Protocol to analyze changes in hippocampal neural stem cell quiescence from single-cell RNA sequencing data

Area of Science:

• Neuroscience
• Stem Cell Biology
• Computational Biology

Background:

• Identifying quiescent neural stem cells (NSCs) in the mouse hippocampus is challenging due to similarities with astrocytes.
• Distinguishing quiescent NSCs from proliferating progenitors is crucial for understanding neurogenesis and brain repair.
• Single-cell RNA sequencing (scRNA-seq) offers a powerful tool for cellular characterization but requires robust computational methods for specific cell populations.

Purpose of the Study:

• To present a computational protocol for isolating mouse hippocampal neural stem cells from scRNA-seq datasets.
• To provide methods for analyzing the impact of perturbations on the depth of NSC quiescence.
• To overcome existing challenges in differentiating quiescent NSCs from astrocytes and proliferating NSCs from progenitors.

Main Methods:

• Development of a computational protocol utilizing scRNA-seq data.
• Implementation of sequential reclustering for cell population refinement.
• Application of pseudotime trajectory analysis to infer cellular states.
• Statistical testing to detect shifts in quiescence distribution (deep vs. shallow).

Main Results:

• Successful isolation of mouse hippocampal neural stem cells using the computational protocol.
• Demonstration of the protocol's ability to analyze perturbation effects on quiescence depth.
• Validation of methods for distinguishing between quiescent NSCs, astrocytes, proliferating NSCs, and progenitor cells.

Conclusions:

• The presented computational protocol provides an effective means to identify and analyze neural stem cells from scRNA-seq data.
• This method facilitates the study of neural stem cell quiescence dynamics and responses to perturbations.
• The protocol addresses key limitations in distinguishing closely related cell types within the neural stem cell lineage.