Background

Studies on plant intracellular trafficking have widely beneficiated from the identification and characterization of proteins that are localized to specific intracellular compartments. These proteins can serve in subsequent studies as compartment markers either using immunofluorescence when antibodies are available, or direct fusion with fluorescent proteins (Dettmer et al., 2006; Geldner et al., 2003; Jaillais et al., 2008; Jaillais et al., 2006). Typically, they can be used in co-localization experiments (Geldner et al., 2009; Simon et al., 2014), but also as reference points to characterize mutants, drugs or growth conditions that might affect intracellular trafficking pathways, such as exocytosis, endocytosis, autophagy, or secretory trafficking. The localization of these marker proteins may vary in different manner, including for example their number, size, shape, clustering or labeling intensity. For example, in root, the fungal toxin Brefeldin A (BFA), an inhibitor of protein recycling, degradation and secretion, induces the aggregation of multiple compartments in or around the so-called ‘BFA compartment’ (Geldner et al., 2003; Geldner et al., 2009). Wortmannin, an inhibitor of PI3 Kinase activity induces the fusion of late endosomal compartments (Jaillais et al., 2006; Tse et al., 2004), while concanamycin A induces TGN swelling (Dettmer et al., 2006). Accordingly similar effects on compartment numbers and/or morphology have been observed in trafficking mutants (Dettmer et al., 2006; Geldner et al., 2003; Jaillais et al., 2007; Sauer et al., 2013).

Automatic spot detection was spearheaded on leaf for the detection of endomembrane rearrangements induced by environmental changes such as dark, cold treatment or biotic stresses (Salomon et al., 2010). This technique was also applied to study the endocytosis of the FLAGELIN-INSENSITIVE2 (FLS2) receptor (Beck et al., 2012; Mbengue et al., 2016; Spallek et al., 2013). Here we described a protocol to computationally detect and count the number of intracellular compartments on Arabidopsis root image. This protocol relies on a macro that runs on the open source image analysis software ImageJ and that can work with a wide variety of images with different image-to-noise signals. In addition, it proposes two different modes of detection; a first one where the macro automatically finds the root area and another one that allows the selection of a user-defined region of interest (ROI). Finally, although this version of the macro is designed to count the number of spots, similar image segmentation can easily be used to measure spot size, to estimate signal intensity, to capture compartment morphology or to automatically quantify co-localization between two or more channels.