Deep learning is becoming an increasingly important tool for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration and super-resolution ...imaging, and discuss how the latest deep learning research could be applied to other image reconstruction tasks. Despite its successes, deep learning also poses substantial challenges and has limits. We discuss key questions, including how to obtain training data, whether discovery of unknown structures is possible, and the danger of inferring unsubstantiated image details.
The mouse embryo has long been central to the study of mammalian development; however, elucidating the cell behaviors governing gastrulation and the formation of tissues and organs remains a ...fundamental challenge. A major obstacle is the lack of live imaging and image analysis technologies capable of systematically following cellular dynamics across the developing embryo. We developed a light-sheet microscope that adapts itself to the dramatic changes in size, shape, and optical properties of the post-implantation mouse embryo and captures its development from gastrulation to early organogenesis at the cellular level. We furthermore developed a computational framework for reconstructing long-term cell tracks, cell divisions, dynamic fate maps, and maps of tissue morphogenesis across the entire embryo. By jointly analyzing cellular dynamics in multiple embryos registered in space and time, we built a dynamic atlas of post-implantation mouse development that, together with our microscopy and computational methods, is provided as a resource.
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•Adaptive light-sheet microscopy captures mouse development at the single-cell level•We analyzed embryo-wide cell dynamics from gastrulation to early organogenesis•We reconstructed high-resolution fate maps and maps of tissue morphogenesis•We created a statistical, dynamic atlas of development from multiple embryos
Adaptive light-sheet microscopy is used to establish a dynamic atlas of post-implantation mouse development at the single-cell level.
Optimal image quality in light-sheet microscopy requires a perfect overlap between the illuminating light sheet and the focal plane of the detection objective. However, mismatches between the ...light-sheet and detection planes are common owing to the spatiotemporally varying optical properties of living specimens. Here we present the AutoPilot framework, an automated method for spatiotemporally adaptive imaging that integrates (i) a multi-view light-sheet microscope capable of digitally translating and rotating light-sheet and detection planes in three dimensions and (ii) a computational method that continuously optimizes spatial resolution across the specimen volume in real time. We demonstrate long-term adaptive imaging of entire developing zebrafish (Danio rerio) and Drosophila melanogaster embryos and perform adaptive whole-brain functional imaging in larval zebrafish. Our method improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions that are not resolved by non-adaptive imaging, adapts to spatiotemporal dynamics of genetically encoded fluorescent markers and robustly optimizes imaging performance during large-scale morphogenetic changes in living organisms.
The field of bioimage analysis is poised for a major transformation, owing to advancements in imaging technologies and artificial intelligence. The emergence of multimodal foundation models — which ...are akin to large language models (such as ChatGPT) but are capable of comprehending and processing biological images — holds great potential for ushering in a revolutionary era in bioimage analysis.
Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, ...the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes.
Many proteins contain disordered regions of low-sequence complexity, which cause aging-associated diseases because they are prone to aggregate. Here, we study FUS, a prion-like protein containing ...intrinsically disordered domains associated with the neurodegenerative disease ALS. We show that, in cells, FUS forms liquid compartments at sites of DNA damage and in the cytoplasm upon stress. We confirm this by reconstituting liquid FUS compartments in vitro. Using an in vitro “aging” experiment, we demonstrate that liquid droplets of FUS protein convert with time from a liquid to an aggregated state, and this conversion is accelerated by patient-derived mutations. We conclude that the physiological role of FUS requires forming dynamic liquid-like compartments. We propose that liquid-like compartments carry the trade-off between functionality and risk of aggregation and that aberrant phase transitions within liquid-like compartments lie at the heart of ALS and, presumably, other age-related diseases.
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•The ALS-associated protein FUS forms liquid compartments in vivo and in vitro•Liquid compartment formation is dependent on the prion-like low-complexity domain•Liquid compartments of FUS convert with time into an aberrant aggregated state•ALS patient mutations accelerate aberrant phase transitions of FUS
The ALS-associated protein FUS assembles into a liquid-like compartment to operate in vivo, but a risk of the functionality conferred by the liquid phase is aggregation to the disease-linked solid phase. Aging diseases caused by aggregation-prone proteins may arise from a failure to maintain liquid-phase homeostasis.
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Recovery of rare earth elements (REE) from acid mine drainage (AMD) could be an alternative to their conventional mining, given that REE are relatively highly concentrated in AMD. ...Their pre-concentration, through AMD active or passive treatment, and further recovery seem promising from both an economic and environmental point of view. This review thoroughly discusses sorption and precipitation as main processes for REE removal from synthetic and real mine water. Nanofiltration and bioaccumulation are also presented as pre-concentration steps prior to the treatment. Promising sorbents especially include biosorbents, but the scientific literature on this topic remains sparse. Even fewer studies cover precipitation as a technique for REE removal from mine water, but its high removal efficiency justifies further research. Large-scale experiments for REE recovery from active or passive AMD remediation systems have already been conducted and this emerging practice has shown economic benefits. Further research is needed to test the performance of these systems.
Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to ...systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization.
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Explaining the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here we present cytoself, a deep-learning approach for fully ...self-supervised protein localization profiling and clustering. Cytoself leverages a self-supervised training scheme that does not require preexisting knowledge, categories or annotations. Training cytoself on images of 1,311 endogenously labeled proteins from the OpenCell database reveals a highly resolved protein localization atlas that recapitulates major scales of cellular organization, from coarse classes, such as nuclear and cytoplasmic, to the subtle localization signatures of individual protein complexes. We quantitatively validate cytoself’s ability to cluster proteins into organelles and protein complexes, showing that cytoself outperforms previous self-supervised approaches. Moreover, to better understand the inner workings of our model, we dissect the emergent features from which our clustering is derived, interpret them in the context of the fluorescence images, and analyze the performance contributions of each component of our approach.
Using network correlation methods, we generate low frequency earthquake templates for a set of 4 composite arrays on Vancouver Island and Washington state that employ data from EarthScope ...(Transportable and Flexible Arrays, Plate Boundary Observatory), POLARIS and permanent network (Canadian National Seismograph Network, Pacific Northwest Seismic Network) sources. On the basis of empirical and semi-analytical arguments, the templates can be viewed as Green's function sections corresponding to moment tensor point sources with step-function time dependence in displacement. Low frequency earthquake hypocentres follow the general epicentral distribution of tremor and occur along tightly defined surfaces in depth with Washington locations averaging slightly deeper than those on Vancouver Island. We invert template waveforms for moment tensor mechanisms and find that data are well modelled by double couple sources. For southern Vancouver Island, with the highest quality templates, the majority of mechanisms are consistent with shallow thrusting in the direction of plate motion. The three other data sets with lower signal to noise levels show predominantly thrust mechanisms with more variable orientations. Taken together with other constraints, our observations support the hypothesis that low frequency earthquakes manifest shear slip on a relatively thin plate boundary.
•Tremor data from EarthScope and other sources used to examine LFEs in N Cascadia.•LFE templates processed as Green's functions.•LFE hypocentres map to thin surface between 29 and 46 km depth.•LFE moment tensors dominated by thrust mechanisms.
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