Enhanced detection of fluorescence fluctuations for high-throughput super-resolution imaging

Abstract

Super-resolution (SR) imaging with high-throughput is invaluable to fast and highprecision 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 an SR method based on the Auto-Correlation with two-step Deconvolution (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 quantitative biology, the microscopy-based high-throughput screening is used to monitor the variability of biological systems as well as examine the heterogeneity 1 , and the advances in three-dimensional (3D) resolution can contribute to the minimization of the uncertainties in analyzing noisy biological processes. In parallel, super-resolution (SR) microscopy techniques relying on the blinking of single fluorophores have been developed to break the diffraction limit, including photoactivated localization microscopy (PALM) 2 and stochastic optical reconstruction microscopy (STORM) 3 . However, precisely localizing individual fluorophores requires tens of thousands of frames to accumulate one final SR view, which inherently limits the throughput and leads to requirements for purpose-built experimental settings 4 . Therefore, although livecell PALM/STORM has been reported 5-7 , excessive illumination power (~10 kW/cm 2 ) 8 , long exposures (>2 s) 9 , and the particular photochemical environment that promotes long dark states to reduce bleaching 9 prevent it from being a general live-cell imaging method 10 with high-throughput.

Dark-based Optical Sectioning assists Background Removal in Fluorescence Microscopy

“…In Fig. 4(i), SCAD 14 (v0.2.0) with 100 frames raw images is used in https://github.com/WeisongZhao/SACDm. In Fig.…”

“…Although optical approaches such as laser-scanning confocal microscopy 9 or multi-photon microscopy 10 provide enhanced optical sectioning performance, they have relatively lower temporal resolution with more photobleaching due to high doses of light. Other imaging techniques relying on the active illumination modulation and WF-based array detection, such as spinning-disk confocal, multi-focal structured illumination microscopy, three-dimensional structured illumination microscopy (SIM) 11, 12 , lightsheet microscopy 13 , or super-resolution optical fluctuation imaging 14, 15 (SOFI), also suffers from the defocused noise and lower imaging speed despite improving axial sectioning.…”

“…Computational approaches have become increasingly important for improving the performance of optical microscopy. Typical computational fluorescence approaches include reconstruction algorithms such as high-and-low-frequency microscopy (HILO), SIM 16, 17 , SOFI 14 , and pre/post-processing algorithms such as deconvolution 18, 19 and denoise 20 . Nearly all these methods require pre-background removal to avoid severe artifacts in the final image 5 .…”

Dense, Continuous Membrane Labeling and Expansion Microscopy Visualization of Ultrastructure in Tissues

“…We then used the SACD ImageJ plugin as provided in the Github (https://github.com/WeisongZhao/SACDj). We used the plugin with the recommended default hyper-parameters 48 (i.e., 1st=10, fourier=2, 2nd=10, order=2, scale=2). Finally, CLAHE was applied for visualization purposes.…”

A head-mounted photoacoustic fiberscope for hemodynamic imaging in mobile mice

“…Yet, applying existing photoacoustic microscopes is largely restricted to sedated animals, while anesthesia can disrupt normal brain metabolism and vascular function 8 . Miniaturized single-photon and multi-photon microscopes have recently emerged as pivotal technologies for recording cortical neuron activities in mobile mice 9 – 12 . Nevertheless, these modalities inadequately measure brain hemodynamics and oxygenation levels.…”