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Optical sectioning in fluorescence microscopy is a cornerstone technique that allows scientists to visualize three-dimensional architectures of specimens with high resolution. The optical sectioning capability has become crucial for advancing our understanding in various technical and scientific fields such as cellular and developmental biology. Several microscopy techniques stand out due to their ability to perform true optical sectioning through distinct optical arrangements. The paper explains formally, how optical sectioning is harnessed in confocal, two-photon, confocal theta, and light sheet-based fluorescence microscopy. The latter’s implementation of optical sectioning fosters detailed studies with morphologically intact fertile specimens as a function of long time periods.

The three-dimensional visualization of cellular architecture by volume electron microscopy (vEM) has reignited interest in morphological descriptions of complex tissue. At the same time, the increasing availability of vEM in life sciences was the foundation for the accelerated development of analysis pipelines with automated software tools for segmentation and 3D reconstruction. This progress results in continuous generation of large amounts of data that hold a treasure box of new scientific insights waiting for discovery. Automated segmentation of morphological architecture provides quantitative readouts of cellular and organellar properties, while open availability of datasets creates the opportunity to address a diversity of research questions. Here, we discuss sample preparation and data analysis strategies in vEM and showcase how this methodology contributed to our knowledge of myelin biology and disease. Furthermore, we intent to inform users about new developments in the field of instrumentation, methods and software development with the potential to contribute to other areas of research.

Materials microstructure analysis is a crucial task for understanding the relationship between a material’s structure and its properties. The quality of the acquired images is vital, as blurring can result in the loss of essential information needed for microstructure analysis. This issue becomes more apparent with higher magnification and the corresponding decrease in depth of field (D.O.F.). To address this, new AI-based approaches are being explored to automatically improve image quality. Experts are testing innovative loss functions for training AI models to improve the image deblurring performance and thus a better image reconstruction. In the research field of image deblurring, it is known that pixel-based loss functions badly correspond with human perception of image quality. Therefore, a perceptual loss term is used for better visual reconstruction results. However, it has been shown that a perceptual loss term can lead to visible artifacts. Current literature has not yet sufficiently investigated why these artifacts are generated. In this work, we tackle this lack of research and provide new insights into the generation of such artifacts. We not only show that artifact generation is caused by the feature map used for the perceptual loss, but we also show that the level of distortion in the input impacts the extent of the artifact generation. Furthermore, we show that common metrics are insensitive to these artifacts, and we propose a method to reduce the extent of the generated artifacts.

Atomic Force Microscopy (AFM) is crucial in mechanobiology for high-resolution imaging and nanomechanical measurements of biological samples, providing insights into their mechanical properties. However, AFM faces challenges such as tip damage and cantilever selection errors, impacting measurement accuracy. This study proposes a methodology using collagen-coated hydrogels with predefined stiffness for calibrating AFM measurements on soft biological materials. By facilitating appropriate cantilever selection, assessing systematic errors, and evaluating tip damage, this approach ensures reliable Young’s modulus measurements. The proof of concept with human lung cells and tissue specimens demonstrates improved accuracy and reliability of AFM-based nanomechanical characterizations, essential for understanding cellular mechanics and disease progression.

Advanced Mineral Identification and Characterisation System (AMICS) is based on energy dispersive X-ray spectroscopy (EDS) utilised in a scanning electron microscope (SEM). A specially designed software controls the sample positioning inside the microscope chamber, EDS spectra acquisition and analysis, phase identification, and results visualisation in the form of maps, graphs and tables. Characterisation can be conducted automatically for up to 28 mounted samples in a Hitachi SU3900 SEM with or without coating. Many options exist for an experienced researcher to influence the analysis process: (i) extract a spectrum for each acquisition point and determine chemical composition at this point, (ii) visualise mineral maps for each phase or a group of phases to characterise their interactions, (iii) determine the particle geometry-chemistry relationship, (iv) conduct statistical analysis of EDS spectra, define presence of solute elements, and visualise distribution of particles with a particular chemical composition, (v) manually develop the spectra database for unknown phases and solid solutions. This paper briefly outlines the AMICS software features and selected results for phase characterisation in minerals, metal alloys, and nanoparticle mixtures (MOFs and ZIFs). The influence of data acquisition methodology on accuracy and depth of characterisation is discussed.