CyberSco.Py an open-source software for event-based, conditional microscopy

Imaging of cellular dynamics from a whole organism to subcellular scale with self-driving, multiscale microscopy

Most biological processes, from development to pathogenesis, span multiple time and length scales. While light-sheet fluorescence microscopy has become a fast and efficient method for imaging organisms, cells and subcellular dynamics, simultaneous observations across all these scales have remained challenging. Moreover, continuous high-resolution imaging inside living organisms has mostly been limited to a few hours, as regions of interest quickly move out of view due to sample movement and growth. Here, we present a self-driving, multiresolution light-sheet microscope platform controlled by custom Python-based software, to simultaneously observe and quantify subcellular dynamics in the context of entire organisms in vitro and in vivo over hours of imaging. We apply the platform to the study of developmental processes, cancer invasion and metastasis, and we provide quantitative multiscale analysis of immune-cancer cell interactions in zebrafish xenografts.

Deep learning-driven automated high-content dSTORM imaging with a scalable open-source toolkit

Super-resolution microscopy offers the ability to visualize molecular structures in biological samples with unprecedented detail. However, the full potential of these techniques is often hindered by a lack of automated, user-independent workflows. Here, we present an open-source toolkit that automates dSTORM super-resolution microscopy using deep learning for segmentation and object detection. This standalone program enables reliable segmentation of diverse biomedical images, even in low-contrast samples, surpassing existing solutions. Integrated into the imaging pipeline, it rapidly processes high-content data in minutes, reducing manual labor. Demonstrated by biological examples, such as microtubules in cell culture and the βII-spectrin in nerve fibers, our approach makes super-resolution imaging faster, more robust, and easy to use, even by nonexperts. This broadens its potential applications in biomedicine, including high-throughput experimentation.

Self-Driving Microscopes: AI Meets Super-Resolution Microscopy

The integration of Machine Learning (ML) with super-resolution microscopy represents a transformative advancement in biomedical research. Recent advances in ML, particularly deep learning (DL), have significantly enhanced image processing tasks, such as denoising and reconstruction. This review explores the growing potential of automation in super-resolution microscopy, focusing on how DL can enable autonomous imaging tasks. Overcoming the challenges of automation, particularly in adapting to dynamic biological processes and minimizing manual intervention, is crucial for the future of microscopy. Whilst still in its infancy, automation in super-resolution can revolutionize drug discovery and disease phenotyping leading to similar breakthroughs as have been recognized in this year's Nobel Prizes for Physics and Chemistry.

Mitochondrial Structure, Dynamics, and Physiology: Light Microscopy to Disentangle the Network

Mitochondria serve as energetic and signaling hubs of the cell: This function results from the complex interplay between their structure, function, dynamics, interactions, and molecular organization. The ability to observe and quantify these properties often represents the puzzle piece critical for deciphering the mechanisms behind mitochondrial function and dysfunction. Fluorescence microscopy addresses this critical need and has become increasingly powerful with the advent of superresolution methods and context-sensitive fluorescent probes. In this review, we delve into advanced light microscopy methods and analyses for studying mitochondrial ultrastructure, dynamics, and physiology, and highlight notable discoveries they enabled.

Live-cell imaging powered by computation

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.

Harnessing artificial intelligence to reduce phototoxicity in live imaging

Fluorescence microscopy is essential for studying living cells, tissues and organisms. However, the fluorescent light that switches on fluorescent molecules also harms the samples, jeopardizing the validity of results - particularly in techniques such as super-resolution microscopy, which demands extended illumination. Artificial intelligence (AI)-enabled software capable of denoising, image restoration, temporal interpolation or cross-modal style transfer has great potential to rescue live imaging data and limit photodamage. Yet we believe the focus should be on maintaining light-induced damage at levels that preserve natural cell behaviour. In this Opinion piece, we argue that a shift in role for AIs is needed - AI should be used to extract rich insights from gentle imaging rather than recover compromised data from harsh illumination. Although AI can enhance imaging, our ultimate goal should be to uncover biological truths, not just retrieve data. It is essential to prioritize minimizing photodamage over merely pushing technical limits. Our approach is aimed towards gentle acquisition and observation of undisturbed living systems, aligning with the essence of live-cell fluorescence microscopy.