Microglia Single-Cell RNA-Seq Enables Robust and Applicable Markers of Biological Aging.

Area of Science:

• Neuroscience
• Genomics
• Computational Biology

Background:

• Traditional biological aging clocks rely on bulk tissue analysis, potentially masking cell-specific aging dynamics.
• Microglia, the brain's immune cells, exhibit functional changes with aging and disease.
• Single-cell RNA sequencing (scRNA-seq) offers unprecedented resolution for studying cellular aging processes.

Purpose of the Study:

• To develop and compare computational approaches for creating robust, single-cell-based biological aging clocks using microglia transcriptomes.
• To assess the accuracy and applicability of these microglia aging clocks across diverse datasets and genomic modalities.
• To explore the potential of these clocks in understanding environmental impacts on brain aging.

Main Methods:

• Leveraged human and mouse scRNA-seq datasets profiling microglia during aging and development.
• Developed and compared unsupervised, frequency-based computational summarization approaches for transcriptome-wide analysis.
• Validated the accuracy of computationally derived microglia aging markers against chronological age across multiple datasets.
• Extrapolated single-cell microglia clock models to bulk RNA-seq data, incorporating environmental factors like early life stress.

Main Results:

• Unsupervised, frequency-based summarization methods provide a balance of accuracy, interpretability, and computational efficiency.
• Computationally derived microglia markers accurately predict chronological age across three distinct scRNA-seq datasets.
• Single-cell microglia aging clocks demonstrate applicability to bulk RNA-seq data, including analysis of environmental inputs.
• Microglia exhibit characteristic age-related gene expression changes that can be summarized into robust aging markers.

Conclusions:

• Single-cell transcriptomic analysis of microglia enables the development of accurate and broadly applicable biological aging clocks.
• These microglia aging clocks offer valuable insights into the determinants of brain aging and the influence of environmental factors.
• The developed computational models hold potential for advancing interventions to modulate brain health and disease trajectories.