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. 2013 Jul;10(7):653-8.
doi: 10.1038/nmeth.2488. Epub 2013 May 26.

Video-rate nanoscopy using sCMOS camera-specific single-molecule localization algorithms

Fang Huang  1 , Tobias M P Hartwich, Felix E Rivera-Molina, Yu Lin, Whitney C Duim, Jane J Long, Pradeep D Uchil, Jordan R Myers, Michelle A Baird, Walther Mothes, Michael W Davidson, Derek Toomre, Joerg Bewersdorf
Affiliations

Video-rate nanoscopy using sCMOS camera-specific single-molecule localization algorithms

Fang Huang et al. Nat Methods. 2013 Jul.

Abstract

Newly developed scientific complementary metal-oxide semiconductor (sCMOS) cameras have the potential to dramatically accelerate data acquisition, enlarge the field of view and increase the effective quantum efficiency in single-molecule switching nanoscopy. However, sCMOS-intrinsic pixel-dependent readout noise substantially lowers the localization precision and introduces localization artifacts. We present algorithms that overcome these limitations and that provide unbiased, precise localization of single molecules at the theoretical limit. Using these in combination with a multi-emitter fitting algorithm, we demonstrate single-molecule localization super-resolution imaging at rates of up to 32 reconstructed images per second in fixed and living cells.

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Figures

Fig. 1
Fig. 1
sCMOS camera specific algorithms enable unbiased SMSN at the theoretical limit. (a) Readout variance map of a 128 × 128 pixel region in the center of a sCMOS camera. (b) Simulation of single emitters on a parallel line pair based on the shown variance map and localized using conventional MLE and MLEsCMOS. (c, d) Uncertainty estimator performance comparison for sCMOS camera data between conventional MLE and CRLB (c) and between MLEsCMOS and CRLBsCMOS (d) for different detected background photon values/pixel (bg) and single-molecule photon numbers incident on the camera chip. (e, f) Comparison of LLR, LLRsCMOS and the theoretically predicted distribution for two different locations on the camera chip. (g, h) Reconstructed super-resolution images of microtubules analyzed using conventional (g) and sCMOS algorithms (h). (i) Maximum projection of raw data used for (g, h) representing the diffraction-limited image. (j) Difference images of (g) and (h). Zero differences are shown in gray. (k) Difference image of another microtubule data set recorded in the same camera region. Artifacts in (g, j, k) correlate with high-noise pixels in (a) as highlighted by the white circles. The yellow boxes in (a, g–k) denote the positions of the enlarged sections shown in the insets. (l) Relative improvement in localization precision predicted by CRLB for EMCCD cameras and CRLBsCMOS for sCMOS, respectively. Color bars: (a, b) 0–400 ADU2; (i) min-max signal; (g, h) normalized to same scale; (j, k) normalized to same scale. Scale bar in (a, g–k): 2 µm.
Fig. 2
Fig. 2
Unbiased, fast SMSN of fixed microtubules and focal adhesions demonstrating high-throughput capabilities. (a) Super-resolution image of microtubules in a 53 × 53 µm2 ROI imaged at 400 fps in 40 s. The data set was processed by the new sCMOS-specific algorithm resulting in about 4.4 million position estimates after filtering. The area marked by the blue box is shown in the inset as a maximum projection of the raw data (entire analyzed data set) representing the diffraction-limited wide-field image and as the super-resolution image. For visualization purposes, the upper bound in the color table has been adjusted for the overview image. (b) Enlarged maximum projection of raw data and super-resolution image of the area highlighted by the green box in (a). (c,d) Line profiles of position estimates in the small yellow boxes shown in (a) and (b), respectively. (e) Maximum projection of a microtubule raw data set recorded in a 6.5 × 6.5 µm2 FOV at 3,200 fps in 2 s total. (f–h) Reconstructed super-resolution images obtained from different subsets of the same raw data stack using sCMOS-specific multi-emitter fitting. (i,j) Maximum projection of a data set showing the distribution of Paxillin in focal adhesions recorded in 3 s and the corresponding super-resolution image, respectively. (k) Enlarged display of the area marked by the white boxes in (i,j). (l) Profile displaying the distribution of position estimates in the yellow box shown in the super-resolution image in (k).
Fig. 3
Fig. 3
Live-cell SMSN at 0.5 to 2 s temporal resolution. (a) Super-resolution image of mEos3.2-labeled clathrin-coated pits (CCPs) in a live HeLa cell. The localization estimates are colored according to their recording time. (b) The enlarged image of the area marked by the yellow box in (a) reveals that movements of the structures over the course of recording obscure the details in the image. (c) Rings representing axial projections of CCPs can be resolved when displaying only a 2-s time window of the data. (d–f) Peroxisome dynamics in a live COS-7 cell labeled by tdEos. (d) Overview image of an 83-s data set. Data has been colored analogous to (a). (e) Super-resolution images and maximum projections of the raw data from the area in the green box in (d) in short 0.5-s time intervals. (f) Super-resolution images at 0.5-s time resolution of the area highlighted by the blue box in (d). Only every second frame is shown. See also Supplementary Fig. 10 and Supplementary Videos 1–5, 8 and 9.
Fig. 4
Fig. 4
Video-rate live-cell nanoscopy of transferrin receptor clusters in live EA.hy926 cells. (a–b) Two examples of super-resolved transferrin cluster dynamics from a larger data set. Asterisks show splitting of single clusters into multiple clusters. Super-resolution images at a slower rate (~4 super-resolution images/s; shown on the left) lead to artifactual structures due to diffusion as visualized by reconstructed sequences of the same data at higher frame rates (~32 super-resolution images/s; on the right).
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