Trait variability of cancer cells quantified by high-content automated microscopy of single cells.

Mapping quantitative cell traits (QCT) to underlying molecular defects is a central challenge in cancer research because heterogeneity at all biological scales, from genes to cells to populations, is recognized as the main driver of cancer progression and treatment resistance. A major roadblock to a multiscale framework linking cell to signaling to genetic cancer heterogeneity is the dearth of large-scale, single-cell data on QCT-such as proliferation, death sensitivity, motility, metabolism, and other hallmarks of cancer. High-volume single-cell data can be used to represent cell-to-cell genetic and nongenetic QCT variability in cancer cell populations as averages, distributions, and statistical subpopulations. By matching the abundance of available data on cancer genetic and molecular variability, QCT data should enable quantitative mapping of phenotype to genotype in cancer. This challenge is being met by high-content automated microscopy (HCAM), based on the convergence of several technologies including computerized microscopy, image processing, computation, and heterogeneity science. In this chapter, we describe an HCAM workflow that can be set up in a medium size interdisciplinary laboratory, and its application to produce high-throughput QCT data for cancer cell motility and proliferation. This type of data is ideally suited to populate cell-scale computational and mathematical models of cancer progression for quantitatively and predictively evaluating cancer drug discovery and treatment.

[1]  R. Pepperkok,et al.  The potential of high‐content high‐throughput microscopy in drug discovery , 2007, British journal of pharmacology.

[2]  Douglas A. Creager,et al.  The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging , 2005, Genome Biology.

[3]  T. Zimmermann,et al.  Live cell spinning disk microscopy. , 2005, Advances in biochemical engineering/biotechnology.

[4]  Hannah H. Chang,et al.  Non-genetic heterogeneity — a mutation-independent driving force for the somatic evolution of tumours , 2009, Nature Reviews Genetics.

[5]  L. Morasca,et al.  INTRAPOPULATION KINETICS OF THE MITOTIC CYCLE , 1965, The Journal of cell biology.

[6]  H. Heng,et al.  Genetic and epigenetic heterogeneity in cancer: A genome‐centric perspective , 2009, Journal of cellular physiology.

[7]  Bod1, a novel kinetochore protein required for chromosome biorientation , 2007, The Journal of cell biology.

[8]  O. Sansom,et al.  Loss of APC induces polyploidy as a result of a combination of defects in mitosis and apoptosis , 2007, The Journal of cell biology.

[9]  Scott R. Eliason.  Maximum likelihood estimation: Logic and practice. , 1994 .

[10]  R. M. Ford,et al.  Turn angle and run time distributions characterize swimming behavior for Pseudomonas putida , 1997, Journal of bacteriology.

[11]  Corinne Laplace-Builhé,et al.  The Origin of Phenotypic Heterogeneity in a Clonal Cell Population In Vitro , 2007, PloS one.

[12]  J. Wikswo,et al.  Migration of isogenic cell lines quantified by dynamic multivariate analysis of single-cell motility , 2008, Cell adhesion & migration.

[13]  Erik Brauner,et al.  Informatics and Quantitative Analysis in Biological Imaging , 2003, Science.

[14]  A. Wells.  Motility in Tumor Invasion and Metastasis — An Overview , 2006 .

[15]  M Kimmel,et al.  Estimating clonal heterogeneity and interexperiment variability with the bifurcating autoregressive model for cell lineage data. , 1997, Mathematical biosciences.

[16]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[17]  H. Berg,et al.  Chemotaxis in Escherichia coli analysed by Three-dimensional Tracking , 1972, Nature.

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

[19]  Roman Tuma,et al.  Step length measurement--theory and simulation for tethered bead constant-force single molecule assay. , 2007, Biophysical journal.

[20]  Scott T. Acton,et al.  Data acceptance for automated leukocyte tracking through segmentation of spatiotemporal images , 2005, IEEE Transactions on Biomedical Engineering.

[21]  H. Kerl,et al.  Correlation of melanoma cell motility and invasion in vitro , 1995, Melanoma research.

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

[23]  Tiffany W Guo,et al.  Epidermal growth factor-induced enhancement of glioblastoma cell migration in 3D arises from an intrinsic increase in speed but an extrinsic matrix- and proteolysis-dependent increase in persistence. , 2008, Molecular biology of the cell.

[24]  Alka A. Potdar,et al.  Bimodal Analysis of Mammary Epithelial Cell Migration in Two Dimensions , 2008, Annals of Biomedical Engineering.

[25]  L. Wasserman,et al.  Practical Bayesian Density Estimation Using Mixtures of Normals , 1997 .

[26]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[27]  Arild O. Gautestad,et al.  Intrinsic Scaling Complexity in Animal Dispersion and Abundance , 2004, The American Naturalist.

[28]  Scott T. Acton,et al.  Level set analysis for leukocyte detection and tracking , 2004, IEEE Transactions on Image Processing.

[29]  Liang Cai,et al.  Coronin 1B Coordinates Arp2/3 Complex and Cofilin Activities at the Leading Edge , 2007, Cell.

[30]  H. Kleinman,et al.  New method for modeling connective-tissue cell migration: improved accuracy on motility parameters. , 2007, Biophysical journal.

[31]  Nicole S. Bryce,et al.  Cortactin Promotes Cell Motility by Enhancing Lamellipodial Persistence , 2005, Current Biology.

[32]  F Guilak,et al.  A method for quantifying cell size from differential interference contrast images: validation and application to osmotically stressed chondrocytes , 2002, Journal of microscopy.

[33]  Alan Dove,et al.  Screening for content—the evolution of high throughput , 2003, Nature Biotechnology.

[34]  P. Friedl,et al.  Tumour-cell invasion and migration: diversity and escape mechanisms , 2003, Nature Reviews Cancer.

[35]  A. Wells.  Cell Motility in Cancer Invasion and Metastasis , 2006 .

[36]  Edward A. Codling,et al.  Random walk models in biology , 2008, Journal of The Royal Society Interface.

[37]  K. Eliceiri,et al.  Bioimage informatics for experimental biology. , 2009, Annual review of biophysics.

[38]  Atsushi Miyawaki,et al.  Visualizing Spatiotemporal Dynamics of Multicellular Cell-Cycle Progression , 2008, Cell.

[39]  E. Wagner,et al.  Disruption of c-Jun reduces cellular migration and invasion through inhibition of c-Src and hyperactivation of ROCK II kinase. , 2008, Molecular biology of the cell.

[40]  Alissa M. Weaver,et al.  Microenvironmental independence associated with tumor progression. , 2009, Cancer research.

[41]  R. Fürth,et al.  Die Brownsche Bewegung bei Berücksichtigung einer Persistenz der Bewegungsrichtung. Mit Anwendungen auf die Bewegung lebender Infusorien , 1920 .

[42]  Paul Matsudaira,et al.  Linking microscopy and high content screening in large-scale biomedical research. , 2007, Methods in molecular biology.

[43]  G. Uhlenbeck,et al.  On the Theory of the Brownian Motion , 1930 .

[44]  H. Stanley,et al.  Optimizing the success of random searches , 1999, Nature.

[45]  Gary G. Borisy,et al.  Antagonism between Ena/VASP Proteins and Actin Filament Capping Regulates Fibroblast Motility , 2002, Cell.

[46]  Anne E Carpenter,et al.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes , 2006, Genome Biology.

[47]  Lani F. Wu,et al.  Image-based multivariate profiling of drug responses from single cells , 2007, Nature Methods.