Camera variance, gain and offset calibration.

We recorded a series of dark images (60,000 frames) with our sCMOS camera (ORCA-Flash 4.0, Hamamatsu Photonics). The automatic pixel correction that is offered by many sCMOS camera models was disabled for all calibration and application measurements to avoid automatic replacement of high-variance pixels by the average of the neighboring pixels. This correction otherwise prevents correct statistical treatment of the pixel signal. The mean ('offset'), o i , and the variance, var i , for each pixel i were calculated by temporal mean and variance operations over the acquired dark frames. The amplification gain, g i , for each pixel was estimated from 15 sets of 20,000 frames each that were recorded at different illumination intensities ranging from approximately 20 to 200 photons per pixel (Supplementary Note).

Simulations of super-resolution sCMOS data sets.

For the simulated line pattern, single emitters were simulated using a pixel-integrated symmetric two-dimensional (2D) Gaussian model13. The switching behavior was simulated using a Markov model with k off (bright-to-dark) and k on (dark-to-bright) rates of 0.8 frame−1 and 10−5 frame−1, respectively. These rates were chosen to ensure nonoverlapping emitters. Single emitters were simulated with 200 photons per molecule incident on the camera, 5 photons detected per pixel as the background, a 2D Gaussian-shaped point-spread function (PSF) with 133-nm s.d., 75% camera quantum efficiency and a pixel size of 103 nm to match our experimental setup. Images of the simulated structure of two lines with 80-nm distance were first generated with Poisson noise, and then pixel-dependent Gaussian noise was added to each pixel in the simulated subregion where the variance, offset and gain values of each pixel had been obtained from a physical subregion of our sCMOS camera. This noise mapping method ensures realistic simulation of sCMOS noise behavior.

Generation of goodness-of-fit–metric (LLR) histograms.

For Figure 1e,f, we simulated 10,000 7 × 7–pixel subregions each with a single emitter (200 total incident photons per single molecule and 5 background photons per pixel) separately in two different subregions. Noise was added to the images using the noise mapping method described above. Localization analysis was performed using MLE and MLE sCMOS , respectively, and LLR and LLR sCMOS were calculated. The χ2 distribution with 45 degrees of freedom (number of pixels – number of fit parameters) (ref. 30) is plotted in Figure 1e,f.

Comparison of MLE results with CRLB.

For Figure 1c,d, at each incident photon level, simulations were performed at 1,000 randomly distributed positions using the noise mapping method. In each subregion, 1,000 single emitters were simulated and fit by MLE and MLE sCMOS , respectively. The localization uncertainty is the s.d. of the localization estimates. The distribution of these values over the 1,000 subregions then provides a mean localization uncertainty and its s.d. The CRLB and CRLB sCMOS were determined as the mean value of the CRLBs calculated from the localization estimates of all 1,000 × 1,000 simulations for each signal photon-background combination for the conventional method and sCMOS method, respectively.

Imaging of fluorescent beads and single-molecule analysis.

A sample of 100-nm fluorescent beads (F-8801, Life Technologies) was prepared on a coverslip and imaged with our custom-built microscope (Supplementary Fig. 6) using a 63×/1.2-NA water-immersion objective (C-Apochromat 63×/1.2 W Corr, Zeiss). The effective pixel sizes were matched between the two cameras for fair comparison using relay lenses (pixel size in the sample plane: 101 nm for EMCCD and 103 nm for sCMOS). A mirror on a magnetic retention base enabled fast switching between the two cameras with minimal disturbance to the imaging system. A 568-nm laser (Innova 300, Coherent) was used at a low intensity (∼0.1 mW measured before the objective) as the excitation source. Images were taken first with the EMCCD and then with the sCMOS camera to rule out a decrease in localization precision caused by photobleaching. Photobleaching was not observed over the acquisition period (data not shown). Data sets from EMCCD and sCMOS data sets (800 frames each), were analyzed using MLE12 and MLE sCMOS (Supplementary Note), respectively.

Plasmid construction.

The Human paxillin sequence (NM_002859.2) was amplified using PCR and cloned into the pCMV-3TAG 1A vector (Agilent Technologies) to obtain a construct expressing an N-terminal Flag-tagged version of paxillin.

mEos3.2 and tdEos fluorescent protein (FP) expression vectors were constructed using C1 and N1 (Clontech-style) cloning vectors. The FP cDNAs were amplified with a 5′ primer encoding an AgeI site and a 3′ primer encoding a NotI site (N1) or a BspEI site (C1) for insertion into the appropriate cloning vector backbone. The PCR products and EGFP-C1 and EGFP-N1 cloning vectors were gel purified and digested with the appropriate enzymes before ligation to generate new cloning vectors with the Eos FP coding regions. All fusions were first constructed using an EGFP variant with mutations designed to enhance folding (mEmerald), which is well behaved with respect to monomeric character and lack of localization artifacts. These vectors served as pilots to demonstrate proper localization of constructs to confirm the performance of the photoconvertible FPs. Thus, an N-terminal fusion (with respect to the targeting protein) was constructed to produce a human clathrin light chain (CLC) fusion (NM_001834.2; gift from G. Patterson, NIH) with a 15-amino-acid linker separating the fluorescent protein from clathrin. CLC cDNA was amplified using the primers listed in Supplementary Table 1. The resulting PCR product and mEmerald-C1 cloning vector were digested with the appropriate restriction enzymes, and the products were ligated to yield mEmerald-CLC. The same process was also used to generate human EB3 (NM_012326.2; OriGene) using the primers in Supplementary Table 1. The PCR product and mEmerald-C1 were digested by the appropriate enzymes and ligated to yield mEmerald-EB3 with an 18-amino-acid linker separating the proteins. In both cases, after proper localization was confirmed, mEos3.2 was substituted for mEmerald through digestion with BglII and NheI enzymes. This yielded mEos3.2-CLC and mEos3.2-EB3.

To produce a pilot C-terminal fusion (with respect to the targeting protein), human peroxisomal membrane protein (PMP) (NM_018663.1; OriGene) was amplified using the primers in Supplementary Table 1. The resulting PCR product and mEmerald-N1 were digested by the appropriate restriction enzymes, and the products were ligated to yield PMP-mEmerald with a 10-amino-acid linker separating the proteins. After confirmation of proper localization, the resulting construct and tdEos-N1 were then sequentially digested with AgeI and NotI and ligated to form PMP-tdEos.

In a similar manner, human pyruvate dehydrogenase alpha 1 (PDHA1) (NM_000284.3; OriGene) was amplified using the primers listed in Supplementary Table 1. The PCR product and mEmerald-N1 were digested by the appropriate enzymes and ligated to yield PDHA1-mEmerald with a 10-amino-acid linker separating the proteins. After we confirmed the proper localization, the resulting construct and tdEos-N1 were then digested with BamHI and NotI and ligated together to form PDHA1-tdEos.

The DNA used for transfection was prepared using the Plasmid Maxi kit (Qiagen). To ensure proper localization, mEos3.2 and tdEos fusion proteins were characterized by transfection in HeLa cells (CCL2 line; ATCC) using Effectene (Qiagen) and ∼1 μg vector. Transfected cells were grown on coverslips in DMEM/F12, fixed after 48 h, and mounted with gelvatol.

Sample preparation for microtubule imaging.

COS-7 cells (ATCC) were grown in DMEM/F12 (Invitrogen) supplemented with 2 mM L-glutamine (Gibco), 10% fetal bovine serum (FBS, ATCC) and 1% penicillin (10,000 IU/mL)/streptomycin (10,000 μg/mL) (P/S, ATCC) at 37 °C with 5% CO 2 . Prior to imaging, cells were grown in 35-mm dishes on no. 1.5 glass coverslips precoated with poly(L-lysine) (MatTek) and fibronectin. For microtubule labeling, cells were washed three times with PBS prewarmed to 37 °C and were pre-extracted with 0.2% saponin (Sigma) in cytoskeleton buffer (CSB, 10 mM MES pH 6.1 (Sigma), 150 mM NaCl, 5 mM MgCl 2 (Sigma), 5 mM EGTA (Sigma), 5 mM glucose) for 1 min at room temperature. After the solution was aspirated, the cells were fixed with 3% paraformaldehyde (PFA, Electron Microscopy Sciences) and 0.1% glutaraldehyde (Electron Microscopy Sciences) diluted in CBS for 15 min. Cells were washed three times for 3-min intervals with PBS and then permeabilized and blocked with blocking buffer (3% BSA (Sigma) and 0.2% TX-100 in PBS) for 30 min while gently rocking. The buffer was aspirated, and the cells were incubated with mouse monoclonal anti–α-tubulin antibody (Sigma T5168, 1:1,000 dilution) at room temperature for 1 h. Cells were washed three times for 3-min intervals using wash buffer (WB; 0.05% TX-100 in PBS) and incubated with Alexa Fluor 647 goat anti-mouse IgG (Invitrogen A-21236) at a concentration of approximately 5 μg/mL for 1 h. Cells were washed with the WB for three 3-min intervals and postfixed with 3% PFA and 0.1% glutaraldehyde diluted in CSB for 10 min. Samples were washed three times in PBS for 3-min intervals and stored in PBS at 4 °C until imaging.

Sample preparation for focal adhesion imaging.

We seeded 2 × 105 HeLa cells grown in DMEM (Invitrogen) with 10% FBS on a 35-mm glass-bottom dish (MatTek; no. 1.5 coverglass). After overnight incubation, the cells were transfected with 500 ng of plasmid DNA expressing N-terminal Flag-tagged focal adhesion marker paxillin using FuGENE HD transfection reagent (Promega; E2311). The cells were fixed 24 h after transfection with 4% PFA and were processed for immunofluorescence. Briefly, the cells were permeabilized with 0.5% Triton X-100, treated with monoclonal antibodies to the Flag epitope (1:1,000 dilution, Clone M2, Sigma-Aldrich; F3165; 1 mg/mL) in PBS containing 3% bovine serum albumin fraction V (American Bioanalytical; AB01088). This was followed by treatment with Alexa Fluor 647–conjugated goat secondary antibodies against mouse (1:2,000 dilution; Invitrogen; A-21236; 2 mg/mL). The cells were then postfixed with 4% PFA. Immediately before image acquisition, the cells were mounted in STORM-imaging buffer and overlaid with mineral oil.

Sample preparation for live-cell imaging with photoswitchable fluorescent proteins.

HeLa and COS-7 cells grown in DMEM (high glucose, phenol red–free; Invitrogen) supplemented with 10% FBS and 1% P/S were seeded on a 35-mm glass-bottom dish (MatTek; no. 1.5 coverglass). After overnight incubation, the cells were transfected with 2 μg of plasmids using FuGENE HD transfection reagent (Promega). Cells were washed with supplemented medium 24 h post-transfection and were incubated overnight. Before imaging, the cells were washed with supplemented growing medium.

Sample preparation for live-cell imaging of transferrin.

EA.hy926 cells were grown in DMEM (high glucose, phenol red–free, Invitrogen) supplemented with 10% FBS and 1% P/S at 37 °C with 5% CO 2 . Prior to imaging, cells were grown in 35-mm dishes with no. 1.5 glass coverslips coated with collagen (MatTek). One hour before labeling, EA.hy926 cells were incubated in DMEM (high glucose, phenol red–free) without serum. Transferrin from human serum, conjugated to Alexa Fluor 647 (Life Technologies), was reconstituted with deionized water to a concentration of 5 mg/mL and stored at 4 °C. The transferrin–Alexa Fluor 647 conjugate was diluted to 10 μg/mL in DMEM (high glucose, phenol red–free) and incubated with EA.hy926 cells for 45 min. After labeling, cells were washed three times with 1× PBS. To remove transferrin–Alexa Fluor 647 bound to the plasma membrane, cells were washed once with 50 mM MES, pH 5.0,150 mM NaCl and twice with 1× PBS. Cells were imaged in 1 mL of DMEM (high glucose, phenol red–free) supplemented with 25 μM 2-ME, 10 μL of glucose oxidase and 4 μL of catalase at room temperature.

Imaging buffer preparation for Alexa Fluor 647–labeled samples.

Oxygen-scavenging enzymes, catalase from bovine liver (Sigma C40) and glucose oxidase from Aspergillus niger (Sigma G2133), were reconstituted in 20 mM Tris pH 7.4 (Sigma), 50 mM NaCl (Sigma) and 28.4 mM 2-mercaptoethanol (2-ME, Sigma). Oxygen scavenging enzymes were stored separately in 50% glycerol at −20 °C at concentrations of 500 kU/mL of catalase and 13.5 kU/mL of glucose oxidase. Oxygen-scavenging enzymes were diluted into imaging buffer (50 mM Tris, pH 8.0, 50 mM NaCl (Sigma), 10% glucose) immediately before use. For imaging, 20 μL of glucose oxidase and 4 μL of catalase stocks were added to 1 mL of 1% (v/v) 2-ME in imaging buffer.

Imaging of fixed and live cells.

All biological images were recorded on a custom-built setup (Supplementary Fig. 6) based on a commercial microscope stand (Axio Observer D1, Carl Zeiss MicroImaging) with a 100×/1.46-NA oil-immersion objective (alpha Plan-Apochromat 100×/1.46 oil, Zeiss). The setup is equipped with lasers emitting at 405 nm (CrystaLaser, 50 mW), 568 nm (Coherent Innova 300, ∼400 mW) and 642 nm (MPB Communications, 500 mW). Fluorescence was recorded by our sCMOS camera through the side port of the stand. All data were recorded at room temperature.

Fixed microtubule structures were imaged in a 128 × 128–pixel ROI for 40,000 frames at 1,600 f.p.s. (Fig. 1g–k), a 512 × 512–pixel ROI for 16,000 frames at 400 f.p.s. (Fig. 2a) and a 64 × 64–pixel ROI for 30,000 frames at 3,200 f.p.s. (Fig. 2d–g). The 642-nm laser was used at average intensities of ∼18 kW/cm2, ∼6 kW/cm2 and ∼7 kW/cm2, respectively. Images were acquired using HCimage software (Hamamatsu). Focal adhesions were recorded in a 256 × 256–pixel ROI for 2,400 frames at 800 f.p.s. with the 642-nm average laser intensity set to ∼9 kW/cm2. Clathrin data were recorded in a 256 × 256–pixel ROI for 50,000 frames at 600 f.p.s. with the 568-nm average laser intensity set to ∼5 kW/cm2. Mitochondria were recorded in a 256 × 256–pixel ROI for 80,000 frames at 400 f.p.s. with the 568-nm average laser intensity set to ∼5 kW/cm2. EB3 data were recorded in a 256 × 256–pixel ROI for 30,000 frames at 600 f.p.s. with the 568-nm average laser intensity set to ∼5 kW/cm2. Peroxisomes were recorded in a 256 × 256–pixel ROI for 50,000 frames at 600 f.p.s. with the 568-nm average laser intensity set to ∼5 kW/cm2. Transferrin data were recorded in a 128 × 128–pixel ROI at 1,600 f.p.s. with the 642-nm average laser intensity set to ∼7 kW/cm2. During imaging, the intensity of the 405-nm activation laser was manually increased from 0 to 0.3 W/cm2 (for a 512 × 512–pixel ROI) and from 0 to 1.8 W/cm2 (for 256 × 256–, 128 × 128– and 64 × 64–pixel ROIs) to ensure optimal particle densities for either single-emitter fitting or multi-emitter fitting31.

Super-resolution analysis and reconstruction for fixed- and live-cell experiments.

For the conventional MLE method, raw images recorded with the sCMOS camera were first Poisson calibrated13 by subtracting a predetermined offset value and then dividing by a constant gain factor (provided by the manufacturer)13,32. The calibrated images were then analyzed using the conventional MLE method. The fit parameters were the emitter position (x, y), the number of detected photons and the background value. The localization precision was estimated by the conventional CRLB. Finally, super-resolution images were reconstructed by plotting pixel-integrated 2D Gaussians13 at each localized position, each with s.d. in the x and y directions equal to the corresponding CRLB estimates of localization uncertainties.

For the sCMOS-specific method, particles were identified and segmented in the raw sCMOS images using the sCMOS-specific image-segmentation algorithm (Supplementary Note) and then fitted using MLE sCMOS . The localization precision was estimated by CRLB sCMOS . Super-resolution images for Figure 1 were reconstructed as described above for conventional MLE.

To obtain the super-resolution images shown in Figures 2,3,4, only the sCMOS-specific algorithms were used. In Figures 2a and 3, single-emitter analysis was performed and localization results were filtered using LLR sCMOS . For reconstruction, localization estimates were binned into a 2D histogram image. The value of each pixel indicates the number of localization estimates in the pixel. The pixel sizes in the 2D histogram images shown were 5.15 nm in Figure 2a, 10.3 nm in Figure 3a–c and 20.6 nm in Figure 3d–f. To aid visualization, each image was convolved with a 2D Gaussian kernel with s.d. σ = 1.5 pixels (Fig. 2a) or 1 pixel (Fig. 3).

In Figures 2d–g and 4, multi-emitter fitting was performed using the following fitting parameters: position (x, y), effective photon count and background. Initial guesses of effective photon count were 250 (Fig. 2d–g) and 200 (Fig. 4). For all data sets, the maximal number of emitter models fitted within a single subregion was set to 4 and σ of the Gaussian emitter model was set to 133 nm. A P value of 10−10 from the LLR sCMOS was used as the rejection threshold. To reconstruct the super-resolution image, localization estimates from the remaining particles were binned into a 2D histogram image with 10.3-nm pixel size. To aid visualization, each resulting image was convolved with a 2D Gaussian kernel with σ = 1 pixel (Fig. 2e–g) or 2 pixels (Fig. 4).

To provide an estimate for the average number of consecutive frames over which single molecules were detected, we considered localization events to stem from the same molecule if they were located within a radius of three times the localization uncertainty.

Generation of difference images.

For Figure 1j,k, after localization using MLE or MLE sCMOS , 2D histogram images were generated with 10.3-nm pixel size. The difference image was obtained by subtracting the MLE sCMOS image from the conventional MLE image. To aid visualization, the resulting images were convolved with a 2D Gaussian kernel (σ = 2 pixels).

Tracking of EB3 and clathrin-coated pits.

The series of EB3 super-resolution images was imported in Velocity 6.2 (Velocity Medical Solutions), and the tips of the growing microtubules were tracked using the manual tracking mode. For the series of super-resolution CCP images, the centers of identified CCPs were tracked manually in Matlab during the individual periods of directed movement (5–20 s).

Emitter density calculation.

To estimate the number of localized emitters per area in Figure 2 and Supplementary Figure 10, we extracted the number of localization estimates from a representative 1-μm segment of straight microtubule using an integration width of 80 nm (Fig. 2a). For the paxillin data, estimates were counted in a 240 nm × 240 nm box centered on a large focal adhesion (Supplementary Fig. 10a). The 1D and 2D localization densities were calculated as the number of localization estimators determined per length unit and per area unit, respectively.

Nyquist resolution measurement.

To estimate the Nyquist resolution in Figure 4a,b, we first determined masks covering the area of each transferrin cluster in each super-resolution image. Determining the number of localization estimates in the clusters and dividing it by the cluster area yielded the localization density. The Nyquist resolution was calculated for the 2D case7 as 2/(localization density)1/2.