sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human subcutaneous and visceral adipose tissues
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.16.549187v1?rss=1

Authors: Sorek, G., Haim, Y., Chalifa-Caspi, V., Lazarescu, O., Ziv, M., Hagemann, T., Nono Nankam, P., Bluher, M., Liberty, I. F., Dukhno, O., Kukeev, I., Yeger-Lotem, E., Rudich, A., Levin, L.

Abstract: Deconvolution algorithms rely on single-cell RNA-sequencing (scRNA-seq) data applied onto bulk RNA-sequencing (bulk RNA-seq) to extract information on the cell-types composition and proportions comprising a certain tissue. Adipose tissues cellular composition exhibits enormous plasticity in response to weight changes and high variance at different anatomical locations (depots). However, adipocytes, the functionally unique cell type of adipose tissue, are not amenable to scRNA-seq, a challenge recently met by applying single-nucleus RNA-sequencing (snRNA-seq). Here we aimed to develop a deconvolution method to estimate the cellular composition of human visceral and subcutaneous adipose tissues (hVAT and hSAT, respectively) using snRNA-seq to assess the true cell-type proportions. To correlate deconvolution-estimated cell-type proportions to true (snRNA-seq -derived) proportions, we analyzed seven hVAT and 5 hSAT samples by both bulk RNA-seq and snRNA-seq. snRNA-seq uncovered 15 distinct cell types in hVAT and 13 in hSAT. Deconvolution tools SCDC, MuSiC, and Scaden exhibited low performance in estimating cell-type proportions (median |R|= 0.12 for estimated vs. true correlations). Notably, estimation accuracy somewhat improved by decreasing the number of cell-types groups, which nevertheless remained low (|R| less than 0.42). We therefore developed sNuConv, a novel method that employs Scaden, a deep-learning tool, trained using snRNA-seq based data corrected by i. snRNA-seq/bulk RNA-seq highly-correlated genes, ii. corrected estimated cell-type proportions based on individual cell-type regression models. Applying sNuConv on our bulk RNA-seq data resulted in cell-type proportion estimation accuracy with median R=0.93 (range:0.76-0.97) for hVAT, and median R=0.95 (range:0.92-0.98) for hSAT. The resulting model was depot-specific, reflecting depot-differences in gene expression patterns. Thus, we present sNuConv, a novel, AI-based, method to deduce the cellular landscape of hVAT and hSAT from bulk RNA-seq data, providing proof-of-concept for producing validated deconvolution algorithms for tissues un-amenable to single-cell RNA sequencing.

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