ABSTRACT
High-throughput single-cell cytometry data are crucial for understanding immune system\'s involvement in diseases and responses to treatment. Traditional methods for annotating cytometry data, specifically manual gating and clustering, face challenges in scalability, robustness, and accuracy. In this study, we propose a single-cell masked autoencoder (scMAE), which offers an automated solution for immunophenotyping tasks including cell type annotation. The scMAE model is designed to uphold user-defined cell type definitions, thereby facilitating easier interpretation and cross-study comparisons. The scMAE model operates on a pre-train and fine-tune approach. In the pre-training phase, scMAE employs Masked Single-cell Modelling (MScM) to learn relationships between protein markers in immune cells solely based on protein expression, without relying on prior information such as cell identity and cell type-specific marker proteins. Subsequently, the pre-trained scMAE is fine-tuned on multiple specialized tasks via task-specific supervised learning. The pre-trained scMAE addresses the shortcomings of manual gating and clustering methods by providing accurate and interpretable predictions. Through validation across multiple cohorts, we demonstrate that scMAE effectively identifies co-occurrence patterns of bound labeled antibodies, delivers accurate and interpretable cellular immunophenotyping, and improves the prediction of subject metadata status. Specifically, we evaluated scMAE for cell type annotation and imputation at the cellular-level and SARS-CoV-2 infection prediction, secondary immune response prediction against COVID-19, and prediction the infection stage in the COVID-19 progression at the subject-level. The introduction of scMAE marks a significant step forward in immunology research, particularly in large-scale and high-throughput human immune profiling. It offers new possibilities for predicting and interpretating cellular-level and subject-level phenotypes in both health and disease.