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bioRxiv · 10.64898/2026.10.31.763288

NEPTUNE: a neuronal cell death pathway prediction tool used to decipher neurodegeneration using single cell RNA-seq datasets

Padvi, A. P.·Brunet, A. A.·Miller, A. L.·Carvalho, L. S.
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Abstract

In the constantly evolving landscape of cell death mechanisms, much of the literature is dominated by research performed in cancer cells and regenerating cell types. However, neurons differ in that they are non-regenerating and post-mitotic. Therefore, assessing neuronal specific disease biomarkers, including cell death markers, without the bias of non-neuronal or cancerous cells, could provide a more biologically accurate neurodegeneration profile. In this study, we developed the Neuronal cEll death Prediction Tool for Understanding Neurodegenerative Events (NEPTUNE), a user-friendly tool for researchers that bioinformatically identifies neuronal cell death markers from user provided differentially expressed gene lists obtained by single cell RNA sequencing data. We applied NEPTUNE to a publicly available single cell RNA sequencing dataset and analysed the progression of photoreceptor degeneration in the rd1 mouse model of severe retinitis pigmentosa. The analysis interpreted the dataset in a neuronal-specific manner and showed that late-stage photoreceptor death is not purely apoptotic but possibly engages overlapping cell death programmes. From the analysis, the user is provided with publishable graphs and a list of cell death markers with fold change, type of impact, cell death type and a strength score. Overall, our tool provides markers specific to neurodegeneration, allows mapping to known WikiPathways and KEGG pathways, performs GO and KEGG enrichment analysis and plots the data for further analysis, greatly facilitating the study of neuron specific degeneration. The tool can be found at: https://neptune.rgtcarvalholab.com/.

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Padvi, A. P., Brunet, A. A., Miller, A. L., Carvalho, L. S.. 2026-11-02. NEPTUNE: a neuronal cell death pathway prediction tool used to decipher neurodegeneration using single cell RNA-seq datasets. https://doi.org/10.64898/2026.10.31.763288

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