@article {70182, title = {iGlucoSnFR2: A genetically encoded fluorescent sensor for measuring intracellular or extracellular glucose in vivo in mouse brain.}, journal = {Sci Adv}, volume = {11}, year = {2025}, month = {2025 Nov 14}, pages = {eadz3889}, abstract = {

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.

}, issn = {2375-2548}, doi = {10.1126/sciadv.adz3889}, url = {https://doi.org/10.1126/sciadv.adz3889}, author = {Marvin, Jonathan S and M{\"a}chler, Philipp and Meng, Chengbo and Ates, Tayfun and Patel, Ronak H and Adhikari, Raghabendra and Makurath, Monika A and Ku, Zaneta and Feliciano, Daniel and Atasoy, Deniz and Cui, Guohong and Kleinfeld, David and Brown, Timothy A} } @article {70190, title = {Imp1 acts as a dosage- and stage-dependent temporal rheostat orchestrating radial glial fate transitions and cortical morphogenesis}, journal = {bioRxiv}, year = {2025}, month = {2025 Nov 18}, abstract = {

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.

}, doi = {10.1101/2025.11.18.688993}, url = {https://www.biorxiv.org/content/early/2025/11/18/2025.11.18.688993}, author = {Azur, Romie Angelo G. and Feliciano, Daniel and Espinosa-Medina, Isabel and Adhikari, Raghabendra and Lilao-Garz{\'o}n, Joaquin and Jensen, Ella and Yang, Ching-Po and Lee, Tzumin} } @article {70039, title = {MicroSplit: Semantic Unmixing of Fluorescent Microscopy Data}, journal = {bioRxiv}, year = {2025}, month = {2025 Jul 21}, abstract = {

Fluorescence microscopy, a key driver for progress in the life sciences, faces limitations due to the microscope{\textquoteright}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.

}, doi = {10.1101/2025.02.10.637323}, url = {https://www.biorxiv.org/content/early/2025/07/21/2025.02.10.637323}, author = {Ashesh, Ashesh and Carrara, Federico and Zubarev, Igor and Galinova, Vera and Croft, Melisande and Pezzotti, Melissa and Gong, Daozheng and Casagrande, Francesca and Colombo, Elisa and Giussani, Stefania and Restelli, Elena and Cammarota, Eugenia and Battagliotti, Juan Manuel and Klena, Nikolai and Di Sante, Moises and Adhikari, Raghabendra and Feliciano, Daniel and Pigino, Gaia and Taverna, Elena and Harschnitz, Oliver and Maghelli, Nicola and Scherer, Norbert and Dalle Nogare, Damian Edward and Deschamps, Joran and Pasqualini, Francesco and Jug, Florian} } @article {69694, title = {Spatial single-cell Organellomics reveals nutrient dependent hepatocyte heterogeneity and predicts pathophysiological status in vivo}, journal = {bioRxiv}, year = {2024}, month = {2024 Dec 08}, abstract = {

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.

}, doi = {10.1101/2024.12.06.627285}, url = {https://doi.org/10.1101/2024.12.06.627285}, author = {Hillsley, Alexander and Adhikari, Raghabendra and Johnson, Alana Dowdell and Espinosa-Medina, Isabel and Funke, Jan and Feliciano, Daniel} }