Fluorescence fluctuation‐based super‐resolution microscopy: Basic concepts for an easy start

Alva, Alma; Brito‐Alarcón, Eduardo; Linares, Alejandro; Torres, Esley; Hernández, Haydee O.; Pinto‐Cámara, Raúl; Martínez, Damián; Hernández‐Herrera, Paul; D’Antuono, Rocco; Wood, Christopher D.; Guerrero, Adán

doi:10.1111/jmi.13135

Cited by 17 publications

(7 citation statements)

References 102 publications

(467 reference statements)

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“…However, the target was a relatively thin area (<200 μm) of the transparent sample. Super-resolution optical fluctuation imaging (SOFI), based on a fluorescence correlation analysis similar to SRRF ( Alva et al, 2022 ), was previously applied to two-photon light-sheet microscopy ( Chen et al, 2016 ). Although SOFI successfully improved the spatial resolution, it required special flicking fluorescent dyes and a custom-made light-sheet microscope, making its in vivo application difficult.…”

Section: Discussionmentioning

confidence: 99%

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Fluorescence radial fluctuation enables two-photon super-resolution microscopy

Tsutsumi,

Takahashi,

Kobayashi

et al. 2023

Front. Cell. Neurosci.

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Despite recent improvements in microscopy, it is still difficult to apply super-resolution microscopy for deep imaging due to the deterioration of light convergence properties in thick specimens. As a strategy to avoid such optical limitations for deep super-resolution imaging, we focused on super-resolution radial fluctuation (SRRF), a super-resolution technique based on image analysis. In this study, we applied SRRF to two-photon microscopy (2P-SRRF) and characterized its spatial resolution, suitability for deep observation, and morphological reproducibility in real brain tissue. By the comparison with structured illumination microscopy (SIM), it was confirmed that 2P-SRRF exhibited two-point resolution and morphological reproducibility comparable to that of SIM. The improvement in spatial resolution was also demonstrated at depths of more than several hundred micrometers in a brain-mimetic environment. After optimizing SRRF processing parameters, we successfully demonstrated in vivo high-resolution imaging of the fifth layer of the cerebral cortex using 2P-SRRF. This is the first report on the application of SRRF to in vivo two-photon imaging. This method can be easily applied to existing two-photon microscopes and can expand the visualization range of super-resolution imaging studies.

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Section: Discussionmentioning

confidence: 99%

“…Including SRRF and SOFI, fluorescence fluctuation-based super-resolution techniques have been developed and improved in recent years ( Alva et al, 2022 ). Throughput, precision of center-of-gravity determination, and reliability of image reconstruction of these techniques have improved over the years.…”

Section: Discussionmentioning

confidence: 99%

Fluorescence radial fluctuation enables two-photon super-resolution microscopy

Tsutsumi,

Takahashi,

Kobayashi

et al. 2023

Front. Cell. Neurosci.

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“…These two limitations make SMLM unattractive for high-throughput imaging of FFPE samples. Albeit not achieving as good resolution as SMLM, FF-SRM requires significantly less number of frames (usually hundreds but as low as 30 frames have been demonstrated 54 ). Thus, the combination of FF-SRM with the excellent optical sectioning provided by TIRF illumination is an attractive route for high-speed imaging of FFPE tissue sections.…”

Section: Contrast Enhancement Approaches For Histologymentioning

confidence: 99%

Super-resolution histology of paraffin-embedded samples via photonic chip-based microscopy

Villegas-Hernández

Dubey

Mao

et al. 2023

Preprint

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Fluorescence-based super-resolution optical microscopy (SRM) techniques allow the visualization of biological structures beyond the diffraction limit of conventional microscopes. Despite its successful adoption in cell biology, the integration of SRM into the field of histology has been deferred due to several obstacles. These include limited imaging throughput, high cost, and the need for complex sample preparation. Additionally, the refractive index heterogeneity and high labeling density of commonly available formalin-fixed paraffin-embedded (FFPE) tissue samples pose major challenges to applying existing super-resolution microscopy methods. Here, we demonstrate that photonic chip-based microscopy alleviates several of these challenges and opens avenues for super-resolution imaging of FFPE tissue sections. By illuminating samples through a high refractive-index waveguide material, the photonic chip-based platform enables ultra-thin optical sectioning via evanescent field excitation, which reduces signal scattering and enhances both the signal-to-noise ratio and the contrast. Furthermore, the photonic chip provides decoupled illumination and collection light paths, allowing for total internal reflection fluorescence (TIRF) imaging over large and scalable fields of view. By exploiting the spatiotemporal signal emission via MUSICAL, a fluorescence fluctuation-based super-resolution microscopy (FF-SRM) algorithm, we demonstrate the versatility of this novel microscopy method in achieving superior contrast super-resolution images of diverse FFPE tissue sections derived from human colon, prostate, and placenta. The photonic chip is compatible with routine histological workflows and allows multimodal analysis such as correlative light-electron microscopy (CLEM), offering a promising tool for the adoption of super-resolution imaging of FFPE sections in both research and clinical settings.

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“…Fluorescence microscopy is the most commonly used technique for observation the dynamics of specific molecular labelled biological structures and is an indispensable tool in biomedical research. [1][2][3] However, due to the optical diffraction limit, there exists an upper bound on the image resolution obtained through conventional fluorescence imaging techniques. 4 The emergence of super-resolution fluorescence microscopy has provided sufficient details for the visualization of numerous subcellular structures, breaking this constraint and fundamentally revolutionizing the field of biological imaging.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of the resolution for low-quality fluorescence microscopy image by novel computational method

Wang,

Zhu,

Zhang

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Applications of Imaging Technologies

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The application of computational methods for enhancement the resolution of fluorescence microscopy images beyond the diffraction limit has emerged in recent years. Among them, super-resolution radial fluctuations (SRRF) and mean-shift super-resolution (MSSR) are two widely used representatives. However, these two methods are often unsatisfactory when dealing with low-quality fluorescence microscopy images, which are prone to artifacts as well as structure discontinuities. Here, we propose an effective computational method named morphological filtering and polynomial fitting (MFPF). MFPF equips excellent denoising and light-field balancing capabilities, which are beneficial for subsequent resolution enhancement. Our results validated on public BioSR dataset show that MFPF not only improves the quality of subcellular structure images such as microtubules and endoplasmic reticulum, but also attenuates the artifacts and discontinuities generated by SRRF radiality map analysis and single-frame MSSR reconstructions while maintaining the comparable resolution. Furthermore, it permits processing of low-quality fluorescence microscopy images, and would have vast applications in long-term super-resolution imaging of live cells with moderate image quality.

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Fluorescence fluctuation‐based super‐resolution microscopy: Basic concepts for an easy start

Cited by 17 publications

References 102 publications

Fluorescence radial fluctuation enables two-photon super-resolution microscopy

Fluorescence radial fluctuation enables two-photon super-resolution microscopy

Super-resolution histology of paraffin-embedded samples via photonic chip-based microscopy

Enhancement of the resolution for low-quality fluorescence microscopy image by novel computational method

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