We applied the optimized workflow to analyze 67 samples from 4 major metabolic tissues in both human and mouse (201,411 nuclei). It showed enhanced cell-type resolution (e.g. adipocytes,) and the identification of biologically relevant pathways. (4/n)

After carefully benchmarking existing methods, we proposed an optimized workflow that includes:
✅ Removal of environmental RNA contamination (CellBender)
✅ Double cell detection (scDblFinder)
✅ Selection of integration methods (scIB framework) (3/n)

Single nucleus transcriptomics (snRNAseq) has become an alternative way to get single cell resolution of these tissues. However, it also brought analytic challenges given the high ambient RNA contamination and doublet rate. (2/n)

Thrilled to share our recent work published on Life Metabolism
@lifemetabolism.bsky.social , lead by my talented PhD student Pengwei Dong with help from Shitong Ding. We propose an optimized upstream workflow for the snRNA-seq analysis in main metabolic tissues.