Automated cytometric gating with human-level performance using bivariate segmentation.

Area of Science:

• Immunology
• Computational Biology
• Biotechnology

Background:

• High-throughput cytometry enables extensive single-cell protein expression analysis.
• Biological and technical variability complicates manual gating, particularly for initial pre-gates dealing with debris and artifacts.

Purpose of the Study:

• To develop an automated framework, UNITO, for rigorous identification of hierarchical cytometric subpopulations.
• To reduce the labor-intensive nature of manual gating in cytometry data analysis.

Main Methods:

• UNITO reframes cell-level classification as an image-based segmentation problem.
• The framework was validated on three independent cohorts: two mass cytometry and one flow cytometry datasets.

Main Results:

• UNITO demonstrated superior performance compared to existing automated methods.
• Its results closely align with the consensus of experienced immunologists, with deviations comparable to individual human performance.

Conclusions:

• UNITO offers a robust and efficient solution for automated cytometry data analysis.
• The framework provides reproducible gating contours for inspection and enables parallel processing for increased speed.