Joint spatiotemporal modeling of perturbation effects in single-cell data

Music City Center 

Single-cell data provide a unique opportunity to dissect complex cellular interactions. However, several existing computational methods lack the building blocks to effectively integrate spatial and temporal dimensions to identify key regulatory genes in sparse single-cell data. To address this gap, we developed pSTNet, a novel framework that integrates network and spatio-temporal models to identify differentially expressed genes induced by perturbation effects. The method incorporates cell spatial organizational structure to identify joint and perturbation-specific driver genes while taking account of single-cell data sparsity, tissue misalignment across multiple samples, and time points. We applied pSTNet to a time-course scRNA-seq dataset obtained from wild-type (WT) and Glis3 knockout (KO) mice to investigate the role of Glis3 in beta-cell development in the study of diabetes mechanism. The pSTNet framework jointly models the gene activation process through a spatiotemporal logistic regression and further models non-zero gene expression using a spatiotemporal model with standard parameterized probability distribution conditioned on a network, allowing for the differentiation between true biological signals and technical noise. The framework identifies key regulatory genes and cell states driving normal and deregulated beta-cell development, providing insights into the molecular mechanisms underlying beta-cell differentiation. Notably, the analysis reveals significant spatial and temporal differential expression patterns in genes central to beta-cell development, such as Ins1, Ins2, and Iapp, and identifies diverse regulatory modules associated with KO beta-cell development. These findings highlight the utility of pSTNet in elucidating the complex dynamics of cellular behavior in response to genetic perturbations.

Spatial model

Network Model

Spatial Transcriptomics

Cell-Cell Interaction

Differential Expression 

Section on Statistical Learning and Data Science