Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX
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Alastair Magness Emma Colliver Katey Enfield Claudia Lee Masako Shimato Emer Daly David A Moore Monica Sivakumar Karishma Valand Dina Levi Crispin Hiley Philip Hobson Febe VanMaldegem James L Reading Sergio A Quezada Julian Downward Erik Sahai Charles Swanton Mihaela AngelovaAbstract
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.
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Journal
Nature Communications
Volume
15
Issue number
1
Pages
5135
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10.1038/s41467-024-48870-5
Europe PubMed Central
38879602
Pubmed
38879602
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