Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Abstract

Cellular processes are complex and result from the interplay between multiple cell types and their environment. Existing cell biology techniques often do not allow for accurate interpretation of this interplay. Using a quantitative imaging-based approach, we present a high-content protocol for characterizing the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations to changes in environmental stimuli. We highlight our ability to distinguish between cell types based upon either fluorescence intensity or inherent morphology features depending on the application. This platform allows for a more comprehensive characterization of subpopulation response to perturbation while utilizing shorter time, smaller amounts of reagents, and lower likelihood of error than traditional cell biology assays. However, in some cases, cell populations may be difficult to identify and quantitate based on complex cellular features and will require additional troubleshooting; we highlight some of these circumstances in the protocol. We demonstrate this application using response to drug in a cancer model; however, it can easily be applied more broadly to other physiological processes. This protocol allows one to identify subpopulations within a co-culture system and characterize the particular response of each to external stimuli.

Anti-EGFR Therapy Induces EGF Secretion by Cancer-Associated Fibroblasts to Confer Colorectal Cancer Chemoresistance

“…We previously established an imaging-based methodology that allows one to study the influence of drugs on heterogeneous cell populations while distinguishing between cell types [13,14]. Briefly, we quantified live and dead cell counts over time and then fit the data to an exponential growth model to determine net birth, death, and growth rates.…”

“…Cells were then segmented based upon the nuclear dye using Harmony software (PerkinElmer, Waltham, MA, USA). In order to differentiate cell types in co-culture assays, morphological features were calculated and used to train a machine-learning algorithm to classify cells as either 'CAF' or 'tumor,' as described in Garvey et al [13,14]. Propidium iodide intensity levels were calculated and cells were classified as 'dead' if their intensity was above the established threshold.…”

Establishing a reference focal plane using beads for trypan‐blue‐based viability measurements

“…The notion of image quality is broad and includes resolution, blurring and image signal‐to‐noise. To ensure repeatable measurements, it is important to control key image quality factors such as these that can impart bias and variability 3–5 . This type of measurement has been used for many decades using trypan blue staining 6 .…”