Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX

Alastair Magness (), Emma Colliver, Katey S. S. Enfield, Claudia Lee, Masako Shimato, Emer Daly, David A. Moore, Monica Sivakumar, Karishma Valand, Dina Levi, Crispin T. Hiley, Philip S. Hobson, Febe Maldegem, James L. Reading, Sergio A. Quezada, Julian Downward, Erik Sahai, Charles Swanton () and Mihaela Angelova ()
Additional contact information
Alastair Magness: The Francis Crick Institute
Emma Colliver: The Francis Crick Institute
Katey S. S. Enfield: The Francis Crick Institute
Claudia Lee: The Francis Crick Institute
Masako Shimato: The Francis Crick Institute
Emer Daly: The Francis Crick Institute
David A. Moore: The Francis Crick Institute
Monica Sivakumar: University College London Cancer Institute
Karishma Valand: The Francis Crick Institute
Dina Levi: The Francis Crick Institute
Crispin T. Hiley: The Francis Crick Institute
Philip S. Hobson: The Francis Crick Institute
Febe Maldegem: The Francis Crick Institute
James L. Reading: University College London Cancer Institute
Sergio A. Quezada: University College London Cancer Institute
Julian Downward: The Francis Crick Institute
Erik Sahai: The Francis Crick Institute
Charles Swanton: The Francis Crick Institute
Mihaela Angelova: The Francis Crick Institute

Nature Communications, 2024, vol. 15, issue 1, 1-20

Abstract: Abstract The growing scale and dimensionality of multiplexed imaging require reproducible and comprehensive yet user-friendly computational pipelines. TRACERx-PHLEX performs deep learning-based cell segmentation (deep-imcyto), automated cell-type annotation (TYPEx) and interpretable spatial analysis (Spatial-PHLEX) as three independent but interoperable modules. PHLEX generates single-cell identities, cell densities within tissue compartments, marker positivity calls and spatial metrics such as cellular barrier scores, along with summary graphs and spatial visualisations. PHLEX was developed using imaging mass cytometry (IMC) in the TRACERx study, validated using published Co-detection by indexing (CODEX), IMC and orthogonal data and benchmarked against state-of-the-art approaches. We evaluated its use on different tissue types, tissue fixation conditions, image sizes and antibody panels. As PHLEX is an automated and containerised Nextflow pipeline, manual assessment, programming skills or pathology expertise are not essential. PHLEX offers an end-to-end solution in a growing field of highly multiplexed data and provides clinically relevant insights.

Date: 2024
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DOI: 10.1038/s41467-024-48870-5

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