ArchVelo: Archetypal Velocity Modeling for Single-cell Multi-omic Trajectories

2025 · doi:10.1101/2025.09.14.676182
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This paper presents ArchVelo, an analytical method to infer dynamic cellular trajectories and gene regulatory programs from static single-cell multi-omic data by jointly modeling scATAC-seq chromatin accessibility and scRNA-seq transcriptomes. Using an archetype-based representation of shared chromatin regulatory programs, the authors report improved inference accuracy and better gene-level latent-time alignment compared with prior approaches, and demonstrate that transcription factor activity can be inferred. ArchVelo is benchmarked on developing mouse brain and human hematopoiesis datasets and applied to CD8 T cells responding to viral infection, where it reveals distinct differentiation and proliferation trajectories and identifies a previously uncharacterized Ccr6− to Ccr6+ progenitor differentiation path shared between acute and chronic infection. The study’s caveat is that it is focused on modeling trajectories in the contexts tested (multi-omic single-cell datasets of the specified types), rather than providing broader biological validation across other systems. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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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.
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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. Competing Interest Statement A.Y.R. is an SAB member and has equity in Sonoma Biotherapeutics, RAPT Therapeutics, Coherus BioSciences, Santa Ana Bio, Odyssey Therapeutics, Nilo Therapeutics, and Vedanta Biosciences; he is also an SAB member of BioInvent and Amgen and a co-inventor of a CCR8+ Treg cell depletion IP licensed to Takeda, which is unrelated to the content of this publication. The remaining authors declare no competing interests.

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