Cytometry masked autoencoder: An accurate and interpretable automated immunophenotyper

Single-cell cytometry data are crucial for understanding the role of the immune system in diseases and responses to treatment. However, traditional methods for annotating cytometry data face challenges in scalability, robustness, and accuracy. We propose a cytometry masked autoencoder (cyMAE), which...

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Vydáno v:	Cell reports. Medicine Ročník 5; číslo 11; s. 101808
Hlavní autoři:	Kim, Jaesik, Ionita, Matei, Lee, Matthew, McKeague, Michelle L., Pattekar, Ajinkya, Painter, Mark M., Wagenaar, Joost, Truong, Van, Norton, Dylan T., Mathew, Divij, Nam, Yonghyun, Apostolidis, Sokratis A., Clendenin, Cynthia, Orzechowski, Patryk, Jung, Sang-Hyuk, Woerner, Jakob, Ittner, Caroline A.G., Turner, Alexandra P., Esperanza, Mika, Dunn, Thomas G., Mangalmurti, Nilam S., Reilly, John P., Meyer, Nuala J., Calfee, Carolyn S., Liu, Kathleen D., Matthy, Michael A., Swigart, Lamorna Brown, Burnham, Ellen L., McKeehan, Jeffrey, Gandotra, Sheetal, Russel, Derek W., Gibbs, Kevin W., Thomas, Karl W., Barot, Harsh, Greenplate, Allison R., Wherry, E. John, Kim, Dokyoon
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Inc 19.11.2024 Elsevier
Témata:	Advanced Basic Science Algorithms automated gating deep learning Flow Cytometry - methods high-dimensional cytometry Humans immunophenotyping Immunophenotyping - methods machine learning mass cytometry representation learning Single-Cell Analysis - methods deep learning automated gating representation learning immunophenotyping high-dimensional cytometry mass cytometry machine learning
ISSN:	2666-3791, 2666-3791
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Popis
Shrnutí:	Single-cell cytometry data are crucial for understanding the role of the immune system in diseases and responses to treatment. However, traditional methods for annotating cytometry data face challenges in scalability, robustness, and accuracy. We propose a cytometry masked autoencoder (cyMAE), which automates immunophenotyping tasks including cell type annotation. The model upholds user-defined cell type definitions, facilitating interpretability and cross-study comparisons. The training of cyMAE has a self-supervised phase, which leverages large amounts of unlabeled data, followed by fine-tuning on specialized tasks using smaller amounts of annotated data. The cost of training a new model is amortized over repeated inferences on new datasets using the same panel. Through validation across multiple studies using the same panel, we demonstrate that cyMAE delivers accurate and interpretable cellular immunophenotyping and improves the prediction of subject-level metadata. This proof of concept marks a significant step forward for large-scale immunology studies. [Display omitted] •Masked cytometry modeling learns relationships among proteins without cell identity•We develop cytometry masked autoencoder (cyMAE) to automate immunophenotyping•cyMAE improves both cell-level and subject-level immune profiling Kim et al. introduces cyMAE, a cytometry masked autoencoder that automates immune cell profiling from single-cell cytometry data. By leveraging unlabeled data for pre-training and fine-tuning on specific tasks, the model improves immune profiling accuracy and enhances prediction of subject-level clinical data, advancing large-scale immune studies.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally Lead contact
ISSN:	2666-3791 2666-3791
DOI:	10.1016/j.xcrm.2024.101808