Sepulveda, L. A. | Search bioRxiv

bioRxiv2026

Comprehensive Lineage Tracing Maps the Landscape of Cell Fate Decisions in Mouse Embryogenesis

A comprehensive cell fate map of mammalian embryogenesis has remained out of reach due to the scale, cellular diversity, and non-deterministic nature of development in utero. Here, we use PEtracer to continuously install heritable genetic marks as cells divide, reconstructing lineage trees that resolve [~]75% of cell divisions across >1.5 million cells from 16 mouse embryos collected at half-day intervals from E7.5-E10.0. We pair these trees with deep transcriptional profiling to chart the landscape of cell fate decisions during gastrulation and early organogenesis. Using these data, we quantify cell fate biases, restriction timing, progenitor pool sizes, and lineage relationships across the embryo, revealing strikingly reproducible lineage architecture across replicate embryos despite the regulative flexibility of mammalian development. We further show how lineage, spatial position, and signaling jointly determine fate outcomes and timing, with their relative influence varying by tissue. This dataset provides a quantitative framework for understanding cell fate specification and a lineage-resolved reference for generating and contextualizing developmental hypotheses at organismal scale.

developmental biology↗

bioRxiv2026

Cell Growth and Division Shape mRNA-Protein Correlations

1Correlations between cellular variables, such as gene-expression levels, provide insights into regulatory mechanisms. We focus here on correlations between mRNA and protein levels and re-examine previously derived analytical predictions. We test this prediction on single-cell E. coli data and see substantial disagreement. We hypothesize that this discrepancy arises from the assumption of constant cell volume and develop a theoretical framework for mRNA-protein correlations in growing and dividing cells. Within this framework, we derive an analytical expression for mRNA- protein correlations and show that explicit incorporation of growth and division substantially alters these correlations. The resulting relation is invariant to upstream transcriptional dynamics, and we validate it using stochastic simulations across multiple gene-regulatory architectures. Finally, we show that the derived predictions are consistent with the E. coli data.

biophysics↗