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A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially-resolved tissue phenotyping at single-cell resolution

Author

Listed:
  • Michele Bortolomeazzi

    (The Francis Crick Institute
    King’s College London)

  • Lucia Montorsi

    (The Francis Crick Institute
    King’s College London)

  • Damjan Temelkovski

    (The Francis Crick Institute
    King’s College London)

  • Mohamed Reda Keddar

    (The Francis Crick Institute
    King’s College London)

  • Amelia Acha-Sagredo

    (The Francis Crick Institute
    King’s College London)

  • Michael J. Pitcher

    (King’s College London)

  • Gianluca Basso

    (IRCCS Humanitas Research Hospital
    IRCCS Humanitas Research Hospital)

  • Luigi Laghi

    (IRCCS Humanitas Research Hospital
    University of Parma)

  • Manuel Rodriguez-Justo

    (University College London Cancer Institute)

  • Jo Spencer

    (King’s College London)

  • Francesca D. Ciccarelli

    (The Francis Crick Institute
    King’s College London)

Abstract

Multiplexed imaging technologies enable the study of biological tissues at single-cell resolution while preserving spatial information. Currently, high-dimension imaging data analysis is technology-specific and requires multiple tools, restricting analytical scalability and result reproducibility. Here we present SIMPLI (Single-cell Identification from MultiPLexed Images), a flexible and technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After raw image processing, SIMPLI performs a spatially resolved, single-cell analysis of the tissue slide as well as cell-independent quantifications of marker expression to investigate features undetectable at the cell level. SIMPLI is highly customisable and can run on desktop computers as well as high-performance computing environments, enabling workflow parallelisation for large datasets. SIMPLI produces multiple tabular and graphical outputs at each step of the analysis. Its containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. Software is available at “SIMPLI [ https://github.com/ciccalab/SIMPLI ]”.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28470-x
    DOI: 10.1038/s41467-022-28470-x
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    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.
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    1. 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.

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