Integrative single-cell analysis of metabolic syndrome reveals novel cellular heterogeneity and differentiation dynamics in adipose tissue

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We analyzed single-nucleus RNA sequencing data from subcutaneous adipose tissue of 84 individuals with MetS from the METSIM cohort, characterizing cell composition, inter-individual variation, adipocyte progenitor differentiation, and cell-cell communication networks. Methods We performed single-nucleus RNA sequencing on subcutaneous adipose tissue samples from 84 individuals with MetS. Clustering analysis was used to define cell types and subpopulations, inter-individual variation in cell composition was assessed, pseudotime trajectory analysis reconstructed adipocyte precursor differentiation pathways, and ligand–receptor interaction analysis mapped intercellular communication networks. Results We identified 12 distinct cell types in MetS adipose tissue and discovered two patient subgroups with differential enrichment of adipocytes/progenitors versus immune cells, suggesting subtypes of MetS with distinct adipose profiles. Pseudotime analysis revealed two adipocyte progenitor subpopulations with altered differentiation trajectories. Cell-cell communication analysis identified WNT signaling from progenitors to adipocytes as a potential differentiation driver, with extracellular matrix pathways mediating progenitor-adipocyte interactions. Conclusion This comprehensive single-cell atlas of MetS adipose tissue reveals previously unrecognized cellular heterogeneity and differentiation dynamics, offering new insights into MetS pathogenesis and highlighting potential therapeutic targets. Metabolic syndrome Adipose tissue Cellular heterogeneity Obesity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Metabolic syndrome (MetS), also known as X syndrome, is characterized by multiple metabolic disturbances, including impaired glucose tolerance, obesity, and dyslipidemia( 1 , 2 ). Although MetS initially emerged in developed countries, its prevalence has since expanded significantly in the Asia-Pacific region( 3 ). Currently, over 25% of the global population satisfies the diagnostic criteria for MetS( 4 ). The syndrome includes many cardiovascular and metabolic risk factors that lead to coronary artery disease and type 2 diabetes, consequently elevating the risk of early mortality. These health complications and their economic consequences place substantial burden on healthcare systems worldwide. Research has demonstrated that individuals with MetS experience persistent, low-grade inflammation in adipose tissue, leading to the release of lipids, dysregulated adipokines, and the synthesis of pro-inflammatory cytokines and chemokines( 5 ). Adipose tissue serves as a vital metabolic organ, governing systemic energy balance and significantly contributing to the pathophysiology of MetS. Historically regarded solely as an energy reservoir, adipose tissue is now acknowledged as a dynamic endocrine organ consisting of adipocytes and the stromal vascular fraction (SVF)( 6 ). The SVF contains diverse cell types, including adipose stem cells (ASCs), immune cells, and endothelial cells, which interact through complex signaling networks. However, technical limitations have restricted our understanding of the functional characteristics of these different cell types and their specific roles in MetS development( 7 ). Single-nucleus RNA sequencing technology has emerged as a powerful tool for exploring cellular heterogeneity in complex tissues( 8 ). This advanced method enables comprehensive analysis of gene expression profiles at the single-cell level, revealing cell subpopulations and functional states that conventional bulk sequencing methods fail to identify. This method has been utilized in adipose tissue research, primarily concentrating on healthy persons or certain disorders( 9 ) (e.g., obesity or diabetes). Therefore, the cellular composition and function of adipose tissue in relation to MetS are yet inadequately investigated. To address this knowledge gap, we performed single-nucleus RNA sequencing on subcutaneous adipose tissue samples from 84 individuals with MetS in the METSIM study cohort. We aimed to: ( 1 ) characterize the cellular landscape of adipose tissue in individuals with MetS; ( 2 ) examine the heterogeneity of adipose tissue cellular composition across individuals; ( 3 ) investigate adipocyte precursor differentiation dynamics; and ( 4 ) decipher intercellular communication network regulatory mechanisms. This research represents the first systematic characterization of cellular heterogeneity in metabolic syndrome -associated adipose tissue. Through pseudotime analysis and cell-cell communication network investigation, we identified potential disease mechanisms, providing new insights into MetS pathogenesis and potential therapeutic targets. 2. Methods 2.1 Single-Nucleus RNA-Sequencing Data Collection and Quality Control Single-nucleus transcriptomic data of MetS were sourced from a publicly accessible dataset encompassing 84 individuals from the METabolic Syndrome In Men (METSIM) study. The METSIM cohort, established between 2005 and 2010, enrolled 10,197 Finnish males aged 45 to 73 years. Subcutaneous abdominal adipose tissue biopsies were randomly procured from 1,410 participants(10). After quality control filtering (excluding nuclei with 6,000 detected genes, or with total counts >25,000) and removing potential doublets, we retained a total of 21,446 high-quality single-nucleus transcriptomes for analysis(11, 12). 2.2 Clustering and Cell-Type Identification The entire analysis was conducted using R (version 4.3.1) and the Seurat(13) (version 4.3) package. Gene expression data were normalized, and the top 2,000 variable genes were selected and scaled for principal component analysis (PCA). The initial 50 principal components were computed, with the first 14 PCs (PC1-PC14) utilized to construct a K-nearest neighbor graph with K = 20 and to identify unsupervised cell clusters(14). Resolution was set to 1.0 to discern different cell clusters. Cluster-specific marker genes were identified using Seurat’s FindMarkers function (criteria: adjusted p-value 0.25). For dimensionality reduction, we applied Uniform Manifold Approximation and Projection (UMAP) to the first 14 principal components to visualize cell clustering. Cell-type identification relied on canonical marker expression patterns for major cell types, with assistance from the SingleR package (version 2.0.0)(15). 2.3 Cellular Proportion Analysis and the Biological Significance of Differential Gene Expression To determine whether MetS is characterized by distinct cellular composition profiles, a thorough analysis of cellular proportions was undertaken. We quantified the relative abundance of each cell type in each participant, then applied hierarchical clustering to classify the 84 individuals based on these cellular distributions. We used the R package pheatmap (version 1.0.12) for clustering, which automatically stratified the subjects into two groups: 'Group 1' (G1) and 'Group 2' (G2). To understand the biological significance of the observed groupings, we conducted a comparative gene expression analysis. Genes were considered differentially expressed if they exhibited an adjusted p-value of less than 0.05 and an absolute log2(fold change) greater than 0.25. Pathway enrichment analysis was performed using G:Profiler web tool(16), which includes pathways from Gene Ontology (GO), KEGG Reactome, and WikiPathways. The Benjamini–Hochberg (BH)-adjusted p-values of <0.05 were considered statistically significant. 2.4 Cell Type-Specific Expression and Gene Set Scoring of Genome-Wide Association Study (GWAS) Loci for Classical Metabolic Syndrome To validate the MetS loci identified through GWAS and to investigate their cell type-specific expression, we examined the most extensive GWAS for MetS conducted within the UK Biobank cohort(17), with 291,107 individuals (59,677 cases and 231,430 controls). From this GWAS, 92 independent loci were identified with a genome-wide threshold of significance ( p-value < 5 x 10 -8 ). Dot plots were employed to illustrate the expression of the 92 genes, based on distinct cell types and cell composition groups (G1 and G2). To precisely quantify the expression of 92 genes, we utilized the Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE) method. This analytical approach, developed by Zheng S et al., determines the approximate mean expression of genes within a signature by utilizing their ranks among all expressed genes. The JASMINE score evaluates the enrichment of the gene sets(18). The JASMINE score evaluates the enrichment of the gene sets. 2.5 Pseudotime Analysis Pseudotime analysis is a computational technique employed to deduce the temporal history of biological development by arranging cells along a virtual pathway(19). This method replicates the dynamic alterations that transpire during cellular differentiation or disease advancement. In this study, we focused on the specific clusters (6, 15, and 17) identified in our previous analysis and subjected them to the following pseudotime analysis. R package Monocle 2(20) (version 2.26.0) was utilized to preprocess the data, perform UMAP reduction, and reduce the dimensionality of the data using the DDRTree algorithm. Cells were ordered along the inferred pseudotime trajectory. To identify genes that significantly changed along the pseudotemporal trajectory, differential gene testing was performed using the formula “~sm.ns(Pseudotime)” with a statistical significance q-value threshold of less than 0.05. Branched expression analysis was further performed and differential genes associated with branches were identified. 2.6 Cell-cell Interaction Analysis We employed cell-cell interaction analysis to elucidate the intercellular communication between distinct cell clusters. The analysis was performed using the CellChat R package(21) (version 1.6.1), which identifies 'sender' and 'receiver' cells participating in defined signaling pathways. Guided by prior research and their well-recognized roles in mediating cell-cell interactions, our focus was directed towards four pivotal signaling pathways including WNT, COLLAGEN, LAMININ, and NEGR. For each pathway, we examined the expression profiles of crucial genes involved in signal transmission and reception. We particularly focused on identifying the primary receptor-ligand pairs that drive the signaling processes within and across different cell types. The expression levels of these genes were visualized to assess their potential roles in facilitating or modulating cell-cell communication. 3. Results 3.1 Clustering and Cell-Type Identification To establish a comprehensive single-cell atlas of MetS, we utilized the single-nucleus data derived from 84 Finnish individuals. Following the stringent quality control procedures previously delineated, we obtained a total of 21,446 cells. After dimensionality reduction, these cells were clustered into 20 distinct clusters and further categorized into 12 different cell types, including adipocytes, adipocyte progenitor cells (APCs), endothelial cells, fibroblasts, B cells, M1 macrophages, M2 macrophages, mast cells, natural killer (NK) cells, T helper (Th) cells, monocytes, and vascular smooth muscle ( Figure 1A, 1C ). A dot plot showcasing the marker genes characteristic of each cell type is presented in Figure 1B . The gene feature plot is shown in Supplementary Figure 1 . Supplementary Table 1 presents a marker genes list of specific cell types. 3.2 Cellular Proportion Analysis Classified 84 Individuals into Two Groups Upon conducting an analysis of individual cellular proportions, we observed substantial differences in the cell type distribution across the 84 individuals ( Figure 1D ). The cell proportion of these participants is shown in Supplementary Table 2. Through unsupervised clustering, these individuals were automatically segregated into two major groups ( Figure 1E ), which we have designated as Group 1 (G1) and Group 2 (G2). Notably, within G1, there was a significant predominance of adipocytes and APCs, concurrent with a relative dearth of T helper (Th) cells and natural killer (NK) cells. In contrast, G2 was characterized by a lower proportion of adipocytes and APCs, yet a higher representation of Th cells and NK cells ( Figure 1F ). To identify differentially expressed genes between G1 and G2, we applied stringent criteria where genes were considered significantly differentially expressed if they had an adjusted p-value of less than 0.05 and an absolute log2(fold change) greater than 0.25. The comprehensive list of differentially expressed genes between the two groups is provided in Supplementary Table 3 . Compared with G2, differentially expressed genes in G1 group were particularly within adipocytes and monocytes ( Figure 2A ). In adipocytes, the upregulated genes were significantly enriched in blood circulation ( padj = 8.94×10 -3 ), circulatory system process ( padj = 2.17×10 -2 ), and tumor necrosis factor production-associated pathways. In monocytes, the upregulated genes were predominantly associated with pentosyltransferase activity ( padj = 4.99×10 -2 ), as detailed in Supplementary Table 4 . 3.3 Marginal Enrichment of Metabolic Syndrome GWAS Genes in Adipocytes and Macrophages without Group-Specific Differences To investigate whether the expression of GWAS risk genes for MetS exhibits cell type specificity, we evaluated the expression profiles of 92 genome-wide significant MetS risk genes across 12 identified cell types. The results showed that these genes were relatively highly expressed in M1 macrophages and adipocytes, yet they did not exhibit pronounced cell type specificity overall ( Figure 2C ). Furthermore, we employed the JASMINE algorithm to calculate gene set scores for these 92 genes. Relative to other cell types, adipocytes and M1/M2 macrophages exhibited marginally elevated scores, indicating a somewhat higher expression of the metabolic syndrome-associated GWAS genes within these cell types, although the disparity was not markedly pronounced ( Figure 2D ). Expression profiles of 92 genes between G1 and G2 were further examined, however, no significant differential expression was detected between the two groups ( Figure 2B ). 3.4 Temporal Remodeling of Adipocyte Progenitor Cell Differentiation into Fibroblasts Our analysis has uncovered a progressive transition in the expression of marker genes associated with APCs and fibroblasts within clusters 6, 15, and 17. Specifically, cluster 6 exhibited a higher expression of adipocyte progenitor cell markers, cluster 15 displayed a balanced expression of both types of markers, and cluster 17 showed a predominance of fibroblast markers ( Figure 3A ). To ascertain whether these clusters represent a temporal sequence of cellular differentiation, we conducted a pseudotime analysis on the three clusters in isolation. Our results indicate that cluster 15 occupies an early position in the pseudotime trajectory, with a subset of cells from cluster 6 demonstrating a gradual progression towards cluster 17 ( Figure 3B-3E ). This implied that some APCs are in an early stage of differentiation, exhibiting a tendency to evolve into late-stage APCs and fibroblasts. The density plot further illustrates the positional distribution of the three clusters along the pseudotime sequence ( Figure 3F ). To differentiate between the two clusters of APCs, we designated cluster 6 as “adipocyte progenitor 1” (APC1) and cluster 15 as “adipocyte progenitor 2” (APC2). A differential expression analysis of genes along the pseudotime trajectory within the three clusters was conducted. The heatmap depicting the top 100 differentially expressed genes is presented in Figure 3G . Additionally, a branched expression analysis was performed within the three distinct branches to identify genes associated with specific stages of cellular differentiation, and a GO (Gene Ontology) pathway enrichment analysis was applied to the differentially expressed genes within each branch ( Figure 3H ). 3.5 Investigating the Driving Mechanisms of Adipocyte Progenitor Cell Differentiation Cell-cell communication analysis has unveiled the intricate interactions among various cell types within adipose tissue ( Figure 4A-4B ). For the interaction strength, a dense network of interactions among cells was found, with particularly strong communication intensity observed between adipocytes themselves, as well as between adipocytes and APC1 and APC2 cells, and between adipocytes and endothelial cells ( Figure 4B ). A heatmap further illustrates the number and strength of these cellular interactions, and no significant differences were observed in signal quantity or intensity between the two adipocyte progenitor cell types ( Figure 4C-4D ). Building upon the comprehensive analysis of cell-cell interactions within adipose tissue, we next explored the specific signaling pathways that drive communication between APCs. 3.5.1 WNT Signaling Drive Adipocyte Progenitor Cell Communication Interestingly, we identified a significant enrichment of the WNT signaling pathway in the signals emanating from APC2 to adipocytes ( Figure 5A ). Within this pathway, the receptor-ligand pairs WNT3A (ligand)-FZD4 and LRP5 (receptors) were found to play a predominant role ( Figure 5B ). Further analysis indicated that this receptor-ligand pair primarily functions in signaling from APC2 to adipocytes ( Figure 5C ), suggesting that the WNT signaling pathway in adipocyte progenitor cell differentiation may act through the WNT3A-FZD4 and LRP5 receptor-ligand interaction. 3.5.2 Three Novel Signaling Pathways Identified as Drivers of Adipocyte Progenitor Cell Communication To explore the crucial signals driving the differentiation of APC2, we examined all significant interactions targeting APC2 and found that the receptor-ligand pairs on the COLLAGEN, LAMININ, and NEGR signaling pathways were markedly upregulated ( Figure 4E ). We focused on the signal transduction, principal receptor-ligand pairs, and expression of key genes within these three pathways. For the collagen pathway, the primary interactions were observed in autocrine signaling from adipocytes and paracrine signaling from both clusters of APCs to adipocytes ( Figure 5D ). Among the many significant receptor-ligand pairs, COL4A2-(ITGA1 and ITGB1) stood out ( Figure 5E-5F ), primarily mediating interactions between fibroblasts and adipocytes. Key genes within this pathway were found to be highly expressed in both APC1, APC2, fibroblasts, and vascular smooth muscle cells ( Figure 5G ). The LAMININ pathway showed similarities to the collagen pathway, with primary interactions in autocrine signaling from adipocytes and paracrine signaling from both clusters of APCs to adipocytes ( Figure 5H ). The most contributing receptor-ligand pair was LAMA4-(ITGA7 and ITGB1), predominantly involved in signaling from APC1 to adipocytes ( Figure 5I-5J ). Crucial genes in this pathway were also highly expressed in APC2, APC1, fibroblasts, and vascular smooth muscle cells ( Figure 5K ). The NEGR pathway exhibited specificity in signal transduction, being confined to interactions between APC1, APC2, and fibroblasts ( Figure 5L ), with the sole receptor-ligand pair NEGR1-NEGR1 playing a role ( Figure 5M-5N ). This gene was found to be expressed exclusively in APC1, APC2 and fibroblasts ( Figure 5O ). Notably, the signal intensity of NEGR1 is the most pronounced among all signals received by APC2. 4. Discussion This study comprehensively revealed the cellular heterogeneity, differentiation dynamics, and molecular regulatory mechanisms in adipose tissue of patients with MetS through single-cell transcriptomics analysis of subcutaneous adipose tissue from 84 Finnish men in the METSIM cohort, combined with GWAS genetic locus analysis and exploration of intercellular communication networks. The following will discuss the results of this study in detail from multiple aspects, including the heterogeneity of adipose tissue cell composition, the cell-specific expression of GWAS genes and their functional significance, the remodeling of the time sequence of adipocyte precursor cell differentiation, and the role of cell-cell interactions in MetS. Our single-cell RNA sequencing analysis revealed that adipose tissue cell composition in individuals with MetS is highly heterogeneous. In fact, the adipose samples from the 84 individuals showed clear differences and were separated by unsupervised clustering into two groups (G1 and G2). Group G1 was characterized by an enrichment of adipocytes and APCs, whereas group G2 was characterized by a higher proportion of Th cells and NK cells. This discovery indicates the presence of subtypes in people with MetS, wherein varying adipose tissue cell compositions align with varied clinical presentations of MetS( 22 ). This outcome is strongly aligned with the current literature on adipose tissue cell heterogeneity. Emont et al. ( 9 ) found via single-cell transcriptome analysis that various adipose cell subsets in adipose tissue are significantly associated with metabolic protective effects, aligning with our observation of an enrichment of G1 group APCs. This indicates that the G1 group may possess a more robust potential for compensatory adipogenesis to address metabolic problems. However, the enrichment of immune cells in the G2 group indicates that this group may have a higher inflammatory immune response, which is consistent with the dynamic changes in the immune microenvironment identified by Jaitin et al. ( 23 ) in obesity models. This finding not only reveals the cellular heterogeneity of MetS, but also provides a novel perspective for the future clinical categorization of MetS. In terms of the functional mechanism, the genes differentially expressed by the G1 group of adipocytes are enriched in pathways associated with blood circulation and tumor necrosis factor production, indicating that the adipose tissue of the G1 group may exhibit a distince pattern of angiogenesis and inflammatory response. The study by Sárvári et al. ( 24 ) shows that the proliferation and differentiation capacity of adipocyte precursor cells directly affects the expansion pattern of adipose tissue, while functional lipogenesis can effectively prevent ectopic lipid deposition. Therefore, the enrichment of G1 phenotype adipocyte precursor cells may represent a compensatory response, in which these cells delay the progression of metabolic disorders by promoting lipid storage. At the same time, the upregulation of monocyte genes in the G1 group is associated with pentose phosphate kinase activity, suggesting that changes in glucose metabolism may be an important mechanism of metabolic disorders in this group. This study also explored the cell-specific expression patterns of 92 metabolic syndrome-related GWAS loci. We found that these GWAS risk genes were highly expressed in M1 macrophages and adipocytes, indicating that these two types of cells play a key role in the pathological process of MetS. However, the results of gene set scoring showed that although the scores of adipocytes and M1/M2 macrophages increased slightly, this difference did not reach significance. This result may reflect the common action of MetS genetic susceptibility risk genes in different cell types, rather than the specific effect of a single cell type. Among the genetic mechanisms of MetS, the NEGR1 gene is of particular interest. NEGR1 was initially identified as an obesity risk gene (GWAS) and thought to act in the central nervous system( 25 , 26 ), but recent studies indicate it also plays a key role in adipose tissue( 5 , 27 ). We found that NEGR1 is highly expressed in adipocyte precursor cells and fibroblasts, and its expression is markedly reduced in MetS adipose tissue – a change closely associated with the development of obesity and related metabolic disorders. This finding is consistent with the latest literature: NEGR1 is upregulated during normal adipocyte differentiation, while in patients with obesity, NEGR1 expression is significantly downregulated, and its expression level is negatively correlated with obesity-related indicators (such as waist-to-hip ratio, waist circumference). Mechanism studies have shown that NEGR1 regulates the uptake and utilization of fatty acids by interacting with the fatty acid transport protein CD36​( 28 ). When NEGR1 expression is downregulated, CD36 levels increase, leading to excessive uptake of fatty acids, which in turn promotes lipid accumulation and the development of insulin resistance. Therefore, changes in the expression pattern of NEGR1 may be an early sign of MetS( 29 , 30 ). In this study, we revealed the gradual transition of adipocyte precursor cells to fibroblasts using pseudotime analysis. The pseudotime trajectory analysis showed that cluster 15 (APC2) was located in the early stage of the pseudotime trajectory, while some cells in cluster 6 (APC1) gradually transformed into cluster 17 (fibroblasts). This finding suggests that under MetS, the fate of precursor cells in adipose tissue is significantly biased—some precursor cells fail to differentiate normally into adipocytes and instead switch to the fibroblast lineage. The mechanism of this transformation may be related to metabolic disorders, especially the progression of metabolic diseases such as obesity and diabetes( 31 , 32 ). This result is consistent with multiple studies, which have shown that in MetS, a chronic inflammatory microenvironment and multiple signaling pathways (such as WNT, TGF-β, and PDGF) may induce the transformation of adipocyte precursor cells into fibroblasts( 33 ). The differentiation of adipocyte precursor cells is not only biased, resulting in a reduction in the lipid storage function of adipocytes, but also may lead to excessive deposition of extracellular matrix such as collagen, further exacerbating tissue fibrosis and metabolic disorders. Marcelin et al. ​( 34 ) found that in conditions of excess adiposity, adipocyte precursor cells exhibit stronger collagen-producing capacity, leading to fibrosis and dysfunction of adipose tissue. This phenomenon may be one of the key factors contributing to adipose tissue dysfunction in MetS. This study reveals the complex communication network among cells in the MetS microenvironment via cell-cell interaction analysis. We found that the interaction between adipocytes, APCs and macrophages plays an important role in MetS, especially under the regulation of WNT, COLLAGEN( 35 ), LAMININ( 36 ) and NEGR signaling pathways, cell-cell signaling may exacerbate adipose tissue inflammation and fibrosis​( 37 , 38 ). In particular, the WNT signaling pathway plays a central role in the interaction between APCs and mature adipocytes( 39 , 40 , 41 ). Our study found that the WNT3A-FZD4/LRP5 receptor-ligand pair may play a key role in the differentiation of APCs into adipocytes, suggesting that the WNT signaling pathway may be involved in the development of MetS by regulating the differentiation direction of APCs. In addition, we also found that the COLLAGEN, LAMININ and NEGR signaling pathways play a special role in the differentiation of adipocyte precursor cells. These pathways may affect the function and metabolic stability of adipocytes by regulating the production of extracellular matrix. These intercellular interactions and changes in signaling pathways not only deepen our understanding of the pathological mechanisms of MetS, but also provide potential targets for future treatment strategies. Regulating the WNT signaling pathway or reversing fibroblast formation may emerge as significant intervention strategies to mitigate MetS( 42 , 43 ). There are several limitations to this study. First, the study subjects were limited to Finnish men, which may limit the generalizability of the results, especially considering the influence of gender and ethnicity in MetS. Second, although single-cell RNA sequencing provides high-resolution cell-type information, our study lacks protein-level validation and functional experiments to confirm the biological significance of the observed cell–cell interactions. In addition, the cross-sectional design of this study cannot reveal the causal relationship between changes in cell composition and the development of MetS. Future research directions include: ( 1 ) expanding the sample size and including individuals of different genders and ethnicities to assess the generalizability of the results; ( 2 ) incorporating spatial transcriptomics to preserve the spatial context of cells in adipose tissue and better understand the microenvironmental context of intercellular interactions; ( 3 ) conducting functional experiments to validate the roles of WNT, COLLAGEN, LAMININ and NEGR signaling pathways in adipocyte precursor cell differentiation; ( 4 ) a longitudinal study to determine whether changes in cell composition are a cause or a consequence of MetS; and ( 5 ) an integrated multi-omics analysis, including genomic, transcriptomic, proteomic and metabolomic data, to gain a comprehensive understanding of the molecular mechanisms underlying MetS. 5.Conclusion This study provides critical insights into the cellular heterogeneity, differentiation dynamics and molecular regulatory mechanisms of MetS in adipose tissue. Our results indicate that the heterogeneity of adipose tissue cell composition, the cell-specific expression patterns of GWAS genes, and the reprogramming of adipocyte precursor cell differentiation trajectories play central roles in the development of MetS. In addition, the complex cell-cell interactions between adipocyte precursor cells and immune cells provide new biomarkers and targets for the early diagnosis and treatment of MetS. Future research needs to further explore the clinical significance of these cell interactions and develop precision therapeutic strategies targeting these pathways. Declarations Acknowledgements: The authors would like to thank the participants and investigators of the METSIM cohort for generating and making publicly available the valuable single-nucleus RNA sequencing dataset used in this study. This research was not supported by any specific grants from public, commercial, or not-for-profit funding agencies. Author Contributions: H.J. was responsible for designing the study, conducting data analysis, performing bioinformatic interpretation, writing the manuscript draft, and creating figures and tables. D.L. contributed by conceptualizing the research questions, supervising the analysis, critically reviewing the interpretation of results, editing the manuscript. Both authors contributed to the review and approval of the final manuscript and agree to be accountable for all aspects of the work. Data Availability Statement: All data analyzed in this study are available from public databases and can be accessed through the corresponding databases and repositories cited in the manuscript. The analyzed data and code are available upon reasonable request to the corresponding author. Competing Interests: The authors declare that they have no competing interests. Consent for publication: Not applicable. Funding: None. Ethics approval and consent to participate: Not applicable. 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Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden. Human Molecular Genetics. 2009;18(8):1489-96. Lee AW, Hengstler H, Schwald K, Berriel-Diaz M, Loreth D, Kirsch M, et al. Functional inactivation of the genome-wide association study obesity gene neuronal growth regulator 1 in mice causes a body mass phenotype. PLoS One. 2012;7(7):e41537. Yoo A, Joo Y, Cheon Y, Lee SJ, Lee S. Neuronal growth regulator 1 promotes adipocyte lipid trafficking via interaction with CD36. Journal of Lipid Research. 2022;63(6). Silverstein RL, Febbraio M. CD36, a scavenger receptor involved in immunity, metabolism, angiogenesis, and behavior. Sci Signal. 2009;2(72):re3. Love-Gregory L, Abumrad NA. CD36 genetics and the metabolic complications of obesity. Curr Opin Clin Nutr Metab Care. 2011;14(6):527-34. Schwalie PC, Dong H, Zachara M, Russeil J, Alpern D, Akchiche N, et al. A stromal cell population that inhibits adipogenesis in mammalian fat depots. Nature. 2018;559(7712):103-8. Hepler C, Shan B, Zhang Q, Henry GH, Shao M, Vishvanath L, et al. Identification of functionally distinct fibro-inflammatory and adipogenic stromal subpopulations in visceral adipose tissue of adult mice. Elife. 2018;7. Sun K, Tordjman J, Clement K, Scherer PE. Fibrosis and adipose tissue dysfunction. Cell Metab. 2013;18(4):470-7. Marcelin G, Silveira ALM, Martins LB, Ferreira AVM, Clément K. Deciphering the cellular interplays underlying obesity-induced adipose tissue fibrosis. Journal of Clinical Investigation. 2019;129(10):4032-40. Buechler C, Krautbauer S, Eisinger K. Adipose tissue fibrosis. World J Diabetes. 2015;6(4):548-53. Li L, Clevers H. Coexistence of quiescent and active adult stem cells in mammals. Science. 2010;327(5965):542-5. Ouchi N, Higuchi A, Ohashi K, Oshima Y, Gokce N, Shibata R, et al. Sfrp5 Is an Anti-Inflammatory Adipokine That Modulates Metabolic Dysfunction in Obesity. Science. 2010;329(5990):454-7. Goddi A, Carmona A, Park S-Y, Dalgin G, Gonzalez Porras MA, Brey EM, et al. Laminin-α4 Negatively Regulates Adipocyte Beiging Through the Suppression of AMPKα in Male Mice. Endocrinology. 2022;163(11). Jeffery E, Church CD, Holtrup B, Colman L, Rodeheffer MS. Rapid depot-specific activation of adipocyte precursor cells at the onset of obesity. Nature Cell Biology. 2015;17(4):376-85. Christodoulides C, Lagathu C, Sethi JK, Vidal-Puig A. Adipogenesis and WNT signalling. Trends Endocrinol Metab. 2009;20(1):16-24. Ross SE, Hemati N, Longo KA, Bennett CN, Lucas PC, Erickson RL, et al. Inhibition of adipogenesis by Wnt signaling. Science. 2000;289(5481):950-3. Prestwich TC, Macdougald OA. Wnt/beta-catenin signaling in adipogenesis and metabolism. Curr Opin Cell Biol. 2007;19(6):612-7. Xu S, Lu F, Gao J, Yuan Y. Inflammation-mediated metabolic regulation in adipose tissue. Obes Rev. 2024;25(6):e13724. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xls Cite Share Download PDF Status: Published Journal Publication published 22 Oct, 2025 Read the published version in Diabetology & Metabolic Syndrome → Version 1 posted Editorial decision: Accepted 21 Sep, 2025 Reviews received at journal 20 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviews received at journal 17 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviews received at journal 12 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviews received at journal 11 Sep, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviewers invited by journal 26 Aug, 2025 Editor assigned by journal 23 Aug, 2025 Submission checks completed at journal 23 Aug, 2025 First submitted to journal 17 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7392416","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508554999,"identity":"db3467ed-3aeb-48be-aac1-6a0b69662c0b","order_by":0,"name":"Hui Jia","email":"","orcid":"","institution":"The Eighth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Jia","suffix":""},{"id":508555000,"identity":"594f0c8f-e5d1-4554-a6e7-557443808a5a","order_by":1,"name":"Dandan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACPmYgwdjAwNjPzHz4AVFa2EBaDgK1zGxnSzMgTgsDVMuG8zwKEsRpYecx/Pxxx2HZzYd5GAwYamyiiXAYj7HEwTOHjbcd5j3wgOFYWm4DEVoMJA62HU7cdpgvwYCx4TBRWox/gLRsbgbqJVaLGdiWDczEa2Erszjblm484zAwkBOI8Qs//+HNNyrbrGX7+w8ffvChxoawFihohlAJRCoHgToS1I6CUTAKRsGIAwCh1T5eSevwrQAAAABJRU5ErkJggg==","orcid":"","institution":"The Eighth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-08-17 12:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7392416/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7392416/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13098-025-01975-3","type":"published","date":"2025-10-22T16:17:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90763047,"identity":"54a3198f-9f97-4275-ad42-69f95dfa913e","added_by":"auto","created_at":"2025-09-07 16:16:52","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":526250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-Nucleus RNA-Sequencing of 84 Finnish Individuals and Cellular Proportion Analysis. \u003c/strong\u003e(A). The dim plot of 20 distinct clusters. (B) Marker genes expressed across each cluster. (C) Identificiation of 12 different cell types. (D) Cell proportion distribution of 84 individuals. (E) Heatmap of cellular proportion clustering. (F) Cellular proportion profiles in Group 1 and Group 2.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7392416/v1/495f9c28740f57dc5be3a1f5.jpeg"},{"id":90762782,"identity":"dd7103f3-3156-4a17-89f2-f215971b8017","added_by":"auto","created_at":"2025-09-07 16:08:52","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":280093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional differentiation between two groups and gene set scoring of 92 metabolic syndrome GWAS loci.\u003c/strong\u003e (A) The volcano plot showed the differentially expressed genes between 2 groups (adjusted p-value\u0026lt; 0.05 and an absolute log2(fold change)\u0026gt; 0.25). (B) Gene set scoring of 92 metabolic syndrome GWAS loci between 2 groups. (C) The dot plot of 92 metabolic syndrome GWAS genes. (D) Gene set scoring of 92 metabolic syndrome GWAS loci among distinct cell types.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7392416/v1/a0e1912911fecc1da1f48ee2.jpeg"},{"id":90762786,"identity":"ed483e05-22e3-4184-b66c-fedead182e3d","added_by":"auto","created_at":"2025-09-07 16:08:52","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":339795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePseudotime analysis between adipocyte progenitor cells and fibroblast.\u003c/strong\u003e(A) The dot plot of clusters 6, 15, and 17.\u003cstrong\u003e \u003c/strong\u003e(B) The cellular density plot along the pseudotemporal axis. (C) The pseudotime trajectory plot of the three states. (D) The pseudotime trajectory plot of the three clusters. (E) The pseudotime dendrogram plot of the three states. (F) The pseudotime trajectory plot of time sequence. (G) The heatmap of the top 100 pseudotime differential genes of the three clusters. (H) The heatmap of pseudotime differential genes of the three branches and associated GO pathways.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7392416/v1/481da0a29195631de5f630af.jpeg"},{"id":90762797,"identity":"b7a2a06a-2754-45ce-8a25-e5a54c7e1b16","added_by":"auto","created_at":"2025-09-07 16:08:53","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":447834,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe cell-cell interaction analysis among the different cell types. \u003c/strong\u003e(A) The circular plot of the number of cellular signaling interactions. (B) The circular plot of the strength of cellular signaling interactions. (C) The heatmap plot of the number of cellular signaling interactions. (D) The heatmap plot of the strength of cellular signaling interactions. (E) The bubble chart of receptor-ligand interactions received by adipocyte progenitor cell 2.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7392416/v1/1a26e80da13a10cbbbad9275.jpeg"},{"id":90763048,"identity":"e5b02d46-d93b-42ba-8960-d5cde95c791f","added_by":"auto","created_at":"2025-09-07 16:16:52","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":359450,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKey pathways in adipocyte development and their cellular interactions. \u003c/strong\u003e(A) The hierarchical plot of the WNT signaling pathway in cellular interactions. (B) The crucial receptor-ligand pairs in the WNT signaling pathway. (C) The hierarchical plot of the WNT3A-(FZD4+LRP5) receptor-ligand pair in cellular interactions. (D) The hierarchical plot of the COLLAGEN signaling pathway in cellular interactions. (E) The crucial receptor-ligand pairs in the COLLAGEN signaling pathway. (F) The hierarchical plot of the COL4A2-(ITGA1+ITGB1) receptor-ligand pair in cellular interactions. (G) Expression levels of the key genes within the COLLAGEN signaling pathway. (H) The hierarchical plot of the LAMININ signaling pathway in cellular interactions. (I) The crucial receptor-ligand pairs in the LAMININ signaling pathway. (J) The hierarchical plot of the LAMA4-(ITGA7+ ITGB1) receptor-ligand pair in cellular interactions. (K) Expression levels of the key genes within the LAMININ signaling pathway. (L) The hierarchical plot of the LAMININ signaling pathway in cellular interactions. (M) The crucial receptor-ligand pairs in the NEGR signaling pathway. (N) The hierarchical plot of the NEGR1–NEGR1 receptor-ligand pair in cellular interactions. (O) Expression levels of the key genes within the NEGR signaling pathway.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7392416/v1/99b566bf96e9f8fc7dac82f4.jpeg"},{"id":94489992,"identity":"97e8a6b9-ff85-4b3b-8649-5a2fd91b52c9","added_by":"auto","created_at":"2025-10-27 17:06:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3129634,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7392416/v1/b59c930a-0879-4717-a466-76a1ed48460c.pdf"},{"id":90762784,"identity":"fba49485-ea6f-4f58-98d4-8e9e044c9c61","added_by":"auto","created_at":"2025-09-07 16:08:52","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":64512,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xls","url":"https://assets-eu.researchsquare.com/files/rs-7392416/v1/be28f60f3c496c7a5dbe2da8.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative single-cell analysis of metabolic syndrome reveals novel cellular heterogeneity and differentiation dynamics in adipose tissue","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetabolic syndrome (MetS), also known as X syndrome, is characterized by multiple metabolic disturbances, including impaired glucose tolerance, obesity, and dyslipidemia(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although MetS initially emerged in developed countries, its prevalence has since expanded significantly in the Asia-Pacific region(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Currently, over 25% of the global population satisfies the diagnostic criteria for MetS(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The syndrome includes many cardiovascular and metabolic risk factors that lead to coronary artery disease and type 2 diabetes, consequently elevating the risk of early mortality. These health complications and their economic consequences place substantial burden on healthcare systems worldwide.\u003c/p\u003e\u003cp\u003eResearch has demonstrated that individuals with MetS experience persistent, low-grade inflammation in adipose tissue, leading to the release of lipids, dysregulated adipokines, and the synthesis of pro-inflammatory cytokines and chemokines(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Adipose tissue serves as a vital metabolic organ, governing systemic energy balance and significantly contributing to the pathophysiology of MetS. Historically regarded solely as an energy reservoir, adipose tissue is now acknowledged as a dynamic endocrine organ consisting of adipocytes and the stromal vascular fraction (SVF)(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The SVF contains diverse cell types, including adipose stem cells (ASCs), immune cells, and endothelial cells, which interact through complex signaling networks. However, technical limitations have restricted our understanding of the functional characteristics of these different cell types and their specific roles in MetS development(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSingle-nucleus RNA sequencing technology has emerged as a powerful tool for exploring cellular heterogeneity in complex tissues(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This advanced method enables comprehensive analysis of gene expression profiles at the single-cell level, revealing cell subpopulations and functional states that conventional bulk sequencing methods fail to identify. This method has been utilized in adipose tissue research, primarily concentrating on healthy persons or certain disorders(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) (e.g., obesity or diabetes). Therefore, the cellular composition and function of adipose tissue in relation to MetS are yet inadequately investigated.\u003c/p\u003e\u003cp\u003eTo address this knowledge gap, we performed single-nucleus RNA sequencing on subcutaneous adipose tissue samples from 84 individuals with MetS in the METSIM study cohort. We aimed to: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) characterize the cellular landscape of adipose tissue in individuals with MetS; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) examine the heterogeneity of adipose tissue cellular composition across individuals; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) investigate adipocyte precursor differentiation dynamics; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) decipher intercellular communication network regulatory mechanisms. This research represents the first systematic characterization of cellular heterogeneity in metabolic syndrome -associated adipose tissue. Through pseudotime analysis and cell-cell communication network investigation, we identified potential disease mechanisms, providing new insights into MetS pathogenesis and potential therapeutic targets.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Single-Nucleus RNA-Sequencing Data Collection and Quality Control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-nucleus transcriptomic data of MetS were sourced from a publicly accessible dataset encompassing 84 individuals from the METabolic Syndrome In Men (METSIM) study. The METSIM cohort, established between 2005 and 2010, enrolled 10,197 Finnish males aged 45 to 73 years. Subcutaneous abdominal adipose tissue biopsies were randomly procured from 1,410 participants(10). After quality control filtering (excluding nuclei with \u0026lt;300 or \u0026gt;6,000 detected genes, or with total counts \u0026gt;25,000) and removing potential doublets, we retained a total of 21,446 high-quality single-nucleus transcriptomes for analysis(11, 12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Clustering and Cell-Type Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe entire analysis was conducted using R (version 4.3.1) and the Seurat(13) (version 4.3) package. Gene expression data were normalized, and the top 2,000 variable genes were selected and scaled for principal component analysis (PCA). The initial 50 principal components were computed, with the first 14 PCs (PC1-PC14) utilized to construct a K-nearest neighbor graph with K\u0026thinsp;=\u0026thinsp;20 and to identify unsupervised cell clusters(14). Resolution was set to 1.0 to discern different cell clusters.\u0026nbsp;Cluster-specific marker genes were identified using Seurat\u0026rsquo;s FindMarkers function (criteria: adjusted \u003cem\u003ep-value\u003c/em\u003e \u0026lt; 0.05 and log2(fold change) \u0026gt; 0.25).\u0026nbsp;For dimensionality reduction, we applied Uniform Manifold Approximation and Projection (UMAP) to the first 14 principal components to visualize cell clustering. Cell-type identification relied on canonical marker expression patterns for major cell types, with assistance from the SingleR package (version 2.0.0)(15).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Cellular Proportion Analysis and the Biological Significance of Differential Gene Expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine whether MetS is characterized by distinct cellular composition profiles, a thorough analysis of cellular proportions was undertaken. We quantified the relative abundance of each cell type in each participant, then applied hierarchical clustering to classify the 84 individuals based on these cellular distributions. We used the R package pheatmap (version 1.0.12) for clustering, which automatically stratified the subjects into two groups: \u0026apos;Group\u0026nbsp;1\u0026apos; (G1) and \u0026apos;Group\u0026nbsp;2\u0026apos; (G2).\u003c/p\u003e\n\u003cp\u003eTo understand the biological significance of the observed groupings, we conducted a comparative gene expression analysis. Genes were considered differentially expressed if they exhibited an adjusted \u003cem\u003ep-value\u003c/em\u003e of less than 0.05 and an absolute log2(fold change) greater than 0.25. Pathway enrichment analysis was performed using G:Profiler web tool(16), which includes pathways from Gene Ontology (GO), KEGG Reactome, and WikiPathways. The Benjamini\u0026ndash;Hochberg (BH)-adjusted \u003cem\u003ep-values\u003c/em\u003e of \u0026lt;0.05 were considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Cell Type-Specific Expression and Gene Set Scoring of Genome-Wide Association Study (GWAS) Loci for Classical Metabolic Syndrome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the MetS loci identified through GWAS and to investigate their cell type-specific expression, we examined the most extensive GWAS for MetS conducted within the UK Biobank cohort(17), with 291,107 individuals (59,677 cases and 231,430 controls). From this GWAS, 92 independent loci were identified with a genome-wide threshold of significance (\u003cem\u003ep-value\u003c/em\u003e \u0026lt; 5 x 10\u003csup\u003e-8\u003c/sup\u003e). Dot plots were employed to illustrate the expression of the 92 genes, based on distinct cell types and cell composition groups (G1 and G2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo precisely quantify the expression of 92 genes, we utilized the Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE) method. This analytical approach, developed by Zheng S et al., determines the approximate mean expression of genes within a signature by utilizing their ranks among all expressed genes. The JASMINE score evaluates the enrichment of the gene sets(18). The JASMINE score evaluates the enrichment of the gene sets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Pseudotime Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePseudotime analysis is a computational technique employed to deduce the temporal history of biological development by arranging cells along a virtual pathway(19). This method replicates the dynamic alterations that transpire during cellular differentiation or disease advancement. In this study, we focused on the specific clusters (6, 15, and 17) identified in our previous analysis and subjected them to the following pseudotime analysis. R package Monocle 2(20) (version 2.26.0) was utilized to preprocess the data, perform UMAP reduction, and reduce the dimensionality of the data using the DDRTree algorithm. Cells were ordered along the inferred pseudotime trajectory. To identify genes that significantly changed along the pseudotemporal trajectory, differential gene testing was performed using the formula\u0026nbsp;\u0026ldquo;~sm.ns(Pseudotime)\u0026rdquo;\u0026nbsp;with a statistical significance \u003cem\u003eq-value\u003c/em\u003e threshold of less than 0.05. Branched expression analysis was further performed and differential genes associated with branches were identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Cell-cell Interaction Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed cell-cell interaction analysis to elucidate the intercellular communication between distinct cell clusters. The analysis was performed using the CellChat R package(21) (version 1.6.1), which identifies \u0026apos;sender\u0026apos; and \u0026apos;receiver\u0026apos; cells participating in defined signaling pathways. Guided by prior research and their well-recognized roles in mediating cell-cell interactions, our focus was directed towards four pivotal signaling pathways including WNT, COLLAGEN, LAMININ, and NEGR. For each pathway, we examined the expression profiles of crucial genes involved in signal transmission and reception. We particularly focused on identifying the primary receptor-ligand pairs that drive the signaling processes within and across different cell types. The expression levels of these genes were visualized to assess their potential roles in facilitating or modulating cell-cell communication.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eClustering and Cell-Type Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo establish a comprehensive single-cell atlas of MetS, we utilized the single-nucleus data derived from 84 Finnish individuals. Following the stringent quality control procedures previously delineated, we obtained a total of 21,446 cells. After dimensionality reduction, these cells were clustered into 20 distinct clusters and further categorized into 12 different cell types, including adipocytes, adipocyte progenitor cells (APCs), endothelial cells, fibroblasts, B cells, M1 macrophages, M2 macrophages, mast cells, natural killer (NK) cells, T helper (Th) cells, monocytes, and vascular smooth muscle (\u003cstrong\u003eFigure 1A, 1C\u003c/strong\u003e). A dot plot showcasing the marker genes characteristic of each cell type is presented in \u003cstrong\u003eFigure 1B\u003c/strong\u003e. The gene feature plot is shown in \u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e. \u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e presents a marker genes list of specific cell types.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Cellular Proportion Analysis Classified 84 Individuals into Two Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon conducting an analysis of individual cellular proportions, we observed substantial differences in the cell type distribution across the 84 individuals (\u003cstrong\u003eFigure 1D\u003c/strong\u003e). The cell proportion of these participants is shown in \u003cstrong\u003eSupplementary Table 2.\u0026nbsp;\u003c/strong\u003eThrough unsupervised clustering, these individuals were automatically segregated into two major groups (\u003cstrong\u003eFigure 1E\u003c/strong\u003e), which we have designated as Group 1 (G1) and Group 2 (G2). Notably, within G1, there was a significant predominance of adipocytes and APCs, concurrent with a relative dearth of T helper (Th) cells and natural killer (NK) cells. In contrast, G2 was characterized by a lower proportion of adipocytes and APCs, yet a higher representation of Th cells and NK cells (\u003cstrong\u003eFigure 1F\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eTo identify differentially expressed genes between G1 and G2, we applied stringent criteria where genes were considered significantly differentially expressed if they had an adjusted \u003cem\u003ep-value\u003c/em\u003e of less than 0.05 and an absolute log2(fold change) greater than 0.25. The comprehensive list of differentially expressed genes between the two groups is provided in \u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e. Compared with G2, differentially expressed genes in G1 group were particularly within adipocytes and monocytes (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). In adipocytes, the upregulated genes were significantly enriched in blood circulation (\u003cem\u003epadj\u003c/em\u003e = 8.94\u0026times;10\u003csup\u003e-3\u003c/sup\u003e ), circulatory system process (\u003cem\u003epadj\u003c/em\u003e = 2.17\u0026times;10\u003csup\u003e-2\u003c/sup\u003e), and tumor necrosis factor production-associated pathways. In monocytes, the upregulated genes were predominantly associated with pentosyltransferase activity (\u003cem\u003epadj\u003c/em\u003e = 4.99\u0026times;10\u003csup\u003e-2\u003c/sup\u003e), as detailed in \u003cstrong\u003eSupplementary Table 4\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e \u003cstrong\u003eMarginal Enrichment of Metabolic Syndrome GWAS Genes in Adipocytes and Macrophages without Group-Specific Differences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate whether the expression of GWAS risk genes for MetS exhibits cell type specificity, we evaluated the expression profiles of 92 genome-wide significant MetS risk genes across 12 identified cell types. The results showed that these genes were relatively highly expressed in M1 macrophages and adipocytes, yet they did not exhibit pronounced cell type specificity overall (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). Furthermore, we employed the JASMINE algorithm to calculate gene set scores for these 92 genes. Relative to other cell types, adipocytes and M1/M2 macrophages exhibited marginally elevated scores, indicating a somewhat higher expression of the metabolic syndrome-associated GWAS genes within these cell types, although the disparity was not markedly pronounced (\u003cstrong\u003eFigure 2D\u003c/strong\u003e). Expression profiles of 92 genes between G1 and G2 were further examined, however, no significant differential expression was detected between the two groups (\u003cstrong\u003eFigure 2B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Temporal Remodeling of Adipocyte Progenitor Cell Differentiation into Fibroblasts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis has uncovered a progressive transition in the expression of marker genes associated with APCs and fibroblasts within clusters 6, 15, and 17. Specifically, cluster 6 exhibited a higher expression of adipocyte progenitor cell markers, cluster 15 displayed a balanced expression of both types of markers, and cluster 17 showed a predominance of fibroblast markers (\u003cstrong\u003eFigure 3A\u003c/strong\u003e). To ascertain whether these clusters represent a temporal sequence of cellular differentiation, we conducted a pseudotime analysis on the three clusters in isolation.\u003c/p\u003e\n\u003cp\u003eOur results indicate that cluster 15 occupies an early position in the pseudotime trajectory, with a subset of cells from cluster 6 demonstrating a gradual progression towards cluster 17 (\u003cstrong\u003eFigure 3B-3E\u003c/strong\u003e). This implied that some APCs are in an early stage of differentiation, exhibiting a tendency to evolve into late-stage APCs and fibroblasts. The density plot further illustrates the positional distribution of the three clusters along the pseudotime sequence (\u003cstrong\u003eFigure 3F\u003c/strong\u003e). To differentiate between the two clusters of APCs, we designated cluster 6 as \u0026ldquo;adipocyte progenitor 1\u0026rdquo; (APC1) and cluster 15 as \u0026ldquo;adipocyte progenitor 2\u0026rdquo; (APC2).\u003c/p\u003e\n\u003cp\u003eA differential expression analysis of genes along the pseudotime trajectory within the three clusters was conducted. The heatmap depicting the top 100 differentially expressed genes is presented in \u003cstrong\u003eFigure 3G\u003c/strong\u003e. Additionally, a branched expression analysis was performed within the three distinct branches to identify genes associated with specific stages of cellular differentiation, and a GO (Gene Ontology) pathway enrichment analysis was applied to the differentially expressed genes within each branch (\u003cstrong\u003eFigure 3H\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Investigating the Driving Mechanisms of Adipocyte Progenitor Cell Differentiation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell-cell communication analysis has unveiled the intricate interactions among various cell types within adipose tissue (\u003cstrong\u003eFigure 4A-4B\u003c/strong\u003e). For the interaction strength, a dense network of interactions among cells was found, with particularly strong communication intensity observed between adipocytes themselves, as well as between adipocytes and APC1 and APC2 cells, and between adipocytes and endothelial cells (\u003cstrong\u003eFigure 4B\u003c/strong\u003e). A heatmap further illustrates the number and strength of these cellular interactions, and no significant differences were observed in signal quantity or intensity between the two adipocyte progenitor cell types (\u003cstrong\u003eFigure 4C-4D\u003c/strong\u003e). Building upon the comprehensive analysis of cell-cell interactions within adipose tissue, we next explored the specific signaling pathways that drive communication between APCs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.1 WNT Signaling Drive Adipocyte Progenitor Cell Communication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInterestingly, we identified a significant enrichment of the WNT signaling pathway in the signals emanating from APC2 to adipocytes (\u003cstrong\u003eFigure 5A\u003c/strong\u003e). Within this pathway, the receptor-ligand pairs WNT3A (ligand)-FZD4 and LRP5 (receptors) were found to play a predominant role (\u003cstrong\u003eFigure 5B\u003c/strong\u003e). Further analysis indicated that this receptor-ligand pair primarily functions in signaling from APC2 to adipocytes (\u003cstrong\u003eFigure 5C\u003c/strong\u003e), suggesting that the WNT signaling pathway in adipocyte progenitor cell differentiation may act through the WNT3A-FZD4 and LRP5 receptor-ligand interaction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.2 Three Novel Signaling Pathways Identified as Drivers of Adipocyte Progenitor Cell Communication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the crucial signals driving the differentiation of APC2, we examined all significant interactions targeting APC2 and found that the receptor-ligand pairs on the COLLAGEN, LAMININ, and NEGR signaling pathways were markedly upregulated (\u003cstrong\u003eFigure 4E\u003c/strong\u003e). We focused on the signal transduction, principal receptor-ligand pairs, and expression of key genes within these three pathways.\u003c/p\u003e\n\u003cp\u003eFor the collagen pathway, the primary interactions were observed in autocrine signaling from adipocytes and paracrine signaling from both clusters of APCs to adipocytes (\u003cstrong\u003eFigure 5D\u003c/strong\u003e). Among the many significant receptor-ligand pairs, COL4A2-(ITGA1 and ITGB1) stood out (\u003cstrong\u003eFigure 5E-5F\u003c/strong\u003e), primarily mediating interactions between fibroblasts and adipocytes. Key genes within this pathway were found to be highly expressed in both APC1, APC2, fibroblasts, and vascular smooth muscle cells (\u003cstrong\u003eFigure 5G\u003c/strong\u003e). The LAMININ pathway showed similarities to the collagen pathway, with primary interactions in autocrine signaling from adipocytes and paracrine signaling from both clusters of APCs to adipocytes (\u003cstrong\u003eFigure 5H\u003c/strong\u003e). The most contributing receptor-ligand pair was LAMA4-(ITGA7 and ITGB1), predominantly involved in signaling from APC1 to adipocytes (\u003cstrong\u003eFigure 5I-5J\u003c/strong\u003e). Crucial genes in this pathway were also highly expressed in APC2, APC1, fibroblasts, and vascular smooth muscle cells (\u003cstrong\u003eFigure 5K\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe NEGR pathway exhibited specificity in signal transduction, being confined to interactions between APC1, APC2, and fibroblasts (\u003cstrong\u003eFigure 5L\u003c/strong\u003e), with the sole receptor-ligand pair NEGR1-NEGR1 playing a role (\u003cstrong\u003eFigure 5M-5N\u003c/strong\u003e). This gene was found to be expressed exclusively in APC1, APC2 and fibroblasts (\u003cstrong\u003eFigure 5O\u003c/strong\u003e). Notably, the signal intensity of NEGR1 is the most pronounced among all signals received by APC2.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study comprehensively revealed the cellular heterogeneity, differentiation dynamics, and molecular regulatory mechanisms in adipose tissue of patients with MetS through single-cell transcriptomics analysis of subcutaneous adipose tissue from 84 Finnish men in the METSIM cohort, combined with GWAS genetic locus analysis and exploration of intercellular communication networks. The following will discuss the results of this study in detail from multiple aspects, including the heterogeneity of adipose tissue cell composition, the cell-specific expression of GWAS genes and their functional significance, the remodeling of the time sequence of adipocyte precursor cell differentiation, and the role of cell-cell interactions in MetS.\u003c/p\u003e\u003cp\u003eOur single-cell RNA sequencing analysis revealed that adipose tissue cell composition in individuals with MetS is highly heterogeneous. In fact, the adipose samples from the 84 individuals showed clear differences and were separated by unsupervised clustering into two groups (G1 and G2). Group G1 was characterized by an enrichment of adipocytes and APCs, whereas group G2 was characterized by a higher proportion of Th cells and NK cells. This discovery indicates the presence of subtypes in people with MetS, wherein varying adipose tissue cell compositions align with varied clinical presentations of MetS(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This outcome is strongly aligned with the current literature on adipose tissue cell heterogeneity. Emont et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) found via single-cell transcriptome analysis that various adipose cell subsets in adipose tissue are significantly associated with metabolic protective effects, aligning with our observation of an enrichment of G1 group APCs. This indicates that the G1 group may possess a more robust potential for compensatory adipogenesis to address metabolic problems. However, the enrichment of immune cells in the G2 group indicates that this group may have a higher inflammatory immune response, which is consistent with the dynamic changes in the immune microenvironment identified by Jaitin et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) in obesity models. This finding not only reveals the cellular heterogeneity of MetS, but also provides a novel perspective for the future clinical categorization of MetS. In terms of the functional mechanism, the genes differentially expressed by the G1 group of adipocytes are enriched in pathways associated with blood circulation and tumor necrosis factor production, indicating that the adipose tissue of the G1 group may exhibit a distince pattern of angiogenesis and inflammatory response. The study by S\u0026aacute;rv\u0026aacute;ri et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) shows that the proliferation and differentiation capacity of adipocyte precursor cells directly affects the expansion pattern of adipose tissue, while functional lipogenesis can effectively prevent ectopic lipid deposition. Therefore, the enrichment of G1 phenotype adipocyte precursor cells may represent a compensatory response, in which these cells delay the progression of metabolic disorders by promoting lipid storage. At the same time, the upregulation of monocyte genes in the G1 group is associated with pentose phosphate kinase activity, suggesting that changes in glucose metabolism may be an important mechanism of metabolic disorders in this group.\u003c/p\u003e\u003cp\u003eThis study also explored the cell-specific expression patterns of 92 metabolic syndrome-related GWAS loci. We found that these GWAS risk genes were highly expressed in M1 macrophages and adipocytes, indicating that these two types of cells play a key role in the pathological process of MetS. However, the results of gene set scoring showed that although the scores of adipocytes and M1/M2 macrophages increased slightly, this difference did not reach significance. This result may reflect the common action of MetS genetic susceptibility risk genes in different cell types, rather than the specific effect of a single cell type.\u003c/p\u003e\u003cp\u003eAmong the genetic mechanisms of MetS, the NEGR1 gene is of particular interest. NEGR1 was initially identified as an obesity risk gene (GWAS) and thought to act in the central nervous system(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), but recent studies indicate it also plays a key role in adipose tissue(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). We found that NEGR1 is highly expressed in adipocyte precursor cells and fibroblasts, and its expression is markedly reduced in MetS adipose tissue \u0026ndash; a change closely associated with the development of obesity and related metabolic disorders.\u003c/p\u003e\u003cp\u003eThis finding is consistent with the latest literature: NEGR1 is upregulated during normal adipocyte differentiation, while in patients with obesity, NEGR1 expression is significantly downregulated, and its expression level is negatively correlated with obesity-related indicators (such as waist-to-hip ratio, waist circumference). Mechanism studies have shown that NEGR1 regulates the uptake and utilization of fatty acids by interacting with the fatty acid transport protein CD36​(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). When NEGR1 expression is downregulated, CD36 levels increase, leading to excessive uptake of fatty acids, which in turn promotes lipid accumulation and the development of insulin resistance. Therefore, changes in the expression pattern of NEGR1 may be an early sign of MetS(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we revealed the gradual transition of adipocyte precursor cells to fibroblasts using pseudotime analysis. The pseudotime trajectory analysis showed that cluster 15 (APC2) was located in the early stage of the pseudotime trajectory, while some cells in cluster 6 (APC1) gradually transformed into cluster 17 (fibroblasts). This finding suggests that under MetS, the fate of precursor cells in adipose tissue is significantly biased\u0026mdash;some precursor cells fail to differentiate normally into adipocytes and instead switch to the fibroblast lineage. The mechanism of this transformation may be related to metabolic disorders, especially the progression of metabolic diseases such as obesity and diabetes(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis result is consistent with multiple studies, which have shown that in MetS, a chronic inflammatory microenvironment and multiple signaling pathways (such as WNT, TGF-β, and PDGF) may induce the transformation of adipocyte precursor cells into fibroblasts(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The differentiation of adipocyte precursor cells is not only biased, resulting in a reduction in the lipid storage function of adipocytes, but also may lead to excessive deposition of extracellular matrix such as collagen, further exacerbating tissue fibrosis and metabolic disorders. Marcelin et al. ​(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) found that in conditions of excess adiposity, adipocyte precursor cells exhibit stronger collagen-producing capacity, leading to fibrosis and dysfunction of adipose tissue. This phenomenon may be one of the key factors contributing to adipose tissue dysfunction in MetS.\u003c/p\u003e\u003cp\u003eThis study reveals the complex communication network among cells in the MetS microenvironment via cell-cell interaction analysis. We found that the interaction between adipocytes, APCs and macrophages plays an important role in MetS, especially under the regulation of WNT, COLLAGEN(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), LAMININ(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) and NEGR signaling pathways, cell-cell signaling may exacerbate adipose tissue inflammation and fibrosis​(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). In particular, the WNT signaling pathway plays a central role in the interaction between APCs and mature adipocytes(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Our study found that the WNT3A-FZD4/LRP5 receptor-ligand pair may play a key role in the differentiation of APCs into adipocytes, suggesting that the WNT signaling pathway may be involved in the development of MetS by regulating the differentiation direction of APCs. In addition, we also found that the COLLAGEN, LAMININ and NEGR signaling pathways play a special role in the differentiation of adipocyte precursor cells. These pathways may affect the function and metabolic stability of adipocytes by regulating the production of extracellular matrix. These intercellular interactions and changes in signaling pathways not only deepen our understanding of the pathological mechanisms of MetS, but also provide potential targets for future treatment strategies. Regulating the WNT signaling pathway or reversing fibroblast formation may emerge as significant intervention strategies to mitigate MetS(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThere are several limitations to this study. First, the study subjects were limited to Finnish men, which may limit the generalizability of the results, especially considering the influence of gender and ethnicity in MetS. Second, although single-cell RNA sequencing provides high-resolution cell-type information, our study lacks protein-level validation and functional experiments to confirm the biological significance of the observed cell\u0026ndash;cell interactions. In addition, the cross-sectional design of this study cannot reveal the causal relationship between changes in cell composition and the development of MetS.\u003c/p\u003e\u003cp\u003eFuture research directions include: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) expanding the sample size and including individuals of different genders and ethnicities to assess the generalizability of the results; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) incorporating spatial transcriptomics to preserve the spatial context of cells in adipose tissue and better understand the microenvironmental context of intercellular interactions; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) conducting functional experiments to validate the roles of WNT, COLLAGEN, LAMININ and NEGR signaling pathways in adipocyte precursor cell differentiation; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) a longitudinal study to determine whether changes in cell composition are a cause or a consequence of MetS; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) an integrated multi-omics analysis, including genomic, transcriptomic, proteomic and metabolomic data, to gain a comprehensive understanding of the molecular mechanisms underlying MetS.\u003c/p\u003e"},{"header":"5.Conclusion","content":"\u003cp\u003eThis study provides critical insights into the cellular heterogeneity, differentiation dynamics and molecular regulatory mechanisms of MetS in adipose tissue. Our results indicate that the heterogeneity of adipose tissue cell composition, the cell-specific expression patterns of GWAS genes, and the reprogramming of adipocyte precursor cell differentiation trajectories play central roles in the development of MetS. In addition, the complex cell-cell interactions between adipocyte precursor cells and immune cells provide new biomarkers and targets for the early diagnosis and treatment of MetS. Future research needs to further explore the clinical significance of these cell interactions and develop precision therapeutic strategies targeting these pathways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors would like to thank the participants and investigators of the METSIM cohort for generating and making publicly available the valuable single-nucleus RNA sequencing dataset used in this study. This research was not supported by any specific grants from public, commercial, or not-for-profit funding agencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e H.J. was responsible for designing the study, conducting data analysis, performing bioinformatic interpretation, writing the manuscript draft, and creating figures and tables. D.L. contributed by conceptualizing the research questions, supervising the analysis, critically reviewing the interpretation of results, editing the manuscript. Both authors contributed to the review and approval of the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eAll data analyzed in this study are available from public databases and can be accessed through the corresponding databases and repositories cited in the manuscript. The analyzed data and code are available upon reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlberti KGMM, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO Consultation. 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Obes Rev. 2024;25(6):e13724.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metabolic syndrome, Adipose tissue, Cellular heterogeneity, Obesity","lastPublishedDoi":"10.21203/rs.3.rs-7392416/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7392416/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMetabolic syndrome (MetS) is characterized by obesity, insulin resistance, and dyslipidemia with adipose tissue inflammation, yet its cellular heterogeneity and intercellular interactions remain poorly understood. We analyzed single-nucleus RNA sequencing data from subcutaneous adipose tissue of 84 individuals with MetS from the METSIM cohort, characterizing cell composition, inter-individual variation, adipocyte progenitor differentiation, and cell-cell communication networks.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe performed single-nucleus RNA sequencing on subcutaneous adipose tissue samples from 84 individuals with MetS. Clustering analysis was used to define cell types and subpopulations, inter-individual variation in cell composition was assessed, pseudotime trajectory analysis reconstructed adipocyte precursor differentiation pathways, and ligand\u0026ndash;receptor interaction analysis mapped intercellular communication networks.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe identified 12 distinct cell types in MetS adipose tissue and discovered two patient subgroups with differential enrichment of adipocytes/progenitors versus immune cells, suggesting subtypes of MetS with distinct adipose profiles. Pseudotime analysis revealed two adipocyte progenitor subpopulations with altered differentiation trajectories. Cell-cell communication analysis identified WNT signaling from progenitors to adipocytes as a potential differentiation driver, with extracellular matrix pathways mediating progenitor-adipocyte interactions.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis comprehensive single-cell atlas of MetS adipose tissue reveals previously unrecognized cellular heterogeneity and differentiation dynamics, offering new insights into MetS pathogenesis and highlighting potential therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Integrative single-cell analysis of metabolic syndrome reveals novel cellular heterogeneity and differentiation dynamics in adipose tissue","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-07 16:08:38","doi":"10.21203/rs.3.rs-7392416/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-09-21T22:12:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-20T11:56:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T04:54:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-17T11:22:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290577805119231495534008048672776468754","date":"2025-09-13T19:04:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-12T15:26:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172140028989737272059634435659910952180","date":"2025-09-12T15:00:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24641480753920714582603216233298666790","date":"2025-09-12T09:36:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-11T22:16:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70469628059760763300605581223364042678","date":"2025-09-09T06:30:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248219717214424031385957654278822058714","date":"2025-09-08T02:26:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15850742390140449460455020563036309128","date":"2025-09-07T18:13:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-27T01:56:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-23T14:30:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-23T14:30:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Diabetology \u0026 Metabolic Syndrome","date":"2025-08-17T12:45:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc4bc176-08d1-4929-9fa3-21865af40d9f","owner":[],"postedDate":"September 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:30:35+00:00","versionOfRecord":{"articleIdentity":"rs-7392416","link":"https://doi.org/10.1186/s13098-025-01975-3","journal":{"identity":"diabetology-and-metabolic-syndrome","isVorOnly":false,"title":"Diabetology \u0026 Metabolic Syndrome"},"publishedOn":"2025-10-22 16:17:06","publishedOnDateReadable":"October 22nd, 2025"},"versionCreatedAt":"2025-09-07 16:08:38","video":"","vorDoi":"10.1186/s13098-025-01975-3","vorDoiUrl":"https://doi.org/10.1186/s13098-025-01975-3","workflowStages":[]},"version":"v1","identity":"rs-7392416","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7392416","identity":"rs-7392416","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}