CelltypeR: A flow cytometry pipeline to characterize single cells from brain organoids

作者：Rhalena A. Thomas, Julien Sirois, Shu-Ming Li, Alexandre Gestin, Ghislaine Deyab, Valerio E. C. Piscopo, Paula Lépine, Meghna Mathur, Carol X.‐Q. Chen, Vincent Soubannier, Taylor Goldsmith, Lama Fawaz, Thomas M. Durcan, Edward A. Fon · 发表于：iScience · 年份：2024 · DOI：10.1016/j.isci.2024.110613 · 被引用次数：5 · 研究领域：Single-cell and spatial transcriptomics、Cell Image Analysis Techniques、Pluripotent Stem Cells Research

Motivated by the cellular heterogeneity in complex tissues, particularly in brain and induced pluripotent stem cell (iPSC)-derived brain models, we developed a complete workflow to reproducibly characterize cell types in complex tissues. Our approach combines a flow cytometry (FC) antibody panel with our computational pipeline CelltypeR, enabling dataset aligning, unsupervised clustering optimization, cell type annotating, and statistical comparisons. Applied to human iPSC derived midbrain organoids, it successfully identified the major brain cell types. We performed fluorescence-activated cell sorting of CelltypeR-defined astrocytes, radial glia, and neurons, exploring transcriptional states by single-cell RNA sequencing. Among the sorted neurons, we identified subgroups of dopamine neurons: one reminiscent of substantia nigra cells most vulnerable in Parkinson's disease. Finally, we used our workflow to track cell types across a time course of organoid differentiation. Overall, our adaptable analysis framework provides a generalizable method for reproducibly identifying cell types across FC datasets in complex tissues.