CelltypeR: A flow cytometry pipeline to annotate, characterize and isolate single cells from brain organoids

Abstract

AbstractMotivated by the growing number of single cell RNA sequencing datasets (scRNAseq) revealing the cellular heterogeneity in complex tissues, particularly in brain and in induced pluripotent stem cell (iPSC)-derived brain models, we have developed a high-throughput, standardized approach for reproducibly characterizing and isolating cell types in complex neuronal tissues based on protein expression levels. Our approach combines a flow cytometry (FC) antibody panel targeting brain cells with a computational pipeline called CelltypeR, which has scripts for aligning and transforming datasets, optimizing unsupervised clustering, annotating, and quantifying cell types, and comparing cells across different conditions. We applied this workflow to human iPSC-derived midbrain organoids and identified the expected brain cell types, including neurons, astrocytes, radial glia and oligodendrocytes. By defining gates based on the expression levels of our protein markers, we were able to perform Fluorescence-Activated Cell Sorting (FACS) astrocytes, radial glia, and neurons, cell types were then confirmed by scRNAseq. Among the sorted neurons, we identified three subgroups of dopamine (DA) neurons, including one which was similar to substantia nigra DA neurons, the cell type most vulnerable in Parkinson’s disease. Overall, our adaptable analysis framework provides a generalizable method for reproducibly identifying cell types across FC datasets.