Optimized Analytical Workflow for Single-Nucleus Transcriptomics in Main Metabolic Tissues
Authors/Creators
- 1. Shanghai Key Laboratory of Metabolic Remodelling and Health, Institute of Metabolism and Integrative Biology, Centre for Evolutionary Biology, Fudan University, Shanghai, 200438, China
Description
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 four main metabolic tissues white adipose tissue, hypothalamus, muscle and liver. We emphasized the importance of key steps including ambient RNA removal, doublet identification, normalization 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.
Files
cellranger_output.zip
Additional details
References
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