Single-cell Ca²⁺ parameter inference reveals how transcriptional states inform dynamic cell fates

Abstract

Inferring the parameters of dynamic models is a cornerstone of systems biology. Large single-cell transcriptomic datasets have opened up many possibilities for new analyses, but their potential to inform parameter inference of molecular or cellular dynamics has not yet been realized. Here, making use of coupled data: single-cell gene expression and dynamic molecular measurements, we develop new methods for parameter inference. We construct cell chains in which the posterior distribution of a cell is used to inform the prior of the subsequent cell in the chain. In application to the Ca²⁺ signaling pathway, we show that cell predecessor-informed priors accelerate inference of the Ca²⁺ model parameters in single cells. Though use of cell chains informed by single-cell gene expression does not improve sampling relative to random chain assignment, we show that the posteriors produced via gene expression-informed cell chains capture distinct properties of the dynamic Ca²⁺ response. By clustering posterior parameters we can identify markers genes that correspond with variable Ca²⁺ responses. Additionally, through analysis of the posterior distributions of hundreds of single cells, we discover that divergent co-variation of parameters within and between cells, highlighting the complex and competing sources of cell heterogeneity. Through the analysis of large populations of posterior distributions we are able to quantify the relationships between single-cell transcriptional states and dynamic cellular responses, paving the way for more detailed mappings between gene expression states and dynamic cell fates.