Nervous tissue is composed of highly heterogeneous types and subtypes of neurons. The advent of single-cell RNA sequencing (scRNA-seq) techniques has provided a way to extensively study the molecular identities and cellular diversity of nervous tissue at the single-cell level. This has led to the generation of large number of scRNA-seq datasets. However, accessing desired scRNA-seq datasets from the main public biodata repository “Gene Expression Omnibus” (GEO) remains challenging due to its complex and nonuniform searching interface.

To promote the re-using and enhance retrieval accessibility of generated scRNA-seq datasets in the field of neuroscience, we developed a comprehensive and user-friendly Shiny App named “Neuro_SCpedia”. Using the NCBI’s web API tool “E-utilites” through R, we automatically compiled metadata of single-cell datasets related to neuroscience available in GEO. We then use “Neuro_SCpedia” (https://cmushiny.unige.ch/zhux/Neuro_SCpedia/) for statistical analysis to reveal trends in the technical development and application of scRNA-seq techniques in neuroscience. Finally, we created automated tools to display the data in Neuro_SCpedia to facilitate the use and analysis of these datas.

We believe that this resource will significantly improve access to single-cell RNA sequencing data in the neuroscience field, promoting the reuse of existing data and facilitating meta-analyses.