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 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.
Authors
- Ajinkya Pattekar (ORCID: https://orcid.org/0000-0002-1416-1230)
- Patryk Orzechowski (ORCID: https://orcid.org/0000-0003-3578-9809)
- Mark M. Painter (ORCID: https://orcid.org/0000-0002-0180-2748)
- Sumita Garai (ORCID: https://orcid.org/0000-0001-6035-4837)
- Matei Ionita (ORCID: https://orcid.org/0000-0002-0514-3364)
- Divij Mathew (ORCID: https://orcid.org/0000-0002-8323-7358)
- Nuala J. Meyer (ORCID: https://orcid.org/0000-0003-4597-5584)
- Jingxuan Bao (ORCID: https://orcid.org/0000-0001-7127-3258)
- Joost Wagenaar (ORCID: https://orcid.org/0000-0003-0837-7120)
- Duy Duong‐Tran (ORCID: https://orcid.org/0009-0009-4496-7575)
- E. John Wherry (ORCID: https://orcid.org/0000-0003-0477-1956)
- Allison R. Greenplate (ORCID: https://orcid.org/0000-0002-2614-3072)
- Junhao Wen (ORCID: https://orcid.org/0000-0003-2077-3070)
- Kenneth Hassinger
- Li Shen (ORCID: https://orcid.org/0000-0002-5443-0503)
- Yinfeng Lu
- Yanbo Feng
- Jiong Chen (ORCID: https://orcid.org/0000-0001-5971-1681)
- Michelle L. McKeague
Institutions
- University of Southern California (US)
- United States Naval Academy (US)
- University of the Arts (US)
- Translational Therapeutics (United States) (US)
- AGH University of Krakow (PL)
- University of Pennsylvania (US)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1038/s41467-025-56622-2
- Citations
- 7
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 11.44
Funders
- University of Pennsylvania
- National Institutes of Health