Hide and (Single-Cell RNA)-Seq: Leveraging Gene Expression Profiles to Characterize Ras/Erk Heterogeneity in Epidermal Populations

Abstract

The mammalian epidermis has been a fundamental model system for observing dynamic Erk signaling pulses and testing their contributions to tissue function and repair. However, no established protocols are yet available for monitoring the consequences of Erk dynamics on downstream gene expression in the epidermis. As a result, it is still unknown whether Erk signaling pulses are functionally “read out” into pulses of downstream gene expression. This thesis explores the hypothesis that if stochastic Erk pulses indeed drive gene expression, correlated signatures of Erk-responsive immediate early gene (IEG) expression should be present in publicly available single-cell RNA sequencing (scRNA-seq) datasets from the mammalian epidermis. I present methods for defining clusters of co-expressing IEGs across spatial subpopulations within the epidermis in a quantifiable manner, identifying this subset of IEGs as a future point of research regarding shared biological function and relevance. Our research contributes to the toolbox of methods available for studying single-cell expression profiles and paves the way for the more commonplace use of leveraging scRNA-seq data to generate a clearer understanding of the expression atlas of IEGs within developing and adult tissues.