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.

Continuous glucose monitors have proven invaluable for monitoring blood glucose levels for diabetics, but they are of limited use for observing glucose dynamics at the cellular (or subcellular) level. We have developed a second generation, genetically encoded intensity-based glucose sensing fluorescent reporter (iGlucoSnFR2). We show that when it is targeted to the cytosol, it reports intracellular glucose consumption and gluconeogenesis in cell culture, along with efflux from the endoplasmic reticulum. It outperforms the original iGlucoSnFR in vivo when observed by fiber photometry in mouse brain and reports transient increase in glucose concentration when stimulated by noradrenaline or electrical stimulation. Last, we demonstrate that membrane localized iGlucoSnFR2 can be calibrated in vivo to indicate absolute changes in extracellular glucose concentration in awake mice. We anticipate iGlucoSnFR2 facilitating previously unobservable measurements of glucose dynamics with high spatial and temporal resolution in living mammals and other experimental organisms.

Cortical neurogenesis proceeds through a precise temporal program in which radial glia sequentially generate distinct neuronal subtypes and later glia, yet how post-transcriptional regulators coordinate these transitions remain poorly understood. We previously identified that a decreasing temporal gradient of the RNA-binding protein Imp encodes neural stem cell age in Drosophila. In this work, we extend our investigation to Imp1, a mammalian homologue of Imp, and its role in murine neocortical development. Using TEMPO to track birth-order dynamics, we demonstrate that sustained Imp1 overexpression during early neurogenesis arrests temporal fate progression, shifting neuronal populations toward deeper cortical layers V-VI. Immunostaining with layer-specific transcription factors Cux1 and Ctip2 confirmed that laminar repositioning results from genuine changes in neuronal identity rather than migratory defects, with neurons adopting molecular identities matching their final positions. Temporal window-specific manipulations reveal distinct stage-specific effects where early-stage Imp1 induction produces cascading effects on fate specification and moderately delays the neuronal-to-gliogenic transition, while mid-stage induction induces neuronal accumulation in the subplate region. Live imaging of organotypic cultures reveals continuous neuronal recruitment within intermediate and ventricular zones, with mid-stage-born neurons accumulating at significantly faster rates than earlier cohorts. Strikingly, mid-stage Imp1 overexpression also induces ectopic glial-like foci distributed throughout the cortical plate, featuring dramatic cellular expansion and morphological heterogeneity. These findings establish Imp1 as a dosage- and stage-dependent temporal rheostat orchestrating developmental transitions in radial glial progenitors, controlling neuronal fate decisions and spatial organization. This work advances our understanding of molecular timing mechanisms governing neuronal diversity in the mammalian cortex.

Cortical neurogenesis proceeds through a precise temporal program in which radial glia sequentially generate distinct neuronal subtypes and later glia, yet how post-transcriptional regulators coordinate these transitions remain poorly understood. We previously identified that a decreasing temporal gradient of the RNA-binding protein Imp encodes neural stem cell age in Drosophila. In this work, we extend our investigation to Imp1, a mammalian homologue of Imp, and its role in murine neocortical development. Using TEMPO to track birth-order dynamics, we demonstrate that sustained Imp1 overexpression during early neurogenesis arrests temporal fate progression, shifting neuronal populations toward deeper cortical layers V-VI. Immunostaining with layer-specific transcription factors Cux1 and Ctip2 confirmed that laminar repositioning results from genuine changes in neuronal identity rather than migratory defects, with neurons adopting molecular identities matching their final positions. Temporal window-specific manipulations reveal distinct stage-specific effects where early-stage Imp1 induction produces cascading effects on fate specification and moderately delays the neuronal-to-gliogenic transition, while mid-stage induction induces neuronal accumulation in the subplate region. Live imaging of organotypic cultures reveals continuous neuronal recruitment within intermediate and ventricular zones, with mid-stage-born neurons accumulating at significantly faster rates than earlier cohorts. Strikingly, mid-stage Imp1 overexpression also induces ectopic glial-like foci distributed throughout the cortical plate, featuring dramatic cellular expansion and morphological heterogeneity. These findings establish Imp1 as a dosage- and stage-dependent temporal rheostat orchestrating developmental transitions in radial glial progenitors, controlling neuronal fate decisions and spatial organization. This work advances our understanding of molecular timing mechanisms governing neuronal diversity in the mammalian cortex.

Fructose-1,6-bisphosphate (FBP) is the product of the first committed step of glycolysis, and its concentration is tightly correlated with glycolytic flux. Glycolytic activity varies across tissues and cell types: some tissues, such as the brain, dynamically regulate glycolysis in response to demand, while others, such as the liver have characterized spatial heterogeneity. Here, we report HYlight2, an improved sensor for FBP developed through random whole-gene mutagenesis in E. coli lysate. After four rounds of screening, we isolated HYlight2, which retains its binding affinity while displaying a ΔR/R \~9 in vitro, a three-fold improvement in mammalian cells, and a two-fold improvement in detecting glycolytic responses during stimulated neuronal activity. We further demonstrate its use in vivo to detect altered glycolytic activity in C. elegans neurons, zebrafish pancreatic islets, and mouse liver.

The cell cycle is tightly regulated by checkpoint mechanisms that ensure faithful duplication and segregation of the genome. Here, we induced cell-cell fusion between mitotic and interphase cells to study how nuclei from different cell cycle stages behave in a shared cytoplasm. We found that mitosis is a dominant cell cycle state: the mitotic cytoplasm can drive interphase nuclei into mitosis, whereas, in high ratios of interphase versus mitotic nuclei, fusion forced mitotic nuclei to exit mitosis. Both outcomes represent checkpoint override events with impactful consequences. Interphase nuclei forced into mitosis form aberrant mitotic spindles, show partially condensed DNA and ultimately undergo mitotic catastrophe. Conversely, forced mitotic exit resulted in reformation of nuclear envelope membranes around condensed chromosomes, forming nuclei with a defective nuclear import machinery. Altogether, cell-cell fusion revealed an unexpected plasticity in cell cycle control and highlight cell-cell fusion experiments as a powerful experimental system to study how competing cytoplasmic states are integrated in a shared cytoplasm.

Fluorescence microscopy is constrained by optical limits, fluorophore chemistry and finite photon budgets, imposing trade-offs between imaging speed, resolution and phototoxicity. Here we introduce MicroSplit, a deep learning-based computational multiplexing method that enables multiple cellular structures to be imaged simultaneously in a single fluorescent channel and then computationally unmixed. We show that MicroSplit separates up to four superimposed noisy structures into distinct, denoised image channels, enabling faster and more photon-efficient imaging. Built on Variational Splitting Encoder-Decoder networks,  MicroSplit models a posterior distribution over solutions, allowing uncertainty-aware predictions and the estimation of spatially resolved prediction errors from posterior variability. We demonstrate robust performance across diverse datasets, noise levels and imaging conditions, and show that  MicroSplit improves downstream analysis while reducing photon exposure. All methods, data and trained models are released as open resources, enabling immediate adoption of computational multiplexing in biological imaging.

Fluorescence microscopy, a key driver for progress in the life sciences, faces limitations due to the microscope’s optics, fluorophore chemistry, and photon exposure limits, necessitating trade-offs in imaging speed, resolution, and depth. Here, we introduce MicroSplit, a computational multiplexing technique based on deep learning that allows multiple cellular structures to be imaged in a single fluorescent channel and then unmixed computationally, allowing faster imaging and reduced photon exposure. We show that MicroSplit efficiently separates up to four superimposed noisy structures into distinct denoised fluorescent image channels. Furthermore, using Variational Splitting Encoder-Decoder (VSE) networks, our approach can sample diverse predictions from a trained posterior of solutions. The diversity of these samples scales with the uncertainty in a given input, allowing us to estimate the true prediction errors by computing the variability between posterior samples. We demonstrate the robustness of MicroSplit across various datasets and noise levels and show its utility to image more, image faster, and improve downstream analysis. We provide MicroSplit along with all associated training and evaluation datasets as open resources, enabling life scientists to benefit from the potential of computational multiplexing and accelerate the pace of scientific discovery.

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.