Computational protocol: Cortical actin contributes to spatial organization of ER–PM junctions

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[…] SIM imaging was performed with a DeltaVision OMX SR imaging system equipped with a PLAPON 60× oil objective and a pco.edge sCMOS camera.For TIRF-SIM imaging experiments, HeLa cells stably expressing MAPPER were seeded on an eight-well Nunc Lab-Tek II chambered coverglass at a density of 2 × 104 cells/well ∼40 h before staining. Alexa Fluor 568-Phallodin staining was performed before imaging according to the manufacturer’s instructions except that 150 μl/well of staining solution at 400× dilution was used.Image acquisition was controlled by AcquireSR, and the following parameters were used: SI TIRF for light path, sequential for image mode, 90 for ring size, 512 × 512 for image size, 1 × 1 for binning, and 286 MHz for readout speed. Suitable cells were identified using 488-nm and 568-nm excitation lasers in combination with AF488 and AF568 emission filters, respectively. Before TIRF-SIM acquisition, the beam concentrator was turned on. For each reconstruction in each channel, the TIRF illumination plane was imaged at three phase shifts in three angles. Images were acquired using 10–20-ms exposure of 488- or 561-nm excitation lasers at 5–20%T. Image reconstruction was performed with softWoRx, and the following parameters were used: SI-TIRF- and channel-specific OTFs and k0 angles, 0.003 for Wiener filter constant, and 30 for bias offset. The reconstruction option “Discard negative intensities” was disabled according to the guideline suggested by . Channel alignment was done using parameters generated from a gold grid registration slide (GE Healthcare, Pittsburgh, PA) and the OMX Image Registration Calibration tool in SoftWoRx. Background subtraction and brightness/contrast adjustment of the reconstructed images was performed in ImageJ distribution Fiji (). [...] ImageJ/Fiji was used to measure lengths of ER–PM junctions from electron micrographs. ER–PM junctions were visually identified as the regions where the gap distance between the ER and the PM is smaller than 20 nm.An integrated software platform developed by was used to extract quantitative information about the morphology of ER–PM junctions from PALM data sets. Those interested in the associated analysis methods are advised to read the original article by and a nice review by . Localization data sets generated by softWoRx of DeltaVision OMX SR imaging system were first reformatted and cleaned using a custom MATLAB (MathWorks, Natick, MA) function. Essentially, molecules satisfying one of the following criteria were removed: 1) derived from frames with poor lateral drift correction (rarely happens if the acquisition/localization is terminated before activated fluorophores become too sparse), 2) with localization precision greater than 20 nm, or 3) identified as repetitive detections due to multiple blinking. Also, to achieve better consistency when performing Ripley’s K analysis and image segmentation by SR-Tesseler, a region of interest (ROI) covered by the cell was cropped from each localization data set. Ripley’s K analysis was performed within the platform, which implements an algorithm optimization based on the Voronoï diagram. The radius of maximal aggregation in each localization data set was identified with two iterations of analysis using step sizes 10 and 1 nm. Voronoï-based segmentation was obtained using the following parameters: density cutoff = 2 and minimal localizations per object = 100. DBSCAN was also performed within the platform and the following parameters were used: search radius ε = 50 nm, minimal localizations per neighborhood = 50 and minimal localizations per object = 100. Equivalent diameters of objects were calculated from the areas reported by SR-Tesseler, using the formula . Principal component analysis was performed, and major and minor axes of objects were normalized from the SDs along principal components by a factor 3.2897, equivalent to 90% of a Gaussian distribution. Aspect ratios were calculated using the formula major axis/minor axis.ImageJ/Fiji, ilastik 1.2.2rc4 () and MATLAB R2016b were used to assess the spatial relationship between ER–PM junctions and cortical actin. ROIs containing individual cells were cropped from raw TIRFM or reconstructed TIRF-SIM images, converted to 16-bit TIFF images, and subjected to background subtraction using ImageJ/Fiji. Next, the distribution/localization of F-actin structures was identified using pixel classification workflow of ilastik. Classifiers were separately trained to segment F-actin structures from the background using representative data sets of both TIRFM and TIRF-SIM images, and then the trained classifiers were used to segment all of the images. Locations of ER–PM junctions were determined by LoG detector built in TrackMate () plug-in of ImageJ/Fiji. Output data sets from both ilastik and TrackMate were then analyzed using MATLAB. Briefly, if the center of an ER–PM junction fell within segmented F-actin regions and was >1.5-pixel away (in L1-norm) from the nearest background region, this junction was considered as superimposed on F-actin. Otherwise, the distance (in L2-norm) from the center of an ER–PM junction to the nearest segmented F-actin region was measured.ImageJ/Fiji and MATLAB R2016b were used to assess the density and subcellular distribution of ER–PM junctions. For a given raw TIRFM image, background was subtracted, ROIs were determined by PM staining of mCherry-KRas-tail, and ER–PM junctions were localized using LoG detector built in TrackMate plug-in of ImageJ/Fiji. Output data sets were further analyzed using MATLAB.ImageJ/Fiji and MATLAB R2016b were used to characterize lateral motility of ER–PM junctions. Junctions were localized using LoG detector and tracked using a simple LAP tracker () built-in TrackMate plug-in of ImageJ/Fiji. MSD analysis was performed with MATLAB class msdanalyzer ().Quantification of PI(4,5)P2 level at the PM during receptor stimulation was performed as previously described (). Briefly, TIRFM images were analyzed with MetaMorph (Molecular Devices, Sunnyvale, CA) to obtain relative change of GFP-PLCδ-PH intensity from single cells.Quantification of STIM1 translocation following thapsigargin was performed as previously described (). Briefly, TIRFM images were analyzed with MetaMorph to obtain relative change of mCherry-STIM1 intensity from single cells.Quantification of intracellular Ca2+ level during ER Ca2+ depletion and SOCE was performed as previously described (). Briefly, widefield fluorescence images were analyzed with MetaMorph to obtain F340/F380 change from single cells.Data visualization and statistical analysis were performed with MATLAB R2016b or GraphPad Prism 7 (GraphPad Software, La Jolla, CA).All custom MATLAB scripts and functions used in this study are available on request. […]

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