Journal Article

DZNE-2026-00990

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png ConvexGating infers gating strategies from clusters in single cell cytometry data.

Friedrich, V. D. ; Mai, K. (First author)DZNE* ; Hofer, T. P. ; Nößner, E. ; Bonaguro, L.DZNE* ; Hartmann, C. L.DZNE* ; Frolov, A.DZNE* ; Carraro, C.DZNE* ; Hamada, D.DZNE* ; Hadaddzadeh Shakiba, M.DZNE* ; Theis, H.DZNE* ; Silva Ribeiro, D. J. ; Wachten, D. ; Wunderlich, F. T. ; Scholz, M. ; Theis, F. J. ; Becker, M.DZNE* ; Beyer, M. D.DZNE* ; Schultze, J. L.DZNE* ; Büttner, M. (Last author)DZNE*

2026
Springer Nature [London]

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Please use a persistent id in citations: doi:10.1038/s41467-026-77360-z

Abstract: Manual expert gating remains common practice for defining specific cell populations in flow cytometry data, but increasing numbers of measured parameters and high inter-rater variability limit consistency across studies. Cluster-based approaches use the full marker space to define cell populations more consistently, but their outputs cannot be directly implemented on a cell sorter. Here we develop ConvexGating, an artificial intelligence tool to address this gap by automatically learning interpretable gating strategies for sorting in an unbiased, data-driven manner, generating low-contamination strategies for both known and previously unknown cell populations, including plasmacytoid dendritic cells identified solely as CD57-CD13-CD45RA+ CD123+ cells. We show that ConvexGating derives sorting strategies for CD8+ subtypes and adipose progenitor cell populations, which we validate experimentally by single-cell sequencing of sorted cells. We also demonstrate that the method transfers effectively to Cytometry by Time of Flight and Cellular Indexing of Transcriptomes and Epitopes by Sequencing data and improves marker panel design for cell sorting.

Keyword(s): Flow Cytometry: methods (MeSH) ; Single-Cell Analysis: methods (MeSH) ; Humans (MeSH) ; Dendritic Cells: cytology (MeSH) ; Dendritic Cells: metabolism (MeSH) ; Clustering Algorithms (MeSH) ; Cell Separation: methods (MeSH) ; Cluster Analysis (MeSH) ; Artificial Intelligence (MeSH) ; CD8-Positive T-Lymphocytes: cytology (MeSH) ; CD8-Positive T-Lymphocytes: metabolism (MeSH)

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Contributing Institute(s):

1. Clinical Single Cell Omics (CSCO) / Systems Medicine (AG Schultze)
2. Molecular and Translational Immunaging (AG Bonaguro)
3. Immunogenomics and Neurodegeneration (AG Beyer)
4. Platform for Single Cell Genomics and Epigenomics (PRECISE)
5. Modular High Performance Computing and Artificial Intelligence (AG Becker)

Research Program(s):

1. 354 - Disease Prevention and Healthy Aging (POF4-354) (POF4-354)
2. 351 - Brain Function (POF4-351) (POF4-351)
3. 352 - Disease Mechanisms (POF4-352) (POF4-352)

Experiment(s):

1. Platform for Single Cell Genomics and Epigenomics at DZNE University of Bonn

Appears in the scientific report 2026

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