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. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Alongside developments of microscopy hardware, computational methods — especially those based on machine learning — are powerful tools to improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.

Live-cell imaging powered by computation.

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.

pdf/harnessing-artificial-intelligence-to-reduce-phototoxicity-4xcqxxo7dv.pdf

Harnessing artificial intelligence to reduce phototoxicity in live imaging.

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. 

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

Many biological processes span multiple time and length scales, including developmental processes and cancer metastasis. While light-sheet fluorescence microscopy (LSFM) has become a fast and efficient method for imaging of organisms, cells and sub-cellular dynamics, simultaneous observations across these scales have remained challenging. Moreover, continuous high-resolution imaging inside living organisms has mostly been limited to few hours as regions of interest quickly move out of view due to sample movement and growth. Here, we present a self-driving, multi-resolution light-sheet microscope platform controlled by a custom Python-based software, to simultaneous observe and quantify sub-cellular dynamics and 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 multi-scale analysis of immune-cancer cell interactions in zebrafish xenografts.

Imaging of cellular dynamics in vitro and in situ: from a whole organism to sub-cellular imaging with self-driving, multi-scale microscopy

Cells are highly dynamic, continually responding to intra- and extracellular signals. Monitoring and measuring the response to these signals at the single-cell level is challenging. Signal transduction is fast, but reporters for downstream gene expression are typically slow, requiring fluorescent proteins to be expressed and to mature. An alternative is to fluorescently tag and then monitor the intracellular locations of transcription factors and other effectors. These proteins move in or out of the nucleus in minutes, after upstream signalling modifies their state of phosphorylation. Although such approaches are being used increasingly, there is no consensus on how to quantify the nuclear and cytoplasmic localisation of these proteins. Using budding yeast, we developed a convolutional neural network that quantifies nuclear localisation from fluorescence and, optionally, bright-field images. Focusing on the cellular response to changing glucose, we generated ground-truth data using strains with both a transcription factor and a nuclear protein tagged with fluorescent markers. We then showed that the neural network based approach outperformed seven published methods, particularly when predicting single-cell time series, which are key to determining how cells respond and adapt. Collectively, our results are conclusive --- using machine learning to automatically determine the appropriate image processing consistently outperforms ad hoc methods. Adopting such methods promises to both improve the accuracy and, with transfer learning, the consistency of single-cell analyses. 

Quantifying the nuclear localisation of fluorescently tagged proteins

Timelapse fluorescence microscopy imaging is routinely used in quantitative cell biology. However, microscopes could become much more powerful investigation systems if they were endowed with simple unsupervised decision-making algorithms to transform them into fully responsive and automated measurement devices. Here, we report CyberSco.Py, Python software for advanced automated timelapse experiments. We provide proof-of-principle of a user-friendly framework that increases the tunability and flexibility when setting up and running fluorescence timelapse microscopy experiments. Importantly, CyberSco.Py combines real-time image analysis with automation capability, which allows users to create conditional, event-based experiments in which the imaging acquisition parameters and the status of various devices can be changed automatically based on the image analysis. We exemplify the relevance of CyberSco.Py to cell biology using several use case experiments with budding yeast. We anticipate that CyberSco.Py could be used to address the growing need for smart microscopy systems to implement more informative quantitative cell biology experiments. 

/pdf/cybersco-py-an-open-source-software-for-event-based-llbec4b6.pdf

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

Natural environments of living organisms are often dynamic and multifactorial, with multiple parameters fluctuating over time. To better understand how cells respond to dynamically interacting factors, we quantified the effects of dual fluctuations of osmotic stress and glucose deprivation on yeast cells using microfluidics and time-lapse microscopy. Strikingly, we observed that cell proliferation, survival and signaling depend on the phasing of the two periodic stresses. Cells divided faster, survived longer and showed decreased transcriptional response when fluctuations of hyperosmotic stress and glucose deprivation occurred in phase than when the two stresses occurred alternatively. We also found that mutants with impaired osmotic stress response were better adapted to alternating stresses than wild-type cells, showing that genetic mechanisms of adaptation to a persistent stress factor can be detrimental under dynamically interacting conditions. Taken together, we demonstrate that glucose availability regulates yeast responses to dynamic osmotic stress. We anticipate that our approach can be extended to other stress responsive pathways to further elucidate the key role of metabolic fluctuations in the dynamics of cellular responses to stress.

/pdf/yeast-cell-responses-and-survival-during-periodic-osmotic-2btvpsty.pdf

Yeast cell responses and survival during periodic osmotic stress are controlled by glucose availability

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

Yeast cell responses and survival during periodic osmotic stress are controlled by glucose availability