Axonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and develop effective therapies. Here, we evaluated the progression of AxD in cortical neurons using a novel microfluidic device together with a deep learning tool that we developed for the enhanced-throughput analysis of AxD on microscopic images. The trained convolutional neural network (CNN) sensitively and specifically segmented the features of AxD including axons, axonal swellings, and axonal fragments. Its performance exceeded that of the human evaluators. In an in vitro model of AxD in hemorrhagic stroke induced by the hemolysis product hemin, we detected a time-dependent degeneration of axons leading to a decrease in axon area, while axonal swelling and fragment areas increased. Axonal swellings preceded axon fragmentation, suggesting that swellings may be reliable predictors of AxD. Using a recurrent neural network (RNN), we identified four morphological patterns of AxD (granular, retraction, swelling, and transport degeneration). These findings indicate a morphological heterogeneity of AxD in hemorrhagic stroke. Our EntireAxon platform enables the systematic analysis of axons and AxD in time-lapse microscopy and unravels a so-far unknown intricacy in which AxD can occur in a disease context.

Inflammation triggers systemic growth restrictions, a process well characterised in tumour cachexia. Whether inflammatory tissue damage also induces growth restrictions, and how regenerating tissue overcome them, is less explored. Using a tissue damage model in Drosophila , we identify metabolic and signaling adaptations that both induce and bypass systemic growth restrictions. Expression of eiger , the Drosophila TNF-α homolog, in imaginal discs causes systemic insulin restriction and insulin resistance, reducing protein translation and proliferation in peripheral tissues. Regenerating cells overcome this by upregulating Pdk1, which is necessary and sufficient to promote protein translation via an Insulin/Akt-independent mechanism. JAK/STAT acts upstream to elevate Pdk1, defining a JAK/STAT-Pdk1-S6K axis essential for regenerative proliferation. Regenerating cells also upregulate amino acid transporters and rely on mTORC1. Similar signatures in Ras ^V12 , scrib tumors indicate that tumors co-opt these pathways to sustain growth under insulin restriction. This physiological program thus integrates systemic nutrient mobilization and local metabolic reprogramming, with implications for tissue repair but also pathologies, such as chronic wounds and cancer.

Background: Axonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and to develop effective therapeutic interventions. Here, we evaluated the progression of AxD in cortical neurons using a novel microfluidic device in combination with a deep learning tool, the EntireAxon, that we developed for the enhanced-throughput analysis of AxD on microscopic images. Results: The EntireAxon convolutional neural network sensitively and specifically segmented the features of AxD, including axons, axonal swellings, and axonal fragments, and its performance exceeded that of human expert raters. In an in vitro model of AxD in hemorrhagic stroke induced by the hemolysis product hemin, we detected the concentration- and time-dependent degeneration of axons leading to a decrease in axon area, while the axonal swelling and axonal fragment area increased. Time course analysis revealed that axonal swellings preceded axon fragmentation, suggesting that swellings may be reliable predictors of AxD. Using a recurrent neural network, we further identified four morphological patterns of AxD (granular, retraction, swelling, and transport degeneration) in cortical axons subjected to hemin. Conclusions: These findings indicate a morphological heterogeneity of AxD under pathophysiologic conditions. The combination of the microfluidic device with the EntireAxon deep learning tool enable the systematic analysis of AxD but also unravel a so far unknown intricacy in which AxD can occur in a disease context.