Automated cytometric gating with human-level performance using bivariate segmentation

Jiong Chen, Matei Ionita, Yanbo Feng, Yinfeng Lu, Patryk Orzechowski, Sumita Garai, Kenneth Hassinger, Jingxuan Bao, Junhao Wen, Duy Duong-Tran, Joost Wagenaar, Michelle L. McKeague, Mark M. Painter, Divij Mathew, Ajinkya Pattekar, Nuala J. Meyer, E. John Wherry, Allison R. Greenplate and Li Shen ()
Additional contact information
Jiong Chen: University of Pennsylvania School of Engineering and Applied Science
Matei Ionita: University of Pennsylvania Perelman School of Medicine
Yanbo Feng: University of Pennsylvania Perelman School of Medicine
Yinfeng Lu: University of Pennsylvania Perelman School of Medicine
Patryk Orzechowski: University of Pennsylvania Perelman School of Medicine
Sumita Garai: University of Pennsylvania Perelman School of Medicine
Kenneth Hassinger: University of Pennsylvania Perelman School of Medicine
Jingxuan Bao: University of Pennsylvania Perelman School of Medicine
Junhao Wen: University of Southern California
Duy Duong-Tran: University of Pennsylvania Perelman School of Medicine
Joost Wagenaar: University of Pennsylvania Perelman School of Medicine
Michelle L. McKeague: University of Pennsylvania Perelman School of Medicine
Mark M. Painter: University of Pennsylvania Perelman School of Medicine
Divij Mathew: University of Pennsylvania Perelman School of Medicine
Ajinkya Pattekar: University of Pennsylvania Perelman School of Medicine
Nuala J. Meyer: University of Pennsylvania
E. John Wherry: University of Pennsylvania Perelman School of Medicine
Allison R. Greenplate: University of Pennsylvania Perelman School of Medicine
Li Shen: University of Pennsylvania Perelman School of Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Recent advances in cytometry have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance in cytometry has posed a formidable challenge during the gating process, especially for the initial pre-gates which deal with unpredictable events, such as debris and technical artifacts. To mitigate the labor-intensive manual gating process, we propose UNITO, a framework to rigorously identify the hierarchical cytometric subpopulations. UNITO transforms a cell-level classification task into an image-based segmentation problem. The framework is validated on three independent cohorts (two mass cytometry and one flow cytometry datasets). We compare its results with previous automated methods using the consensus of at least four experienced immunologists. UNITO outperforms existing methods and deviates from human consensus by no more than any individual does. UNITO can reproduce a similar contour compared to manual gating for post-hoc inspection, and it also allows parallelization of samples for faster processing.

Date: 2025
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DOI: 10.1038/s41467-025-56622-2

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