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

Abstract Dynamic 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-α stimulated cells, we discovered a rapid, heterogeneity-driven bifurcation in the NF-κB 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. Competing Interest Statement Author [KA] and Author [WW] have a pending patent application related to the research presented in this paper. All other authors declare no competing interests.