2026/06/06 by Maria Avdeeva, Sarah Walker, Joris van der Veeken +2 · 1 voice
Biochemistry, Genetics and Molecular Biology · Decision Sciences · #Gene Regulatory Network Analysis #Microbial Metabolic Engineering and Bioproduction #Simulation Techniques and Applications
paper · doi:10.1038/s41467-026-74000-4
openalex created_date 2025/10/10 · openalex publication_date 2026/06/06 · openalex updated_date 2026/07/30
Abstract Inferring dynamic cellular processes from static single-cell measurements remains a central challenge in genomics. Here we introduce ArchVelo, a new method for modeling gene regulation and inferring cell trajectories using single-cell simultaneous chromatin accessibility (scATAC-seq) and transcriptomic (scRNA-seq) profiling. ArchVelo represents chromatin accessibility as a set of archetypes—shared regulatory programs—and models their dynamic influence on transcription. Compared to previous methods, ArchVelo improves inference accuracy and gene-level latent time alignment, and enables identification of the underlying transcription factor activity. We benchmark ArchVelo on developing mouse brain and human hematopoiesis datasets and apply it to CD8 T cells responding to viral infection, revealing distinct trajectories of differentiation and proliferation. Focusing on the progenitor CD8 T cell population with key roles in sustaining immune responses and translationally linked to immunotherapy outcomes, we identify a previously uncharacterized differentiation trajectory from Ccr6 − to Ccr6 + progenitors, shared between acute and chronic infection. In sum, ArchVelo provides a principled framework for modeling dynamic gene regulation in multi-omic single-cell data across biological systems.