Single-cell transcriptomic analysis of human colonic macrophages reveals niche-specific subsets.

Macrophages are a heterogeneous population of cells involved in tissue homeostasis, inflammation, and cancer. Although macrophages are densely distributed throughout the human intestine, our understanding of how gut macrophages maintain tissue homeostasis is limited. Here we show that colonic lamina propria macrophages (LpMs) and muscularis macrophages (MMs) consist of monocyte-like cells that differentiate into multiple transcriptionally distinct subsets. LpMs comprise subsets with proinflammatory properties and subsets with high antigen-presenting and phagocytic capacity. The latter are strategically positioned close to the surface epithelium. Most MMs differentiate along two trajectories: one that upregulates genes associated with immune activation and angiogenesis, and one that upregulates genes associated with neuronal homeostasis. Importantly, MMs are located adjacent to neurons and vessels. Cell-cell interaction and gene network analysis indicated that survival, migration, transcriptional reprogramming, and niche-specific localization of LpMs and MMs are controlled by an extensive interaction with tissue-resident cells and a few key transcription factors.

Cellsnake: a user-friendly tool for single-cell RNA sequencing analysis.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. The analysis of scRNA-seq data requires the utilization of numerous computational tools. However, nonexpert users usually experience installation issues, a lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines. RESULTS: We have developed cellsnake, a comprehensive, reproducible, and accessible single-cell data analysis workflow, to overcome these problems. Cellsnake offers advanced features for standard users and facilitates downstream analyses in both R and Python environments. It is also designed for easy integration into existing workflows, allowing for rapid analyses of multiple samples. CONCLUSION: As an open-source tool, cellsnake is accessible through Bioconda, PyPi, Docker, and GitHub, making it a cost-effective and user-friendly option for researchers. By using cellsnake, researchers can streamline the analysis of scRNA-seq data and gain insights into the complex biology of single cells.