CelltypeR: A flow cytometry pipeline to annotate, characterize and isolate single cells from brain organoids
Bibliographic record
Abstract
Abstract Motivated by the growing number of single cell RNA sequencing datasets (scRNAseq) revealing the cellular heterogeneity in complex tissues, particularly in brain and induced pluripotent stem cell (iPSC)-derived brain models, we developed a high-throughput, standardized approach for reproducibly characterizing 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, with functions for aligning and transforming datasets, optimizing unsupervised clustering, annotating and quantifying cell types, and statistical comparisons. We applied this workflow to human iPSC-derived midbrain organoids and identified the expected brain cell types, including neurons, astrocytes, radial glia, and oligodendrocytes. Defining gates based on the expression levels of our protein markers, we performed Fluorescence-Activated Cell Sorting of astrocytes, radial glia, and neurons, cell types were then confirmed by scRNAseq. Among the sorted neurons, we identified three subgroups of dopamine (DA) neurons; one reminiscent of substantia nigra DA neurons, the cell type most vulnerable in Parkinson’s disease. Finally, we use 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".