histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data

Schapiro, Denis; Jackson, Hartland W.; Raghuraman, Swetha; Fischer, Jana; Zanotelli, Vito Riccardo Tomaso; Schulz, Daniel; Giesen, Charlotte; Catena, Raúl; Varga, Zsuzsanna; Bodenmiller, Bernd

doi:10.1038/nmeth.4391

Cited by 496 publications

(666 citation statements)

References 25 publications

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“…Bodenmiller et al . are developing multivariate computational tools to visualize and analyze multiplexed images of human tissue sections generated by IMC . However, there is no single software package or analysis workflow that could currently be applied to answer specific biological questions.…”

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confidence: 99%

Multidimensional profiling of drug‐treated cells by Imaging Mass Cytometry

2019

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In pharmaceutical research, high‐content screening is an integral part of lead candidate development. Measuring drug response in vitro by examining over 40 parameters, including biomarkers, signaling molecules, cell morphological changes, proliferation indices, and toxicity in a single sample, could significantly enhance discovery of new therapeutics. As a proof of concept, we present here a workflow for multidimensional Imaging Mass Cytometry™ (IMC™) and data processing with open source computational tools. CellProfiler was used to identify single cells through establishing cellular boundaries, followed by histoCAT™ (histology topography cytometry analysis toolbox) for extracting single‐cell quantitative information visualized as t‐SNE plots and heatmaps. Human breast cancer‐derived cell lines SKBR3, HCC1143, and MCF‐7 were screened for expression of cellular markers to generate digital images with a resolution comparable to conventional fluorescence microscopy. Predicted pharmacodynamic effects were measured in MCF‐7 cells dosed with three target‐specific compounds: growth stimulatory EGF, microtubule depolymerization agent nocodazole, and genotoxic chemotherapeutic drug etoposide. We show strong pairwise correlation between nuclear markers pHistone3 S28 , Ki‐67, and p4E‐BP1 T37/T46 in classified mitotic cells and anticorrelation with cell surface markers. Our study demonstrates that IMC data expand the number of measured parameters in single cells and brings higher‐dimension analysis to the field of cell‐based screening in early lead compound discovery.

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confidence: 99%

Multidimensional profiling of drug‐treated cells by Imaging Mass Cytometry

2019

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“…In fact, using this histological multiplexing approach we do not isolate stromal cells and therefore overcome the risk of damaging at least a fraction of stromal cells by preserving the microenvironment, in which they are nested. By means of classified pixel segmentation, masks are generated with Ilastik 1.1.9 and CellProfiler 2.1.1 in order to create heat maps of protein expression (43). 3).…”

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confidence: 99%

Multiplexed fluorescence microscopy reveals heterogeneity among stromal cells in mouse bone marrow sections

et al. 2018

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The bone marrow (BM) consists of multiple, structured micro-environmental entities-the so called niches, which contain hematopoietic cells as well as stromal cells. These niches fulfill a variety of functions, such as control of the hematopoietic stem cell pool, differentiation of hematopoietic cells, and maintenance of immunological memory. However, due to the molecular and cellular complexity and a lack of suitable histological multiplexing methods, the composition of the various BM niches is still elusive. In this study, we apply multiepitope-ligand-cartography (MELC) on bone sections from mice. We combine multiplexed immunofluorescence histology data with various object-based segmentation approaches in order to define irregularly shaped, net-like structures of stromal cells. We confirm MELC as a robust histological method and validate our automated segmentation algorithms using flow cytometry and manual evaluation. By means of MELC multiplexing, we reveal heterogeneous expression of leptin receptor (LpR), BP-1, and VCAM-1 in the stromal network. Moreover, we demonstrate by quantification a preferential contact of B cell subsets as well as of plasma cells to processes of CXCL12-expressing stromal cells, compared with stromal somata. In summary, our approach is suitable for spatial analysis of complex tissue structures.

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“…For this purpose, histology topography cytometry analyses toolbox (histoCAT) has been developed by the University of Zurich researchers [64]. This software relies on the preprocessing of images, such as cell segmentation by CellProfiler [65] or other methods, followed by pixel based processing in histoCAT to recreate spatial co-localization of the cells.…”

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confidence: 99%

“…Schapiro, et al used histoCAT to analyze 49 diverse breast cancer specimens and matched normal controls, which were imaged via multiplexed IMC. They could identify 29 unique phenotype clusters as defined by expression of specific epitopes, and subsequently focused on clusters containing tumor associated macrophages (TAMs) [64] due to their known involvement in tumor progression or inhibition thereof [66,67]. Among other findings, their analysis indicated that the immediate cellular environment of TAMs is distinctly proliferative and hypoxic, as evident by expression of Ki-67 and carbonic anhydrase IX respectively [64].…”

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confidence: 99%

“…They could identify 29 unique phenotype clusters as defined by expression of specific epitopes, and subsequently focused on clusters containing tumor associated macrophages (TAMs) [64] due to their known involvement in tumor progression or inhibition thereof [66,67]. Among other findings, their analysis indicated that the immediate cellular environment of TAMs is distinctly proliferative and hypoxic, as evident by expression of Ki-67 and carbonic anhydrase IX respectively [64]. Because both intra-tumor hypoxia and hyper proliferation are common hallmarks of breast and other cancers [68], these findings support proof of concept for multiplex tissue staining and data analysis tools.…”

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confidence: 99%

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Advances in high-dimensional mass cytometry cell and tissue analyses for translational biomarker discovery

Elma¹,

Kariv²

2017

Integr Cancer Sci Therap

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The systematic profiling of cellular functions and phenotypes, especially for cancer and inflammation diseases, enables patient stratification strategies and personalized medicine approaches. These studies include quantification of cell surface phenotypic and activation markers, and detection of secreted proteins and peptides. This information can aid discovery of both clinical biomarkers, as well as Ex Vivo/ In Vitro read-outs to guide development of novel therapeutics. The focus of this review is to highlight the value of the precision medicine data generated using novel high-dimensional technologies, such as mass cytometry (MC) and imaging MC, and to review emerging data tools which enable comprehensive data analysis and elucidation. Integration of these novel multiplex read-outs and corresponding mathematical analyses has facilitated discovery of previously unrecognized prognostic clinical biomarkers. During the last two decades our knowledge of cellular homeostasis during disease origination and progression has significantly advanced due to the systematic profiling of cellular functions and phenotypes, especially for immune-cell mediated pathologies [1,2]. In fact, many clinical trials now include cellular immunophenotyping as a biomarker component [3,4]. Patient specimens, often as small as a core needle biopsies or superfluous volumes of routinely collected fluids such as blood or sputum [5], are tested by a multitude of single cell profiling methods focusing on gaining a better understating of the cellular milieu. These studies include quantification of cell surface phenotypic and activation markers [6], ex-vivo response analysis to therapies [7], and detection of secreted proteins and peptides [8]. Multiplexed analyses throughout clinical trials have enabled the collection of valuable information from precious and limited patient specimens applied to investigate cellular disease perturbations. Ultimately these data aid in the development of disease diagnostic and prognostic tools, and more importantly determine patient stratification strategies which enable personalized medicine approaches. The focus of this review is to highlight the value of the translational data generated using novel technologies, such as mass cytometry (MC) and imaging MC, which has brought single cell immunophenotyping and imaging of the tumor microenvironment and other tissues to a new forefront, and to review emerging data tools which enable comprehensive data analysis and interpretation, often identifying previously unrecognized connections between cellular phenotypes and functions in healthy and diseased states [9][10][11][12].Although still in early development, MC based single cell immunophenotyping and analysis are steadily gaining recognition and becoming a mainstay in not only immune-mediated disease research, but in interrogating basic biology of other therapeutic targets [13][14][15][16]. MC or cytometry by time of flight (CyTOF) merges concepts of both flow cytometry and mass spectrometry [17,18]. This method r...

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histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data

Cited by 496 publications

References 25 publications

Multidimensional profiling of drug‐treated cells by Imaging Mass Cytometry

Multidimensional profiling of drug‐treated cells by Imaging Mass Cytometry

Multiplexed fluorescence microscopy reveals heterogeneity among stromal cells in mouse bone marrow sections

Advances in high-dimensional mass cytometry cell and tissue analyses for translational biomarker discovery

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