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Narayan, A. P.

Publications and source records attributed to Narayan, A. P..

2 recordsLinked to original sources

Combinatorial Control of Corticospinal Axon Growth by Retinoic Acid Receptors

Regeneration of central nervous system (CNS) axons depends on Transcription Factors (TFs) that reactivate developmental growth programs, yet most such factors remain unknown. By intersecting developmental chromatin binding with pro-growth gene networks, we identified two retinoic acid receptor transcription factors, RARA and RARG, whose occupancy at growth-associated genes is progressively lost as neurons mature. Restoring both factors together increased neurite outgrowth beyond either alone in two independent systems, the Neuro-2a cell line and primary cortical neurons. In vivo, the same combination drove cross-midline sprouting after pyramidotomy and long-tract regeneration after thoracic spinal cord crush, with concordant recovery of hindlimb gait and grip strength. Interestingly, neither receptor alone was sufficient, hinting at combinatorial regulation. Single-nucleus transcriptomics delineated that only the combination reactivated relevant cytoskeletal and gene-expression programs, while genome-wide binding maps showed that RARA and RARG partition the regulatory landscape, with RARG dominating promoters and RARA occupying distal enhancers, so that neither receptor reconstitutes the developmental growth state alone. Intriguingly, this cooperative requirement was specific to the CNS: in peripheral sensory neurons, RARG alone was sufficient and RARA was dispensable. These data identify RARA and RARG as novel cooperative regulators of regenerative axon growth in mammalian CNS and PNS neurons and potential targets for therapeutic intervention.

neuroscience↗

Deep Neurite Analysis Tool (DeNAT): A machine-learning framework for precise automated neurite outgrowth measurement

Accurate quantification of neurite sprouting after injury is a critical step in axon regeneration research. Yet it remains a major bottleneck, as the current gold standard relies on manual counting by multiple blinded observers. This process is slow, labor-intensive, and prone to variability. While some software can measure total neurite length, they arent made to specifically measure new growth in complicated images from real-life injury models, like the thoracic crush and pyramidotomy model. Existing software can measure total neurite length in culture, but it is not designed to capture new growth in complex images from injury models, such as thoracic crush or pyramidotomy. Crucially, these tools lack the ability to selectively analyze growth within user-defined regions, a key requirement for injury paradigms. To address this need, we developed the Deep Neurite Analysis Tool (DeNAT), an accessible deep-learning-based platform that automatically measures neurite outgrowth after injury. DeNAT allows users to define regions of interest to specifically quantify sprouting in images from common spinal cord injury paradigms. We benchmarked its performance against manual scoring and conventional automated approaches. DeNAT achieved 87 percent accuracy in detecting neurite sprouts relative to manual counts, while reducing variability and labor. By combining user-guided region selection with automated deep learning analysis, DeNAT offers an accurate, reproducible, and efficient solution for measuring neurite outgrowth in injury models.

bioinformatics↗