Simultaneous Multiplexed Imaging of mRNA and Proteins with Subcellular Resolution in Breast Cancer Tissue Samples by Mass Cytometry

Schulz, Daniel; Zanotelli, Vito Riccardo Tomaso; Fischer, Jana; Schapiro, Denis; Engler, Stefanie; Lun, Xiao-Kang; Jackson, Hartland W.; Bodenmiller, Bernd

doi:10.1016/j.cels.2017.12.001

Cited by 229 publications

(176 citation statements)

References 53 publications

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“…Compared with high‐throughput sequencing, the experimental conditions for only one or few of genes are better designed and optimized, e.g., BaseScope, ARMS PCR. Slides for multiple molecular biomarkers with different fluorescent labeled probes have been released separately in a relevant study on breast cancer and may imply expansive application in cancer research and molecular diagnosis in the future.…”

Section: Discussionmentioning

confidence: 99%

EGFR T790M detection in formalin‐fixed paraffin‐embedded tissues of patients with lung cancer using RNA‐based in situ hybridization: A preliminary feasibility study

Shi

et al. 2019

Thoracic Cancer

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Background Following drug resistance in patients with lung cancer treated by EGFR TKIs, a biopsy is required to obtain sufficient cancer tissue for T790M detection in order to select potential beneficiaries suitable for third‐generation EGFR TKIs, such as osimertinib. The purpose of this study was to explore the feasibility of using a new in situ analysis technique based on RNA target sequences to detect EGFR T790M in lung cancer. Methods A total of 28 formalin‐fixed paraffin‐embedded (FFPE) samples from 24 lung adenocarcinoma patients archived in Peking Union Medical College Hospital from 2015 to 2016 were collected. The BaseScope T790M detection technique by in situ hybridization on FFPE slides was used to analyze the mutation of EGFR T790M, and then the results were compared with the data acquired by Scorpions ARMS assay, which is the so‐called gold standard for EGFR gene mutation testing. The sensitivity and specificity of the BaseScope T790M detection technique were preliminarily evaluated. Results Of the 28 FFPE specimens, the average proportion of T790M‐positive cells was 35.78% ± 17.68% in 18 samples with EGFR T790M, confirmed by Scorpions ARMS assay, Compared with real‐time PCR assay, the sensitivity and specificity of BaseScope T790M were all 100% in our cohort. Conclusion BaseScope T790M assay could be completed on only one FFPE slide and the visualized molecular result overplayed with histomorphological information perfectly, so it may be the alternative method for EGFR T790M evaluation. BaseScope assay has potential clinical utility, and it will be necessary to carry out validation with a large number of cases.

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Section: Discussionmentioning

confidence: 99%

EGFR T790M detection in formalin‐fixed paraffin‐embedded tissues of patients with lung cancer using RNA‐based in situ hybridization: A preliminary feasibility study

Shi

et al. 2019

Thoracic Cancer

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“…In recent years, we witnessed an exponential growth in high‐dimensional (HD) technologies, which resulted in a wide range of medical and biological applications. HD cytometers such as mass cytometry (Cytometry by time of flight, CyTOF) and flow cytometry (hereafter termed HDcyto) can detect more than 40 parameters (with the promise for detection of up to 100 parameters) with single‐cell resolution.…”

mentioning

confidence: 99%

The end of omics? High dimensional single cell analysis in precision medicine

et al. 2019

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High-dimensional single-cell (HDcyto) technologies, such as mass cytometry (CyTOF) and flow cytometry, are the key techniques that hold a great promise for deciphering complex biological processes. During the last decade, we witnessed an exponential increase of novel HDcyto technologies that are able to deliver an in-depth profiling in different settings, such as various autoimmune diseases and cancer. The concurrent advance of custom data-mining algorithms has provided a rich substrate for the development of novel tools in translational medicine research. HDcyto technologies have been successfully used to investigate cellular cues driving pathophysiological conditions, and to identify disease-specific signatures that may serve as diagnostic biomarkers or therapeutic targets. These technologies now also offer the possibility to describe a complete cellular environment, providing unanticipated insights into human biology. In this review, we present an update on the current cutting-edge HDcyto technologies and their applications, which are going to be fundamental in providing further insights into human immunology and pathophysiology of various diseases. Importantly, we further provide an overview of the main algorithms currently available for data mining, together with the conceptual workflow for high-dimensional cytometric data handling and analysis. Overall, this review aims to be a handy overview for immunologists on how to design, develop and read HDcyto data.Keywords: bioinformatic tools r diagnostic biomarkers r flow cytometry r mass cytometry (CyTOF) r single cell analysisIn recent years, we witnessed an exponential growth in highdimensional (HD) technologies, which resulted in a wide range of medical [1][2][3][4][5][6][7][8][9][10][11] and biological [12][13][14] applications. HD cytometers such as mass cytometry (Cytometry by time of flight, CyTOF) and flow cytometry (hereafter termed HDcyto) can detect more than 40 parameters (with the promise for detection of up to 100 parameters) with single-cell resolution. The data produced by HDcyto techniques together with appropriate computational methods of analysis have initiated a new era for translational medicine, enabling the unsupervised discovery of new cell pop-

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“…There is a growing appreciation of the role of spatial distribution of proteins, transcripts and other molecules in determining tissues functioning and its deregulation in disease, with potential value as predictors of clinical outcomes (Bodenmiller, 2016) . This is largely driven by vigorous development of novel technologies that enable us to capture such data (Aichler & Walch, 2015;Bodenmiller, 2016;Goltsev et al, 2018;Lin et al, 2017;Schulz et al, 2018) .…”

Section: Resultsmentioning

confidence: 99%

“…However, these methods do not allow for directly assessing cell-cell interactions. On the other hand, there are methods that explain dependencies between cells based on cell-type assignments and predefined cellular neighbourhoods Schulz et al, 2018) . While these approaches provide qualitative insights into interactions between cell types, they do not allow for quantifying the impact of spatial effects on individual genes or proteins.…”

Section: Different Technologies Allow For Generating Spatially Resolvmentioning

confidence: 99%

Modelling cell-cell interactions from spatial molecular data with spatial variance component analysis

Arnol

Schapiro

Bodenmiller

et al. 2018

Preprint

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Technological advances allow for assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing physiological tissue contexts of single cell variation. While methods for assaying spatial expression profiles are increasingly accessible, there is a lack of computational approaches that allow for studying the relevance of the spatial organization of tissues on cellcell heterogeneity. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA estimates signatures of spatial variance components , thereby quantifying the effect of cellcell interactions, as well as environmental and intrinsic cell features on the expression levels of individual molecules. In application to a breast cancer Imaging Mass Cytometry dataset, our model yields robust spatial variance signatures, identifying cellcell interactions as a major driver of expression heterogeneity. We also apply SVCA to highdimensional imagingderived RNA data, where we identify molecular pathways that are linked to cellcell interactions.

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Simultaneous Multiplexed Imaging of mRNA and Proteins with Subcellular Resolution in Breast Cancer Tissue Samples by Mass Cytometry

Cited by 229 publications

References 53 publications

EGFR T790M detection in formalin‐fixed paraffin‐embedded tissues of patients with lung cancer using RNA‐based in situ hybridization: A preliminary feasibility study

EGFR T790M detection in formalin‐fixed paraffin‐embedded tissues of patients with lung cancer using RNA‐based in situ hybridization: A preliminary feasibility study

The end of omics? High dimensional single cell analysis in precision medicine

Modelling cell-cell interactions from spatial molecular data with spatial variance component analysis

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