Journal of microscopy最新文献

Localisation microscopy often relies on detailed models of point-spread functions. For applications such as deconvolution or PSF engineering, accurate models for light propagation in imaging systems with a high numerical aperture are required. Different models have been proposed based on 2D Fourier transforms or 1D Bessel integrals. The most precise ones combine a vectorial description of the electric field and accurate aberration models. However, it may be unclear which model to choose as there is no comprehensive comparison between the Fourier and Bessel approaches yet. Moreover, many existing libraries are written in Java (e.g., our previous PSF generator software) or MATLAB, which hinders their integration into deep learning algorithms. In this work, we start from the original Richards-Wolf integral and revisit both approaches in a systematic way. We present a unifying framework in which we prove the equivalence between the Fourier and Bessel strategies and detail a variety of correction factors applicable to both of them. Then, we provide a high-performance implementation of our theoretical framework in the form of an open-source library that is built on top of PyTorch, a popular library for deep learning. It enables us to benchmark the accuracy and computational speed of different models and allows for an in-depth comparison of the existing models for the first time. We show that the Bessel strategy is optimal for axisymmetric beams, while the Fourier approach can be applied to more general scenarios. Our work enables the efficient computation of a point-spread function on CPU or GPU, which can then be included in simulation and optimisation pipelines.

Recently developed secondary nanobodies or single-domain antibodies present a powerful tool for immunodetection. Unlike traditional antibodies, their monovalence enables pre-association with primary antibodies prior to sample staining, presenting a straightforward affinity-based antibody labelling solution. This not only simplifies and streamlines immunoassays, it also supports multiplexed techniques where conflicts in the species of the desired primary antibodies preclude standard indirect immunostaining. Despite these advantages, the use of secondary nanobodies remains sparse, due perhaps to a lack of evaluation on their suitability for assays requiring quantification and an assessment of optimal protocols for their use. Here, we propose a set of experiments spanning total internal reflection fluorescence and confocal microscopies that can be used to validate secondary nanobody binding, specificity, and their propensity for mis-targeted binding in multiplex assays. Using these tools, we analysed the binding properties of commercially available secondary nanobodies and outline optimised protocols for their robust use.

Expansion microscopy (ExM) is a powerful high-resolution imaging technique that enhances the spatial resolution of conventional light microscopy by physically enlarging biological specimens by embedding and cross-linking them in a swellable polymer network. This review explores the combination of ExM with commonly used advanced fluorescence imaging modalities, including light sheet fluorescence microscopy (LSFM), stimulated emission depletion (STED), structured illumination microscopy (SIM), single-molecule localisation microscopy (SMLM), and computational super-resolution radial fluctuations (SRRF) to push the boundaries of achievable resolution in biological imaging. By integrating ExM with these optical and analytical approaches, researchers can visualise subcellular structures and molecular complexes with unprecedented clarity, enabling the study of intricate biological processes that are otherwise inaccessible with conventional light microscopy methods. The review covers the theoretical resolutions attainable with each combined technique, example biological questions they can address, and key considerations for optimising their use. Together, these advancements offer novel insights into nanoscale cellular and subcellular structures, opening new avenues for exploration in fields such as neuroscience, cancer research, and developmental biology.

Diatom frustules exhibit highly organised silica architectures with submicron features, which are widely studied. Nowadays, scanning electron microscopy (SEM) is commonly used for studying their morphology. However, SEM provides a two-dimensional projection that distorts the true size of three-dimensional features. In addition, samples for SEM observations are usually prepared by removing organic parts and drying the remaining parts made of bio-silica (so-called frustule). Here, we combine high-resolution SEM and liquid-cell SEM imaging with confocal microscopy to construct a curvature-aware correction model that accounts for the surface area distortion in SEM imaging caused by the in-plane projection of three-dimensional frustule geometry, demonstrated using Diploneis didyma frustules as a case study. The paper describes how automated image analysis enabled the detection of individual pores, the localisation of their centroids, and the calculation of curvature-corrected surface areas. True diameters of the frustules are provided, and drying-induced shrinkage of pores is obtained. Species length-based normalisation of morphometric data reduced variability and allow to infer systematic dependence of pore sizes on position on frustules. The results establish a reliable framework for quantitative characterisation of diatom frustules and can be applied across disciplines requiring accurate morphometrics of biogenic silica structures. LAY DESCRIPTION: Curvature-corrected SEM morphometrics, paired with hydrated/dehydrated imaging, offer a reliable quantitative framework for diatom frustule pore-size assessment. Drying shrinks pores by ∼10%, so dry-sample measurements should be corrected when targeting living/wet morphology. Curvature correction compensates SEM projection distortion, with uncorrected pores underestimated by ∼34% on average, enabling realistic edge-to-centre comparisons. The protocol is broadly transferable beyond Diploneis didyma with minor adjustments and supports applications in taxonomy, biomonitoring, and bioinspired design.

Locating dopants in 3D is of great interest as advanced materials and devices increasingly rely on control at atomic dimensions, and microscopy tools are constantly in development for this purpose. One potential tool is electron energy loss spectroscopy (EELS) depth sectioning, where a core-loss signal is collected as a function of electron probe defocus along an atomic column in scanning transmission electron microscopy. Here we revisit its prospects through simulation with particular attention to dose considerations. We discuss pitfalls of by-inspection interpretation of EELS depth sectioning and resolve them by comparing with simulated references in a Bayesian framework. While direct electron detectors are starting to enable energy-filtered momentum-resolved maps, we show that momentum resolution offers no real advantage for depth determination. We extend the Bayesian framework to infer the depths of multiple dopants along a column without simulating all possible doping concentrations and configurations. We further show that zero-loss depth sectioning is too sensitive to residual aberrations to usefully allow the application of our Bayesian framework.

Atomic force microscopy (AFM) has transcended its role as a mere provider of high-resolution imaging in medical research, catalysing a paradigm shift from 'morphological observation' to 'quantitative mechanics' and 'single molecule manipulation'. AFM enables us to directly decipher the 'mechanical language' of living systems, acquiring information on sample topography, mechanical properties, and molecular interactions under near physiological conditions with nanoscale resolution. This review systematically elaborates on how AFM, by providing quantitative, functional, and dynamic nanoscale data, is reshaping the understanding of disease mechanisms. It is also fostering novel precision medicine strategies guided by 'mechanobiology' in areas such as cardiovascular diseases, cancer, and pathogen recognition. AFM not only expands the dimensions of medical research but also provides unprecedented tools and perspectives for disease diagnosis, drug development, and cellular intervention.

Multiplex immunohistofluorescence (mIHF) allows to investigate protein (co)-localisation in the tumour microenvironment, which can facilitate research on carcinogenesis. Most available technologies for multiplex staining are expensive and immutable. We aim to demonstrate the feasibility and the flexibility of a low-cost mIHF protocol through the illustrated fine-tuning of two six-plex panels on human breast cancer samples. We detail a workflow combining two fluorescence amplification steps - (1) a peroxidase-labelled polymer conjugated to secondary antibodies and (2) tyramide signal amplification technology - to overcome the high autofluorescence of FFPE sections. The optimised slide scanner configuration enables single-run acquisition of up to six markers plus a nuclear dye from one tissue section. The resulting native scans can be directly used for analysis without the need for extensive or complex image processing. As a proof of concept, this protocol was applied on breast tissue samples from 19 patients diagnosed with ductal carcinoma in situ (DCIS), of various size, age and fat content. Two six-plex panels highlighted proteins expressed in tumours cells, extracellular matrix, and lymphocytes. The 12 proteins were first individually validated by immunohistochemistry, subsequently by immunohistofluorescence and finally combined in two six-plex panels. Only one sample could not be interpreted. Some samples displayed tissue detachment, cold zones or heterogeneous immunoreactivity, independently of the fat content, surgical procedure or specimen age. Here, we propose an open, flexible and cost-effective six-plex protocol (Flex-6 mIHF) and a validation workflow. We demonstrated its feasibility on challenging tissue containing fat such as breast cancer tissues.

Image noise is a fundamental problem in fluorescence microscopy analysis, especially in live cell imaging applications where the number of detected photons is limited due to low power of excitation lasers to prevent phototoxicity during extended imaging experiments. The noise increases measurement uncertainty and complicates further image processing routines such as deconvolution, object detection and segmentation. State-of-the-art denoisers are computationally expensive and require training using large datasets, which are not available in cases of typical biological imaging experiments with rather scarce and unlabelled data. Here, we show that a denoiser can be trained using a single image containing a cropped out object of interest, where we exploit the symmetry often present in biological structures at molecular scales. As only a single example is used during training, our method can be trained even with limited computational resources, obtaining competitive denoising performance compared to the state-of-the-art methods.