MATISSE: An analysis protocol for combining imaging mass cytometry with fluorescence microscopy to generate single-cell data

Krijgsman, Daniëlle; Sinha, Neeraj; Baars, Matthijs J.D.; Dam, Stephanie van; Amini, Mojtaba; Vercoulen, Yvonne

doi:10.1016/j.xpro.2021.101034

Cited by 12 publications

(16 citation statements)

References 7 publications

(3 reference statements)

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“…In recent years, the development of multiplex imaging technologies has increased the need for methods to analyze imaging data, particularly spatial single-cell analysis. Im-ageJ [86] is the major open-source software package used for many different image analysis tasks, including single-cell segmentation, tumor grading of clinical images originating from magnetic resonance imaging (MRI), or staining quantification [87][88][89]. A more recent open-source software package specifically developed for tissue analysis, QuPath [90], is now being applied to multiple spatial tissue analysis tasks, including region annotation, cell segmentation, marker expression, and distance calculation.…”

Section: Challenges and Developments In Spatial Data Analysismentioning

confidence: 99%

“…These algorithms include user-guided machine learning pipelines with Illastik [92] and CellProfiler [93]. These pipelines have been used in the analysis of datasets that combine IMC and fluorescent microscopy [16,89,91]. More recently, deep-learning algorithms that use convolutional neural networks (CNNs) have become available; these include DeepCell [17,94], U-NET [95], and DeepImageJ [96].…”

Section: Cell Segmentationmentioning

confidence: 99%

“…This risk of segmentation in too many fragments is increasing when the imaging data has a lower resolution. To improve single-cell segmentation quality in dense areas in lower resolution images, the MATISSE pipeline has recently been released [89,91]. A final challenge that is faced in single-cell data generation is the segmentation of irregularly shaped cell types, such as macrophages.…”

Section: Conclusion and Future Outlookmentioning

confidence: 99%

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Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Dam

Baars

Vercoulen

2022

Cancers

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The tumor microenvironment is a complex ecosystem containing various cell types, such as immune cells, fibroblasts, and endothelial cells, which interact with the tumor cells. In recent decades, the cancer research field has gained insight into the cellular subtypes that are involved in tumor microenvironment heterogeneity. Moreover, it has become evident that cellular interactions in the tumor microenvironment can either promote or inhibit tumor development, progression, and drug resistance, depending on the context. Multiplex spatial analysis methods have recently been developed; these have offered insight into how cellular crosstalk dynamics and heterogeneity affect cancer prognoses and responses to treatment. Multiplex (imaging) technologies and computational analysis methods allow for the spatial visualization and quantification of cell–cell interactions and properties. These technological advances allow for the discovery of cellular interactions within the tumor microenvironment and provide detailed single-cell information on properties that define cellular behavior. Such analyses give insights into the prognosis and mechanisms of therapy resistance, which is still an urgent problem in the treatment of multiple types of cancer. Here, we provide an overview of multiplex imaging technologies and concepts of downstream analysis methods to investigate cell–cell interactions, how these studies have advanced cancer research, and their potential clinical implications.

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Section: Challenges and Developments In Spatial Data Analysismentioning

confidence: 99%

Section: Cell Segmentationmentioning

confidence: 99%

Section: Conclusion and Future Outlookmentioning

confidence: 99%

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Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Dam

Baars

Vercoulen

2022

Cancers

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“…2A). Using the MATISSE cell segmentation pipeline that simultaneously takes advantage of highresolution DAPI nuclear imaging in combination with high-multiplexity IMC, 22,23 we identified 215,293 single cells across all patient samples. After removal of cells from excluded tissue regions (including artefacts, lymphoid follicles and submucosa), 184,975 single cells remained for data normalization, scaling and lineage determination.…”

Section: Cd8 + T Cells Are the Dominant Immune Cell Population In αPd...mentioning

confidence: 99%

Highly multiplexed spatial analysis identifies tissue-resident memory T cells as drivers of ulcerative and immune checkpoint inhibitor induced colitis

Eijs

Linde

Baars

et al. 2023

Preprint

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Colitis is a prevalent adverse event associated with immune checkpoint inhibitor (ICI) therapy with similarities to inflammatory bowel disease. Incomplete mechanistic understanding of ICI-colitis curtails evidence-based treatment. Given the often-overlooked connection between tissue architecture and mucosal immune cell function, we here applied imaging mass cytometry (IMC) to gain spatial proteomic insight in ICI-colitis in comparison to ulcerative colitis (UC). Using a cell segmentation pipeline that simultaneously utilizes high-resolution nuclear imaging and high-multiplexity IMC, we show that CD8+T cells are significantly more abundant (and dominant) in anti-PD-1 +/- anti-CTLA-4-induced colitis compared to anti-CTLA-4-induced colitis and UC. We identified activated, cycling CD8+tissue-resident memory T (TRM) cells at the lamina propria-epithelial interface as drivers of cytotoxicity in ICI-colitis and UC. Moreover, we found that combined ICI-induced colitis featured highest granzyme B levels both in tissue and serum. Together, these data reinforce CD8+TRMcells as potentially targetable drivers of ICI-colitis.

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“…There are a number of published "end to end" pipelines for IMC data analysis (8)(9)(10)(11)(12) that utilise open source software for segmentation such as Ilastik (13) and CellProfiler (14,15), as well as StarDist (16) and IMC-specific approaches that utilise deep learning (17). There have also been attempts to use matched fluorescent images of the nuclei using DAPI co-staining to improve segmentation accuracy (18) as well as removing image noise (19,20). Nonetheless, it has been shown that, due to the nature of tissue imaging, simple approaches to single cell segmentation are often highly effective (21).…”

Section: Introductionmentioning

confidence: 99%

OPTIMAL: An OPTimised Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration

Hunter

Nicorescu

Foster

et al. 2023

Preprint

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Analysis of Imaging Mass Cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single cell segmentation and sub-optimal approaches for data visualisation and exploration. This can lead to inaccurate identification of cell phenotypes, states or spatial relationships compared to reference data from single cell suspension technologies. To this end we have developed the "OPTIMAL" framework to determine the best approaches for cell segmentation, parameter transformation, batch effect correction, data visualisation/clustering and spatial neighbourhood analysis. Using a panel of 27 metal-tagged antibodies recognising well characterised phenotypic and functional markers to stain the same FFPE human tonsil sample Tissue Microarray (TMA) over 12 temporally distinct batches we tested a total of four cell segmentation models, a range of different arcsinh cofactor parameter transformation values, five different dimensionality reduction algorithms and two clustering methods. Finally we assessed the optimal approach for performing neighbourhood analysis. We found that single cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bi-variate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximised the statistical separation between negative and positive signal distributions and a simple Z-score normalisation step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing Phenograph in terms of cell type identification. We also found that neighbourhood analysis was influenced by the method used for finding neighbouring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates .FCS files from the segmentation output, allows for single cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.

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MATISSE: An analysis protocol for combining imaging mass cytometry with fluorescence microscopy to generate single-cell data

Cited by 12 publications

References 7 publications

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Highly multiplexed spatial analysis identifies tissue-resident memory T cells as drivers of ulcerative and immune checkpoint inhibitor induced colitis

OPTIMAL: An OPTimised Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration

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