Person:

Shah, Kavya

Heterochronic parabiosis, a surgical process connecting the circulatory systems of young and old mice, is used to study the regulation of tissue aging and regeneration via protein factors found in blood. Several factors have been shown to improve central nervous system function and ameliorate the effects of aging in mice by increasing stem cell regeneration, but it is less well known which genes are differentially expressed in old mice to induce this “aging reversal” phenotype. We performed an in silico analysis of single cell RNAsequencing (scRNA-seq) data from parabiosed mice to identify transcription factors (TFs) whose expression is significantly dysregulated between old mice and old-young parabiosed mice, which represent the reversed aging state. We employed single-cell regulatory network inference and clustering (SCENIC), a computational framework which identified gene regulatory networks (GRNs) from scRNA-seq data. Regulatory networks from old, old-old parabiosed (as a control), and old-young parabiosed mice were identified from scRNA-seq gene expression matrices spanning 31 cell types. Network activity was then scored by SCENIC as a function of gene expression levels in the networks. These scores formed the basis of comparison of transcription factors between old mice and parabiosed mice. The TFs identified thus far were verified against a set of TFs thought to be dysregulated with parabiosis via differential gene expression analysis. Preliminary results indicated that GRN analysis provides a robust platform for identifying TFs whose expression is significantly altered with parabiosis, and completion of this study will yield a comprehensive understanding of these TFs. In the future, these TFs can serve as therapeutic targets, as compounds can be administered to modulate the expression of these TFs and the genes they regulate in old mice to match the expression profiles identified in old-young parabiosed mice, potentially achieving the aging reversal phenotype.