posted on 2024-06-18, 11:42authored byAlastair Magness, Emma Colliver, Katey SS Enfield, Claudia Lee, Masako Shimato, Emer Daly, David A Moore, Monica Sivakumar, Karishma Valand, Dina Levi, Crispin T Hiley, Philip S Hobson, Febe van Maldegem, James L Reading, Sergio A Quezada, Julian Downward, Erik Sahai, Charles Swanton, Mihaela Angelova
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.
Funding
Crick (Grant ID: CC2041, Grant title: Swanton CC2041)
Crick (Grant ID: CC1062, Grant title: STP Flow Cytometry)
Crick (Grant ID: CC2040, Grant title: Sahai CC2040)
Crick (Grant ID: CC2097, Grant title: Downward CC2097)
Novo Nordisk UK Research Foundation (Grant ID: NNF15OC0016584, Grant title: NovoNordisk Foundation 16584)
European Research Council (Grant ID: 835297 - PROTEUS, Grant title: ERC 835297 - PROTEUS)
European Research Council (Grant ID: 101019366 - CAN_ORGANISE, Grant title: ERC 101019366 - CAN_ORGANISE)
European Commission (Grant ID: 838540 - TRACERxTIME, Grant title: EC 838540 - TRACERxTIME)