolic health, which will be addressed in the next chapter.

CLINICAL IMPLICATIONS OF ASPC HETEROGENEITY

ASPCs are the most abundant cell class in AT and play essential roles in AT-remodeling and metabolic regulation (Fig. 2A). Their abundance and functional state are linked to depot and metabolic health, suggesting that specific ASPC-subtypes may contribute differently to obesity, insulin resistance, and fibrosis. Emerging sn/scRNAseq-studies revealed both shared and depot-specific responses to metabolic stress. Despite inconsistencies among studies, an emerging picture suggests: ASPCs adapt dynamically to local and systemic metabolic cues, and their dysregulation may represent an early cellular correlation with metabolic dysfunction.

Inverse regulation of SAT and VAT ASPCs

WAT-depots, such as SAT and VAT, exhibit distinct functions and could be considered as functionally distinct organs. This depot-specific specialization extends to their ASPC-compartments. Reinisch et al. [23] observed a lower overall abundance of ASPCs in SAT from metabolically unhealthy obese (MUO) individuals, consistent with findings by Loft et al. [53], who reported an enrichment of SAT-ASPCs following bariatric surgery-induced WL. Similarly, Kar et al. [44] noted a reduction in ASPC-proportions upon impaired adipogenesis, linked to altered expression of genes associated with both obesity and aging. In contrast, Emont et al. [3] found a positive correlation between ASPC-abundance and body mass index (BMI) in SAT.

In VAT, ASPC levels were increased in MUO [23], whereas Emont et al. [3] reported a negative association between VAT ASPC abundance and BMI. Metabolically, gluteofemoral ASPCs were enriched in lipid and cholesterol metabolism pathways, and displayed high proliferative potential, while abdominal ASPCs were more preadipocyte-committed and enriched for ECM and ribosomal gene expression [25]. Subtype-level comparisons further highlight these depot-specific differences: UMPs, in line with their ubiquitous cross-organ relevance as multipotent progenitors [139], were shown to be similarly abundant across multiple depots, while IPAs, CPAs, and PreAd were more depot-specific [139]. The latter, as well as Aregs, were found to be more abundant in SAT versus VAT [3,4,16].

Despite some discrepancies, collective evidence supports inverse regulation of ASPC-abundance and differentiation potential in SAT versus VAT, linking depot-specificity to metabolic health (Fig. 2B).

ASPC subtype abundance reflects metabolic state

Emont et al. [3] and Massier et al. [34] reported a positive correlation between UMPs and BMI, supported by Loft et al. [53], who observed a decline in UMP abundance following WL. Given that adipocyte number is largely established during childhood [140], an expanded UMP pool may predispose to increased adiposity by enhancing the capacity for lipid storage (Fig. 2C).

Following the adipogenic trajectory, TXNIP⁺-ASPCs (Pre-Ads/fibroblast-like) have been negatively correlated with BMI [34], in line with more recent data that reported increased PPARG⁺-IPAs/CPAs after WL [53]. Conversely, abundance of CXCL14⁺-PreAd declined following WL [53], consistent with Emont et al. [3], who found Aregs and ASPCs expressing sarcoglycan zeta (SGCZ), PIEZO2, twist family bHLH transcription factor 1 (TWIST1) positively associated with BMI [3]. Cluster (VII) (inflammatory ASPCs) was more abundant in breast AT in young women, pre-menopause, and obesity [56]. This apparent dichotomy between ASPC-states is mirrored in metabolic health: Reinisch et al. [23] identified PreAds (CD36⁺, PPARG⁺) enriched in metabolically unhealthy obesity (MUO) versus metabolically healthy obesity (MHO) and IPAs (ICAM1⁺, low density lipoprotein receptor [LDLR]⁺) reduced in the same cohort [23].

Beyond adipogenesis, PreAds contribute to complement activation, serving as a major source of circulating CFD (CFD/ adipsin) [22,34,48,53]. Additionally, PreAds secrete further regulators of the alternative complement pathway (C3, complement factor H [CFH]), and downstream complement-associated factors (APOD, gelsolin [GSN], CXCL14, CXCL12) [24,56]. In contrast, UMPs secrete CD55, an inhibitor of C3-C3b conversion, thereby inhibiting the complement cascade [141]. UMPs display immunoregulatory responsiveness, reacting to chemokines (CCL2, CCL5, CXCL1, CXCL2, CXCL8), and cytokines (TNFα, interleukin 1 beta [IL1β], IL-15), linking ASPCs to AT-inflammation and metabolic deterioration [4,22,35,52,56,142,143]. Elevated stress-response signaling (FOS, JUN, signal transducer and activator of transcription 3 [STAT3]) in UMPs, IPAs and PreAds in obesity, with reversal after WL, further supports the role of ASPCs in healthy AT-remodeling [17,32,53].

Taken these findings together, Aregs showed a negative correlation with BMI whereas clusters (I), (VI) and (VII) were positively correlated. The remaining clusters exhibited either positive or negative associations with BMI or no demonstratable correlation (Fig. 2C).

Specialized and adaptive ASPC states

Beyond their role in adipogenesis, ASPCs acquire distinct functional states that sustain ECM integrity and maintain AT-homeostasis. Under inflammatory or metabolic stress, they can acquire antigen-presenting features characterized by major histocompatibility complex (MHC) class II expression (major histocompatibility complex, class II, DR alpha [HLA-DRA], HLA-DRB1, HLA-DRB5) [4,28,34,52] or adopt macrophage-like profiles [4,22,34,35]. Functional validation of these states, however, remains limited. Evidence from mouse studies showed polarization into keratin 23 (Krt23)⁺-ASPCs, secreting CCL2, CCL6, and CCL9 and recruiting neutrophils and macrophages [144]. Persistent dysregulation of fibroblast-like ASPCs may further promote pathological fibrosis through excessive ECM deposition and remodeling.

Positively BMI-correlated ASPC-subclusters, such as UMPs [21], Aregs [3], CD83⁺ [24], and IGFBP2⁺ [20], display adaptive states responding to metabolic health: WL leads to reduced hypoxia and decreased expression of profibrotic (TGFB) and anti-adipogenic genes (WNT), particularly in PPARG⁺- ASPCs [32]. Further, IPAs and profibrotic ASPCs were elevated in T2DM and obesity [51].

Community databases such as the AT Knowledge Portal (adiposetissue.org) enable clinical association analyses of subtype-specific marker genes, revealing substantial heterogeneity even within defined clusters (Fig. 3) [145]. Notably, adaptive, inflammatory and profibrotic subtypes show the strongest correlations with homeostatic model assessment of insulin resistance, triglyceride levels, adipocyte volume, BMI, waist-to-hip ratio, and circulating inflammation, measured by C-reactive protein. Depot-level comparisons further suggest weaker clinical associations for VAT-specific markers, implying depot-specific roles in tissue adaptation and a more pronounced dysregulation of SAT-ASPCs with adverse clinical parameters. These trends should be interpreted cautiously, given the predominance of SAT-derived datasets.

Sex-dependent ASPC differences in adipose distribution and function

Sexual dimorphism is a major determinant of AT distribution and metabolic disease risk [146]. Emerging evidence indicates contribution of ASPCs to these sex-specific differences. A female-specific Areg polarization occurred exclusively in MUO women [23]. This might explain conflicting findings on Areg abundance, being increased [3] or decreased [51] upon obesity.

Further evidence for sex-linked ASPC-specialization comes from depot-specific analyses revealing that PPARγ phosphorylation status differs between inguinal depots of men and premenopausal women [140], suggesting sex-specific regulation of adipogenic signaling. Moreover, ASPC-subclusters isolated from infrapatellar fat pads exhibit transcriptional differences associated with both local inflammation and sex [46]. Collectively, these findings point towards sex-specific ASPC-transcriptomes that may underlie the well-recognized differences in fat distribution and metabolic flexibility between men and women. Nevertheless, systematic functional studies in human cohorts remain scarce. A better understanding of sex-dependent ASPC-biology could illuminate the cellular basis for divergent cardiometabolic risks observed between women and men and identify new opportunities for individualized therapeutic strategies.

ASPC aging and cellular senescence impair regenerative and metabolic function

Aging profoundly alters the abundance and functionality of ASPCs, contributing to impaired AT-plasticity and metabolic decline. Evidence from both in vitro and in vivo studies indicates ASPCs progressively acquiring senescent features with age. In cultured SAT-derived ASPCs, Mitterberger et al. [147] reported classical senescence characteristics, including enlarged, flattened morphology, elevated β-galactosidase activity and reduced adipogenic potential. Similarly, Choudhery et al. [148] demonstrated that ASPCs from older individuals display reduced proliferative capacity, fewer population doublings, and higher senescence marker expression compared with those from young donors. Aging also compromises the paracrine support of ASPCs: senescent cells secrete lower levels of angiogenic factors, resulting in impaired endothelial network formation [148]. At transcriptomic level, Maredziak et al. [149] found that while the global gene-expression profile of aged ASPCs remains relatively stable, specific changes occur in cell-cycle regulators, accompanied by a shortened G1 phase and increased nascent protein synthesis.

In vivo data confirmed aging-related loss of ASPC-abundance and -functionality. Kar et al. [44] reported a significant decline of SAT-ASPC abundance with age, an effect most evident in lean individuals. Interestingly, obesity appeared to abolish the typical age-related decline, as young obese participants already displayed low ASPC-levels comparable to those of older adults, suggesting that obesity induces a premature aging phenotype. The same study identified an age-dependent gene network linking ASPC-loss to impaired adipogenesis and metabolic dysfunction, highlighting potential therapeutic targets to mitigate obesity-related AT-deterioration [44].

OUTLOOK

Collectively, current evidence indicates that ASPCs constitute a highly adaptive and plastic cellular compartment that dynamically responds to local tissue demands and systemic metabolic cues. Despite the rapid expansion of single-cell studies, many ASPC-subclusters, particularly those reported in only one or a few datasets, remain insufficiently validated across independent cohorts. While the lack of replication may reflect technical artefacts or differences in bioinformatic pipelines, it may equally arise from cohort-specific factors such as depot origin, metabolic state, age, or sex. Resolving these ambiguities will require larger, well-powered studies employing standardized and multiplexed single-cell and spatial profiling approaches across diverse human populations.

A major limitation of the current literature is the limited spatial context for most ASPC-subtypes. Understanding how ASPC-subpopulations are organized within AT, how they relate to vasculature, immune cells, and adipocytes, and how these spatial relationships change in disease states will be essential for assigning functional relevance. High-resolution STx and integrated multi-omic approaches are therefore expected to play a central role in future investigations.

Beyond descriptive profiling, the field must increasingly focus on functional characterization. ASPCs exhibit remarkable plasticity, yet their differentiation trajectories, lineage hierarchies, and context-dependent fate decisions remain incompletely defined. In this regard, lineage-tracing and perturbation experiments in model organisms will remain indispensable for establishing causality and dissecting regulatory pathways that cannot be resolved from human observational data alone.

From a translational perspective, a critical open question is whether ASPC-subpopulations differ in their responsiveness to pharmacological or lifestyle interventions. It remains unclear whether distinct subtypes share common receptor repertoires and signaling pathways or whether selective modulation of specific ASPC states is feasible. Addressing this question could open new avenues for targeted therapies aimed at restoring healthy AT-remodeling, improving adipogenic capacity, or limiting fibrosis and inflammation in metabolic disease.

Finally, ASPCs should not be studied in isolation. Their phenotype and function are shaped by continuous interactions with neighboring adipocytes, immune cells, ECs, and the ECM. Future studies integrating spatial, functional, and intercellular communication analyses will be essential to capture ASPCs as part of a dynamic tissue ecosystem rather than as discrete cell states. Such an integrated framework will be key to translating ASPC heterogeneity into mechanistic insight and therapeutic opportunity.

SUPPLEMENTARY MATERIALS

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

FUNDING

This study was supported by a starting grant from the Swedish Research Council (VR), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy–EXC-3105/1–533765739, the German Diabetes Association (DDG) and the European Association for the Study of Diabetes (EASD).

ACKNOWLEDGMENTS

None

Fig. 1.

Overview of identified adipose stromal and progenitor cell (ASPC) subpopulations in human white adipose tissue. (A) Overview of classified ASPC subclusters and respective names, proposed in this review. Cluster-specific marker genes are indicated below the subtype. (B) Conceptual model of differential expression of traditional applied ASPC markers adjusted to adipogenic differentiation states. Data from Whytock et al. [54], Zhu et al. [125], and the adiposetissue.org portal [144] has been used to design the plot. SMC, smooth muscle cell; UMP, uncommitted, multipotent progenitor cell; IPA, intermediate preadipocyte; CPA, committed, intermediate preadipocyte; PreAd, premature adipocyte; Ad, adipocyte.

Fig. 2.

Depot-specific abundance of adipose stromal and progenitor cells (ASPCs) and clinical correlation of ASPC heterogeneity. (A) ASPC proportions across white adipose tissue depots. Data from Massier et al. [34] and Jalkanen et al. [76]. (B) Overall ASPC abundance across subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT), in line with Reinisch et al. [23]. (C) Conceptual model of subtype abundance correlated to body mass index (BMI). Data from Liu et al. [28], Emont et al. [3], Massier et al. [34] and Loft et al. [53] was utilized to design this figure. PVAT, perivascular adipose tissue; UMP, uncommitted, multipotent progenitor cell; IPA, intermediate preadipocyte; CPA, committed, intermediate preadipocyte; PreAd, premature adipocyte.

Fig. 3.

Correlation of adipose stromal and progenitor cell (ASPC) subtype specific marker genes across human white adipose tissue depots with clinical parameters. Data was obtained through the adipose tissue knowledge portal [144], meta-analysis correlation across all cohorts in the portal was extracted through the clinical module (https://adiposetissue.org/clinical) and visualized. UMP, uncommitted, multipotent progenitor cell; IPA, intermediate preadipocyte; CPA, committed, intermediate preadipocyte; PreAd, premature adipocyte; Areg, adipogenic regulatory cell; FAP, fibro-adipogenic progenitor; WHR, waist-to-hip ratio; BMI, body mass index; HOMA-IR, homeostatic model assessment of insulin resistance; CRP, C-reactive protein; HbA1c, glycosylated hemoglobin.

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Albert M, Nalir K, Zhong J, Massier L. Heterogeneity and Clinical Relevance of Human Adipose Stromal and Progenitor Cells. Diabetes Metab J. 2026;50(2):217-234.

Received: Nov 21, 2025; Accepted: Jan 28, 2026

DOI: https://doi.org/10.4093/dmj.2025.1182.

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