The method operates almost free of parameters users only need to provide an estimate of the PSF (in pixels) of the optical system, choose the MSSR order, and decide whether a temporal analysis will take place ( Supplementary Material and Supplementary Methods). Open-source implementations of MSSR are provided for ImageJ (as a plugin), Python, R, and MATLAB, some of which take advantage of the parallel computing capabilities of regular desktop computers (Supplementary Note 7). Additionally, we demonstrate the extended-, enhanced- and super-resolving capabilities of MSSR as a standalone method for a variety of fluorescence microscopy applications, through a single-frame and temporal stack analysis, allowing resolution improvements toward a limit of 40 nm. As a result of that, the spatial distribution becomes ‘refined’ (i.e., for a Gaussian distribution of fluorescence its width shrinks). MSSR extends the resolution of any single fluorescence image up to 1.6 times, including its use as a resolution and contrast enhancement complement after the application of other super-resolution methods.īy computing the local magnitude of the Mean Shift vector, MSSR generates a probability distribution of fluorescence estimates whose local magnitude peaks at the source of information. Here, we introduce the Mean Shift Super- Resolution principle for digital images ‘MSSR’ (pronounced as messer), derived from the Mean Shift (MS) theory 17, 18. In this scenario, the problem of spatial resolution gets reduced to the problem of finding modes of information, regardless of the shape of the distribution, hence, disconnecting the problem of optical resolution from the diffraction boundary 16. ![]() In the case of fluorescence microscopy, the process of photon emission from point sources (fluorescence emitters) can be considered as a discrete distribution of information, where the unitary element of the distribution is the photon 15. The problem of spatial resolution in optical microscopy can be addressed from the statistical point of view. However, these methods also present some limitations, such as the possible introduction of artifacts 14, the requirement for high signal-to-noise ratio (SNR) data and the acquisition of tens to hundreds of frames 10, 11, 12, 13, which limit their applicability to reconstruct fast dynamical processes. Both, the quantity and performance of these methods have increased over the past decade given the advantages they present, such as their low barriers to entry and generic applicability to data acquired with a variety of microscopy modalities (widefield, confocal, or light-sheet). ![]() Some SRM computational methods have few or no demands on hardware or sample preparation and provide resolution improvements beyond the diffraction limit, i.e., fluorescence fluctuation-based super-resolution microscopy (FF-SRM) approaches 10, 11, 12, 13. Single-molecule localization microscopy (SMLM) methods (e.g., STORM, PAINT, PALM) 6, 7, 8, 9 localize individual emitters with nanometer precision but require temporal analysis of several hundred-to-thousands of images and are prone to error due to fast molecular dynamics within live specimens. These techniques can be used for live imaging although they require specialized hardware and dedicated personnel for maintenance and operation. ![]() Instrumentation-based techniques, such as SIM and STED, exceed the diffraction limit by engineering the illumination or the point spread function (PSF) 3, 4, 5. There are several approaches to SRM which vary in terms of the final attainable spatial and temporal resolution, photon efficiency, as well as in their capacity to image live or fixed samples at depth 1, 2. Super-resolution Microscopy (SRM), which encompasses a collection of methods that circumvent Abbe’s optical resolution limit, has dramatically increased our capability to visualize the architecture of cells and tissues at the molecular level. Nature Communications volume 13, Article number: 7452 ( 2022) Extending resolution within a single imaging frame
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