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A main determinant of the spatial resolution of live-cell super-resolution (SR) microscopes is the maximum photon flux that can be collected. To further increase the effective resolution for a given photon flux, we take advantage of a priori knowledge about the sparsity and continuity of biological structures to develop a deconvolution algorithm that increases the resolution of SR microscopes nearly twofold. Our method, sparse structured illumination microscopy (Sparse-SIM), achieves ~60-nm resolution at a frame rate of up to 564 Hz, allowing it to resolve intricate structures, including small vesicular fusion pores, ring-shaped nuclear pores formed by nucleoporins and relative movements of inner and outer mitochondrial membranes in live cells. Sparse deconvolution can also be used to increase the three-dimensional resolution of spinning-disc confocal-based SIM, even at low signal-to-noise ratios, which allows four-color, three-dimensional live-cell SR imaging at ~90-nm resolution. Overall, sparse deconvolution will be useful to increase the spatiotemporal resolution of live-cell fluorescence microscopy. The resolution of fluorescence microscopy is increased by incorporating prior information into deconvolution algorithms.

Super-resolution (SR) imaging with high-throughput is invaluable to fast and high-precision profiling in a wide range of biomedical applications. However, prevalent SR methods require sophisticated acquisition devices and specific imaging control, and may cost a fairly long time on a single field-of-view. These essentially increase the construction difficulty, including challenges in imaging throughput, system establishment, and automation. Using the natural photophysics of fluorescence, fluctuation-based microscopy techniques can routinely break the diffraction limit with no need for additional optical components, but its long acquisition time still poses a challenge for high-throughput imaging or visualizing transient organelle dynamics. Here, we propose anSR method based on theAuto-Correlation with two-stepDeconvolution (SACD) that reduces the number of frames required by maximizing the detectable fluorescence fluctuation behavior in each measurement, with further removal of tunable parameters by a Fourier ring correlation analysis. It only needs 20 frames for twofold lateral and axial resolution improvements, while the SR optical fluctuation imaging (SOFI) needs more than 1000 frames. By capturing raw images for ∼10 minutes, we record an SR image with ∼128 nm resolution that contains 2.4 gigapixels covering an area of ∼2.0 mm × 1.4 mm, including more than 2,000 cells. Beyond that, by applying continuity and sparsity joint constraint, the Sparse deconvolution-assisted SACD enables 4D live-cell SR imaging of events such as mitochondrial fission and fusion. Overall, as an open-sourced module, we anticipate SACD can offer direct access to SR, which may facilitate the biology studies of cells and organisms with high-throughput and low-cost.

In fluorescence microscopy, computational algorithms have been developed to suppress noise, enhance contrast, and even enable super-resolution (SR). The local quality of the images may vary on multiple scales and these differences can lead to misconceptions. However, current mapping methods fail to finely estimate the local quality, challenging to associate the SR scale content. Here, we develop a rolling Fourier ring correlation (rFRC) to quantify reconstruction quality down to the SR scale. To visually pinpoint regions with low reliability, we combine a filtered rFRC with a modified resolution scaled error map (RSM), providing a comprehensive and concise map for further examination. We demonstrate their performances on different SR imaging modalities, and the resulting quantitative maps enable better SR images integrated from different reconstructions. Beyond that, as a model-independent assessment, we show its applications in evaluating the local restoration qualities for the deep-learning methods.

The spatial resolutions of live-cell super-resolution microscopes are limited by the maximum collected photon flux. Taking advantage of a priori knowledge of the sparsity and continuity of biological structures, we develop a deconvolution algorithm that further extends the resolution of super-resolution microscopes under the same photon budgets by nearly twofold. As a result, sparse structured illumination microscopy (Sparse-SIM) achieves ~60 nm resolution at a 564 Hz frame rate, allowing it to resolve intricate structural intermediates, including small vesicular fusion pores, ring-shaped nuclear pores formed by different nucleoporins, and relative movements between the inner and outer membranes of mitochondria in live cells. Likewise, sparse deconvolution can be used to increase the three-dimensional resolution and contrast of spinning-disc confocal-based SIM (SD-SIM), and operates under conditions with the insufficient signal-to-noise-ratio, all of which allows routine four-color, three-dimensional, ~90 nm resolution live-cell super-resolution imaging. Overall, sparse deconvolution may be a general tool to push the spatiotemporal resolution limits of live-cell fluorescence microscopy.

A main determinant of the spatial resolution of live-cell super-resolution (SR) microscopes is the maximum photon flux that can be collected. To further increase the effective resolution for a given photon flux, we take advantage of a priori knowledge about the sparsity and continuity of biological structures to develop a deconvolution algorithm that increases the resolution of SR microscopes nearly twofold. Our method, sparse structured illumination microscopy (Sparse-SIM), achieves ~60-nm resolution at a frame rate of up to 564 Hz, allowing it to resolve intricate structures, including small vesicular fusion pores, ring-shaped nuclear pores formed by nucleoporins and relative movements of inner and outer mitochondrial membranes in live cells. Sparse deconvolution can also be used to increase the three-dimensional resolution of spinning-disc confocal-based SIM, even at low signal-to-noise ratios, which allows four-color, three-dimensional live-cell SR imaging at ~90-nm resolution. Overall, sparse deconvolution will be useful to increase the spatiotemporal resolution of live-cell fluorescence microscopy.