Optimized upstream analytical workflow for single-nucleus transcriptomics in main metabolic tissues

Abstract

Single-nucleus RNA sequencing (snRNA-seq) has emerged as a powerful approach for studying cellular heterogeneity in metabolic tissues. However, snRNA-seq analysis remains challenging due to low gene expression and data complexity. Here, we introduce an optimized analytical workflow for snRNA-seq data from 67 samples across white adipose tissue, muscle, liver and the hypothalamus. We emphasized the importance of key steps including ambient RNA removal, doublet identification and data integration to ensure accurate downstream analysis. This workflow offers a valuable resource for researchers in metabolism, facilitating deeper insights into cellular diversity and metabolic function through rigorous snRNA-seq analysis.