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

Tissue functionality is determined by the characteristics of tissue-resident cells and their interactions within their microenvironment. Imaging Mass Cytometry offers the opportunity to distinguish cell types with high precision and link them to their spatial location in intact tissues at sub-cellular resolution. This technology produces large amounts of spatially-resolved high-dimensional data, which constitutes a serious challenge for the data analysis. We present an interactive visual analysis workflow for the end-to-end analysis of Imaging Mass Cytometry data that was developed in close collaboration with domain expert partners. We implemented the presented workflow in an interactive visual analysis tool; ImaCytE. Our workflow is designed to allow the user to discriminate cell types according to their protein expression profiles and analyze their cellular microenvironments, aiding in the formulation or verification of hypotheses on tissue architecture and function. Finally, we show the effectiveness of our workflow and ImaCytE through a case study performed by a collaborating specialist.

[1]  David Gotz,et al.  Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics , 2014, IEEE Transactions on Visualization and Computer Graphics.

[2]  Ullrich Köthe,et al.  Ilastik: Interactive learning and segmentation toolkit , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[3]  Jock D. Mackinlay,et al.  Automating the design of graphical presentations of relational information , 1986, TOGS.

[4]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[5]  Tobias Schreck,et al.  A System for Interactive Visual Analysis of Large Graphs Using Motifs in Graph Editing and Aggregation , 2009, VMV.

[6]  G. Nolan,et al.  Automated Mapping of Phenotype Space with Single-Cell Data , 2016, Nature Methods.

[7]  Tamara Munzner,et al.  A Multi-Level Typology of Abstract Visualization Tasks , 2013, IEEE Transactions on Visualization and Computer Graphics.

[8]  Mark M. Davis,et al.  Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE) , 2013, Proceedings of the National Academy of Sciences.

[9]  O. Ornatsky,et al.  Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. , 2009, Analytical chemistry.

[10]  Marc Streit,et al.  Opening the Black Box: Strategies for Increased User Involvement in Existing Algorithm Implementations , 2014, IEEE Transactions on Visualization and Computer Graphics.

[11]  Polina Golland,et al.  CellProfiler Analyst: data exploration and analysis software for complex image-based screens , 2008, BMC Bioinformatics.

[12]  Sarah A. Teichmann,et al.  Faculty Opinions recommendation of histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. , 2017 .

[13]  Elmar Eisemann,et al.  Cytosplore: Interactive Immune Cell Phenotyping for Large Single‐Cell Datasets , 2016, Comput. Graph. Forum.

[14]  Philippe Castagliola,et al.  On the Readability of Graphs Using Node-Link and Matrix-Based Representations: A Controlled Experiment and Statistical Analysis , 2005, Inf. Vis..

[15]  F S Fay,et al.  Visualization of single RNA transcripts in situ. , 1998, Science.

[16]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Min Chen,et al.  Glyph-based Visualization: Foundations, Design Guidelines, Techniques and Applications , 2013, Eurographics.

[18]  Sean C. Bendall,et al.  Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis , 2015, Cell.

[19]  Tamara Munzner,et al.  Visualizing dimensionally-reduced data: interviews with analysts and a characterization of task sequences , 2014, BELIV.

[20]  Ron Lenk Practical Guide to Instrumentation , 2005 .

[21]  J. Hooton,et al.  Randomization tests: statistics for experimenters. , 1991, Computer methods and programs in biomedicine.

[22]  P. Mazzarello A unifying concept: the history of cell theory , 1999, Nature Cell Biology.

[23]  S. Lewandowsky,et al.  Displaying proportions and percentages , 1991 .

[24]  J. Buhmann,et al.  Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry , 2014, Nature Methods.

[25]  Boudewijn P F Lelieveldt,et al.  Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data , 2016, Proceedings of the National Academy of Sciences.

[26]  Piet Demeester,et al.  FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data , 2015, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[27]  Ben Shneiderman,et al.  Motif simplification: improving network visualization readability with fan, connector, and clique glyphs , 2013, CHI.

[28]  M. Tiemann,et al.  Non-specific binding of antibodies in immunohistochemistry: fallacies and facts , 2011, Scientific reports.

[29]  Alexander van Oudenaarden,et al.  Spatially resolved transcriptomics and beyond , 2014, Nature Reviews Genetics.

[30]  Kun Zhang,et al.  Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues , 2015, Nature Protocols.

[31]  Tamara Munzner,et al.  MulteeSum: A Tool for Comparative Spatial and Temporal Gene Expression Data , 2010, IEEE Transactions on Visualization and Computer Graphics.

[32]  Elmar Eisemann,et al.  Approximated and User Steerable tSNE for Progressive Visual Analytics , 2015, IEEE Transactions on Visualization and Computer Graphics.

[33]  Stefan Steinerberger,et al.  Clustering with t-SNE, provably , 2017, SIAM J. Math. Data Sci..

[34]  Simon Ameer-Beg,et al.  Biomedical Imaging: From Nano to Macro , 2008 .

[35]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[36]  U. Landegren,et al.  In situ genotyping individual DNA molecules by target-primed rolling-circle amplification of padlock probes , 2004, Nature Methods.

[37]  Elmar Eisemann,et al.  Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types , 2017, Nature Communications.

[38]  Mats Nilsson,et al.  Network Visualization and Analysis of Spatially Aware Gene Expression Data with InsituNet. , 2018, Cell systems.

[39]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[40]  Sean C. Bendall,et al.  viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia , 2013, Nature Biotechnology.

[41]  Eli R. Zunder,et al.  A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. , 2015, Cell stem cell.

[42]  Edward R. Tufte,et al.  Envisioning Information , 1990 .

[43]  Matthew A. Hibbs,et al.  Visualization of omics data for systems biology , 2010, Nature Methods.

[44]  Timo Ropinski,et al.  Survey of glyph-based visualization techniques for spatial multivariate medical data , 2011, Comput. Graph..

[45]  HeerJeffrey,et al.  D3 Data-Driven Documents , 2011 .