Rezanejad, M. | bioRxiv Science

bioRxiv2026

Breaking the link between demyelination and axon loss: SARM1 inhibition as a neuroprotective strategy for multiple sclerosis

Axonal degeneration is a principal driver of irreversible neurological disability in multiple sclerosis (MS), yet current treatments fail to target this neurodegenerative phase directly. Sterile alpha and TIR domain-containing protein 1 (SARM1) has emerged as an executioner of programmed axon destruction and is overexpressed in grey and white matter MS tissue, making its inhibition a promising therapeutic strategy. However, the effects of SARM1 inhibition on localized neurodegeneration versus systemic and central inflammation remain poorly understood in complex autoimmune environments like MS. Here, we investigated SARM1 pathology using global SARM1-/- knockout (SARM1-/-) mice, AAV-mediated CRISPR knockdown and overexpression of SARM1 in retinal ganglion cells (RGCs), and the small-molecule SARM1 inhibitor 5-iodoisoquinoline (5IIQ), across optic nerve crush (ONC) and experimental autoimmune encephalomyelitis (EAE) models. While global SARM1-/- did not alter the overall clinical course of EAE, it revealed a complex phenotype characterized by an altered peripheral inflammatory cytokine profile and persistent CNS immune infiltration, alongside preserved axonal and myelin integrity. RGC-restricted SARM1 knockdown partially preserved RGCs and axons during EAE, whereas SARM1 overexpression worsened both retinal function and axonal injury. Pharmacological inhibition with 5IIQ preserved axonal integrity and restored visual function in both the ONC and EAE models, as confirmed by electrophysiology, and reduced serum neurofilament light chain (NfL) in EAE, without affecting demyelination. Together, these findings demonstrate that SARM1 inhibition uncouples axonal self-destruction from demyelination and gross neuroinflammation while providing robust structural and functional neuroprotection. This study establishes SARM1 as a viable target for neuroprotective co-therapies designed to complement existing immunomodulatory and remyelinating regimens in MS and related neurodegenerative disorders.

neuroscience↗

bioRxiv2022

Machine learning meets classical computer vision for accurate cell identification

High-parameter multiplex immunostaining techniques have revolutionized our ability to image healthy and diseased tissues with unprecedented depth; however, accurate cell identification and segmentation remain significant downstream challenges. Identifying individual cells with high precision is a requisite to reliably and reproducibly interpret acquired data. Here we introduce CIRCLE, a cell identification pipeline that combines classical and modern machine learning-based computer vision algorithms to address the shortcomings of current cell segmentation tools for 2D images. CIRCLE is a fully automated hybrid cell detection model, eliminating subjective investigator bias and enabling high-throughput image analysis. CIRCLE accurately distinguishes cells across diverse tissues microenvironments, resolves low-resolution structures, and can be applied to any 2D image that contains nuclei. Importantly, we quantitatively demonstrate that CIRCLE outperforms current state-of-the-art image segmentation tools using multiple accuracy measures. As high-throughput multiplex imaging grows closer toward standard practice for histology, integration of CIRCLE into analysis protocols will deliver unparalleled segmentation quality.

cell biology↗