rcell2: Microscopy‐Based Cytometry in R

Abstract

This article describes a method for quantifying various cellular features (e.g., volume, curvature, total and sub-cellular fluorescence localization) of individual cells from sets of microscope images, and for tracking them over time-course microscopy experiments. One purposely defocused transmission image (sometimes referred to as bright-field or BF) is used to segment the image and locate each cell. Fluorescence images (one for each of the color channels or z-stacks to be analyzed) may be acquired by conventional wide-field epifluorescence or confocal microscopy. This method uses a set of R packages called rcell2. Relative to the original release of Rcell (Bush et al., 2012), the updated version bundles, into a single software suite, the image-processing capabilities of Cell-ID, offers new data analysis tools for cytometry, and relies on the widely used data analysis and visualization tools of the statistical programming framework R.