Automated cytometric gating with human-level performance using bivariate segmentation

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 d...

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Bibliographic Details
Published in:Nature communications Vol. 16; no. 1; pp. 1576 - 15
Main Authors: Chen, Jiong, Ionita, Matei, Feng, Yanbo, Lu, Yinfeng, Orzechowski, Patryk, Garai, Sumita, Hassinger, Kenneth, Bao, Jingxuan, Wen, Junhao, Duong-Tran, Duy, Wagenaar, Joost, McKeague, Michelle L., Painter, Mark M., Mathew, Divij, Pattekar, Ajinkya, Meyer, Nuala J., Wherry, E. John, Greenplate, Allison R., Shen, Li
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 12.02.2025
Nature Publishing Group
Nature Portfolio
Subjects:
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631/114/1305
631/114/1314
631/114/2397
631/250/2520
631/250/38
Algorithms
Antibodies
Automation
Biological activity
Bivariate analysis
Classification
Data collection
Datasets
Deep learning
Flow cytometry
Flow Cytometry - methods
Gating
Human performance
Humanities and Social Sciences
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Immunology
Medicine
multidisciplinary
Protein expression
Proteins
Science
Science (multidisciplinary)
Single-Cell Analysis - methods
Single-cell protein
Software
Subpopulations
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Summary: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. High-throughput cytometry generates complex single-cell data with challenging manual gating process. Here, authors introduce UNITO, a framework that transforms cell classification into image segmentation, outperforming existing methods in identifying cytometric subpopulations across diverse datasets.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-025-56622-2