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From the development of high-throughput imaging platforms that integrate machine learning algorithms for the analysis of 3D organoids and immune cell co-cultures, to the creation of novel software tools like Trapalyzer for the quantitative analysis of neutrophil extracellular trap formation, the advancements in precision microscopy are revolutionizing the way we study and understand complex biological systems across millions of cells, through 2D to 3D tissue spatial dimensions, incorporating temporal aspects. The integration of advanced imaging techniques, multiplex staining methods, and AI-driven analysis for patient samples, pre-clinical models and in vitro cell cultures is not only enhancing our ability to visualize and analyze biological structures with unprecedented precision but is also paving the way for significant advancements in personalized medicine and precision diagnostics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

In cancer, vulnerable breast epithelium malignance tendency correlates with number and activation of ErbB receptor tyrosine kinases. In the presented work, we observe ErbB receptors activated by irradiation-induced DNA injury or neuregulin- 1 β application, or alternatively, attenuated by a therapeutic antibody using high resolution fluorescence localization microscopy. The gap junction turnover coinciding with ErbB receptor activation and co-transport is simultaneously recorded. DNA injury caused by 4 Gray of 6 MeV photon γ -irradiation or alternatively neuregulin- 1 β application mobilized ErbB receptors in a nucleograde fashion—a process attenuated by trastuzumab antibody application. This was accompanied by increased receptor density, indicating packing into transport units. Factors mobilizing ErbB receptors also mobilized plasma membrane resident gap junction channels. The time course of ErbB receptor activation and gap junction mobilization recapitulates the time course of non-homologous end-joining DNA repair. We explain our findings under terms of DNA injury-induced membrane receptor tyrosine kinase activation and retrograde trafficking. In addition, we interpret the phenomenon of retrograde co-trafficking of gap junction connexons stimulated by ErbB receptor activation.

The optical resolution of conventional far field fluorescence light microscopy is restricted to about 200 nm laterally and 600 nm axially and has been thought for many decades to be an insurmountable barrier for the quantitative spatial analysis of cellular and hence also nuclear constituents. Novel approaches in light microscopy have now overcome this barrier. Here, we report on a special method of localisation microscopy, spectral precision distance/position determination microscopy and its combination with fluorescence in situ hybridization to analyse the spatial distribution of specific DNA sequences in human cell nuclei at the macromolecular optical resolution level. As an example, repetitive DNA sequence DYZ2 located within the heterochromatin region on human chromosome Yq12 was labelled with clone pHY2.1. Between 300 and 700 single-probe molecules were resolved in individual chromatin domains, corresponding to a detected molecule density around 500/μm², i.e., many times higher than resolvable by conventional fluorescence microscopy. A mean localisation accuracy of about 20 nm indicated a mean optical resolution in the 50 nm range. Beyond new perspectives for light microscopic studies of specific chromatin nanostructures, this may open a new avenue towards the general analysis of copy number of specific DNA sequences in small regions of individual interphase nuclei.

The localization precision is a crucial and important parameter for single-molecule localization microscopy (SMLM) and directly influences the achievable spatial resolution. It primarily depends on experimental imaging conditions and the registration potency of the algorithm used. We propose a new and simple routine to estimate the average experimental localization precision in SMLM, based on the nearest neighbor analysis. By exploring different experimental and simulated targets, we show that this approach can be generally used for any 2D or 3D SMLM data and that reliable values for the localization precision σ SMLM are obtained. Knowing σ SMLM is a prerequisite for consistent visualization or any quantitative structural analysis, e.g., cluster analysis or colocalization studies.

Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice.

Although scanning transmission electron microscopy (STEM) images of individual heavy atoms were reported 50 years ago, the applications of atomic-resolution STEM imaging became wide spread only after the practical realization of aberration correctors on field-emission STEM/TEM instruments to form sub-Ångstrom electron probes. The innovative designs and advances of electron optical systems, the fundamental understanding of electron–specimen interaction processes, and the advances in detector technology all played a major role in achieving the goal of atomic-resolution STEM imaging of practical materials. It is clear that tremendous advances in computer technology and electronics, image acquisition and processing algorithms, image simulations, and precision machining synergistically made atomic-resolution STEM imaging routinely accessible. It is anticipated that further hardware/software development is needed to achieve three-dimensional atomic-resolution STEM imaging with single-atom chemical sensitivity, even for electron-beam-sensitive materials. Artificial intelligence, machine learning, and big-data science are expected to significantly enhance the impact of STEM and associated techniques on many research fields such as materials science and engineering, quantum and nanoscale science, physics and chemistry, and biology and medicine. This review focuses on advances of STEM imaging from the invention of the field-emission electron gun to the realization of aberration-corrected and monochromated atomic-resolution STEM and its broad applications.

This review covers the advancements of optical super-resolution microscopy (SRM) on formalin-fixed paraffin-embedded (FFPE) histological samples. We cover the implementation of various SRM strategies in histology, including wide field methods such as structured illumination microscopy, single-molecule localization microscopy and fluorescence fluctuations-based SRM, as well as the point-scanning stimulated emission depletion microscopy. We also cover the recent developments in FFPE-based expansion microscopy. The review highlights the advantages and challenges of these SRM methods in FFPE histology, and provides insights into emerging optical and computational techniques that can potentially open avenues for understanding disease mechanisms, tailoring treatments, and advancing personalized medicine across disciplines. This review article is intended for a broad audience, including histopathologists, biologists, physiologists, and physicists. The review covers cutting-edge advancements in optical super-resolution microscopy (SRM) on formalin-fixed paraffin-embedded (FFPE) histological samples.

The 3D organization of the genome plays a critical role in regulating gene expression, maintaining cellular identity, and mediating responses to environmental cues. Advances in super-resolution microscopy and genomic technologies have enabled unprecedented insights into chromatin architecture at nanoscale resolution. However, the complexity and volume of data generated by these techniques necessitate innovative computational strategies for effective analysis and interpretation. In this review, we explore the transformative role of deep learning in the analysis of 3D genome organization, highlighting how deep learning models are being leveraged to enhance image reconstruction, segmentation, and dynamic tracking in chromatin research. We provide an overview of deep learning-enhanced methodologies that significantly improve spatial and temporal resolution of images, with a special focus on single-molecule localization microscopy. Furthermore, we discuss deep learning’s contribution to segmentation accuracy, and its application in single-particle tracking for dissecting chromatin dynamics at the single-cell level. These advances are complemented by frameworks that enable multimodal integration and interpretability, pushing the boundaries of chromatin biology into clinical diagnostics and personalized medicine. Finally, we discuss emerging clinical applications where deep learning models, based on chromatin imaging, aid in disease stratification, drug response prediction, and early cancer detection. We also address the challenges of data sparsity, model interpretability and propose future directions to decode genome function with higher precision and impact.

In the European Union, nanomaterials are regulated through different pieces of sectoral legislation. This legislation often requires risk assessments and thus reliable characterization data, for which regulatory guidance generally recommend electron microscopy. The guidance provides best practices for measurements but lacks requirements on how many particles to measure. Using transmission electron microscopy data of nanomaterials, a strategy based on repeated subsampling is proposed to establish, for different particle size and shape measurands, mathematical relationships between particle count and precision, and subsequently to determine the minimum particle count. Our results confirm that the minimum particle count generally depends on the width of the size and shape distributions and that the median of the distribution can be determined with the highest precision compared to other percentiles. Upon combining the precision uncertainty related to particle number with uncertainties from other sources, such as sample preparation, calibration and trueness, we reach an optimal particle count above which additional particle measurements only yield negligible improvements to the combined measurement uncertainty. Our findings offer an experimental approach for determining the minimum particle count to measure particle size and shape by electron microscopy. It enables efficient analyses and facilitates compliance with legislation addressing nanomaterials across various application domains.

Cardiac fibroblasts (CFs) play critical roles in heart development, homeostasis, and disease. The limited availability of human CFs from native heart impedes investigations of CF biology and their role in disease. Human pluripotent stem cells (hPSCs) provide a highly renewable and genetically defined cell source, but efficient methods to generate CFs from hPSCs have not been described. Here, we show differentiation of hPSCs using sequential modulation of Wnt and FGF signaling to generate second heart field progenitors that efficiently give rise to hPSC-CFs. The hPSC-CFs resemble native heart CFs in cell morphology, proliferation, gene expression, fibroblast marker expression, production of extracellular matrix and myofibroblast transformation induced by TGFβ1 and angiotensin II. Furthermore, hPSC-CFs exhibit a more embryonic phenotype when compared to fetal and adult primary human CFs. Co-culture of hPSC-CFs with hPSC-derived cardiomyocytes distinctly alters the electrophysiological properties of the cardiomyocytes compared to co-culture with dermal fibroblasts. The hPSC-CFs provide a powerful cell source for research, drug discovery, precision medicine, and therapeutic applications in cardiac regeneration. Cardiac fibroblasts (CFs) play critical roles in heart development, homeostasis, and disease. Here the authors efficiently differentiate human pluripotent stem cells through second heart field progenitors to CFs that exhibit features and functional properties similar to native CFs.