Felce, C. | bioRxiv Science

bioRxiv2025

Biophysical constraints on mRNA decay rates shape macroevolutionary divergence in steady-state abundances

Interspecific comparisons of cell-type-specific gene expression levels can provide information about the evolutionary processes that drove divergence between species. From these comparisons, it is now evident that the predominant mode of gene expression evolution has been stabilizing selection, both on the steady-state (mean) protein levels as well as on the mRNA levels with additional lineage-specific shifts resulting from directional selection. However, as all previous work has used bulk RNA measurements, it has been impossible to determine which of the many cellular processes that contribute to mean abundances are highly constrained and which are more evolutionary labile. Assessing this is further complicated by the expectation that components of complex systems will evolve over time independent of changes in the selective regime so long as the net output of a system (i.e., mean expression) remains near the evolutionary optima. This process, known as evolutionary systems drift (ESD), has been frequently invoked as a non-adaptive explanation for changes in cellular phenotypes but has never been quantitatively tested or accounted for in any statistical test for selective constraints. Here, we develop a new paradigm that addresses both of these open problems simultaneously. Using single-cell expression data and biophysical models, we estimate mRNA transcriptional bursting rates, splicing rates, and decay rates across multiple vertebrate species. We then derive new mathematical results that describe how these various biophysical parameters are expected to co-evolve under ESD and then test whether we need additional evolutionary constraints to explain the divergences in these parameters. We find evidence that the biophysical parameters are indeed evolving in a coordinated manner as predicted by ESD and that there are additional strong constraints on transcriptional bursting, likely as a consequence of selection to reduce noise in expression. More broadly, this work opens up a whole new approach for studying the evolutionary dynamics of complex cellular systems.

evolutionary biology↗

bioRxiv2025

Joint Biophysical Modeling of Paired Single-Cell RNA and Protein Measurements

Surface protein measurements can supplement gene expression information from single-cell RNA sequencing to provide a more complete assessment of cell identity and function. Recently developed multiomic assays facilitate such measurements, and can, in principle, be utilized to understand the dynamics of transcription and translation. We develop a framework for biophysical modeling of transcription jointly with translation from single-cell data, along with a suitable technical noise model for sequencing data. We demonstrate its efficacy using simulations, and illustrate how it can be useful in practice with 10x multiomic data. Our proof-of-principle highlights the potential for jointly modeling transcription and translation as data quality and measurement accuracy improves.

biophysics↗

bioRxiv2024

A Biophysical Model for ATAC-seq Data Analysis

The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) can be used to identify open chromatin regions, providing complementary information to RNA-seq which measures gene expression by sequencing. Single-cell "multiome" methods offer the possibility of measuring both modalities simultaneously in cells, raising the question of how to analyze them jointly, and also the extent to which the information they provide is better than unregistered data where single-cell ATAC-seq and single-cell RNA-seq are performed on the same sample, but on different cells. We propose and motivate a biophysical model for chromatin dynamics and subsequent transcription that can be used with multiome data, and use it to assess the benefits of multiome data over unregistered single-cell RNA-seq and single-cell ATAC-seq. We also show that our model provides a biophysically grounded approach to integration of open chromatin data with other modalities.

bioinformatics↗