A high-content image-based method for quantitatively studying context-dependent cell population dynamics.

Colleen M Garvey,Nathan C Choi,Jasmine Foo,Danika Lindsay,David B Agus,Parag Mallick,Shannon M Mumenthaler,Erin Spiller,Chun-Te Chiang

doi:10.1038/srep29752

Abstract

Tumor progression results from a complex interplay between cellular heterogeneity, treatment response, microenvironment and heterocellular interactions. Existing approaches to characterize this interplay suffer from an inability to distinguish between multiple cell types, often lack environmental context, and are unable to perform multiplex phenotypic profiling of cell populations. Here we present a high-throughput platform for characterizing, with single-cell resolution, the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations both during standard growth and in response to multiple, co-occurring selective pressures. The speed of this platform enables a thorough investigation of the impacts of diverse selective pressures including genetic alterations, therapeutic interventions, heterocellular components and microenvironmental factors. The platform has been applied to both 2D and 3D culture systems and readily distinguishes between (1) cytotoxic versus cytostatic cellular responses; and (2) changes in morphological features over time and in response to perturbation. These important features can directly influence tumor evolution and clinical outcome. Our image-based approach provides a deeper insight into the cellular dynamics and heterogeneity of tumors (or other complex systems), with reduced reagents and time, offering advantages over traditional biological assays.

Highlights

IntroductionYSpecifically, many sources of cell-to-cell variation exist in tumors including different types of cells (e.g. fibroblasts versus epithelial cells) or the same ‘type’ of cell (e.g. epithelial) with several clones that have acquired different mutations
YSpecifically, many sources of cell-to-cell variation exist in tumors including different types of cells or the same ‘type’ of cell with several clones that have acquired different mutations
Image-based High content screening (HCS) platform improves upon current cell viability assays by providing a more comprehensive picture of diverse cellular dynamics within the contexts of a heterogeneous tumor microenvironment

Summary

Introduction

YSpecifically, many sources of cell-to-cell variation exist in tumors including different types of cells (e.g. fibroblasts versus epithelial cells) or the same ‘type’ of cell (e.g. epithelial) with several clones that have acquired different mutations. HCS platforms have typically been limited to assaying the effects of a single perturbation on a single cell-type[16], a considerable effort has been placed on designing drug screens that implement 3D organotypic cultures, which more closely mimic the tumor microenvironment[17,18] While these assays are beginning to provide significant insight into the complexities of the microenvironment, detailed methodologies capable of measuring the dynamic phenotypic responses of heterogeneous cell populations exposed to physiologically relevant tumor conditions, are largely unavailable. To our knowledge at time of writing, this study represents the first application of HCS for dynamic, multi-parameter analysis to quantify the heterogeneity of cellular phenotypes (i.e. cell birth, death, and changes in morphology) in 2D and 3D co-culture systems[16] The power of this approach comes from the ability to and quantitatively examine cellular phenotypes across different timescales and in high-throughput providing novel insights into cellular dynamics that were overlooked when directly compared to traditional assay readouts from MTS and flow cytometry. The quantitative nature of this platform provides a more accurate representation of biological processes and can feed into additional applications (e.g., mathematical model predictions) that provide further insights into population dynamics

Methods

Results

Conclusion