Embryo-scale Visual Cell Sorting reveals a conserved transcriptomic signature of nucleolar size linked to proteostasis

is poorly understood. Here, we develop a high-throughput Visual Cell Sorting (VCS) workflow for fixed cells and nuclei that combines antibody-based photoconversion; GPU-accelerated, real-time image analysis; and three-level single-cell combinatorial indexing RNA-seq (sci-RNA-seq3) to link nuclear compartment morphology to single-nucleus transcriptomes at embryo scale. We use VCS to analyze and sort over 1 million mouse embryo-derived nuclei by nucleolar, nuclear speckle, or nuclear size and construct a transcriptional atlas annotated with nuclear compartment phenotypes. Nuclear compartment size varies both between and within lineages and is shaped by proliferation and differentiation. In extracellular matrix protein-producing cell types such as fibroblasts, chondrocytes, and osteoblasts, nucleolar enlargement is uncoupled from cell cycle, and in erythroid cells exhibit a sharp nucleolar contraction preceding cell-cycle exit. We identify a 41-gene transcriptional signature whose expression tracks nucleolar size, enriched for ribosome biogenesis, mitochondrial metabolism, unfolded protein response, stress granule, and ubiquitin-proteasome pathway components. We used this nucleolar transcriptional signature to annotate mouse, zebrafish and human developmental atlases with nucleolar size information, revealing a conserved coupling between nucleolar activity and proteostasis programs. Our work establishes Visual Cell Sorting as a scalable platform for mapping image-based phenotypes to molecular programs; details the relationship between nuclear compartment phenotypes and development; and provides a transcriptional signature to estimate nucleolar size from existing single-cell datasets.