Protocol for automated graph-based clustering of single-cell RNA-seq data with application in mouse intestinal stem...

Area of Science:

• Gastroenterology
• Molecular Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity in the intestine.
• Existing protocols for isolating intestinal epithelial cells can be challenging and may impact downstream analysis.
• Efficient and accurate cell population identification is needed for large scRNA-seq datasets.

Purpose of the Study:

• To present a robust protocol for isolating highly purified mouse intestinal crypt epithelial cells.
• To adapt the protocol for various regions of the mouse intestine, including the jejunum and colon.
• To integrate automated community detection for efficient analysis of scRNA-seq data.

Main Methods:

• Development of a detailed protocol for dissociating mouse intestinal tissue.
• Optimization for isolating crypt epithelial cells from the jejunum, adaptable to other intestinal regions.
• Implementation of the automated community detection of cell populations (ACDC) Python package for clustering scRNA-seq data.

Main Results:

• Successful isolation of highly purified crypt epithelial cells.
• Demonstration of the protocol's adaptability across different intestinal segments.
• Effective identification of cellular populations in an intestinal stem cell dataset using ACDC.
• Generation of publication-ready figures for scRNA-seq analysis.

Conclusions:

• The presented protocol provides a reliable method for obtaining pure intestinal epithelial cells for scRNA-seq.
• Automated community detection (ACDC) offers an efficient approach for analyzing large scRNA-seq datasets.
• This work facilitates deeper insights into intestinal cell biology and stem cell research.