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

Author

Listed:
  • 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
    University College London Cancer Institute
    University College London Hospitals)

  • 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
    University College London Cancer Institute)

  • Philip S. Hobson

    (The Francis Crick Institute)

  • Febe Maldegem

    (The Francis Crick Institute
    Location VUMC
    Cancer Biology and Immunology
    Cancer Immunology)

  • James L. Reading

    (University College London Cancer Institute
    University College London Cancer Institute
    Department of Haematology, University College London Cancer Institute)

  • Sergio A. Quezada

    (University College London Cancer Institute
    Department of Haematology, University College London Cancer Institute)

  • Julian Downward

    (The Francis Crick Institute)

  • Erik Sahai

    (The Francis Crick Institute)

  • Charles Swanton

    (The Francis Crick Institute
    University College London Cancer Institute
    University College London Hospitals)

  • Mihaela Angelova

    (The Francis Crick Institute)

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.

Suggested Citation

  • 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. Hobso, 2024. "Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48870-5
    DOI: 10.1038/s41467-024-48870-5
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    References listed on IDEAS

    as
    1. Febe Maldegem & Karishma Valand & Megan Cole & Harshil Patel & Mihaela Angelova & Sareena Rana & Emma Colliver & Katey Enfield & Nourdine Bah & Gavin Kelly & Victoria Siu Kwan Tsang & Edurne Mugarza &, 2021. "Characterisation of tumour microenvironment remodelling following oncogene inhibition in preclinical studies with imaging mass cytometry," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    2. Michele Bortolomeazzi & Lucia Montorsi & Damjan Temelkovski & Mohamed Reda Keddar & Amelia Acha-Sagredo & Michael J. Pitcher & Gianluca Basso & Luigi Laghi & Manuel Rodriguez-Justo & Jo Spencer & Fran, 2022. "A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially-resolved tissue phenotyping at single-cell resolution," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Robert Bentham & Kevin Litchfield & Thomas B. K. Watkins & Emilia L. Lim & Rachel Rosenthal & Carlos Martínez-Ruiz & Crispin T. Hiley & Maise Al Bakir & Roberto Salgado & David A. Moore & Mariam Jamal, 2021. "Using DNA sequencing data to quantify T cell fraction and therapy response," Nature, Nature, vol. 597(7877), pages 555-560, September.
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