Visualizing quantitative microscopy data: History and challenges

Abstract Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods.

[1]  Robert F Murphy,et al.  CellOrganizer: Image-derived models of subcellular organization and protein distribution. , 2012, Methods in cell biology.

[2]  Robert F. Murphy,et al.  A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells , 2001, Bioinform..

[3]  Luca Maria Gambardella,et al.  Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.

[4]  R. Murphy,et al.  Automated subcellular location determination and high-throughput microscopy. , 2007, Developmental cell.

[5]  J. Mitchison.  Cell Biology , 1964, Nature.

[6]  S. Counce.  The Strategy of the Genes , 1958, The Yale Journal of Biology and Medicine.

[7]  V. Garovic,et al.  Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm , 2015, PLoS biology.

[8]  Lani F. Wu,et al.  Characterizing heterogeneous cellular responses to perturbations , 2008, Proceedings of the National Academy of Sciences.

[9]  Albert Gough,et al.  High-Content Screening: A New Approach to Easing Key Bottlenecks in the Drug Discovery Process , 1997 .

[10]  Bang Wong,et al.  Points of view: Heat maps , 2012, Nature Methods.

[11]  Lit-Hsin Loo,et al.  Heterogeneity in the physiological states and pharmacological responses of differentiating 3T3-L1 preadipocytes , 2009, The Journal of cell biology.

[12]  Chris Bakal,et al.  Visualizing cellular imaging data using PhenoPlot , 2015, Nature Communications.

[13]  Anatole Chessel,et al.  A genomic Multiprocess survey of machineries that control and link cell shape, microtubule organization, and cell-cycle progression. , 2014, Developmental cell.

[14]  Chris Bakal,et al.  Cross-talk between Rho and Rac GTPases drives deterministic exploration of cellular shape space and morphological heterogeneity , 2014, Open Biology.

[15]  Bang Wong,et al.  Points of view: Power of the plane , 2012, Nature Methods.

[16]  Gary D Bader,et al.  The Genetic Landscape of a Cell , 2010, Science.

[17]  K. Pearson.  Contributions to the Mathematical Theory of Evolution. II. Skew Variation in Homogeneous Material , 1895 .

[18]  P. Liberali,et al.  Population context determines cell-to-cell variability in endocytosis and virus infection , 2009, Nature.

[19]  Stuart A. Kauffman,et al.  The origins of order , 1993 .

[20]  Herman Chernoff,et al.  The Use of Faces to Represent Points in k- Dimensional Space Graphically , 1973 .

[21]  Lucas Pelkmans,et al.  A Hierarchical Map of Regulatory Genetic Interactions in Membrane Trafficking , 2014, Cell.

[22]  Lani F. Wu,et al.  Patterns of basal signaling heterogeneity can distinguish cellular populations with different drug sensitivities , 2010, Molecular systems biology.

[23]  Lucas Pelkmans,et al.  Using Cell-to-Cell Variability—A New Era in Molecular Biology , 2012, Science.

[24]  C. Bakal,et al.  How cells explore shape space: A quantitative statistical perspective of cellular morphogenesis , 2014, BioEssays : news and reviews in molecular, cellular and developmental biology.

[25]  R. Durbin,et al.  A high-content platform to characterise human induced pluripotent stem cell lines , 2016, Methods.

[26]  Julie A. Theriot,et al.  Mechanism of shape determination in motile cells , 2008, Nature.

[27]  Stuart A. Kauffman,et al.  ORIGINS OF ORDER , 2019, Origins of Order.

[28]  K. Pearson.  Contributions to the Mathematical Theory of Evolution , 1894 .

[29]  N M Larson,et al.  Classification of cultured mammalian cells by shape analysis and pattern recognition. , 1980, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Aabid Shariff,et al.  Automated Image Analysis for High-Content Screening and Analysis , 2010, Journal of biomolecular screening.

[31]  D Lansing Taylor,et al.  Past, present, and future of high content screening and the field of cellomics. , 2007, Methods in molecular biology.

[32]  Varpu Marjomäki,et al.  Molecular Systems Biology Peer Review Process File Single-cell Analysis of the Population Context Advances Rnai Screening at Multiple Levels Transaction Report , 2022 .

[33]  Chris Bakal,et al.  Cell shape and the microenvironment regulate nuclear translocation of NF-κB in breast epithelial and tumor cells. , 2015, Molecular systems biology.

[34]  Bang Wong,et al.  Points of view: Color coding , 2010, Nature Methods.

[35]  Eva Crane,et al.  The World History of Beekeeping and Honey Hunting , 1999 .

[36]  Bang Wong,et al.  Points of view: Mapping quantitative data to color , 2012, Nature Methods.

[37]  Lucas Pelkmans,et al.  Predicting functional gene interactions with the hierarchical interaction score , 2013, Nature Methods.

[38]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[39]  P. Liberali,et al.  Single-cell and multivariate approaches in genetic perturbation screens , 2014, Nature Reviews Genetics.

[40]  Arthur T. Johnson,et al.  Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring , 2012, Diagnostic Pathology.

[41]  Chris Bakal,et al.  Apolar and polar transitions drive the conversion between amoeboid and mesenchymal shapes in melanoma cells , 2015, Molecular biology of the cell.

[42]  C. Bakal,et al.  Quantitative Morphological Signatures Define Local Signaling Networks Regulating Cell Morphology , 2007, Science.

[43]  M. Boutros,et al.  Microscopy-Based High-Content Screening , 2015, Cell.

[44]  Chris Bakal,et al.  Molecular Systems Biology Peer Review Process File Cell Shape and the Microenvironment Regulate Nuclear Translocation of Nf-κ B in Breast Epithelial and Tumor Cells Transaction Report , 2022 .

[45]  Robert F. Murphy,et al.  Joint modeling of cell and nuclear shape variation , 2015, Molecular biology of the cell.

[46]  Stephen T. C. Wong,et al.  A Screen for Morphological Complexity Identifies Regulators of Switch-like Transitions between Discrete Cell Shapes , 2013, Nature Cell Biology.

[47]  Lani F. Wu,et al.  Multidimensional Drug Profiling By Automated Microscopy , 2004, Science.

[48]  Y. Kalaidzidis,et al.  Systems survey of endocytosis by multiparametric image analysis , 2010, Nature.

[49]  Satwik Rajaram,et al.  SimuCell: a flexible framework for creating synthetic microscopy images , 2012, Nature Methods.

[50]  Lani F. Wu,et al.  Cellular Heterogeneity: Do Differences Make a Difference? , 2010, Cell.

[51]  Jieyue Li,et al.  Automated Learning of Subcellular Variation among Punctate Protein Patterns and a Generative Model of Their Relation to Microtubules , 2015, PLoS Comput. Biol..

[52]  Fletcher W. . Hewes.  Scribner's statistical atlas of the United States, showing by graphic methods their present condition and their political, social and industrial development , 1883 .

[53]  Z. P. I N C U S,et al.  Comparison of quantitative methods for cell-shape analysis , 2007 .