Rare · Published 2025-01-01 · DOI 10.1016/j.rare.2025.100114
Whole exome and genome sequencing, coupled with refined bioinformatic pipelines, have enabled improved diagnostic yields for individuals with Mendelian conditions and led to the rapid identification of novel syndromes. Yet, for many Mendelian neurodevelopmental disorders (NDDs), a lack of pre-existing model system data hinders systematic model selection through a phenotype- and genotype-informed approach. Single-cell RNA sequencing data can be an informative tool, as it can indicate which cell types express a gene of interest at the highest levels across time. A particularly valuable single-cell RNA sequencing dataset generated from second trimester developing human brain tissue was produced by Bhaduri et al. in 2021, but access to these data can be limited by computing power and the steep learning curve of single-cell data analysis. To reduce these barriers for translational research on Mendelian NDDs, we built the web-based visualization tool, Neurodevelopment in Trimester 2 - VIsualization of Single cell Data Online Tool (NeuroTri2-VISDOT, found at https://bhojlab.shinyapps.io/NeuroTri2-VISDOT), for exploring percent and average expression data across nine cell types in this dataset. We demonstrate the utility of NeuroTri2-VISDOT by employing it in several settings, including for the chromatinopathy Rubinstein-Taybi Syndrome; the spectrum of syndromes characterized as Noonan Syndrome-like RASopathies; and NDDs caused by germline variants in histone genes. NeuroTri2-VISDOT expands the accessibility of a valuable single-cell dataset by compressing the data into a web-based shinyApp. Our objective is for clinicians and translational researchers to use this tool to both inform model system selection and disentangle the neuro-phenotypic heterogeneity of historically grouped Mendelian NDDs.
Abstract from DOAJ. Public domain (CC0 1.0).
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