Rcell2: Microscopy‐Based Cytometry in R. Issue 4 (19th April 2023)

• Record Type: Journal Article
• Authors: Méndez, Nicolás A.; Beldorati, German; Constantinou, Andreas; Colman‐Lerner, Alejandro
• Is Part Of: Current protocols. Volume 3:Issue 4(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-04-19
• Subjects: Cell‐ID -- fluorescence microscopy -- image processing -- R; Life sciences -- Laboratory manuals -- Periodicals; Biology -- Laboratory manuals -- Periodicals; Life sciences -- Technique -- Periodicals; Biology -- Technique -- Periodicals; 570.028
• DOI: 10.1002/cpz1.726
• Languages: English
• ISSNs: 2691-1299

Abstract

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. © 2023 Wiley Periodicals LLC. Basic Protocol : Extracting quantitative information from single cells Support Protocol 1 : Obtaining and installing Cell‐ID and R Support Protocol 2 : Preparing cells for imaging