ImaCytE: Visual Exploration of Cellular Micro-Environments for Imaging Mass Cytometry Data

Somarakis, Antonios; Unen, Vincent van; Koning, Frits; Lelieveldt, Boudewijn P. F.; Höllt, Thomas

doi:10.1109/tvcg.2019.2931299

Cited by 70 publications

(98 citation statements)

References 39 publications

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“…In the present study we have used the MCD TM viewer software to visualize the images of the tissue sections. In addition cell segmentation approaches based on the identification of nuclei have been developed to aid in the visualization of IMC data (17,18) as well as computational approaches to identify and quantify cell-cell interactions like Imacyte and Histocat (19,20). Together this allows for an in depth investigation of cellular interactions in a variety of tissues.…”

Section: Discussionmentioning

confidence: 99%

A 34-Marker Panel for Imaging Mass Cytometric Analysis of Human Snap-Frozen Tissue

et al. 2020

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Imaging mass cytometry (IMC) is able to quantify the expression of dozens of markers at sub-cellular resolution on a single tissue section by combining a novel laser ablation system with mass cytometry. As such, it allows us to gain spatial information and antigen quantification in situ, and can be applied to both snap-frozen and formalin-fixed, paraffin-embedded (FFPE) tissue sections. Herein, we have developed and optimized the immunodetection conditions for a 34-antibody panel for use on human snap-frozen tissue sections. For this, we tested the performance of 80 antibodies. Moreover, we compared tissue drying times, fixation procedures and antibody incubation conditions. We observed that variations in the drying times of tissue sections had little impact on the quality of the images. Fixation with methanol for 5 min at −20 • C or 1% paraformaldehyde (PFA) for 5 min at room temperature followed by methanol for 5 min at −20 • C were superior to fixation with acetone or PFA only. Finally, we observed that antibody incubation overnight at 4 • C yielded more consistent results as compared to staining at room temperature for 5 h. Finally, we used the optimized method for staining of human fetal and adult intestinal tissue samples. We present the tissue architecture and spatial distribution of the stromal cells and immune cells in these samples visualizing blood vessels, the epithelium and lamina propria based on the expression of α-smooth muscle actin (α-SMA), E-Cadherin and Vimentin, while simultaneously revealing the colocalization of T cells, innate lymphoid cells (ILCs), and various myeloid cell subsets in the lamina propria of the human fetal intestine. We expect that this work can aid the scientific community who wish to improve IMC data quality.

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Section: Discussionmentioning

confidence: 99%

A 34-Marker Panel for Imaging Mass Cytometric Analysis of Human Snap-Frozen Tissue

et al. 2020

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“…To understand the tissue architecture, it is necessary to have prior knowledge on which cell types can be present and what their physical relationship to one another could be. Several computational approaches have been developed to enable data analysis of spatially-resolved multiplexed tissue measurements including HistoCAT (148) and ImaCytE (149). These approaches apply cell segmentation masks [using a combination of Ilastik (150) and CellProfiler (151)] to extract single-cell data from each image, which allow for deep characterization using multidimensional reduction tools such as t-SNE combined with the assessment of spatial localization and cellular interactions.…”

Section: Spatially-resolved Datamentioning

confidence: 99%

Unraveling the Complexity of the Cancer Microenvironment With Multidimensional Genomic and Cytometric Technologies

et al. 2020

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Cancers are characterized by extensive heterogeneity that occurs intratumorally, between lesions, and across patients. To study cancer as a complex biological system, multidimensional analyses of the tumor microenvironment are paramount. Single-cell technologies such as flow cytometry, mass cytometry, or single-cell RNA-sequencing have revolutionized our ability to characterize individual cells in great detail and, with that, shed light on the complexity of cancer microenvironments. However, a key limitation of these single-cell technologies is the lack of information on spatial context and multicellular interactions. Investigating spatial contexts of cells requires the incorporation of tissue-based techniques such as multiparameter immunofluorescence, imaging mass cytometry, or in situ detection of transcripts. In this Review, we describe the rise of multidimensional single-cell technologies and provide an overview of their strengths and weaknesses. In addition, we discuss the integration of transcriptomic, genomic, epigenomic, proteomic, and spatially-resolved data in the context of human cancers. Lastly, we will deliberate on how the integration of multi-omics data will help to shed light on the complex role of cell types present within the human tumor microenvironment, and how such system-wide approaches may pave the way toward more effective therapies for the treatment of cancer.

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“…This approach allows the qualitative comparison of pixel intensities across images. Observing image-to-image differences in total pixel intensities can indicate batch effects in staining efficiency 21 or biological features such as the expected loss of proinsulin signal over T1D progression (Fig. 4).…”

Section: Figure 3: Distribution Of Islet Cell Types Along T1d Progresmentioning

confidence: 99%

“…The grid-like visualisation is not restricted to uniform image dimensions and signal intensities can be qualitatively compared across images. This approach is crucial to identify technical or biological staining differences between images, batches or conditions 21 .…”

Section: A Shiny Application For Gating and Visualization Of Cellsmentioning

confidence: 99%

“…Custom scripts 4,11,14,15 and image analysis software such as ImageJ 16 , CellProfiler 17 , ilastik 18 and QuPath 19 are commonly used to process and analyse multiplexed imaging data. More recently, specialised tools based on graphical user interfaces (GUIs) have been developed to facilitate the analysis of high-dimensional spatial expression data [20][21][22][23] . The GUI toolbox histoCAT was specifically designed for IMC data to visualise multi-channel images and segmentation masks, and to link cellular phenotypes to their location within tissues 24 .…”

Section: Introductionmentioning

confidence: 99%

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cytomapper: an R/Bioconductor package for visualisation of highly multiplexed imaging data

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Damond

Hoch

et al. 2020

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SUMMARYHighly multiplexed imaging technologies enable spatial profiling of dozens of biomarkers in situ. Standard data processing pipelines quantify cell-specific features and generate object segmentation masks as well as multi-channel images. Therefore, multiplexed imaging data can be visualised across two layers of information: pixel-intensities represent the spatial expression of biomarkers across an image while segmented objects visualise cellular morphology, interactions and cell phenotypes in their microenvironment.Here we describe cytomapper, a computational tool that enables visualisation of pixel- and cell-level information obtained by multiplexed imaging. The package is written in the statistical programming language R, integrates with the image and single-cell analysis infrastructure of the Bioconductor project, and allows visualisation of single to hundreds of images in parallel. Using cytomapper, expression of multiple markers is displayed as composite images, segmentation masks are coloured based on cellular features, and selected cells can be outlined in images based on their cell type, among other functions. We illustrate the utility of cytomapper by analysing 100 images obtained by imaging mass cytometry from a cohort of type 1 diabetes patients and healthy individuals. In addition, cytomapper includes a Shiny application that allows hierarchical gating of cells based on marker expression and visualisation of selected cells in corresponding images. Together, cytomapper offers tools for diverse image and single-cell visualisation approaches and supports robust cell phenotyping via gating.

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ImaCytE: Visual Exploration of Cellular Micro-Environments for Imaging Mass Cytometry Data

Cited by 70 publications

References 39 publications

A 34-Marker Panel for Imaging Mass Cytometric Analysis of Human Snap-Frozen Tissue

A 34-Marker Panel for Imaging Mass Cytometric Analysis of Human Snap-Frozen Tissue

Unraveling the Complexity of the Cancer Microenvironment With Multidimensional Genomic and Cytometric Technologies

cytomapper: an R/Bioconductor package for visualisation of highly multiplexed imaging data

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