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Dual-modality Molecular Cartography: Integrating Multiplex mRNA Detection with Protein Imaging Mass Cytometry

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Cleanet: Robust Doublet Detection in Cytometry Data Based on Protein Expression Patterns.

Matei Ionita¹, Michelle L McKeague¹, Mark M Painter^1,2

¹Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Pennsylvania, USA.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|October 9, 2025

Summary

This summary is machine-generated.

Cleanet automates doublet detection in cytometry data, improving immune cell profiling. This method distinguishes single cells from doublets, enabling more accurate analysis of large-scale studies and cell interactions.

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Area of Science:

Immunology
Computational Biology
Biotechnology

Background:

Single-cell analysis using flow and mass cytometry is crucial for understanding human immunology.
Analyzing large-scale studies (hundreds/thousands of samples) presents data analysis bottlenecks.
Distinguishing single cells from doublets (multiple cells detected as one event) is a key preprocessing step.

Purpose of the Study:

To develop an automated method for accurate doublet detection and classification in cytometry data.
To address the limitations of traditional bivariate gating methods for doublet identification.
To enable more efficient and precise analysis of large cytometry datasets.

Main Methods:

Proposed Cleanet, a novel automated approach inspired by single-cell transcriptomics.
Cleanet simulates doublet events and identifies true events with similar expression profiles.
Validated the method on mass cytometry, flow cytometry, and imaging flow cytometry datasets.

Main Results:

Cleanet accurately detects both homotypic and heterotypic doublets across different cytometry techniques.
Imaging flow cytometry confirmed that predicted doublets consist of multiple cells.
Demonstrated Cleanet's ability to classify doublets and detect cell-cell interactions, exemplified by a treatment-specific increase in interactions.

Conclusions:

Cleanet offers an automated, accurate solution for doublet detection and classification in cytometry.
The method streamlines data analysis for large-scale studies, enhancing immune cell profiling.
Cleanet facilitates the study of cell-cell interactions by extracting information from discarded doublet events.