Dietary restriction increases the longevity of many organisms but the cell signaling and organellar mechanisms underlying this capability are unclear. We demonstrate that to permit long-term survival in response to sudden glucose depletion, yeast cells activate lipid-droplet (LD) consumption through micro-lipophagy (µ-lipophagy), in which fat is metabolized as an alternative energy source. AMP-activated protein kinase (AMPK) activation triggered this pathway, which required Atg14p. More gradual glucose starvation, amino acid deprivation or rapamycin did not trigger µ-lipophagy and failed to provide the needed substitute energy source for long-term survival. During acute glucose restriction, activated AMPK was stabilized from degradation and interacted with Atg14p. This prompted Atg14p redistribution from ER exit sites onto liquid-ordered vacuole membrane domains, initiating µ-lipophagy. Our findings that activated AMPK and Atg14p are required to orchestrate µ-lipophagy for energy production in starved cells is relevant for studies on aging and evolutionary survival strategies of different organisms.

Yes-associated protein (YAP) is a transcriptional co-activator that regulates cell proliferation and survival by binding to a select set of enhancers for target gene activation. How YAP coordinates these transcriptional responses is unknown. Here, we demonstrate that YAP forms liquid-like condensates in the nucleus. Formed within seconds of hyperosmotic stress, YAP condensates compartmentalized the YAP transcription factor TEAD1 and other YAP-related co-activators, including TAZ, and subsequently induced the transcription of YAP-specific proliferation genes. Super-resolution imaging using assay for transposase-accessible chromatin with photoactivated localization microscopy revealed that the YAP nuclear condensates were areas enriched in accessible chromatin domains organized as super-enhancers. Initially devoid of RNA polymerase II, the accessible chromatin domains later acquired RNA polymerase II, transcribing RNA. The removal of the intrinsically-disordered YAP transcription activation domain prevented the formation of YAP condensates and diminished downstream YAP signalling. Thus, dynamic changes in genome organization and gene activation during YAP reprogramming is mediated by liquid-liquid phase separation.

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.

During development, regulatory factors appear in a precise order to determine cell fates over time. Consequently, to investigate complex tissue development, it is necessary to visualize and manipulate cell lineages with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here, we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labeling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation and inactivation of reporters and/or effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.