Epifluorescence microscopy was performed as previously described [7] using an AxioObserver Z1 microscope (Carl Zeiss, Jena, Germany) equipped with a 636 1.4 oil immersion objective, an additional 1.66 lens and an Orca-R2 camera (Hamamatsu, Hamamatsu City, Japan). Confocal sections were acquired using two custom spinning disk setups based on TE2000-U or TI-Eclipse inverted microscopes (Nikon, Tokyo, Japan), equipped with temperature control chambers (The Cube, Life Imaging Services, Basel, Switzerland or Okolab, Quarto, Italy), 1006oil immersion objectives (NA 1.45 or 1.49), CSU22 or CSU-X1 spinning disk heads (Yokogawa Electric Corporation, Tokyo, Japan), CoolSnap HQ2 or Evolve EM-CCD cameras (Photometrics, Tucson, AZ), and operated by Metamorph 7.1.4 or 7.6 (Molecular Devices, Sunnyvale, CA), respectively. The TE2000-U was equipped with a piezoelectric translator mounted on the objective to control the position of the confocal plane (Physik Instruments, Karlsruhe, Germany). For dual-color live imaging, channels were acquired sequentially and, for each time point, several planes were acquired and projected to compensate the exit of endosomes from the confocal plane. Analyses of images and movies were performed using Metamorph and ImageJ softwares (http://rsb.ingo.nih.gov/ij/). When indicated, 3D deconvolution was performed using the ‘Adaptative Blind’ algorithm of Autoquant 62 software (Media- Cybernetics, Bethesda, MD). 3D reconstructions were performed using Amira software (Visage Imaging Inc., San Diego, CA) on deconvolved images. Movies were edited with Adobe Premiere software. 3D analysis of endosome volume and WASH domains For preliminary quantification (Fig. 1), confocal slices (0.2 mm increment) were acquired for WASH and Transferrin (Tf) channels. Background of images was homogenously subtracted in ImageJ. In order to enhance the detection of small and dim objects, stacks were then processed using a wavelet ‘a` trous’ filter (‘ImproveKymo’ ImageJ plugin, developed by Fabrice Cordelie`re, Institut Curie, Orsay). 3D segmentation was then performed using ‘3D object counter’ ImageJ plugin (Bolte and Cordelie`res, 2006), keeping the intensity threshold constant for the WASH channel. Measurement of WASH fluorescence intensity over the segmented regions was redirected to background-subtracted images. Single peripheral Tf-positive endosomes were manually selected and their corresponding WASH integrated intensity averaged. For automated analysis on scattered endosomes (Figs. 2 and 4, S2 and S3), the image processing workflow is presented in Fig. 2B. The first processing steps were identical to those of the preliminary analysis. Since Tf is a membrane marker, large endosomes were detected as hollow structures that were subsequently filled for accurate volume measurement (‘Fill Holes’ ImageJ function). Each WASH domain was then assigned, in 3D, to its proper endosome using a custom MATLAB code (see paragraph below), and the total WASH fluorescence intensity of domains associated to the endosome was measured. Due to unavailability of one microscopy setup, we had to use a different one to acquire data quantified in Figs. 2, S3 and Figs. 4, S2. We thus normalized the volumes of both datasets to obtain the same endosome volume bins between experiments. The normalization factor was calculated based on the average endosome size in control cases conditions, using the LatA dataset (Fig. 2) as a reference. To assign each WASH domain to its endosome, we developed a custom-made code in MATLAB (Mathworks, Natick, MA). First, a common pixel yellow stack is generated from the segmented images of both green (WASH) and red (Tf) channels. This stack is then segmented and each yellow region is assigned to its red and Actin Organizes WASH Domains green object, from which the association between the red and the green regions is deduced. Two geometrical methods were used to assign each Yellow object to its Green or Red object. First, a ‘Minimum Distance’ method finds the closest Green and Red objects for each Yellow object, based on the 3D-distance between geometrical centers. Second, a ‘Bounding Box’ method assigns a Yellow object to a Green (or Red) object if the fluorescence centroid of the Yellow object is contained within the minimum bounding box containing the Green (or Red) object. We excluded all the objects for which both methods gave different results (less than 2% of total WASH domains). This MATLAB code is available on request. For cells expressing mCherry-Rab5Q79L (Fig. 7), image analysis was conducted essentially as above, except that the wavelet ‘a` trous’ filter was replaced by a deconvolution step (‘Adaptative Blind’ Autoquant algorithm). Apparent surface of each subdomain was measured by counting the number of common pixels between the outline of the Rab5Q79L endosome, obtained using the ‘outline’ ImageJ function, and the solid WASH domain. Since WASH domains are usually detected on the side and not on the top or on the bottom of endosomes, we converted pixel number in a surface using a pixel size of 1266200 nm2 (XY- spacing6Z-spacing). Histograms and statistics Data were plotted using Matlab and statistical tests were performed using Matlab or Sigmastat (Systat software). Endosome volume distributions were compared with the Chi-2 test while pooling volume bins superior to 1.4 to have more than 5 endosomes per bin. The preliminary analysis of the influence of actin depolymerization on the average WASH domain intensity (Fig. 1B) was analyzed using a one way ANOVA after a Log10 transformation. The influence of both actin depolymerizating treatments and endosome volume on the average intensity of WASH domains, their ‘apparent concentration’ or their ‘apparent surface’, was analyzed after a Log10 transformation using a two- way ANOVA with treatments and volumes as parameters. In all cases, pairwise comparisons were performed with the Tukey Test using an a factor of 0.05. Since the number of WASH domains per endosome is a discrete value, the influence of actin depolymer- ization on domain number (Fig. 7B) was analyzed using a non parametric Kruskal–Wallis one-way analysis of variance on ranks and pairwise comparisons were performed with the Dunn Test using an a factor of 0.05. FRAP FRAP experiments were performed on the second spinning disk confocal microscope setup, using a 1006 1.40 PlanAPO objective and a FRAP module equipped with 491 and 642 nm lasers (Roper Scientific SARL, Evry, France). Circular regions centered on WASH domains (10 px in diameter) were selected (,5–10 regions/cell), and bleached for 50 ms using the 491 nm laser. Recovery was monitored on a single plane for 3 min (1 min at 1 frame/s, then 2 min at 0.5 frame/s), acquiring GFP-WASH and Alexa647-Tf channels sequentially using a ms-AOTF switch. FRAP analysis was performed with ImageJ using custom-made macros unless stated otherwise. First, the background was homogenously subtracted and the global photobleaching was corrected over the entire image. Then, WASH domains were manually tracked using the ‘MTrackJ’ ImageJ plugin developed by Eric Meijering. Finally, the fluorescence of WASH domains was integrated at each time point in a circular region (6 px in diameter) centered on the fluorescence centroid. Each WASH domain trajectory was examined and compared to the trajectory of its PLoS ONE | www.plosone.org 14 June 2012 | Volume 7 | Issue 6 | e39774