We have recently characterized co-existing membrane microdomains that are labeled by different proteins in living plant cells (Jarsch et al., 2014). For this approach we first created a digital fingerprint for each of the twenty marker proteins using quantitative image analysis. Here we recorded parameters such as domain size, density and shape based on image segmentation. We found highly reproducible patterns of any of the proteins over a large number of biological replicates. Furthermore we exclusively acquired images from lowly expressing cells and chose our imaging conditions in a way that resulted in images where no pixel was saturated.
This protocol describes in detail the methods that have been used to analyze quantitative differences in localization of members of the remorin protein family in membrane microdomains of Arabidopsis thaliana and Nicotiana benthamiana (Jarsch et al., 2014). The proteins were either individually or pairwise expressed as fluorophore fusions in the respective plant. Image acquisition was performed using standard Confocal Laser Scanning Microscopy (CLSM) and image analysis was performed using ImageJ.
[Introduction] Since confocal laser-scanning or other state-of-the-art fluorescence microscopes are nowadays often regarded as standard equipment a modern research institution should have, the amount of published cell biological data has massively increased over the last years. This certainly also correlates with the availability of an increasing number of fluorophores and corresponding expression vectors that have made it comparably easy to generate large numbers of tagged proteins. One main concern about showing microscopy images in publications is the subjectivity they have been selected with. In addition, and certainly very unfortunate in several cases, the scientific community as well as reviewers of manuscripts have requested ‘no background-high fluorescence’ images from the authors. As a consequence researchers often started selecting the images based on aesthetic aspects rather than showing the most representative ones. Furthermore the majority of images are based on strong over-expression of proteins. Therefore quantitative image analysis has become an absolute requirement in order to make robust statements on cell biological observations and the frequency with which they have been observed. However, this does not only require gaining novel skills but also high numbers of biological repetitions in a standardized way. Furthermore, it should be the ultimate goal to work under conditions where the protein of interest is expressed at native levels. While this may have to be overcome for lowly abundant proteins, researchers should nevertheless aim for similar levels and may thus accept more background noise in the images.
It should be noted that all parameters and protocol specifications provided within this protocol have been optimized for the expression we used in a current study (Jarsch et al., 2014). Most likely they have to be adapted for any analyses in different laboratories.
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