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

Michele Bortolomeazzi; Lucia Montorsi; Damjan Temelkovski; Mohamed Reda Keddar; Amelia Acha-Sagredo; Michael J. Pitcher; Gianluca Basso; Luigi Laghi; Manuel Rodriguez-Justo; Jo Spencer; Francesca D. Ciccarelli

doi:10.1038/s41467-022-28470-x

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

Michele Bortolomeazzi, Lucia Montorsi, Damjan Temelkovski, Mohamed Reda Keddar, Amelia Acha-Sagredo, Michael J. Pitcher, Gianluca Basso, Luigi Laghi, Manuel Rodriguez-Justo, Jo Spencer, Francesca D. Ciccarelli

Research output: Contribution to journal › Article › peer-review

21 Citations (Scopus)

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 ]".

Original language	English
Article number	781
Number of pages	1
Journal	Nature Communications
Volume	13
Issue number	1
Early online date	9 Feb 2022
DOIs	https://doi.org/10.1038/s41467-022-28470-x
Publication status	Published - 9 Feb 2022

Keywords

IMAGE-ANALYSIS
Cell segmentation
Cell clustering
pixel-analysis
multiplex-imaging
high-dimensional data

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/s41467-022-28470-x

https://www.biorxiv.org/content/10.1101/2021.04.01.437886v2Licence: CC BY

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Bortolomeazzi, M., Montorsi, L., Temelkovski, D., Keddar, M. R., Acha-Sagredo, A., Pitcher, M. J., Basso, G., Laghi, L., Rodriguez-Justo, M., Spencer, J., & Ciccarelli, F. D. (2022). A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially-resolved tissue phenotyping at single-cell resolution. Nature Communications, 13(1), Article 781. https://doi.org/10.1038/s41467-022-28470-x

@article{0743bc745e0c459ab0e398eb52521301,

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

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 ]{"}.",

keywords = "IMAGE-ANALYSIS, Cell segmentation, Cell clustering, pixel-analysis, multiplex-imaging, high-dimensional data",

author = "Michele Bortolomeazzi and Lucia Montorsi and Damjan Temelkovski and Keddar, {Mohamed Reda} and Amelia Acha-Sagredo and Pitcher, {Michael J.} and Gianluca Basso and Luigi Laghi and Manuel Rodriguez-Justo and Jo Spencer and Ciccarelli, {Francesca D.}",

note = "Funding Information: We thank Sharavan Vishaan Venkateswaran for testing SIMPLI. This work was supported by Cancer Research UK (C43634/A25487, F.D.C.), the Cancer Research UK King{\textquoteright}s Health Partners Centre at King{\textquoteright}s College London (C604/A25135, F.D.C.), the Cancer Research UK City of London Centre (C7893/A26233, F.D.C.), innovation programme under the Marie Sk{\l}odowska-Curie (grant agreement No CONTRA-766030, F.D.C.) and the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001002, F.D.C.), the UK Medical Research Council (FC001002, F.D.C.), and the Wellcome Trust (FC001002, F.D.C.) and Crohn{\textquoteright}s and Colitis UK (M2019/3, J.S.). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Funding Information: We thank Sharavan Vishaan Venkateswaran for testing SIMPLI. This work was supported by Cancer Research UK (C43634/A25487, F.D.C.), the Cancer Research UK King?s Health Partners Centre at King?s College London (C604/A25135, F.D.C.), the Cancer Research UK City of London Centre (C7893/A26233, F.D.C.), innovation programme under the Marie Sk?odowska-Curie (grant agreement No CONTRA-766030, F.D.C.) and the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001002, F.D.C.), the UK Medical Research Council (FC001002, F.D.C.), and the Wellcome Trust (FC001002, F.D.C.) and Crohn?s and Colitis UK (M2019/3, J.S.). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

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day = "9",

doi = "10.1038/s41467-022-28470-x",

language = "English",

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Bortolomeazzi, M , Montorsi, L , Temelkovski, D , Keddar, MR , Acha-Sagredo, A , Pitcher, MJ, Basso, G, Laghi, L, Rodriguez-Justo, M, Spencer, J & Ciccarelli, FD 2022, 'A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially-resolved tissue phenotyping at single-cell resolution', Nature Communications, vol. 13, no. 1, 781. https://doi.org/10.1038/s41467-022-28470-x

TY - JOUR

T1 - A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially-resolved tissue phenotyping at single-cell resolution

AU - Bortolomeazzi, Michele

AU - Montorsi, Lucia

AU - Temelkovski, Damjan

AU - Keddar, Mohamed Reda

AU - Acha-Sagredo, Amelia

AU - Pitcher, Michael J.

AU - Basso, Gianluca

AU - Laghi, Luigi

AU - Rodriguez-Justo, Manuel

AU - Spencer, Jo

AU - Ciccarelli, Francesca D.

N1 - Funding Information: We thank Sharavan Vishaan Venkateswaran for testing SIMPLI. This work was supported by Cancer Research UK (C43634/A25487, F.D.C.), the Cancer Research UK King’s Health Partners Centre at King’s College London (C604/A25135, F.D.C.), the Cancer Research UK City of London Centre (C7893/A26233, F.D.C.), innovation programme under the Marie Skłodowska-Curie (grant agreement No CONTRA-766030, F.D.C.) and the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001002, F.D.C.), the UK Medical Research Council (FC001002, F.D.C.), and the Wellcome Trust (FC001002, F.D.C.) and Crohn’s and Colitis UK (M2019/3, J.S.). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Funding Information: We thank Sharavan Vishaan Venkateswaran for testing SIMPLI. This work was supported by Cancer Research UK (C43634/A25487, F.D.C.), the Cancer Research UK King?s Health Partners Centre at King?s College London (C604/A25135, F.D.C.), the Cancer Research UK City of London Centre (C7893/A26233, F.D.C.), innovation programme under the Marie Sk?odowska-Curie (grant agreement No CONTRA-766030, F.D.C.) and the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001002, F.D.C.), the UK Medical Research Council (FC001002, F.D.C.), and the Wellcome Trust (FC001002, F.D.C.) and Crohn?s and Colitis UK (M2019/3, J.S.). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Publisher Copyright: © 2022, The Author(s).

PY - 2022/2/9

Y1 - 2022/2/9

N2 - 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 ]".

AB - 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 ]".

KW - IMAGE-ANALYSIS

KW - Cell segmentation

KW - Cell clustering

KW - pixel-analysis

KW - multiplex-imaging

KW - high-dimensional data

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DO - 10.1038/s41467-022-28470-x

M3 - Article

C2 - 35140207

AN - SCOPUS:85124276289

SN - 2041-1723

VL - 13

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 781

ER -

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

Abstract

Keywords

UN SDGs

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