Leveraging Single-Cell RNA-Seq to Generate Robust Microglia Aging Clocks.

Area of Science:

• Neuroscience
• Immunology
• Computational Biology

Background:

• Traditional biological aging clocks rely on bulk tissue analysis, limiting cell-specific insights.
• Microglia, the brain's immune cells, undergo functional changes with aging and disease.
• Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptional data for aging studies.

Purpose of the Study:

• To develop and compare computational methods for creating robust microglia aging clocks from scRNA-seq data.
• To assess the accuracy, interpretability, and efficiency of different featurization approaches.
• To demonstrate the applicability of single-cell-derived microglia clocks to bulk RNA sequencing data.

Main Methods:

• Leveraged existing scRNA-seq datasets profiling microglia during aging.
• Developed and compared various transcriptome-wide summarization and featurization approaches.
• Validated microglia aging clocks on bulk RNA-seq data, incorporating environmental factors.

Main Results:

• Unsupervised, frequency-based featurization methods achieved a balance of accuracy, interpretability, and computational efficiency.
• Successfully extrapolated single-cell-derived microglia clocks to bulk RNA-seq data.
• Demonstrated the utility of microglia aging clocks in understanding brain aging determinants.

Conclusions:

• Single-cell-derived microglia aging clocks offer a powerful tool for studying brain aging.
• These clocks can provide insights into factors influencing brain health and disease.
• The developed methods facilitate the exploration of interventions to modulate aging trajectories.