sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human adipose tissues

Gil Sorek; Yulia Haim; Vered Chalifa-Caspi; Or Lazarescu; Maya Ziv-Agam; Tobias Hagemann; Pamela Arielle Nono Nankam; Matthias Blüher; Idit F Liberty; Oleg Dukhno; Ivan Kukeev; Esti Yeger-Lotem; Assaf Rudich; Liron Levin

doi:10.1016/j.isci.2024.110368

sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human adipose tissues

iScience. 2024 Jun 24;27(7):110368. doi: 10.1016/j.isci.2024.110368. eCollection 2024 Jul 19.

Affiliations

¹ Bioinformatics Core Facility, llse Katz Institute for Nanoscale Science and Technology, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
² Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
³ Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany.
⁴ Soroka University Medical Center, and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

Abstract

Deconvolution algorithms mostly rely on single-cell RNA-sequencing (scRNA-seq) data applied onto bulk RNA-sequencing (bulk RNA-seq) to estimate tissues' cell-type composition, with performance accuracy validated on deposited databases. Adipose tissues' cellular composition is highly variable, and adipocytes can only be captured by single-nucleus RNA-sequencing (snRNA-seq). Here we report the development of sNucConv, a Scaden deep-learning-based deconvolution tool, trained using 5 hSAT and 7 hVAT snRNA-seq-based data corrected by (i) snRNA-seq/bulk RNA-seq highly correlated genes and (ii) individual cell-type regression models. Applying sNucConv on our bulk RNA-seq data resulted in cell-type proportion estimation of 15 and 13 cell types, with accuracy of R = 0.93 (range: 0.76-0.97) and R = 0.95 (range: 0.92-0.98) for hVAT and hSAT, respectively. This performance level was further validated on an independent set of samples (5 hSAT; 5 hVAT). The resulting model was depot specific, reflecting depot differences in gene expression patterns. Jointly, sNucConv provides proof-of-concept for producing validated deconvolution models for tissues un-amenable to scRNA-seq.

Keywords: Biocomputational method; Classification of bioinformatical subject; Integrative aspects of cell biology; Machine learning; Transcriptomics.