Cytometry of Reaction Rate Constant (CRRC) is a method for studying heterogeneity of cell populations with regards to activity of cellular reactions. It is based on time-lapse fluorescence microscopy which facilitates following reaction kinetics in individual cells. The current CRRC workflow utilizes a single fluorescence image to manually identify cell contours; these contours are then used to determine fluorescence intensity of individual cells in the entire time-stack of images. This workflow can only be used reliably if the cells maintain their positions during the time-lapse measurements; if the cells move, the results of a CRRC experiment will be inaccurate. The requirement of invariant cell positions during a prolonged imaging is impossible to satisfy for motile cells. Here we report on developing an advanced workflow that makes CRRC applicable to motile cells. The new workflow combines fluorescence microscopy with brightfield (BF) microscopy and utilizes automated processing and analysis of images. A BF image is taken right after every fluorescence image and used to determine cell contours. The contours are tracked through the time-stack of BF images to account for cell movement. A set of contours, which is unique for every image, is then used to determine fluorescence intensity of cells in the associated fluorescent image. Finally, time dependencies of intracellular fluorescence intensities are used to determine the rate constant and plot a kinetic histogram “number of cells vs rate constant”. The robustness of the new workflow to cell movement was confirmed experimentally by conducting a CRRC study of cross-membrane transport in motile cells. The new workflow makes CRRC applicable to a wide range of cell types and eliminates the influence of cell motility on the accuracy of results.

Cytometry of Reaction Rate Constant (CRRC) is a method for studying cell-population heterogeneity using time-lapse fluorescence microscopy, which allows one to follow reaction kinetics in individual cells. The current and only CRRC workflow utilizes a single fluorescence image to manually identify cell contours which are then used to determine fluorescence intensity of individual cells in the entire time-stack of images. This workflow is only reliable if cells maintain their positions during the time-lapse measurements. If the cells move, the original cell contours become unsuitable for evaluating intracellular fluorescence and the CRRC experiment will be inaccurate. The requirement of invariant cell positions during a prolonged imaging is impossible to satisfy for motile cells. Here we report a CRRC workflow developed to be applicable to motile cells. The new workflow combines fluorescence microscopy with transmitted-light microscopy and utilizes a new automated tool for cell identification and tracking. A transmitted-light image is taken right before every fluorescence image to determine cell contours, and cell contours are tracked through the time-stack of transmitted-light images to account for cell movement. Each unique contour is used to determine fluorescence intensity of cells in the associated fluorescence image. Next, time dependencies of the intracellular fluorescence intensities are used to determine each cell’s rate constant and construct a kinetic histogram “number of cells vs rate constant.” The new workflow’s robustness to cell movement was confirmed experimentally by conducting a CRRC study of cross-membrane transport in motile cells. The new workflow makes CRRC applicable to a wide range of cell types and eliminates the influence of cell motility on the accuracy of results. Additionally, the workflow could potentially monitor kinetics of varying biological processes at the single-cell level for sizable cell populations. Although our workflow was designed ad hoc for CRRC, this cell-segmentation/cell-tracking strategy also represents an entry-level, user-friendly option for a variety of biological assays (i.e., migration, proliferation assays, etc.). Importantly, no prior knowledge of informatics (i.e., training a model for deep learning) is required.