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

Area of Science:

• Neuroscience
• Stem Cell Biology
• Computational Biology

Background:

• Cellular heterogeneity in complex tissues, especially brain and induced pluripotent stem cell (iPSC)-derived models, presents challenges for reproducible cell type characterization.
• Accurate identification of diverse cell populations is crucial for understanding tissue development, function, and disease mechanisms.

Purpose of the Study:

• To develop and validate a comprehensive workflow for reproducible cell type characterization in complex tissues.
• To apply this workflow to human iPSC-derived midbrain organoids for detailed cell population analysis.
• To identify specific neuronal subtypes, including those relevant to neurodegenerative diseases like Parkinson's disease.

Main Methods:

• Integration of a flow cytometry (FC) antibody panel with a computational pipeline (CelltypeR) for dataset alignment, unsupervised clustering, cell type annotation, and statistical comparison.
• Application of the workflow to human iPSC-derived midbrain organoids.
• Fluorescence-activated cell sorting (FACS) of identified cell populations (astrocytes, radial glia, neurons) followed by single-cell RNA sequencing (scRNA-seq) for transcriptional profiling.
• Time-course analysis of cell type dynamics during organoid differentiation.

Main Results:

• The workflow successfully identified major brain cell types in iPSC-derived midbrain organoids.
• Single-cell RNA sequencing revealed distinct transcriptional states within sorted neuronal populations.
• A specific subgroup of dopamine neurons was identified, resembling substantia nigra cells implicated in Parkinson's disease.
• The workflow demonstrated adaptability in tracking cell type changes over the course of organoid differentiation.

Conclusions:

• The developed workflow provides a generalizable and reproducible method for identifying cell types across flow cytometry datasets in complex tissues.
• This approach enhances the characterization of cellular heterogeneity in brain organoid models.
• The findings offer a valuable tool for studying neurodevelopment, disease modeling, and drug discovery in complex biological systems.