Jambor, A. N. | Search bioRxiv

bioRxiv2026

Latent Regulatory Programs Generate Synthetic T Cell States with Enhanced Therapeutic Potential

Transcription factors (TFs) govern cell fate through coordinated gene-regulatory networks, yet the full potential of these networks to generate non-native, therapeutically advantageous cell states in vivo remains largely unexplored. We hypothesized that systematic gain-of-function (GOF) overexpression of TFs in CD8 T cells, central mediators of immune protection, could reveal latent, or "hidden," regulatory programs capable of generating synthetic T cell states with therapeutic utility. To test this, we developed single-cell GOF sequencing (scGOF-seq), a multiplexed platform for unbiased, in vivo mapping of GOF effects on T cell fate in immunocompetent mouse models of infection and cancer. scGOF-seq uncovered unexpected regulators of T cell differentiation and accumulation, including SOX2, OCT4, and GATA2, which are normally silenced during T cell differentiation. Notably, outside its native regulatory context, supraphysiologic cMyc GOF reprogrammed CD8 T cells into a synthetic stem-effector hybrid state, enabling >5,000-fold antigen-dependent expansion and antitumor activity, contrasting sharply with its native function in driving terminal differentiation. scGOF-seq further identified TF modules that cooperate with cMyc GOF to promote robust CD8 T cell responses in solid tumors. Together, these findings establish GOF perturbation as a powerful strategy for revealing latent immune regulatory programs and engineering synthetic immune states with therapeutic potential. One-Sentence SummaryIn vivo single-cell gain-of-function screening reveals latent transcriptional programs that can reprogram T cells into highly functional synthetic states.

immunology↗

bioRxiv2025

Minute-scale single-cell transcriptomics enables dynamic modeling of cellular behavior

AbstractDynamic cellular processes such as signaling, fate decisions, and intercellular communication unfold on minute timescales, a regime inaccessible to conventional transcriptomic methods. This temporal gap has fundamentally limited the development of predictive, causal models of cell behavior. Here, we bridge this gap by introducing ChronoSeq, an automated single-cell RNA sequencing platform that achieves genome-wide profiling with a temporal resolution as brief as seven minutes. By integrating automated live-cell sampling with molecular time-barcoding, ChronoSeq captures rapid transcriptional dynamics with high fidelity. Applying ChronoSeq to TNF- stimulated cells, we discovered a rapid, heterogeneity-driven bifurcation in the NF-{kappa}B response that was previously unobservable. We further demonstrate that the high-density temporal data generated by ChronoSeq enables a new class of computational models that dramatically outperform existing methods in inferring the directionality and targets of post-translationally regulated transcription factors. Finally, in a multicellular co-culture, ChronoSeq resolved a paracrine signaling cascade in real time, identifying both the timing and molecular identity of the intercellular relay. By providing a framework to measure dynamics, infer regulation, and model communication at the true pace of biology, ChronoSeq establishes a new foundation for dynamic systems biology.

bioinformatics↗