Abstract Collagen hydrogels are widely used as biomimetic three‐dimensional matrices whose mechanical behaviour depends strongly on fibrillar micro‐ and nanoarchitecture and crosslinking state. Electron microscopy offers high spatial resolution for structural analysis, but sample preparation can substantially distort highly hydrated collagen networks, thereby confounding interpretation. Here, we compared common SEM and TEM preparation workflows for their ability to preserve structurally subtle, mechanically validated differences in collagen type I hydrogels. Collagen hydrogels containing riboflavin were photochemically crosslinked by blue‐light exposure, and the resulting increase in bulk stiffness was first confirmed by rotational rheometry. Samples were then prepared using various workflows for SEM and TEM analysis based on classical glutaraldehyde fixation or high‐pressure freezing. As control experiments, we performed nitrogen plunge freezing to visualise the effect of ice crystal formation, and high‐pressure freezing with ethanol cryoprotection to rule out the influence of ice crystal artefacts. The results show that sample preparation‐induced artefacts strongly influence structural interpretation in electron microscopy of collagen hydrogels. The methodology based on glutaraldehyde fixation altered network morphology, while the nitrogen plunge freezing based process introduced severe freezing artefacts attributed to ice crystal formation, obscuring the subtle pore‐size changes associated with riboflavin/blue‐light crosslinking. The high‐pressure freezing based process with and without cryoprotectant retained the ability to resolve crosslinking‐associated structural differences in these soft collagen hydrogels. This indicates that high‐pressure frozen samples of the collagen hydrogels used in this study are already free of visible ice crystal damage and that the use of additional cryoprotectants with unknown structural effects is unnecessary. More broadly, the study supports combining mechanical validation with electron microscopy when assessing soft hydrogel architectures to minimise the potential to interpret preparation‐based artefacts as structural features.
Abstract Since its introduction 40 years ago, atomic force microscopy (AFM) has transformed nanoscale imaging across the physical and life sciences. In biology, AFM has provided unprecedented insights into nanoscale biomolecular organisation under near‐physiological conditions, revealing structures and interactions inaccessible to many conventional approaches. However, biological function is inherently dynamic, and understanding molecular mechanisms requires moving beyond static structural snapshots towards direct observation of processes across multiple spatial and temporal scales. In this review, we discuss emerging approaches that are extending the capabilities of AFM beyond traditional imaging, pushing the limits of temporal resolution, spatial precision, quantitative measurement, and data analysis. We highlight advances in speed that enable direct visualisation of molecular dynamics, approaches for sub‐millisecond measurements, methods that overcome conventional resolution limits, and computational approaches that enhance image analysis, automation, and information extraction from complex datasets. Together, these developments are transforming AFM from a technique focused primarily on high‐resolution structural imaging into a platform for dynamic, quantitative, and high‐throughput investigation of biological systems. Finally, we consider how these advances may shape the next 40 years of AFM, enable autonomous measurements and reveal biological mechanisms across previously inaccessible spatial and temporal scales.
Abstract Quantitative analysis of embryonic morphogenesis often requires handling terabyte‐scale 4D microscopy datasets, posing major computational and methodological challenges. Such analyses involve complex workflows for image restoration, segmentation, and tracking of thousands of cells in 3D, sometimes assembled from multiple generic tools and custom scripts. In their study ‘Deconstructing gastrulation at single‐cell resolution ,’ the authors present an integrated computational framework for cell segmentation, tracking and modelling of cell–cell interactions during Drosophila gastrulation. Their approach combines both generic tools such as ilastik, FIJI, Cellpose, MATLAB code and tools such as ImSAnE to reconstruct whole‐embryo, single‐cell morphogenetic atlas with unprecedented detail. While the methodology is transparent and well‐documented, its implementation relies on multiple software environments and some non–open‐source components, limiting full reproducibility and generalisability. Nevertheless, their ‘computational framework’ exemplifies rigorous data sharing, clear documentation, and methodological transparency aligned with several community guidelines for reproducible image analysis. We discuss this work in the context of reproducibility, replicability, generalisability, transparency, validity, and robustness, emphasising how emerging open‐source and popular solutions could improve accessibility and replicability of similar large‐scale bioimage analysis workflows in developmental biology.
