Mutschler, F. | Search bioRxiv

bioRxiv2026

Recording proteome dynamics in the late-stage embryo at hour-timescale, spatial, and single-cell resolution with MEMBRYO

Cell fate during development is directed by new gene expression states that replace prior ones. When and where a protein is first translated, and how long it will remain in the cell, dictate the timing and extent of a gene's phenotypic effect. Accessing protein synthesis and turnover information in the rapidly developing embryo remains a major challenge. Here we present MEMBRYO: a method for continuous stable isotope labeling (SILAC) of newly synthesized proteins throughout the live embryo at the hour-timescales of developmental phenotypes. We extend embryo culture into the late stages of organogenesis, thereby accessing the proteome of neurogenesis in the forebrain ex utero. Variations in protein synthesis and turnover activity lead to expression patterns that diverge from the corresponding transcripts, impacting molecular pathways and key milestones of cortical neuron differentiation. The high labeling depth enabled both spatial and single-cell SILAC proteomics analysis of cortical cells, revealing distinct layers of post-transcriptional gene expression regulation in progenitors and neurons. By continuous metabolic labeling in the live embryo, MEMBRYO tracks the past, present, and future of the developmental proteome.

developmental biology↗

bioRxiv2026

Global quantification of mammalian gene expression noise

Even cells of the same type growing in the same environment show cell-to-cell differences in protein abundance, a phenomenon known as gene expression noise. This variability can be decomposed into intrinsic components, reflecting molecular randomness, and extrinsic components, arising from differences in cellular state. While gene expression noise has been studied genome-wide in microbes, its global organization remains largely unknown in mammalian cells. Here, we develop a spike-in-based stable isotope single-cell proteomics approach that enables robust quantification of protein-level gene expression noise across thousands of human proteins. We find that protein noise scales inversely with abundance until reaching a plateau, consistent with an extrinsic noise floor and conserved scaling principles observed in bacteria and yeast. Cell cycle stage and cell size contribute substantially to protein variability but do not fully account for the observed heterogeneity. Gene-specific features such as mRNA and protein half-lives and translation efficiency show only weak associations with protein noise, and variability at the mRNA level is a weak predictor of protein variability. Instead, protein noise is largely extrinsic, with coordinated variation across proteins encoding biologically organized cellular states. Consistently, coordinated proteome programs predict intercellular differences in proteome dynamics, linking protein variability to cellular function. Together, these results provide a proteome-wide view of gene expression noise in mammalian cells, establishing that protein-level variability encodes structured and functionally relevant differences in cellular state.

systems biology↗