Trait Variability of Cancer Cells Quantified by High-Content Automated Microscopy of Single Cells

Abstract

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 highthroughput 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.

Quantitative Approaches to Heterogeneity and Growth Variability in Cell Populations

“…3), has been observed for some time (see, e.g., Altschuler [1], Quaranta [12] and references therein). However, quantitative evaluation of this cell-to-cell variability (heterogeneity) poses technical challenges that only recently are being overcome [1,12]. Furthermore, mathematical foundations for interpreting these quantitative experimental observations of heterogeneity are in need of development.…”

“…Variability of growth rates, among other indicators of heterogeneity in growth kinetics of individual tumours, has long been detected, but precision in quantification may have been made possible only in the past few years by methods developed by, among others, Quaranta and his group (see [12,19]). For instance, a team from Verona, Italy, quantified growth variability of tumour cell clones from a human leukaemia cell line, by cloning Molt3 cells, and measuring the growth of 201 clonal populations by microplate spectrophotometry.…”

Beyond Genetics in Personalized Cancer Treatment: Assessing Dynamics and Heterogeneity of Tumor Responses

“…Alternative approaches to simultaneously examine multiple cell fates by direct single-cell visualization in the presence of drugs require time-lapse video microscopy, but have long been thwarted by low-throughput and labor-intensive analysis. These challenges have now been largely overcome owing to the development of commercially available instruments for high-throughput microscopy, major improvements in computational power and the development of analytical approaches that facilitate the interpretation of time-lapse imaging data in terms of the rates of various cellular fates (cell division, death and entry into quiescence) [26,27]. Several studies have demonstrated significant variability of single-cell responses to treatment within a genetically constrained cell population [26,28–31], highlighting the importance of studying the nongenetic basis of response variability.…”

“…Thus, without directly quantifying the true cellular effects of a drug (e.g., amount of cell death, quiescence and division) comparisons of the effects of drugs across cell lines (or patient tumors) will be extremely difficult to interpret. Fortunately, several tools are now at our disposal to quantify these parameters in a high-throughput manner [26,27]. …”

Measurement and Modeling of Signaling at the Single-Cell Level

“…Live-cell tracking data obtained by the Quaranta group using high-throughput automated microscopy showed that invasive cancer cell lines were marked by a greater spread in observed motility, with a few cells showing much higher motility than the majority of the population. It is not yet clear whether these particular cells are the ones responsible for metastasis; further single-cell investigation will be needed to confirm or refute this idea (16). …”