Abstract
Understanding adipose tissue cellular heterogeneity and homeostasis is essential to comprehend the cell type dynamics in metabolic diseases. Cellular subpopulations in the adipose tissue have been related to disease development, but efforts towards characterizing the adipose tissue cell type composition are limited. Here, we identify the cell type composition of the adipose tissue by using gene expression deconvolution of large amounts of publicly available transcriptomics level data. The proposed approach allows to present a comprehensive study of adipose tissue cell type composition, determining the relative amounts of 21 different cell types in 1282 adipose tissue samples detailing differences across four adipose tissue depots, between genders, across ranges of BMI and in different stages of type-2 diabetes. We compare our results to previous marker-based studies by conducting a literature review of adipose tissue cell type composition and propose candidate cellular markers to distinguish different cell types within the adipose tissue. This analysis reveals gender-specific differences in CD4+ and CD8+ T cell subsets; identifies adipose tissue as rich source of multipotent stem/stromal cells; and highlights a strongly increased immune cell content in epicardial and pericardial adipose tissue compared to subcutaneous and omental depots. Overall, this systematic analysis provides comprehensive insights into adipose tissue cell-type heterogeneity in health and disease.
Highlights
Progression in complex diseases as well as inter-individual differences in disease etiology, paving the way for improved subtyping of patients for targeted therapies
We perform deconvolution analysis with a set of 779 adipose tissue samples (Supplementary Data S2) from four different adipose tissue depots (SAT, OAT, pericardial adipose tissue (PAT), and EAT) and check how well the results agree with the body of literature
This second analysis shows that the adipose tissue samples are predicted to have on average 14.5% adipose stem/ stromal cells (ASCs), while the sum of the three related cell types is below 1% on average, revealing our ability to correctly distinguish between these cell types (Supplementary Fig. S4)
Summary
Progression in complex diseases (such as heart disease or diabetes) as well as inter-individual differences in disease etiology, paving the way for improved subtyping of patients for targeted therapies. The markers used to define a subpopulation of cells within the adipose tissue can differ greatly across studies, impeding reproducibility and leading to discrepancies across studies. Our proposed TissueDecoder framework builds upon a recently published gene expression deconvolution algorithm[5], and facilitates reuse of published gene expression data for determining adipose tissue cell type composition across various depots and phenotypic traits (Fig. 1). In this way, we are able to determine the relative fraction of 21 different cell types in 1282 adipose tissue samples, presenting the most comprehensive study of adipose tissue cell type composition to date. We demonstrate the applicability and reproducibility of our approach in RNA-Seq level data by using independent studies
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