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.
Quaranta
Vito
V
Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Integrative Cancer Biology Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Tyson
Darren R
DR
Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Integrative Cancer Biology Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Garbett
Shawn P
SP
Vanderbilt Integrative Cancer Biology Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Weidow
Brandy
B
Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Integrative Cancer Biology Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Harris
Mark P
MP
Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Georgescu
Walter
W
Vanderbilt Integrative Cancer Biology Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Biomedical Engineering, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism; D002460:Cell Line; D049109:Cell Proliferation; D022082:Cellular Structures / Q000648:ultrastructure; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation* / Q000379:methods* / Q000592:standards; D008856:Microscopy, Fluorescence / Q000295:instrumentation* / Q000379:methods* / Q000592:standards; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D010641:Phenotype; D011786:Quality Control
D014408:Biomarkers, Tumor