A massively parallel synthetic gene atlas for learning compact cis-regulatory grammar across cellular contexts

Authors

Hagen, T., Liu, I., Nagainis, A., Khosrojerdi, N., Yousefi, H., Gunnels, T., Moarefi, A., Green, K., Azimi, N., Momen-Roknabadi, A., Kipp, K. R., Goodarzi, H.

Abstract

Virtual-cell models increasingly learn from large perturbation atlases, but their view of cis regulation remains limited to endogenous genes embedded in broad native regulatory contexts. Here we introduce Therna Biosciences' Chronos platform and its first public dataset release, comprising two complementary modules of a massively parallel synthetic gene atlas: Penta-47x27K for 5' UTRs/internal-promoter elements and Tria-47x28K for 3' UTR stability elements. Together, these datasets measure ~60,000 compact cis-regulatory elements across approximately 50 cell lines in one pooled experiment. The choice of cell lines here, provided to us courtesy of Tahoe Tx, was rooted in our aim to make this dataset maximally useful for the virtual-cell modeling community. At Therna, we routinely apply Chronos -- part of our RNA-Logix(TM) platform -- beyond cancer cell-line pools, to systems such as primary cells and organoids, via LNP-formulated mRNA delivery. Chronos captures both transcriptional and post-transcriptional gene expression control. Episomal DNA delivery measures DNA-normalized mRNA output, while direct RNA delivery with longitudinal sampling reliably quantifies RNA decay. The RNA-delivery arm uses chemically modified synthetic mRNA incorporating N1-methylpseudouridine, a modification widely used in mRNA therapeutics. To resolve high-complexity libraries of up to 30,000 elements, Chronos pushes the sensitivity limits of single-cell RNA sequencing to quantify individual RNA molecules within single cells. By massively expanding gene regulatory networks with synthetic genes whose variable regulatory code is short and defined, Chronos provides an auxiliary cis-regulatory lens for virtual-cell modeling and enables context-specific cis-trans regulatory interactions to be learned directly.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 15 Sep 2026.

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