Bot, E. | bioRxiv Science

bioRxiv2025

NeuRoDev resolves lifelong temporal and cellular variation in human cortical gene expression

Understanding how the human brain develops and functions requires direct analysis of human cells. Single-cell atlases open unprecedented opportunities to survey cell physiological molecular states as the brain develops. However, technical challenges limit their potential. We present NeuRoDev, a computational resource with highly curated transcriptomic data and novel analytical tools to investigate neuronal and glial development in the human cortex. NeuRoDev compresses [~]1M single-cell transcriptomes into integrative summary networks of reproducible cell clusters that capture temporal and cellular variation across all stages of human brain development. It provides a reference framework to directly interrogate cellular maturation dynamics, contextualize gene function, and interpret experimental organoid models. We use NeuRoDev to investigate developmental variation in cell physiology, reconstruct genesis and maturation dynamics in neuronal and glial cells, and interpret time-series data from organoid models. NeuRoDev is provided as a freely available software package and as web applications for interactive data analysis.

neuroscience↗

bioRxiv2025

Gene specificity landscapes for comparative transcriptomic analysis across tissues, cell types, and species

Gene expression specificity is a biological parameter relevant for understanding the molecular basis of evolutionary constraints and tissue-selective pathogenesis. Many efforts have tried to quantify the degree of tissue specificity of individual genes. The growing availability of single-cell transcriptomic data greatly expands the context in which expression specificity can be assessed. We present a computational strategy to globally analyse and compare the specificity of genes and groups of related genes across different contexts. By representing expression profiles in terms of expression level-breadth (L-B) relationships, we are able to quantify and construct 2D landscapes that provide a globally consistent coordinate system to map specificity patterns. We characterize these landscapes at different levels of resolution and across species to demonstrate simple strategies for comparative transcriptomics. We use this approach to investigate the tissue, cell type, and neuronal specificity of human genes and generate reference specificity landscapes. Finally, by comparing the specificity of brain cell subtypes across 4 primate species we find that their degree of conservation mirrors evolutionary divergence times. Our analysis framework and data resources are available in the R package GeneSLand.

bioinformatics↗