Publisher Correction to: MATISSE: a method for improved single cell segmentation in imaging mass cytometry

Baars, Matthijs J.D.; Sinha, Neeraj; Amini, Mojtaba; Pieterman-Bos, Annelies; Dam, Stephanie van; Ganpat, Maroussia M. P.; Oldenburg, Bas; Vercoulen, Yvonne

doi:10.1186/s12915-021-01065-6

Cited by 2 publications

(1 citation statement)

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“…cell segmentation including the tools DeepCell ( Van Valen et al 2016 ), Ilastik ( Berg et al 2019 ), MATISSE ( Baars et al 2021 ), and CellPose ( Stringer et al 2021 );…”

Section: Introductionmentioning

confidence: 99%

LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies

Ehsani,

Jonassen,

Akslen

et al. 2023

Bioinformatics Advances

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Motivation Recent advances in highly multiplexed imaging have provided unprecedented insights into the complex cellular organization of tissues, with many applications in translational medicine. However, downstream analyses of multiplexed imaging data face several technical limitations, and although some computational methods and bioinformatics tools are available, deciphering the complex spatial organisation of cellular ecosystems remains a challenging problem. Results To mitigate this problem, we develop a novel computational tool, LOCATOR (anaLysis Of CAncer Tissue micrOenviRonment), for spatial analysis of cancer tissue microenvironments using data acquired from mass cytometry imaging (MCI) technologies. LOCATOR introduces a graph-based representation of tissue images to describe features of the cellular organisation and deploys downstream analysis and visualisation utilities that can be used for data-driven patient risk stratification. Our case studies using MCI data from two well-annotated breast cancer cohorts re-confirmed that the spatial organisation of the tumour-immune microenvironment is strongly associated with the clinical outcome in breast cancer. In addition, we report interesting potential associations between the spatial organization of macrophages and patients’ survival. Our work introduces an automated and versatile analysis tool for MCI data with many applications in future cancer research projects. Availability and implementation Datasets and codes of LOCATOR are publicly available at https://github.com/RezvanEhsani/LOCATOR. Supplementary information Supplementary data are available at Bioinformatics Advances online

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“…cell segmentation including the tools DeepCell ( Van Valen et al 2016 ), Ilastik ( Berg et al 2019 ), MATISSE ( Baars et al 2021 ), and CellPose ( Stringer et al 2021 );…”

Section: Introductionmentioning

confidence: 99%

LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies

Ehsani,

Jonassen,

Akslen

et al. 2023

Bioinformatics Advances

View full text Add to dashboard Cite

show abstract

LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies

Ehsani

Jonassen

Akslen

et al. 2023

Preprint

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Motivation: Recent advances in highly multiplexed imaging have provided unprecedented insights into the complex cellular organization of tissues, with many applications in translational medicine. However, downstream analyses of multiplexed imaging data introduce several technical barriers, and although some computational methods and bioinformatics tools are available, deciphering the complex spatial organisation of cellular ecosystems remains a challenging problem. Results: We developed a novel computational method, LOCATOR (anaLysis Of CAncer Tissue micrOenviRonment), for spatial analysis of cancer microenvironments using data acquired from mass cytometry imaging (MCI) technologies. LOCATOR introduces a graph-based representation of tissue images to describe features of the cellular organisation and deploys downstream analysis and visualisation utilities that can be used for data-driven patient risk stratification. Our case studies using MCI data from two well-annotated breast cancer cohorts re-confirmed that the spatial organisation of the tumour-immune microenvironment is strongly associated with the clinical outcome in breast cancer. In addition, we report interesting potential associations between the cellular organization of macrophages and patients survival. Our work introduces an automated and versatile analysis framework for MCI data with many applications in future cancer research projects. Availability: Datasets and codes of LOCATOR are publicly available at https://github.com/RezvanEhsani/LOCATOR.

show abstract

Publisher Correction to: MATISSE: a method for improved single cell segmentation in imaging mass cytometry

Cited by 2 publications

References 1 publication

LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies

LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies

LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies

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