Quantitative mapping and minimization of super-resolution optical imaging artifacts

Culley, Siân; Albrecht, David; Jacobs, Caron; Pereira, Pedro M.; Leterrier, Christophe; Mercer, Jason; Henriques, Ricardo

doi:10.1038/nmeth.4605

Cited by 286 publications

(363 citation statements)

References 25 publications

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“…1), NanoJ-SQUIRREL error mapping analysis ( Supplementary Fig. 1) 18 , and visual inspection, we found PSSR very effectively restored the low resolution images ( Fig. 1c).…”

Section: Resultsmentioning

confidence: 67%

“…Fourier-Ring-Correlation (FRC) analysis. NanoJ-SQUIRREL 19 was used to calculate image resolution using FRC method on real-world testing examples with two independent acquisitions of fixed samples (Fig. 1b-c, 3c and Fig.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…. NanoJ-SQUIRREL 19 was used to calculate the RSE and RSP for both semi-synthetic and real-world acquired low (LR), bilinear interpolated (LR-Bilinear), and PSSR (LR-PSSR) images versus ground truth high resolution (HR) images. Difference error maps were also calculated ( Supplementary Fig.…”

Section: Resolution Scaled Error (Rse) and Resolution Scaled Pearson'mentioning

confidence: 99%

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Deep Learning-Based Point-Scanning Super-Resolution Imaging

Fang

Monroe²,

Novak

et al. 2019

Preprint

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Point scanning imaging systems (e.g. scanning electron or laser scanning confocal microscopes) are perhaps the most widely used tools for high resolution cellular and tissue imaging. Like all other imaging modalities, the resolution, speed, sample preservation, and signal-to-noise ratio (SNR) of point scanning systems are difficult to optimize simultaneously. In particular, point scanning systems are uniquely constrained by an inverse relationship between imaging speed and pixel resolution. Here we show these limitations can be mitigated via the use of deep learning-based super-sampling of undersampled images acquired on a point-scanning system, which we termed point-scanning super-resolution (PSSR) imaging. Oversampled, high SNR ground truth images acquired on scanning electron or Airyscan laser scanning confocal microscopes were 'crappified' to generate semi-synthetic training data for PSSR models that were then used to restore real-world undersampled images. Remarkably, our EM PSSR model could restore undersampled images acquired with different optics, detectors, samples, or sample preparation methods in other labs. PSSR enabled previously unattainable 2 nm resolution images with our serial block face scanning electron microscope system. For fluorescence, we show that undersampled confocal images combined with a multiframe PSSR model trained on Airyscan timelapses facilitates Airyscan-equivalent spatial resolution and SNR with ~100x lower laser dose and 16x higher frame rates than corresponding high-resolution acquisitions. In conclusion, PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed, and sensitivity. Fig. 1 | Restoration of semi-synthetic and real-world EM testing data using PSSR model trained on semi-synthetically generated training pairs. a, Overview of the general workflow.Training pairs were semi-synthetically created by applying a degrading function to the HR images taken from a scanning electron microscope in transmission mode (tSEM) to generate LR counterparts (left column). Semi-synthetic pairs were used as training data through a dynamic ResNet-based U-Net architecture (middle column). Real-world LR and HR image pairs were both manually acquired under a SEM (right column). The output from PSSR (LR-PSSR) when LR is served as input is then compared to HR to evaluate the performance of our trained model. b, Restoration performance on semi-synthetic testing pairs from tSEM. Shown is the same field of view of a representative bouton region from the synthetically created LR input with the pixel size of 8 nm (left column), a 16x bilinear upsampled image with 2 nm pixel size (second column), 16x PSSR upsampled result with 2 nm pixel size (third column) and the HR ground truth acquired at the microscope with the pixel size of 2 nm (fourth column). A close view of the same vesicle in each image is highlighted. The Peak-Signal-to-Noise-Ratio (PSNR) and the Structural Similarity (SSIM) quantification of the semi-synthetic testing sets are shown (right). c, Restor...

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“…1), NanoJ-SQUIRREL error mapping analysis ( Supplementary Fig. 1) 18 , and visual inspection, we found PSSR very effectively restored the low resolution images ( Fig. 1c).…”

Section: Resultsmentioning

confidence: 67%

Section: Evaluation Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Point-Scanning Super-Resolution Imaging

Fang

Monroe²,

Novak

et al. 2019

Preprint

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“…Y.-M. Huang et al, 2017;Leterrier et al, 2015) as well as the mechanisms of slow axonal transport (Chakrabarty et al, 2019;Ganguly et al, 2017). We have also helped developing SMLM imaging modalities and analysis strategies (Culley et al, 2018;Laine et al, 2019). During these years, we have refined our SMLM workflow by optimizing sample preparation, imaging and analysis.…”

Section: Introductionmentioning

confidence: 99%

About samples, giving examples: Optimized Single Molecule Localization Microscopy

Jimenez

Friedl

Leterrier

2019

Preprint

Self Cite

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Super-resolution microscopy has profoundly transformed how we study the architecture of cells, revealing unknown structures and refining our view of cellular assemblies. Among the various techniques, the resolution of Single Molecule Localization Microscopy (SMLM) can reach the size of macromolecular complexes and offer key insights on their nanoscale arrangement in situ. SMLM is thus a demanding technique and taking advantage of its full potential requires specifically optimized procedures. Here we describe how we perform the successive steps of an SMLM workflow, focusing on single-color Stochastic Optical Reconstruction Microscopy (STORM) as well as multicolor DNA Points Accumulation for imaging in Nanoscale Topography (DNA-PAINT) of fixed samples. We provide detailed procedures for careful sample fixation and immunostaining of typical cellular structures: cytoskeleton, clathrin-coated pits, and organelles. We then offer guidelines for optimal imaging and processing of SMLM data in order to optimize reconstruction quality and avoid the generation of artifacts. We hope that the tips and tricks we discovered over the years and detail here will be useful for researchers looking to make the best possible SMLM images, a pre-requisite for meaningful biological discovery.

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“…Traditionally, such issues have been tackled by repeating the measurement several times, but true repetitive imaging is difficult to achieve in the face of issues such as biological heterogeneity, photobleaching and phototoxicity. Several computational techniques for handling this uncertainty have been described in literature [9][10][11][12][13][14][15]. However, these techniques are either specific to other approaches, or are otherwise more generic and therefore not able to take advantage of direct knowledge of the SOFI imaging process.…”

Section: Introductionmentioning

confidence: 99%

SOFIevaluator: a strategy for the quantitative quality assessment of SOFI data

Moeyaert

Vandenberg

Dedecker

2019

Preprint

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Super-resolution fluorescence imaging techniques allow optical imaging of specimens beyond the diffraction limit of light. Super-resolution optical fluctuation imaging (SOFI) relies on computational analysis of stochastic blinking events to obtain a super-resolved image. As with some other super-resolution methods, this strong dependency on computational analysis can make it difficult to gauge how well the resulting images reflect the underlying sample structure. We herein report SOFIevaluator, an unbiased and parameter-free algorithm for calculating a set of metrics that describes the quality of super-resolution fluorescence imaging data for SOFI. We additionally demonstrate how SOFIevaluator can be used to identify fluorescent proteins that perform well for SOFI imaging under different imaging conditions.

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Quantitative mapping and minimization of super-resolution optical imaging artifacts

Cited by 286 publications

References 25 publications

Deep Learning-Based Point-Scanning Super-Resolution Imaging

Deep Learning-Based Point-Scanning Super-Resolution Imaging

About samples, giving examples: Optimized Single Molecule Localization Microscopy

SOFIevaluator: a strategy for the quantitative quality assessment of SOFI data

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