Deciphering the Microglia Transcriptome: Unraveling the Consequences of Early Life Stress and Aging (2024)

Undergraduate: Sneha Jaikumar

Faculty Advisor: Natalie Stanley
Department: Computer Science

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Understanding the intricate relationship between microglia gene expressions and early life stress (ELS) is critical for advancing targeted therapies and personalized medicine aimed at alleviating trauma. Despite the potential impact of such insights, there is a scarcity of literature exploring the interplay between microglia, innate immune cells of the brain, and trauma. This study employed a mouse model to investigate the long-term implications of early life stress utilizing bulk and single-cell RNA sequencing datasets. Motivated by results acquired from analyzing the bulk RNA dataset alone, we adopted a pseudobulk algorithm aggregating representations of gene measurements to train a model on a complementary single-cell dataset of aging in mice. This pseudobulk model was ultimately applied to the original bulk RNA sequencing data to specifically investigate the interplay between aging and early life stress by 1) Predicting phenotypes on samples profiled with bulk RNA sequencing and trained on the pseudobulk model, 2) Correlating variations in genetic expressions between mice exposed to ELS and older mice, and 3) Identifying genetic markers indicative of mice exposed to ELS aging at a faster rate. Key features highlighted from these analyses underscore the role that adverse childhood experiences play in developing age-associated immune diseases such as Alzheimer's Disease, Parkinson’s Disease, and cancer. The findings from this research can contribute to the development of more effective therapeutic interventions and personalized treatment strategies for individuals affected by trauma.