Optimized Analytical Workflow for Single-Nucleus Transcriptomics in Main Metabolic Tissues

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.单细胞核RNA测序（single-nucleus RNA sequencing, snRNA-seq）现已成为探究代谢组织细胞异质性的高效研究手段。然而，受限于较低的基因表达水平与复杂的数据特征，单细胞核RNA测序分析仍存在诸多挑战。本研究针对源自白色脂肪组织（white adipose tissue）、下丘脑（hypothalamus）、肌肉（muscle）及肝脏（liver）这4类主要代谢组织的67份样本的单细胞核RNA测序数据，提出了一套优化后的分析流程。研究着重强调了环境RNA去除、双细胞（doublet）识别、标准化及数据整合等关键步骤的必要性，以确保后续分析的准确性。该流程可为代谢领域的科研人员提供极具价值的参考资源，助力其通过严谨的单细胞核RNA测序分析，更深入地解析细胞多样性与代谢功能。