Computational protocol: α-SNAP regulates dynamic, on-site assembly and calcium selectivity of Orai1 channels

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[…] Resting or store-depleted cells were fixed with 2% PFA for 10 min at room temperature, followed by three or four washes of 1× phosphate-buffered saline (PBS). Transfected MEF cells were first screened using the mEos green fluorescence to identify cells expressing weak levels of mEos-Orai1 at the PM. Cells were further screened for intracellular presence of Cy5-labeled SCR or α-SNAP siRNA. For each cell, mEos-Orai1 molecules were photoconverted to the red form using the 405-nm laser at an intensity of 7 W/cm2 (measured at the sample) for 6 s. Photobleaching of the mEos red form was initiated by turning on the 561-nm laser at 2 W/cm2 with simultaneous image acquisition at 10-Hz frequency and was stopped when the intensity of all molecules reached background.Photobleaching movies were processed to subtract background, enhance contrast, and automatically identify fluorescent spots, using the following steps. All steps were performed in Matlab, unless mentioned otherwise.Noise removal and spot identification. In the first stage, each movie was processed to remove noise in the temporal and spatial scales to unambiguously detect all fluorescent spots in the cell. First, a Kalman filter was applied in ImageJ (http://rsb.info.nih.gov/ij/plugins/kalman.html) to maintain transient signal changes due to bleaching while reducing camera noise across the time series. To further remove spatial blurring and increase contrast between the spot signals and background, we averaged the first 10 frames of the movie and filtered this averaged image with a Laplacian of Gaussian (LoG) kernel filter to generate an image mask (). This image mask was used to locate spot positions and filter spots in subsequent steps.Filter spots. We removed spots using the following criteria: Size. We removed spots of <2 × 2 pixels and >4 × 4 pixels. Smaller spots are likely due to nonspecific fluorescence on the coverslip surface, and larger spots could arise from an overlap of multiple nearby spots. Consistent with this, we did not observe discrete bleaching steps from most spot sizes of 5 × 5 and larger pixel dimensions.Shape and distance. We further performed a two-dimensional elliptical Gaussian fitting using a 3 × 3–pixel region of interest (ROI) to extract center positions, x- and y-widths (Wx and Wy, respectively), and peak intensity for each spot. We removed spots if their centroid distance was <4 pixels and elliptical spots since these could arise from a merger of two neighboring spots. Elliptical shapes were defined as follows: Intensity. We removed extremely weak spots whose intensity was <10% of background as a low-intensity threshold.Application of these filters typically resulted in the elimination of 10–20% of spots.Step detection: To automatically detect steps during the bleaching process, we adopted the progressive step detection algorithm as described by ) and combined it with nonparametric Bayesian inference () to estimate the existing noise level in the bleaching time series and identify the number of discrete bleaching states. For each bleaching trace, we averaged the observed step sizes and used half of the average step size to set as a noise level threshold for automated step level detection. Each fluctuation in the fluorescence signal was compared with noise level to determine whether it was due to bleaching of the molecule or noise in the signal. Subsequently, through iterated scanning, annealing, and reevaluation phases, all fluctuations from noise were averaged and bleaching events found. Automating these steps avoids subjective bias in selecting spots as well as determining step counts.Correction of step overcounting/undercounting. Finally, we manually examined the data to identify cases of overcounting or undercounting of steps. Because a small fraction of mEos molecules can spontaneously photoreactivate after bleaching and the intensity after photoreactivation would be larger than the averaged step size, the automated algorithm could falsely assign such a change in fluorescence state as a bleaching step. We identified such cases, and as long as the spot intensity had reached the background CCD noise, we regarded the molecule as completely bleached. Undercounting of steps could be identified when two molecules bleach simultaneously. These could be identified as a large step that was roughly twice the size of the average step value for that trace and was regarded as two steps instead of one.Data representation and stoichiometry modeling. Examples of spots exhibiting step-photobleaching fluorescence traces are shown in , where fluorescence values for each spot are integrated from a 3 × 3–pixel ROI. Bleaching steps from multiple cells in each group were pooled and plotted as a histogram in Origin software. The probability of observing k steps each subunit follows a binomial distribution: where n is the number of homogeneous subunits in one complex (e.g., n = 2 for dimers and n = 6 for hexamers) and p denotes the fraction of active-state fluorescent proteins. In case of a dynamic process in which Orai1 molecules can reconstitute into multiple coexisting subunit configurations, we considered other possibilities to model the distribution of bleaching steps. For instance, in case of a combination of dimer, tetramer, and hexamer, the probability of observing k steps is where a, b, and c are fractions of dimer, tetramer, and hexamer, respectively. Excluding all dark state subunits, the predicted distribution is Noise removal and spot identification. In the first stage, each movie was processed to remove noise in the temporal and spatial scales to unambiguously detect all fluorescent spots in the cell. First, a Kalman filter was applied in ImageJ (http://rsb.info.nih.gov/ij/plugins/kalman.html) to maintain transient signal changes due to bleaching while reducing camera noise across the time series. To further remove spatial blurring and increase contrast between the spot signals and background, we averaged the first 10 frames of the movie and filtered this averaged image with a Laplacian of Gaussian (LoG) kernel filter to generate an image mask (). This image mask was used to locate spot positions and filter spots in subsequent steps.Filter spots. We removed spots using the following criteria: Size. We removed spots of <2 × 2 pixels and >4 × 4 pixels. Smaller spots are likely due to nonspecific fluorescence on the coverslip surface, and larger spots could arise from an overlap of multiple nearby spots. Consistent with this, we did not observe discrete bleaching steps from most spot sizes of 5 × 5 and larger pixel dimensions.Shape and distance. We further performed a two-dimensional elliptical Gaussian fitting using a 3 × 3–pixel region of interest (ROI) to extract center positions, x- and y-widths (Wx and Wy, respectively), and peak intensity for each spot. We removed spots if their centroid distance was <4 pixels and elliptical spots since these could arise from a merger of two neighboring spots. Elliptical shapes were defined as follows: Intensity. We removed extremely weak spots whose intensity was <10% of background as a low-intensity threshold.Application of these filters typically resulted in the elimination of 10–20% of spots.Size. We removed spots of <2 × 2 pixels and >4 × 4 pixels. Smaller spots are likely due to nonspecific fluorescence on the coverslip surface, and larger spots could arise from an overlap of multiple nearby spots. Consistent with this, we did not observe discrete bleaching steps from most spot sizes of 5 × 5 and larger pixel dimensions.Shape and distance. We further performed a two-dimensional elliptical Gaussian fitting using a 3 × 3–pixel region of interest (ROI) to extract center positions, x- and y-widths (Wx and Wy, respectively), and peak intensity for each spot. We removed spots if their centroid distance was <4 pixels and elliptical spots since these could arise from a merger of two neighboring spots. Elliptical shapes were defined as follows: Intensity. We removed extremely weak spots whose intensity was <10% of background as a low-intensity threshold.Application of these filters typically resulted in the elimination of 10–20% of spots.Step detection: To automatically detect steps during the bleaching process, we adopted the progressive step detection algorithm as described by ) and combined it with nonparametric Bayesian inference () to estimate the existing noise level in the bleaching time series and identify the number of discrete bleaching states. For each bleaching trace, we averaged the observed step sizes and used half of the average step size to set as a noise level threshold for automated step level detection. Each fluctuation in the fluorescence signal was compared with noise level to determine whether it was due to bleaching of the molecule or noise in the signal. Subsequently, through iterated scanning, annealing, and reevaluation phases, all fluctuations from noise were averaged and bleaching events found. Automating these steps avoids subjective bias in selecting spots as well as determining step counts.Correction of step overcounting/undercounting. Finally, we manually examined the data to identify cases of overcounting or undercounting of steps. Because a small fraction of mEos molecules can spontaneously photoreactivate after bleaching and the intensity after photoreactivation would be larger than the averaged step size, the automated algorithm could falsely assign such a change in fluorescence state as a bleaching step. We identified such cases, and as long as the spot intensity had reached the background CCD noise, we regarded the molecule as completely bleached. Undercounting of steps could be identified when two molecules bleach simultaneously. These could be identified as a large step that was roughly twice the size of the average step value for that trace and was regarded as two steps instead of one.Data representation and stoichiometry modeling. Examples of spots exhibiting step-photobleaching fluorescence traces are shown in , where fluorescence values for each spot are integrated from a 3 × 3–pixel ROI. Bleaching steps from multiple cells in each group were pooled and plotted as a histogram in Origin software. The probability of observing k steps each subunit follows a binomial distribution: where n is the number of homogeneous subunits in one complex (e.g., n = 2 for dimers and n = 6 for hexamers) and p denotes the fraction of active-state fluorescent proteins. In case of a dynamic process in which Orai1 molecules can reconstitute into multiple coexisting subunit configurations, we considered other possibilities to model the distribution of bleaching steps. For instance, in case of a combination of dimer, tetramer, and hexamer, the probability of observing k steps is where a, b, and c are fractions of dimer, tetramer, and hexamer, respectively. Excluding all dark state subunits, the predicted distribution is For each model comprising pure dimers, tetramers, hexamers, and combinations thereof, we initially iterated the value of p and the relative fraction of each molecule to calculate the chi- square error between the experimentally observed distribution and the expected distribution from each model. A model was found to fit best when the chi-square error between the observed and expected distribution was minimum. In all cases, we found that models fited best at p around 0.7. [...] Single-particle tracking (SPT) was performed in live HEK293 cells cultured on coverslip bottom dishes. Cells were transiently cotransfected with mEos-Orai1 along with Stim1-myc or empty vector, and imaging was performed at 12–16 h posttransfection. Sparse single molecules of photoconverted mEos-Orai1 were imaged using continuous TIR illumination of the 561-nm laser at 200 W/cm2 plus the 405-nm laser at 0.3 W/cm2 (Supplemental Video S2). Image acquisition was performed at 50-Hz frequency and stopped at 6 min after addition of TG. This SPT movie was processed to subtract uneven illumination background by using the rolling ball algorithm in ImageJ. Subpixel localizations of single particles were extracted using the comet detection algorithm and u-track software (; ). Particle localization in adjacent frames was tracked and trajectories linked using u-track and the following empirically determined criterion: to account for molecule blinking, gap length was set at six frames, search radius to capture displacement was set with an upper bound of 3 pixels, and lower bound was taken as equal to the localization uncertainty of 30 nm. Only trajectories >20 points were taken into consideration for analysis. Diffusion coefficients were obtained using the moment scaling spectrum algorithm () implemented in u-track. The cumulative frequency distribution was plotted for all trajectories, as well as the mean square displacement as a function of time lapse. [...] Two- and three-channel STORM images were acquired on the same imaging setup. Coverslips with cells expressing mEos-Orai1 and Stim1-myc were fixed after store depletion, washed, permeabilized with 3% bovine serum albumin plus 0.1% NP-40 in PBS, and immunolabeled with the 9E10 anti-myc antibody (Developmental Studies Hybridoma Bank, Iowa City, IA). Primary antibody was detected using a donkey anti-mouse immunoglobulin G (IgG) secondary antibody (Jackson ImmunoResearch, West Grove, PA) labeled with the reporter and activator fluorescent dyes Alexa 647 and Cy2. A coverslip was inverted into a slide with an imaging buffer of 100 mM Tris-HCl, pH 8.0, and 150 mM NaCl and containing an oxygen-scavenging system comprising glucose, glucose oxidase, catalase, and the reducing agent 2-mercaptoethylamine (). After removal of excess buffer, edges of the coverslip were sealed with nail polish before imaging. Sparse single- molecule images were acquired at 60-Hz frequency by alternating pairs of imaging plus activator laser sequence: the 642-nm laser at 560 W/cm2 plus weak 488-nm laser for the dye-labeled antibody and the 561-nm laser at 250 W/cm2 plus weak 405-nm laser for mEos. The amount of 488- or 405-nm activation laser was adjusted to ensure sparse single-molecule events in each camera frame. Alexa 647 and mEos-red fluorescence emissions were filtered using an et561/640m dual-bandpass emission filter (Chroma). Raw image stacks were fitted with the DAOSTORM algorithm (; ) to determine the centroid positions of fluorescent intensity peaks. These STORM localizations were rendered as images using custom software.For three-channel STORM, cells transfected with mEos-Orai1, Stim1-myc, and CFP–α-SNAP were immunolabeled with rabbit anti-GFP and mouse anti-myc primary antibodies, followed by donkey anti-rabbit and donkey anti-mouse IgG-specific secondary antibodies (Jackson ImmunoResearch). Secondary reagents were custom conjugated with the fluorescent dye pairs Cy2–Alexa 647 and Alexa 405–Alexa 647. Sequential image acquisition was performed to first image the Alexa 647 antibodies as a two-channel STORM sequence similar to one previously described (), followed by imaging of the mEos fluorescent protein using the 561- and 405-nm lasers. To compensate for drift during image acquisition, we used the redundancy cross-correlation drift correction algorithm in Matlab (). For antibody channels, drift in all frames was corrected to align with the last frame, whereas localizations from all mEos frames were aligned to their first frame. 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Pipeline specifications

Software tools ImageJ, u-track, DAOSTORM
Applications Super-resolution imaging, Microscopic phenotype analysis
Organisms Mus musculus
Chemicals Calcium, Sodium