Identification of a regulatory pathway inhibiting adipogenesis via RSPO2

Integration of APC scRNA-seq data reveals heterogeneity of adipocyte progenitor cells

In a previous study9, we defined LinSca1+CD142+ APCs as adipogenesis regulatory (Areg) cells and demonstrated that these cells are both refractory toward adipogenesis and control adipocyte formation of APCs through paracrine signaling. In contrast, Merrick et. al.4 observed that LinCD142+ cells could differentiate into adipocytes. To study these seemingly inconsistent observations, we re-examined 10X scRNA-seq of Lin cells with the most recent computational algorithms12,13,14,15,16,17, which by unsupervised clustering of the transcriptomes revealed seven distinct subpopulations (Fig. 1a). The newly identified clusters P1-1, P1-2 and P1-3, which express Cd55 and Dpp4 (Fig. 1b) represent an early progenitor population and resemble the previously identified cluster G1/G49/Group 1 (ref. 4). The previously identified G29/Group 2 (ref. 4) cells, which represent committed APCs, could be divided into two Pparg-expressing subgroups (Extended Data Fig. 1a,b) P2-1 (Cd142) and P2-2 (Cd142+) based on their Cd142 expression (Fig. 1b). The newly defined cluster P3 represents a subset of the previously identified Areg9/Group 3 cluster4, with specific expression of Cd142, Clec11a and Fmo2 (Fig. 1b) and which is separated from the P2-2 cluster. The proliferating P4 cluster expresses high levels of cell-cycle genes (Extended Data Fig. 1c).

Fig. 1: Integration of different scRNA-seq data further reveal the heterogeneity of adipocyte progenitors.

a, Uniform Manifold Approximation and Projection (UMAP) two-dimensional map of cells derived from 10X dataset in our previous study9 shows several distinct clusters, including Cd55+ progenitor cells (P1-1, P1-2 and P1-3), two subpopulations of committed pre-adipocytes (P2-1 and P2-2), P3 cells and dividing cells expressing cell-cycle genes of S phase (P4). b, Violin plots showing the expression of marker genes. Cd55 and Dpp4 (marker of cluster P1-1–P1-3); Vap1 and Icam1 (marker of cluster P2-1–P2-2); and Cd142, Clec11a and Fmo2 (marker of cluster P3). c,d, Cell trajectory analysis of Lin cells by Velocyto and scVELO (c) and Monocle 3 (d). e,f, Single-nucleus RNA-seq (snRNA-seq) of human deep neck adipose tissue. Unsupervised clustering of pre-adipocyte populations shown as UMAP plot (e). P3 score (f) calculated as the sum of F3, CLEC11A, FMO2, GAS6, CYGB, PPL and STEAP4 for each cell. g, Feature plots of H1 (FMO2, FGF10, COL4A2 and PPARG), H2 (CD55, KCNAB1, KCNB2 and CREB5) and H3 (NR4A1, S100A10, S100A6 and CD81) markers in preadipocyte nuclei of human deep neck adipose tissue.

To define the relationships between these cells, we next predicted the cellular trajectories of these cell clusters through dynamical modeling of RNA splicing events by Velocyto12 and scVELO13 (Fig. 1c). The models suggested that P1 cells can transit to P3 cells and further to either P2-1 or P2-2 cells. Another trajectory analysis using Monocle16 (Fig. 1d and Extended Data Fig. 1d) inferred that P1 cells can transition through the first branch point to become proliferating P4 cells or through the second branch point to become P2-1 cells. Some P1 cells continue transit through the third branch point to form the cluster of P2-1, P2-2 or P3 cells (Fig. 1d). These data are consistent with previous experimental findings4,9,18 that demonstrated that P1 cells define a group of early adipocyte progenitors, whereas Pparg-expressing P2 cells (Extended Data Fig. 1a,b) represent committed preadipocytes.

Given the newly identified separation of Cd142+ APCs into P3 and P2-2 cells, we hypothesized that the discrepancies reported by us and Merrick et. al. could be due to the fact that these two populations were analyzed as mixtures. To address this, we aligned data from the two mouse datasets from Merrick et. al. to our own data using canonical correlation analysis15 (Extended Data Fig. 1e) and performed clustering based on the aligned results. Indeed, we observed two distinct clusters of Cd142+ cells in the integrated analysis (cluster 0 and 2) (Extended Data Fig. 1f,g). Cluster 2, but not other Cd142+ cells, express Cd142, Clec11a and Fmo2, similar to the newly defined P3 cells. Cluster 0 was similar to P2-2, which expresses Cd142, Icam1 and Vap1 (Extended Data Fig. 1f,g).

We next wanted to extend our analyses to the human situation, as little is known about the presence of either early or late adipocyte progenitors. Therefore, we resolved the adipocyte heterogeneity in human deep neck subcutaneous adipose tissue (SAT), which allowed us to define 12 subpopulations19 (Extended Data Fig. 1j). The pre-adipocyte cluster featured by the pre-adipocyte marker PDGFRA20 (Extended Data Fig. 1k) could be further subdivided into three subsets, termed H1–H3 (Fig. 1e). We failed to correlate the mouse P3 cluster with either H1–H3 clusters, as the overlaps were not statistically significant (Extended Data Table 1). Alternatively, using the P3 score as a sum of mouse P3 signature genes (Fig. 1f) and Cd142 expression (Extended Data Fig. 1l) indicated that mouse P3 cells were enriched in cluster H1 and H3. Based on these findings, it would be worthwhile to investigate, whether H1 or H3 cells are functionally similar to mouse P3 cells. The enrichment of PPARG in the H1 cluster, suggests that these cells might constitute the committed pre-adipocytes within human SAT (Fig. 1g). Enrichr analysis of the H3 signature denotes this cluster as a smooth-muscle-cell-like population (Extended Data Fig. 1m) with enriched pathways such as VEGFA–VEGFR2 signaling or the matrix metalloproteinase pathway, which might regulate adipose tissue microenvironment (Extended Data Fig. 1n) and the expression of known adipogenesis regulatory genes such as NR4A1 (ref. 21) and FSTL1 (ref. 22) (Extended Data Fig. 1o). Taken together, these data suggest that H3 might constitute a regulatory cell type within human SAT; however, more studies will be needed to delineate the function of H3 cells.

In-depth functional analysis of the different cell populations within mouse adipose tissue

Caution needs to be used when employing CD142 as a marker to isolate P3 cells, as adipogenic CD142-expressing P2-2 cells will also be collected. This fact might explain the divergent findings regarding the adipogenic potential of P3 cells4,9,23. In our previous study9, we observed a continuum of CD142-expressing cells within the LinSca1+ (enriched pool of APCs) cell fraction (Extended Data Fig. 2a). This is supported by the finding that LinSca1+CD142++ cells are more similar to P3 cells compared to LinSca1+CD142+ cells based on P3 signature gene expression (Extended Data Fig. 2b). Furthermore, abundant P1 and P2 cells are admixed to the LinSca1+CD142+ fraction, while fewer are observed in the LinSca1+CD142++ population (Extended Data Fig. 2c), which was confirmed by the analysis of P1 and P2 marker gene expression (Extended Data Fig. 2d,e). Thus, for gating of P3-like LinSca1+CD142++ cells, CD142 staining within the Lin+ population could be used as a reference control (Extended Data Fig. 2a). Based on this strategy we next examined the adipogenic capacity of the following cell populations with different cocktails (Extended Data Table 2): LinSca1+ cells, LinSca1+CD142VAP1+ cells (P2-1), LinSca1+CD142+ (P2-2) and LinSca1+CD142++ cells (P3). We observed LinSca1+CD142++ cells were refractory toward adipogenesis similar to the previously described Areg population9 upon adipogenic cocktail induction, whereas LinSca1+CD142+ cells could form adipocytes with previously used induction strategies4 (Extended Data Fig. 2f,g).

To isolate P3 cells more reliably and to further investigate the function of cell populations defined in our combined analysis (Fig. 1a and Extended Data Fig. 1e), we established a new FACS strategy to purify the different subpopulations. As shown in Fig. 2a, enriched-P1 (eP1) composed of P1-1 to P1-3 cells, were isolated using a LinSca1+CD55+VAP1CD142 gating strategy. VAP1+ cells were further separated into P2-1 and enriched-P2 (eP2) cells, which are (LinSca1+CD55VAP1+CD142) or P2-2 cells (LinSca1+CD55VAP1+CD142+), whereas enriched-P3 (eP3) cells were isolated using a LinSca1+CD55VAP1CD142+ strategy. Upon adipogenic cocktail induction, eP1, eP2 and VAP1+CD142+ (P2-2) cells showed an adipogenic capacity and we furthermore observed that removal of eP3 cells from LinSca1+VAP1CD55 (or VAP1CD55) cells, LinSca1+VAP1CD55CD142 (or DN:CD142) showed markedly increased adipocyte formation, reminiscent of the fact that eP3 cells are refractory toward adipogenesis (Fig. 2b,c). Moreover, gene expression analysis demonstrated that P3-specific genes are enriched in the eP3 population (Fig. 2d). In conclusion, we were able to isolate eP1, eP2, P2-2 and eP3 cells, which represent the P1, P2-1, P2-2 and P3 cells identified by the 10X scRNA-seq approach (Fig. 1a). EP1 cells, when treated with a minimal adipogenic cocktail (C cocktail; Extended Data Table 2) exhibited a lower adipogenic potential compared to eP2 cells, whereas no differences in adipogenesis were detected using robust adipogenic conditions (A cocktail) (Fig. 2e–g). These data imply that eP1 cells, which express low levels of Pparg (Extended Data Fig. 1a,b), are at an earlier stage of adipogenesis and might have not committed to the adipocyte lineage.

Fig. 2: Classification of different cell populations within the adipose tissue.

a, Flow cytometry dot plots show the new gating strategy used to sort eP1, eP2 and eP3. b, Quantification of adipogenesis (left) and cell number (right) of LinSca1+ cells, eP1 cells, eP2 cells, VAP1+CD142+ APCs, VAP1CD55 cells, DN:CD142 cells and eP3 induced by A Cocktail (1 μM dexamethasone, 0.5 mM isobutylmethylxanthine and 1 μM insulin). Data are shown as mean ± s.e.m., n = 8 independent wells. Data were analyzed with one-way analysis of variance (ANOVA); F(6,49) = 69.7002, P < 0.0001. c, Microscopy images of different cell populations shown in b on differentiation day 6. Experiment was repeated twice. d, Relative mRNA levels of P1 marker (Cd55, Pcsk6, Efhd1, Pi16 and Smpd3), P2 markers (Vap1, Col4a1, Sparcl1 and Sdc1) and Areg cell-specific marker (Cd142, Gdf10, Igfbp3, Fmo2 and Clec11a) genes in different cell populations; n = 3 biological replicates. Data show mean ± s.e.m. e, Quantification of adipogenesis (left) and cell number (right) of LinSca1+ cells, eP1 cells, eP2 cells and eP3 induced by A Cocktail. Data show mean ± s.e.m.; n = 6 independent wells. Data were analyzed with one-way ANOVA; F(3,20) = 280.8, P < 0.0001 (left); multicomparison with LinSca1+ group was performed by two-stage step-up method with false discovery rate (FDR) = 0.05. F(3,20) = 2.838, P = 0.064 (right). f, Quantification of adipogenesis (left) and cell numbers (right) of LinSca1+ cells, eP1 cells, eP2 cells and eP3 induced by C Cocktail (1 μM insulin). Data are shown as mean ± s.e.m., n = 6 independent wells. Data were analyzed with one-way ANOVA. F(3,20) = 48.27, P < 0.0001 (left), multicomparison with LinSca1+ group was performed by two-stage step-up method with FDR = 0.05. F(3,20) = 0.189, P = 0.903 (right). g, Microscopy images of different cell populations shown in e and f on differentiation day 6. In all panels, nuclei were stained with Hoechst 33342 (blue) and lipids were stained with LD540 (yellow). Scale bars, 100 μm.

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Identification of Rspo2 as a new marker of P3 cells

We previously could show that Areg/P3 cells regulate adipogenesis in a paracrine fashion through Spink2 and Rtp3 (ref. 9). To characterize P3 cells in more detail, we compared bulk RNA-seq of Areg (LinSca1+CD142++) cells to LinSca1+CD142 cells from mouse ingWAT9. A total of 216 differently regulated genes, of which 56 encoded secreted proteins, were enriched in Areg/P3 cells (Extended Data Fig. 3a). The list was reduced to 41 genes after exclusion of candidates expressed in mature adipocytes or other cell populations. When filtered for factors expressed in eP1 and eP2 cells, 13 candidates remained (Extended Data Fig. 3b), which were exclusiverly enriched in eP3 cells (Fig. 3a and Extended Data Fig. 3c).

Fig. 3: Identification of Rspo2 as a new marker of P3.

a, Cd142 Vap1 and Rspo2 expression in eP3-depleted SVF, VAP1+CD142+ APCs and eP3. Data show the mean ± s.e.m., n = 6 biological replicates. Cd142: F(2,15) = 88.9; Vap1: F(2,15) = 686.4; Rspo2: F(2,15) = 79.5 using one-way ANOVA. bd, The ratio (b) and representative images (d) of adipocytes after knocking down of Rspo2 in ingWAT SVF. Rspo2 mRNA expression (c) 48 h after transfection. Data show mean ± s.e.m., analyzed by two-tailed Student’s t-test. n = 2 biological replicates (b), n = 4 biological replicates (c). Ctrl, control. eh, Scheme of Transwell co-culture experiments (e). The ratio (f) and representative images (h) of CD142 cells on differentiation day 8. Rspo2 mRNA levels (g) in siRNA-transfected eP3. Data show mean ± s.e.m., analyzed with two-tailed Student’s t-test, n = 2 biological replicates (f,g). ik, Experimental scheme (i) for rec.RSPO2 treatment experiment. The ratio (j) and microscopy images (k) of adipocytes in SVF-treated ± rec.SPO2. Data shown as mean ± s.e.m., n = 6 independent wells. Data were analyzed with one-way ANOVA followed by Tukey’s multiple comparisons test. F(6,35) = 10.18. Spearman r correlation between RSPO2 level in medium and adipocyte ratio (j right). NS, not significant. ln, Experimental scheme (l) for knocking down of Lgr4, Lgr5 and Lgr6 in ingWAT SVF treated with or without rec.RSPO2. Representative images (m) and the ratio (n) of mature adipocytes per well. Data shown as mean ± s.e.m., n = 6 independent wells. F(3,30) = 1.07, P = 0.377 using two-way ANOVA. Multicomparsion between groups was performed by two-stage step-up method with FDR = 0.05. o, Heat map of Lgr4, Lgr5 and Lgr6 expression in eP1 and eP2 cells, n = 3–5 biological replicates. pq, Ratio (p) and representative images (q) of adipocytes in cells treated ± rec.RSPO2. Data shown as mean ± s.e.m., n = 6 independent wells. F(2,20) = 26.22, P < 0.0001 using two-way ANOVA. Multicomparison between groups was performed by two-stage step-up method with FDR = 0.05. In all panels, nuclei were stained with Hoechst 33342 (blue) and lipids were stained with LD540 (yellow). Scale bars, 100 μm.

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To test the functional relevance of the 13 candidates, we first used siRNA to decrease their expression in ingWAT SVF. We observed increased adipogenesis of SVF after knockdown of Rspo2 (Fig. 3b–d), Cpb1, Lgi1, Nog, S100a9, Cgref1, or Serpinb6c (Extended Data Fig. 3d). Next, to test which candidates could modulate the inhibitory potential of P3 cells in a paracrine manner, we co-cultured eP3 cells with LinSca1+CD142 APCs in a Transwell system (Fig. 3e). We noted that short interfering RNA (siRNA)-mediated ablation of Rspo2 (Fig. 3f–h), Cgref1 or Serpinb6c (Extended Data Fig. 3e) in eP3 cells significantly increased adipogenesis of CD142 APCs in the other compartment. Furthermore, ablation of Rspo2, Cgref1 or Serbinb6c lowered the paracrine inhibitory potential of eP3 cells, comparable to the previously identified Spink2 (ref. 9) (Extended Data Fig. 3f). Therefore, P3 cells likely regulate adipogenesis in a paracrine fashion through the potential effectors Rspo2, Cgref1, Serbinb6c or Spink2.

Recombinant RSPO2 protein inhibits adipogenesis through Lgr4 in primary SVF cells

RSPO2 could be detected in eP3 cell culture medium at 500–600 pg ml−1 (Extended Data Fig. 3h). To test whether RSPO2 inhibits adipogenesis in vitro, we added recombinant RSPO2 (rec.RSPO2) in an SVF differentiation assay starting from 2 d before until 2 d after cocktail induction (Fig. 3i) and observed that increasing amounts of RSPO2 led to a progressive decrease in adipocyte formation (Fig. 3j,k) without affecting cell numbers (Extended Data Fig. 3i). When rec.RSPO2 was inactivated by heat its inhibitory effect on adipogenesis was lost (Fig. 3j). Among the three receptors of RSPO2 leucine-rich repeat-containing G protein-coupled receptor 4–6 (LGR4–6), we found that only Lgr4 was expressed at high levels in Lin cells (Extended Data Fig. 3j). Depletion of Lgr4 expression induced more adipocyte formation in SVF, whereas knockdown of either Lgr5 or Lgr6 did not alter adipogenesis (Fig. 3m,n). Moreover, SVF adipogenesis was not inhibited by rec.RSPO2 when Lgr4 expression was ablated (Fig. 3m,n).

Rspo2 is an enhancer of the Wnt signaling pathway, which plays a key role in regulation of adipocyte commitment. As single-cell trajectory analysis (Fig. 1c,d) suggests a transition of P1 to committed P2 cells, we next aimed to identify mechanisms that regulate this transition. Enrichr24,25 analysis of differentially expressed genes of eP1 and eP2 cells suggest an enrichment of the Wnt signaling pathway in eP1 cells (Extended Data Fig. 3k,l) and adipogenesis genes in eP2 cells (Extended Data Fig. 3m,n). Notably, Rspo2 receptor Lgr4 was enriched in P1 cells compared to P2 cells (Fig. 3o and Extended Data Fig. 3j), which suggests that P1 and not P2 cells might be the target of RSPO2. In accordance with our hypothesis, when exposed to rec.RSPO2 during adipogenesis (Fig. 3i), eP1, but not eP2 cells, exhibited less adipocyte formation (Fig. 3p–q). Because eP1 cells are at an earlier stage of adipogenesis compared to eP2 cells, we assumed that RSPO2 might affect adipocyte commitment and late-phase adipocyte formation. This was confirmed by the finding that adipogenesis was unaltered when cells were exposed to rec.RSPO2 during differentiation from day 3 to 6 (late phase) (Extended Data Fig. 3r–s).

We found that rec.RSPO2 upregulated Wnt signals by inducing β-catenin levels in a time-dependent manner, independent of cell number changes (Extended Data Fig. 3t–w). Similarly, rec.RSPO2 upregulated Wnt signals in eP1 cells 24 h after treatment (Extended Data Fig. 3x,y) and the effect was blunted after ablation of Lgr4 by siRNA (Extended Data Fig. 3x–z). Collectively, these data demonstrate that RSPO2 inhibits P1 commitment during adipogenesis, possibly by regulation of the Wnt/β-catenin signaling pathway through Lgr4.

Rspo2 inhibits adipogenesis of eP1 cells in vivo

To extend our data to the in vivo situation, we first generated an adeno-associated virus (AAV) system to express RSPO2 under the chicken β-actin promoter (CAG), while pAAV–CAG–GFP was used as infection control. Next, eP1 or eP2 cells from ingWAT were resuspended in Matrigel, which contained either pAAV–CAG–Rspo2 or pAAV–CAG–GFP and was transplanted subcutaneously into mice (Fig. 4a). In addition, rec.RSPO2 was supplemented into the Matrigel to ensure that cells were exposed to RSPO2 during the initial phases of adipogenesis as AAV-mediated expression requires at least 5 d26. Mice were exposed to a high-fat diet (HFD) for 4 weeks to induce adipocyte formation after transplantation. pAAV–CAG–Rspo2 significantly increased Rspo2 messenger RNA levels in Matrigel plugs (Fig. 4b) and reduced adipocyte formation of eP1 cells (Fig. 4c,d and Extended Data Fig. 4a). In accordance with the in vitro data, RSPO2 did not inhibit adipogenesis of eP2 cells in mice (Fig. 4e,f and Extended Data Fig. 4b).

Fig. 4: Rspo2 inhibits adipogenesis of eP1 cells in vivo.

af, Experimental scheme (a) for cell transplantation in Matrigel. Rspo2 expression in eP1 Matrigel plugs and in eP2 Matrigel plugs (b). Quantification of adipocytes and cell number in eP1 Matrigel plugs (c) and eP2 Matrigel plugs (e). Representative hematoxylin and eosin (H&E) staining of eP1 Matrigel plugs (d) and eP2 Matrigel plugs (f). Data show mean ± s.e.m., n = 3 biological replicates (b), n = 5 biological replicates (c,e). Data analysis was performed using a two-tailed Student’s t-test. Scale bar, 100 μm. gk, Experimental scheme for overexpression of RSPO2 in AdipoCre-NucRed mice fed with HFD or chow diet. Western blot images (h) and quantification (i) of RSPO2 protein in liver and ingWAT; HSP90 bands were used as loading control. Quantification of adipocyte numbers in ingWAT (j) and visWAT (k) of mice shown in g. Data are shown as mean ± s.d., n = 6 mice. Data analysis was performed by two-tailed Student’s t-test (i) and one-way ANOVA (j,k). In j, Total cell number, F(3,20) = 14.4, P < 0.0001; adipocyte, F(3,20) = 15.50, P < 0.0001; non-adipocyte, F(3,20) = 14.1, P < 0.0001. In k, total cell number, F(3,20) = 14.4, P < 0.0001; adipocyte, F(3,20) = 15.50, P < 0.0001; non-adipocyte, F(3,20) = 14.1, P < 0.0001. lo, Experimental scheme (l) for overexpression of RSPO2 in ingWAT by injection of AAV into ingWAT of AdipoCre-NucRed mice. Western blot images (m) and quantification (n) of RSPO2 protein in ingWAT of mice shown in l. HSP90 bands were used as loading control. Quantification of cell numbers by quantitative PCR in ingWAT (o). Data shows mean ± s.d., n = 5–6 mice. Data were analyzed using a two-tailed Student’s t-test.

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To investigate adipocyte formation within ingWAT during obesity, 6-week-old AdipoCre-NucRed mice received either pAAV–CAG–GFP or pAAV–CAG–Rspo2 by tail-vein injection and 2 weeks after infection, mice were switched to HFD or continued on a chow diet for another 10 weeks (Fig. 4g). By using AdipoqCre-NucRed transgenic mice we quantified adipocyte numbers using qPCR27 (Extended Data Fig. 4c,d). At 12 weeks after AAV infection, higher RSPO2 level were detected in liver and ingWAT (Fig. 4h,i). At 10 weeks after HFD, adipocyte numbers significantly increased in ingWAT (Fig. 4j; HFD CAG–GFP group versus chow CAG–GFP group). Meanwhile, a reduced number of adipocytes was detected in RSPO2 overexpression mice (Fig. 4j; HFD CAG–GFP group versus HFD CAG–RSPO2 group). Higher levels of RSPO2 in chow-diet-fed mice did not alter adipocyte numbers in ingWAT. Moreover, in visWAT, reduced adipocyte numbers were detected in RSPO2-overexpressing mice under both HFD and chow diet conditions (Fig. 4k).

Tail-vein-mediated delivery of AAVs led to RSPO2 overexpression in ingWAT, but also in the liver, circulation and possibly other organs. Thus, to achieve RSPO2 overexpression within ingWAT, AAVs were injected directly into ingWAT (Fig. 4l), which led to a twofold increase in RSPO2 protein levels in ingWAT (Fig. 4m,n) with a minimal increase in circulating RSPO2 (Extended Data Fig. 4e). RSPO2 expression in the liver was not affected (Extended Data Fig. 4f). As a result of RSPO2 overexpression, HFD-induced adipocyte formation was decreased within ingWAT (Fig.4o).

To evaluate whether Rspo2 increased adipocyte apoptosis, which in turn might reduce adipocyte numbers, staining for the apoptosis marker cleaved caspase-3 (ref. 28) was performed in ingWAT. We did not observe any significant difference of adipocyte apoptosis between HFD CAG–GFP mice and HFD CAG–Rspo2 mice (Extended Data Fig. 4g). Collectively, these data suggest that Rspo2 inhibits adipocyte formation in HFD-induced obesity.

Rspo2 inhibits transition of P1 cells to P2 cells in vivo

Single-cell trajectory analysis (Fig. 1c,d) as well as previous work4,9, suggests that P1 cells can transition to P2 cells. To establish a model to study the transition of P1 to P2 cells in vivo, we isolated tdTomato+ eP1 cells from ROSAmT/mG mice and transplanted them into ingWAT of wild-type mice (Fig. 5a). Ten days after transplantation, flow cytometry analysis demonstrated that approximately 23% of implanted eP1 cells had transitioned into eP2 cells (Fig. 5b). A careful evaluation of VAP1+ P2 cells, derived from eP1 cells, showed that they lost expression of P1 markers (CD55, Dpp4, Pi16 and Pcsk6) (Fig. 5c) and acquired expression of P2 markers (Vap1, Icam1, Col4a1 and Sparcl1) (Fig. 5d) as well as committed pre-adipocytes markers such as Pparg and Cebpa (Fig. 5e). However, neither VAP1+ nor VAP1 cells derived from eP1 cells, expressed P3 markers (Fig. 5f). These experiments validate our model system as a tool to study the P1 to P2 transition in vivo.

Fig. 5: Rspo2 inhibits transition of eP1 cells to eP2 cells.

af, Experimental scheme (a) for transplantation of tdTomato+ eP1 cells into inguinal adipose tissue of wild-type (WT) mice. FACS analysis (b) of VAP1 and CD142 expression in tdTomato+ eP1 cells 10 d after transplantation. Expression of P1 marker genes (c) (Cd55, Dpp4, Pi16 and Psck6), P2 marker genes (d) (Vap1, Icam1, Col4a1 and Sparcl1), Pparg and Cebpa (e) and P3 marker genes (f) (Cd142, Gdf10, Clec11a and Igfbp3) in eP1 cells (from donor mice), eP2 cells (from donor mice), eP3 cells (from donor mice), VAP1+ cells (derived from implanted eP1 cells) and VAP1 cells (derived from implanted eP1 cells). Data are shown as mean ± s.e.m., n = 4 biological replicates. gm, Experimental scheme (g) for injection of AAVs into ingWAT for overexpression of RSPO2. Western blot images (h) and quantification (i) of RSPO2 protein and Rspo2 mRNA (j) in ingWAT. FACS analysis of eP1/SVF (k), eP2/SVF (l), CD55+VAP1+ (m) in ingWAT. Data are shown as mean ± s.e.m., n = 6 mice (h,i), n = 5–6 mice (j), n = 5 mice (km). Data were analyzed using two-tailed Student’s t-test. n,o, Experimental scheme (n) for transplantation of tdTomato+ eP1 cells into RSPO2 overexpression mice. FACS analysis of (VAP1+:tdTomato+) cells in tdTomato+ eP1 cells (o). Data are shown as mean ± s.e.m., n = 5 biological replicates. Data were analyzed using a two-tailed paired Student’s t-test.

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When pAAV–CAG–Rspo2 was injected into one depot of ingWAT in mice, while the other depot received pAAV–CAG–GFP as a control (Fig. 5g), RSPO2 protein levels were twofold higher (Fig. 5h,i), whereas mRNA levels were around 100-fold higher than pAAV–CAG–GFP-injected depots (Fig. 5j) 6 weeks after infection. FACS analysis revealed higher numbers of eP1 cells (Fig. 5k) and lower numbers of eP2 cells in the RSPO2-overexpressing ingWAT depot (Fig. 5l), even though CD55+VAP1+ cell numbers did not differ (Fig. 5m) and the eP1/eP2 ratio suggests that Rspo2 inhibited eP1 to eP2 conversion. As eP1 or SVF cell numbers did not change when treated with rec.RSPO2 in vitro (Extended Data Fig. 3i,s) we hypothesized that the increase of eP1 cells might not be due to elevated proliferation. Next, we transplanted eP1 cells from ROSAmT/mG mice into the ingWAT in which RSPO2 expression was modulated by AAVs (Fig. 5n). Ten days after cell transplantation, flow cytometry analysis of transplanted tdTomato+ cells showed that approximately 45% of eP1 cells acquired P2 marker gene (Vap1) expression, demonstrating that they had transitioned to P2 cells, whereas only 30% of eP1 cells acquired P2 marker gene expression in RSPO2-overexpressing ingWAT (Fig. 5o). Taken together, cellular crosstalk of different subpopulations within the adipose tissue SVF fine-tunes adipocyte formation by regulating maturation of early progenitor cells to committed preadipocytes through paracrine RSPO2-mediated changes in the Wnt signaling pathway.

Rspo2 leads to unhealthy adipose tissue expansion and insulin resistance in vivo

As Rspo2 inhibits adipocyte formation in vivo, we next investigated, whether Rspo2 influences adipose tissue expansion during obesity. Therefore, pAAV–CAG–Rspo2 was injected into 8-week-old diet-induced obese mice (Extended Data Fig. 5a), which led to a fivefold increase in RSPO2 protein levels in the liver (Fig. 6a,b) and plasma (Fig. 6c) and a twofold increase in ingWAT (Fig. 6d) and visWAT (Fig. 6e). We observed decreased weight gain in pAAV–CAG–Rspo2-infected mice (Fig. 6f), accompanied by reduced fat mass (Fig. 6g) both in ingWAT and visWAT (Fig. 6h and Extended Data Fig. 5b) independent of food intake (Extended Data Fig. 5c) or energy expenditure (Extended Data Fig. 5d). In addition, RSPO2 overexpression not only reduced adipocyte formation (Fig. 4j) but also led to adipocyte hypertrophy (Fig. 6i–k). Notably, even though RSPO2 overexpression reduced weight gain, higher levels of RSPO2 exhibited a worsened insulin sensitivity during an insulin tolerance test (ITT) (Fig. 6l,m), without affecting fasting blood glucose (Fig. 6n) or hepatic glucose secretion (Extended Data Fig. 5g,h). Fasting triglyceride (TG) levels were unaltered between the two groups (Fig. 6o), whereas less TG accumulated in the livers of RSPO2-overexpression mice (Extended Data Fig. 5e,f) independent of any changes in hepatic TG secretion (Extended Data Fig. 5i,j). These data suggest that adipocyte hypertrophy due to increased RSPO2 levels might be one factor contributing to the worsened metabolic phenotype.

Fig. 6: Circulating RSPO2 leads to unhealthy expansion of adipose tissue and insulin resistance in vivo.

ao, RSPO2 overexpression in mice by tail-vein delivery of pAAV–CAG–Rspo2. Representative immunoblots (a) and quantification of RSPO2 and HSP90 in liver (b), circulation (c), ingWAT (d) and visWAT (e) in RSPO2-overexpression mice. Body weight curve (f), lean mass and fat mass (g) and ingWAT and visWAT tissue weight (h) of AAV-infected mice. Representative H&E staining images (i), average of adipocytes size (μm2) and adipocyte size frequency distribution of ingWAT. Blood glucose normalized to initial blood glucose after insulin injection (l) in ITT and area under the curve (AUC) was quantified as shown in m. Fasting blood glucose (n) and triglycerides (o) in AAV-injected mice. Data are shown as mean ± s.e.m., n = 6 mice. Data were analyzed using a two-tailed Student’s t-test. Scale bar, 100 μm. pr, RSPO2 overexpression by injection into ingWAT. Adipocyte size frequency distribution (p) and representative H&E staining of ingWAT. Data are shown as mean ± s.e.m. Glucose levels in blood in ITT and glucose was normalized to time point 0 (r). Data are shown as mean ± s.d. Comparison of AUC (r, right) in ITT. Data are shown as mean ± s.e.m., n = 5 mice (CAG–GFP), n = 6 mice (CAG–Rspo2). Data analysis was performed using a two-tailed Student’s t-test. s, Circulating RSPO2 levels in insulin-sensitive and insulin-resistant individuals. Data are shown as mean ± s.d., n = 11 (male, insulin sensitive), n = 10 (male, insulin resistant), n = 18 (female, insulin sensitive), n = 21 (male, insulin resistant). Data analysis was performed using a two-tailed Student’s t-test. tv, Spearman correlation coefficient analysis of circulating RSPO2 and glucose infusion rate (t), visceral fat area (u) and max adipocyte volume (v). P values are corrected by two-stage step-up method of Benjamini, Krieger and Yekutieli with an FDR = 0.05.

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Therefore, we next investigated whether RSPO2 overexpression might impair insulin sensitivity in mice whose ingWAT was targeted by pAAV–CAG–Rspo2 to increase intra-tissue RSPO2 levels (Extended Data Fig. 5k). RSPO2 overexpression in ingWAT (Extended Data Fig. 5l) did not affect body weight (Extended Data Fig. 5n) but slightly decreased ingWAT tissue weight (Extended Data Fig. 5o) without altering energy expenditure (Extended Data Fig. 5p). Furthermore, an increased number of large adipocytes was observed in ingWAT in RSPO2 overexpressing mice after 7 weeks of HFD feeding (Fig. 6p–q). However, the observed hypertrophy of the ingWAT did not impair insulin sensitivity (Fig. 6r). Taken together, we show that Rspo2 might affect systemic insulin sensitivity, possibly in part by regulation of de novo adipocyte formation and adipose tissue expansion.

Serum RSPO2 correlates with insulin resistance in individuals with obesity

Given the fact that higher circulating RSPO2 in obese mice led to insulin resistance, we queried this association using serum from obese metabolically healthy and unhealthy individuals. Sixty patients (body mass index (BMI) = 45.6 ± 5.6 kg m2) were divided into an insulin-sensitive group (HOMA-IR = 0.9 ± 0.4) and an insulin-resistant group (HOMA-IR = 3.8 ± 0.9). In men, RSPO2 levels were significantly higher in the insulin-resistant group and we observed the same trend in women (Fig. 6s). Similar to the mouse study, circulating RSPO2 levels exhibited an inverse correlation with the glucose infusion rate (Fig. 6t) in men but not women. In line with the observation from mice, we noted that circulating RSPO2 levels correlated with the visceral fat area (Fig. 6u) and maximal adipocyte volume (Fig. 6v) in men but not women.

Single-nucleus sequencing revealed Rspo2 inhibit adipocyte formation in vivo

To comprehensively evaluate the effects of Rspo2 on different APC populations we performed 10X snRNA-seq on nuclei isolated from ingWAT of pAAV–CAG–GFP- and pAAV–CAG–Rspo2-infected mice. Unsupervised clustering identified seven clusters of cells (Fig. 7a), which were annotated on the basis of known cell marker genes (Fig. 7b). Among all clusters, adipocyte markers (Adipoq, Lep, Plin1, Cidec and Dgat2) were found in the adipocyte clusters (Fig. 7b and Extended Data Fig. 6c), while pre-adipocyte markers (Ly6a and Pdgfra) were found in the APC clusters (Fig. 7b). 10X snRNA-seq analysis revealed reduced adipocyte numbers (Fig. 7c) in pAAV–CAG–Rspo2-infected ingWAT, which underscores our previous findings and suggests that RSPO2 can inhibit adipocyte formation. Besides reducing adipocyte numbers, RSPO2 facilitated macrophage recruitment into ingWAT (Fig. 7a,c), which might contribute to the observed insulin resistance. RSPO2 overexpression also affected many of the identified clusters with regards to their gene expression profile (Fig. 7d and Extended Data Fig. 6e–g).

Fig. 7: snRNA-seq reveals Rspo2 reducing adipocytes number in vivo.

a, Integrated analysis of snRNA-seq, including 14,303 nuclei from ingWAT in mice fed on HFD with chronic expression of GFP or RSPO2 by AAV, yielding 2,218 genes (median). Unsupervised clustering shown as a UMAP plot, seven populations were identified, including adipocytes (adipo) (red), pre-adipocytes (PreA) (blue), macrophages (macro) (green) and natural killer (NK) cells (orange). b, Dot plots for representative markers of each cluster. Expression level (indicated by red color) refers to the log normalized ratio of gene expression reads, normalized to the sum of all reads within each nucleus. Percent expressed refers to the ratio of cells within each cluster that express the genes listed in x axis. c, Cluster compositions in CAG–GFP (n = 7,190 nuclei) and CAG–Rspo2 (n = 7,143 nuclei) conditions. d, Violin plots for Acss2, Nkain2, Sntg1, S100a6, Mrc1 and Gpx1, which are differentially expressed between CAG–GFP and CAG–Rspo2 conditions. e, Subclustering analysis of preadipocyte populations. Unsupervised subclustering of 6,411 preadipocyte nuclei from ingWAT, yielding 2,577 (median) genes. Five subpopulations of preadipocytes (PA-1–PA-5) were identified. f, Feature plots for Dpp4, Pparg and Fmo2, shown as separated plots by conditions. g,h, Pre-adipocyte cluster compositions in CAG–GFP (n = 3,539 nuclei) and CAG–Rspo2 (n = 2,872 nuclei) conditions.

Next, we clustered pre-adipocyte nuclei into five subpopulations named PA-1–PA-5 (Fig. 7e). PA-1 represent noncommitted APCs (P1) with expression of P1 marker genes (Fig. 7f and Extended Data Fig. 6h), Sema3e, Pi16 (Extended Data Fig. 6h) and low Pparg expression (Fig. 7f). PA-2 represents the P2-2 population. Even though P2-2 marker Vap1 or Icam1 were barely detectable in PA-2 nuclei (data not shown), the committed preadipocyte marker Pparg was highly expressed in PA-2 nuclei (Fig. 7f). Similar to P2-2, we observed that some PA-2 nuclei expressed P3 markers such as Fmo2 and Cd142 (Fig. 7f and Extended Data Fig. 6h). The PA-3 population represents the P3 population based on expression of marker genes Cd142, Fmo2 and Meox2 (Fig. 7f and Extended Data Fig. 6h). The PA-4 population defines a cluster of proliferating cells with high levels of cell-cycle genes such as Top2a and Mki67 (Extended Data Fig. 6k). PA-5 represents another cluster of committed pre-adipocytes, which expresses Pparg (Fig. 7f and Extended Data Fig. 6j). Overexpression of RSPO2 led to more active Wnt signaling indicated by higher expression of Cttnb1 (Extended Data Fig. 6i) and led to a higher proportion of adipocyte progenitors (PA-1) and reduced committed pre-adipocytes PA-5 (Fig. 7g,h). These data are in line with our cell transplant experiments, which suggest that RSPO2 inhibits P1 transition into committed preadipocytes (Fig. 5n,o). Collectively, our data suggest that Rspo2 inhibits adipocyte formation during obesity, which leads to adipocyte hypertrophy and macrophage infiltration into adipose tissue. We propose that a combination of these factors contribute to the development of insulin resistance in RSPO2-overexpressing mice.

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