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Summary: Structured‐illumination microscopy allows widefield fluorescence imaging with resolution beyond the classical diffraction limit. Its linear form extends resolution by a factor of two, and its nonlinear form by an in‐principle infinite factor, the effective resolution in practice being determined by noise. In this paper, we analyse the noise properties and achievable resolution of linear and nonlinear 1D and 2D patterned SIM from a frequency–space perspective. We develop an analytical theory for a general case of linear or nonlinear fluorescent imaging, and verify the analytical calculations with numerical simulation for a special case where nonlinearity is produced by photoswitching of fluorescent labels. We compare the performance of two alternative implementations, using either two‐dimensional (2D) illumination patterns or sequentially rotated one‐dimensional (ID) patterns. We show that 1D patterns are advantageous in the linear case, and that in the nonlinear case 2D patterns provide a slight signal‐to‐noise advantage under idealised conditions, but perform worse than 1D patterns in the presence of nonswitchable fluorescent background. Lay Description: Structured‐illumination microscopy (SIM) is a high‐resolution light microscopy technique that allows imaging of fluorescence at a resolution about twice the classical diffraction limit. There are various ways that the illumination can be structured, but it is not obvious how the choice of illumination pattern affects the final image quality, especially in view of the noise. We present a detailed performance analysis considering two illumination techniques: sequential illumination with line‐gratings that are shifted and rotated during image acquisition and two‐dimensional (2D) illumination structures requiring only shift operations. Our analysis is based on analytical theory, supported by simulations of images considering noise. We also extend our analysis to a nonlinear variant of SIM, with which enhanced resolution can be achieved, limited only by noise. This includes nonlinear SIM based on the light‐induced switching of the fluorescent molecules between a bright and a dark state. We find sequential illumination with line‐gratings to be advantageous in ordinary (linear) SIM, whereas 2D patterns provides a slight signal‐to‐noise advantage under idealised conditions in nonlinear SIM if there is no nonswitching background. [ABSTRACT FROM AUTHOR]

Mitochondria are intracellular organelles that act as powerhouses by breaking down nutrition molecules to produce adenosine triphosphate (ATP) as cellular fuel. They have their own genetic material called mitochondrial DNA. Alterations in mitochondrial DNA can result in primary mitochondrial diseases, including neurodegenerative disorders. Early detection of these abnormalities is crucial in slowing disease progression. With recent advances in data acquisition techniques such as focused ion beam scanning electron microscopy, it has become feasible to capture large intracellular organelle volumes at data rates reaching 4Tb/minute, each containing numerous cells. However, manually segmenting large data volumes (gigapixels) can be time-consuming for pathologists. Therefore, there is an urgent need for automated tools that can efficiently segment mitochondria with minimal user intervention. Our article proposes an ensemble of two automatic segmentation pipelines to predict regions of interest specific to mitochondria. This architecture combines the predicted outputs from both pipelines using an ensemble learning-based entropy-weighted fusion technique. The methodology minimizes the impact of individual predictions and enhances the overall segmentation results. The performance of the segmentation task is evaluated using various metrics, ensuring the reliability of our results. We used four publicly available datasets to evaluate our proposed method's effectiveness. Our proposed fusion method has achieved a high score in terms of the mean Jaccard index and dice coefficient for all four datasets. For instance, in the UroCell dataset, our proposed fusion method achieved scores of 0.9644 for the mean Jaccard index and 0.9749 for the Dice coefficient. The mean error rate and pixel accuracy were 0.0062 and 0.9938, respectively. Later, we compared it with state-of-the-art methods like 2D and 3D CNN algorithms. Our ensemble approach shows promising segmentation efficiency with minimal intervention and can potentially aid in the early detection and mitigation of mitochondrial diseases., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gupta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The relentless pursuit of understanding matter at ever-finer scales has pushed optical microscopy to surpass the diffraction limit and realize super-resolution microscopy, which enables visualizing structures shorter than the wavelength of the light emitted by the sample. In the present work, we harnessed extreme ultraviolet beams to create sub-μm grating structures, which were revealed by extreme ultraviolet structured illumination microscopy. We establish that the resolution extension is achievable in the extreme ultraviolet, thereby opening the door to significant resolution enhancement, mainly defined by the wavelength employed.

The knowledge of the exact structure of the optical system point spread function (PSF) enables a high-quality image reconstruction in fluorescence microscopy. Accurate PSF models account for the vector nature of light and the phase and amplitude modifications. Most existing real-space-based PSF models fall into a sampling pitfall near the center position, yielding to the violation of energy conservation. In this work, we present a novel, to the best of our knowledge, Fourier-based techniques for computing vector PSF and compare them to the state-of-the-art. Our methods are shown to satisfy the physical condition of the imaging process. They are reproducible, computationally efficient, easy to implement, and easy to modify to represent various imaging modalities.

Image reconstruction in fluorescence microscopy is highly sensitive to the accuracy of the impulse response, defined as the point spread function (PSF), of the optical system under which the image to reconstruct was acquired. In our previous work, we developed a MATLAB toolbox for accurately calculating realistic vector Fourier-based PSF accounting for any type of aberrations [arXiv, arXiv:2301.13515 (2023)10.48550/arXiv.2301.13515]. In this work, we present a fundamental experimental validation of these numerical methods. The simulated results are found to fit experimental data under different image acquisition conditions at an accuracy higher than 0.97 in normalized cross-correlation. These methods enable a relative contrast of up to 95%.

The point spread function (PSF) of a microscope describes the image of a point emitter. Knowing the accurate PSF model is essential for various imaging tasks, including single-molecule localization, aberration correction and deconvolution. Here we present universal inverse modeling of point spread functions (uiPSF), a toolbox to infer accurate PSF models from microscopy data, using either image stacks of fluorescent beads or directly images of blinking fluorophores, the raw data in single-molecule localization microscopy (SMLM). Our modular framework is applicable to a variety of microscope modalities and the PSF model incorporates system- or sample-specific characteristics, for example, the bead size, field- and depth- dependent aberrations, and transformations among channels. We demonstrate its application in single or multiple channels or large field-of-view SMLM systems, 4Pi-SMLM, and lattice light-sheet microscopes using either bead data or single-molecule blinking data., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The background light from out-of-focus planes hinders resolution enhancement in structured illumination microscopy when observing volumetric samples. Here we used selective plane illumination and reversibly photoswitchable fluorescent proteins to realize structured illumination within the focal plane and eliminate the out-of-focus background. Theoretical investigation of the imaging properties and experimental demonstrations show that selective plane activation is beneficial for imaging dense microstructures in cells and cell spheroids., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The recently developed photoinduced force microscopy for mid-infrared (PiF-IR) offers high spectral resolution in combination with surface sensitivity and a spatial resolution in the range of a few nanometers. Although PiF-IR has primarily been applied to polymer materials, this technology presents significant potential for the chemical characterization of cellular structures approaching single-molecule sensitivity. We applied PiF-IR to differently polymerized F-Actin samples finding general agreement with FTIR spectra from the same samples. Single PiF-IR spectra of F-Actin show variations in the amide I band spectral region, which is related to secondary protein structure. Local variations are also seen in PiF-IR hyperspectra in this region. Such high sensitivity is a necessary requirement for discriminating Actin organization into bundles and other networks in cells and tissue. We applied PiF-IR to mouse liver tissue ex vivo. Single-frequency PiF-IR scans at three different IR frequencies show significant variations in local contrast. However, the presence of other proteins and the unique spatial resolution of PiF-IR pose a challenge to interpreting and validating such data. Careful design of model systems and further theoretical understanding of PiF-IR data far from bulk averages are needed to fully unfold the potential of PiF-IR for high-resolution chemical investigation in the Life Sciences., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: Retinal pigment epithelium (RPE) cells show strong autofluorescence (AF). Here, we characterize the AF spectra of individual RPE cells in healthy eyes and those affected by age-related macular degeneration (AMD) and investigate associations between AF spectral response and the number of intracellular AF granules per cell., Methods: RPE-Bruch's membrane flatmounts of 22 human donor eyes, including seven AMD-affected eyes (early AMD, three; geographic atrophy, one; neovascular, three) and 15 unaffected macula (<51 years, eight; >80 years, seven), were imaged at the fovea, perifovea, and near-periphery using confocal AF microscopy (excitation 488 nm), and emission spectra were recorded (500-710 nm). RPE cells were manually segmented with computer assistance and stratified by disease status, and emission spectra were analyzed using cubic spline transforms. Intracellular granules were manually counted and classified. Linear mixed models were used to investigate associations between spectra and the number of intracellular granules., Results: Spectra of 5549 RPE cells were recorded. The spectra of RPE cells in healthy eyes showed similar emission curves that peaked at 580 nm for fovea and perifovea and at 575 and 580 nm for near-periphery. RPE spectral curves in AMD eyes differed significantly, being blue shifted by 10 nm toward shorter wavelengths. No significant association coefficients were found between wavelengths and granule counts., Conclusions: This large series of RPE cell emission spectra at precisely predefined retinal locations showed a hypsochromic spectral shift in AMD. Combining different microscopy techniques, our work has identified cellular RPE spectral AF and subcellular granule properties that will inform future in vivo investigations using single-cell imaging.