ArticleiScience2024
CelltypeR: A flow cytometry pipeline to characterize single cells from brain organoids.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Single nuclear RNA sequencing shows altered microglial and astrocytic functions in post-mortem Parkinson's disease tissue.BMC genomic data · 2026Article
- Single-Cell Protein Assays in Context: From 2D to 3D and In Situ Analysis.Annual review of analytical chemistry (Palo Alto, Calif.) · 2026Review
- Human organoids for Risk Group 4 virus research: a new frontier in investigating Nipah virus infection of the central nervous system.Journal of virology · 2025Article
- Towards a Treatment for Leukodystrophy Using Cell-Based Interception and Precision Medicine.Biomolecules · 2024Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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