Journal: Nature Genetics

Article Title: Human subcutaneous and visceral adipocyte atlases uncover classical and nonclassical adipocytes and depot-specific patterns

doi: 10.1038/s41588-024-02048-3

Figure Lengend Snippet: a , Clinical characteristics of the 15 samples used to build the atlases. Samples were collected from female and male donors with varying age and BMIs, as described in the pie charts. Characteristics were measured directly or extracted from donors’ medical records. b , The expression of the adipocyte marker adiponectin ( ADIPOQ ) in Uniform Manifold Approximation and Projection (UMAP) representations of hSAT and hVAT snRNA-seq atlases containing 37,879 and 83,731 nuclei, respectively. In both depots, adipocytes constitute a well-defined cluster. c , The expression of cell-specific markers in different clusters of each depot. Adipocytes were distinguished by multiple markers (marked in orange). d , The fraction of adipocytes per depot, stratified by sex, age and BMI in the snRNA-seq in-house data (hSAT, hVAT) and on estimating adipocyte proportions using deconvolution (Decon.) analysis of 73 paired bulk RNA-seq profiles (‘Decon’; hSAT:hVAT). Adipocytes were significantly more prevalent in hSAT versus hVAT in both datasets (two-sided Mann–Whitney U -test P = 0.0013 and P = 1.205 × 10 −13 , respectively), and tended to be more prevalent in females versus males according to the snRNA-seq data (two-sided Mann–Whitney U -test P = 0.044 in hVAT only). Other differences were not statistically significant. The boxplot central band indicates the median, the box limits the 25th to 75th percentiles and the whiskers 1.5× the interquartile range.

Article Snippet: Adipocytes were distinguished by multiple markers (marked in orange). d , The fraction of adipocytes per depot, stratified by sex, age and BMI in the snRNA-seq in-house data (hSAT, hVAT) and on estimating adipocyte proportions using deconvolution (Decon.) analysis of 73 paired bulk RNA-seq profiles (‘Decon’; hSAT:hVAT).

Techniques: Expressing, Marker, RNA Sequencing, MANN-WHITNEY

Journal: Nature Genetics

Article Title: Human subcutaneous and visceral adipocyte atlases uncover classical and nonclassical adipocytes and depot-specific patterns

doi: 10.1038/s41588-024-02048-3

Figure Lengend Snippet: Percentages of classical and non-classical adipocytes in each depot separately were estimated in an independent set of samples analyzed by bulk RNA-seq (n = 73), using sNucConv deconvolution algorithm (a validated deconvolution tool for human adipose tissue based on paired samples sequenced by both bulk and snRNA-seq – see Methods for more detail). The percentage of classical adipocytes in hSAT was negatively correlated with waist-to-hip ratio (WHR) and HOMA-IR (Spearman r = -0.4 and -0.55, adjusted p = 0.17 and 0.06, respectively). The percentage of non-classical adipocytes in hSAT was positively correlated with WHR and HOMA-IR (Spearman r = 0.34 and 0.6, adjusted p = 0.17 and 0.05, respectively). The percentage of classical adipocytes in hVAT was negatively correlated with decease in triglycerides (TG) post bariatric surgery (BS) (Spearman r = 0.49, adjusted p = 0.1). Shaded grey areas mark a confidence interval of 0.95. P-values were adjusted using Benjamini-Hochberg procedure.

Article Snippet: Adipocytes were distinguished by multiple markers (marked in orange). d , The fraction of adipocytes per depot, stratified by sex, age and BMI in the snRNA-seq in-house data (hSAT, hVAT) and on estimating adipocyte proportions using deconvolution (Decon.) analysis of 73 paired bulk RNA-seq profiles (‘Decon’; hSAT:hVAT).

Techniques: RNA Sequencing

Journal: The Journal of Biological Chemistry

Article Title: Multi-modal mechanisms of the metastasis suppressor, NDRG1: Inhibition of WNT/β-catenin signaling by stabilization of protein kinase Cα

doi: 10.1016/j.jbc.2024.107417

Figure Lengend Snippet: Triple co-localization of β-catenin, NDRG1, and PKCα demonstrates the formation of a possible metabolon, decreasing β-catenin levels and nuclear localization. A – H , NDRG1 overexpression in PANC-1 cells significantly increases the co-localization between β-catenin, NDRG1, and PKCα. A and B , VC and NDRG1 overexpressing PANC-1 cells were incubated for 24 h/37 °C in the presence and absence of WNT3a (100 ng/ml). The cells were then examined for β-catenin ( green ), NDRG1 ( red ), and PKCα ( blue ) expression and triple co-localization ( white ) using confocal immunofluorescence microscopy. Studies were performed using a 100× objective at the same acquisition setting with Olympus Fluoview FV3000 software. Images were digitally magnified for better demonstration of the possible co-localization. The scale bar = 3 μm for all images except for the inset images, where the scale bar = 9 μm. C and D , deconvolution analysis was then implemented on the images in ( A and B ) using Olympus CellSens imaging software. White arrows demonstrate triple co-localization and possible formation of the metabolon. The scale bar = 3 μm for all deconvolution images except for the inset images, where the scale bar = 1.8 μm. In all studies, the images are representative of three experiments, and the quantitative analysis of the pixel intensities is shown for ( E ) β-catenin; ( F ) NDRG1; ( G ) PKCα; and ( H ) Co-localization between β-catenin, NDRG1, and PKCα. Quantitative analyses were performed using ImageJ software and were presented as the mean ± SD ( n = 3). Analysis of pixel intensity and co-localization were performed using 24 cells. Statistical significance is denoted as ∗∗ p < 0.01 and ∗∗∗ p < 0.001 comparing NDRG1 overexpressing PANC-1 cells in the presence and absence of WNT3a to VC cells in the absence of WNT3a; or ### p < 0.001 comparing NDRG1 overexpressing PANC-1 cells to VC cells in the presence of WNT3a.

Article Snippet: The images of visualized cells were then examined using Olympus Fluoview software, and in some studies, images were processed for deconvolution analysis with CellSens software (Olympus) to improve contrast and image resolution.

Techniques: Over Expression, Incubation, Expressing, Immunofluorescence, Microscopy, Software, Imaging