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
Affiliations
Gil Sorek
Bioinformatics Core Facility, llse Katz Institute for Nanoscale Science and Technology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Yulia Haim
Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Vered Chalifa-Caspi
Bioinformatics Core Facility, llse Katz Institute for Nanoscale Science and Technology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Or Lazarescu
Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Maya Ziv-Agam
Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Tobias Hagemann
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
Pamela Arielle Nono Nankam
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
Matthias Blüher
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
Idit F. Liberty
Soroka University Medical Center, and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Oleg Dukhno
Soroka University Medical Center, and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Ivan Kukeev
Soroka University Medical Center, and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Esti Yeger-Lotem
Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Assaf Rudich
Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Corresponding author
Liron Levin
Bioinformatics Core Facility, llse Katz Institute for Nanoscale Science and Technology, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Corresponding author
Summary: 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.
