W103 - An Automated and Scalable Pipeline for High-dimensional Immune Cell Phenotyping in Mass Cytometry Datasets

Abstract Text: Context: Efficient single-cell phenotyping has become essential for the identification of complex cell populations. Traditionally, these populations are identified through manual gating, a time-consuming process, subject to variability and a lack of reproducibility. Here, we adapted a deep learning model to devise an automated gating pipeline with the goal of enhancing the accuracy, speed, and reproducibility of immune cell gating.

Methods: Our pipeline was evaluated on two mass-cytometry datasets (>5M cells, 35 markers), that had been previously gated by experts for comparison purposes. Phenotypic markers were used for identification, while the median expression of intracellular markers was used to determine the functional properties of populations.

Results: The automated gating pipeline achieved high overall accuracy comparable to expert manual gating (n=30 cell populations) (0.917 and 0.921 for dataset 1 and 2, respectively). Processing time was significantly reduced: < 12min for both datasets (8CPUs, 8GB of RAM). High-level populations (n=10) were identified with excellent accuracy (e.g. Tcells, Bcells with f1-score of 0.993, 0.937 on dataset 1 and 0.996, 0.934 on dataset 2). However, rare populations (n=20) showed higher discrepancies (e.g. intermediate monocytes, DC with f1-scores of 0.864, 0.816 and 0.678, 0.552 on dataset 1 and 2, respectively). Differences in identification scores had low effect on the functional properties of populations, as 91.4% of the functional properties defined from our pipeline were highly correlated (r>0.75) with those derived from manual gating.

Conclusion: This automated approach eliminates operator biases and handles multiple markers simultaneously, offering reliable and efficient analyses for research and clinical applications.