Cellular heterogeneity within complex tissues and organs is essential to coordinate biological processes across biological scales. The effect of local cues and tissue microenvironments on cell heterogeneity has been mainly studied at the transcriptional level. However, it is within the subcellular scale - the organelles - that lays the machinery to conduct most metabolic reactions and maintain cells alive, ensuring proper tissue function. How changes in subcellular organization under different microenvironments define the functional diversity of cells within organs remains largely unexplored. Here we determine how organelles adapt to different microenvironments using the mouse liver as model system, in combination with computational approaches and machine-learning. To understand organelle adaptation in response to changing nutritional conditions, we analyzed 3D fluorescent microscopy volumes of liver samples labeled to simultaneously visualize mitochondria, peroxisomes, and lipid droplets from mice subjected to different diets: a control diet, a high-fat diet, and a control diet plus fasting. A Cellpose based pipeline was implemented for cell and organelle segmentation, which allowed us to measure 100 different organelle metrics and helped us define subcellular architectures in liver samples at the single cell level. Our results showed that hepatocytes display distinct subcellular architectures within different regions of the liver-close to the central vein, in the middle region, and near the portal vein- and across the various diet groups, thus reflecting their adaptation to specific nutritional inputs. Principal component analysis and clustering of hepatocytes based on organelle signatures revealed 12 different hepatocyte categories within the different experimental groups, highlighting a reduction in hepatocyte heterogeneity under nutritional perturbations. Finally, using single cell organelle signatures exclusively, we generated machine learning models that were able to predict with high accuracy different hepatocyte categories, diet groups, and the stages of MASLD. Our results demonstrate how organelle signatures can be used as hallmarks to define hepatocyte heterogeneity and their adaptation to different nutritional conditions. In the future, our strategy, which combines subcellular resolution imaging of liver volumes and machine learning, could help establish protocols to better define and predict liver disease progression.

Haploid larvae in non-mammalian vertebrates are lethal, with characteristic organ growth retardation collectively called 'haploid syndrome'. In contrast to mammals, whose haploid intolerance is attributed to imprinting misregulation, the cellular principle of haploidy-linked defects in non-mammalian vertebrates remains unknown. Here, we investigated cellular defects that disrupt the ontogeny of gynogenetic haploid zebrafish larvae. Unlike diploid control larvae, haploid larvae manifested unscheduled cell death at the organogenesis stage, attributed to haploidy-linked p53 upregulation. Moreover, we found that haploid larvae specifically suffered the gradual aggravation of mitotic spindle monopolarization during 1-3 days post-fertilization, causing spindle assembly checkpoint-mediated mitotic arrest throughout the entire body. High-resolution imaging revealed that this mitotic defect accompanied the haploidy-linked centrosome loss occurring concomitantly with the gradual decrease in larval cell size. Either resolution of mitotic arrest or depletion of p53 partially improved organ growth in haploid larvae. Based on these results, we propose that haploidy-linked mitotic defects and cell death are parts of critical cellular causes shared among vertebrates that limit the larval growth in the haploid state, contributing to an evolutionary constraint on allowable ploidy status in the vertebrate life cycle.