Biological super-resolution microscopy is a new generation of imaging techniques that overcome the ~200 nm diffraction limit of conventional light microscopy in spatial resolution. By providing novel spatial or spatiotemporal information on biological processes at nanometer resolution with molecular specificity, it plays an increasingly important role in biomedical sciences. However, its technical constraints also require trade-offs to balance its spatial resolution, temporal resolution, and light exposure of samples. Recently, deep learning has achieved breakthrough performance in many image processing and computer vision tasks. It has also shown great promise in pushing the performance envelope of biological super-resolution microscopy. In this brief review, we survey recent advances in using deep learning to enhance the performance of biological super-resolution microscopy, focusing primarily on computational reconstruction of super-resolution images. Related key technical challenges are discussed. Despite the challenges, deep learning is expected to play an important role in the development of biological super-resolution microscopy. We conclude with an outlook into the future of this new research area.
Fluorescence microscopy has become a routine tool in biology for interrogating life activities with minimal perturbation. While the resolution of fluorescence microscopy is in theory governed only by the diffraction of light, the resolution obtainable in practice is also constrained by the presence of optical aberrations. The past two decades have witnessed the advent of super-resolution microscopy that overcomes the diffraction barrier, enabling numerous biological investigations at the nanoscale. Adaptive optics, a technique borrowed from astronomical imaging, has been applied to correct for optical aberrations in essentially every microscopy modality, especially in super-resolution microscopy in the last decade, to restore optimal image quality and resolution. In this review, we briefly introduce the fundamental concepts of adaptive optics and the operating principles of the major super-resolution imaging techniques. We highlight some recent implementations and advances in adaptive optics for active and dynamic aberration correction in super-resolution microscopy.
Ferroptosis is a novel form of programmed cell death characterized by iron-dependent lipid peroxidation accumulation. It is morphologically, biochemically, and genetically distinct from other known cell death, such as apoptosis, necrosis, and pyroptosis. Its regulatory mechanisms include iron metabolism, fatty acid metabolism, mitochondrial respiration, and antioxidative systems eliminating lipid peroxidation, such as glutathione synthesis, selenium-dependent glutathione peroxidase 4, and ubiquinone. The disruption of cellular redox systems causes damage to the cellular membrane leading to ferroptotic cell death. Recent studies have shown that numerous pathological diseases, like tumors, neurodegenerative disorders, and ischemia-reperfusion injury are associated with ferroptosis. As such, pharmacological regulation of ferroptosis either by activation or by suppression will provide a vast potential for treatments of relevant diseases. This review will discuss the advanced progress in ferroptosis and its regulatory mechanisms from both the antioxidative and oxidative sides. In addition, the roles of ferroptosis in various tumorigenesis, development, and therapeutic strategies will be addressed, particularly to chemotherapy and immunotherapy, as well as the discoveries from Traditional Chinese Medicine. This review will lead us to have a comprehensive understanding of the future exploration of ferroptosis and cancer therapy.
Recently, there has been a lot of interest by using nanobodies (heavy chain-only antibodies produced naturally from the Camelidae) as targeting molecules for molecular imaging, especially for the nuclear medicine imaging. A radiolabeled method that generates a homogeneous product is of utmost importance in radiotracer development for the nuclear medicine imaging. The conventional method for the radiolabeling of nanobodies is non-specifically, which conjugates the radioisotope chelating group to the side chain ɛ-amine group of lysine or sulfhydryl of cysteine of nanobodies, with a shortcoming of produce of the heterogeneous radiotracer. Here we describe a method for the site-specific radioisotope 99mTc labeling of nanobodies by transpeptidase Sortase A. The radiolabeling process includes two steps: first step, NH2-GGGGK(HYNIC)-COOH peptide (GGGGK = NH2-Gly-Gly-Gly-Gly-Lys-COOH, HYNIC = 6-hydrazinonicotinyl) was labeled with 99mTc to obtain GGGGK-HYNIC-99mTc; second step, Sortase A catalyzes the formation of a new peptide bond between the peptide motif LPETG (NH2-Leu-Pro-Glu-Thr-Gly-COOH) expressed C-terminally on the nanobody and the N-terminal of GGGGK-HYNIC-99mTc. After a simple purification process, homogeneous single-conjugated and stable 99mTc-labeled nanobodies were obtained in >50% yield. This approach demonstrates that the Sortase A-mediated conjugation is a valuable strategy for the development of site-specifically 99mTc-labeled nanobodies.
Multicolor super-resolution (SR) microscopy plays a critical role in cell biology research and can visualize the interactions between different organelles and the cytoskeleton within a single cell. However, more color channels bring about a heavier budget for imaging and sample preparation, and the use of fluorescent dyes of higher emission wavelengths leads to a worse spatial resolution. Recently, deep convolutional neural networks (CNNs) have shown a compelling capability in cell segmentation, super-resolution reconstruction, image restoration, and many other aspects. Taking advantage of CNN’s strong representational ability, we devised a deep CNN-based instant multicolor super-resolution imaging method termed IMC-SR and demonstrated that it could be used to separate different biological components labeled with the same fluorophore, and generate multicolor images from a single super-resolution image in silico. By IMC-SR, we achieved fast three-color live-cell super-resolution imaging with ~100 nm resolution over a long temporal duration, revealing the complicated interactions between multiple organelles and the cytoskeleton in a single COS-7 cell.
When imaging the nucleus structure of a cell, the out-of-focus fluorescence acts as background and hinders the detection of weak signals. Light-sheet fluorescence microscopy (LSFM) is a wide-field imaging approach which has the best of both background removal and imaging speed. However, the commonly adopted orthogonal excitation/detection scheme is hard to be applied to single-cell imaging due to steric hindrance. For LSFMs capable of high spatiotemporal single-cell imaging, the complex instrument design and operation largely limit their throughput of data collection. Here, we propose an approach for high-throughput background-free fluorescence imaging of single cells facilitated by the Immersion Tilted Light Sheet Microscopy (ImTLSM). ImTLSM is based on a light-sheet projected off the optical axis of a water immersion objective. With the illumination objective and the detection objective placed opposingly, ImTLSM can rapidly patrol and optically section multiple individual cells while maintaining single-molecule detection sensitivity and resolution. Further, the simplicity and robustness of ImTLSM in operation and maintenance enables high-throughput image collection to establish background removal datasets for deep learning. Using a deep learning model to train the mapping from epi-illumination images to ImTLSM illumination images, namely PN-ImTLSM, we demonstrated cross-modality fluorescence imaging, transforming the epi-illumination image to approach the background removal performance obtained with ImTLSM. We demonstrated that PN-ImTLSM can be generalized to large-field homogeneous illumination imaging, thereby further improving the imaging throughput. In addition, compared to commonly used background removal methods, PN-ImTLSM showed much better performance for areas where the background intensity changes sharply in space, facilitating high-density single-molecule localization microscopy. In summary, PN-ImTLSM paves the way for background-free fluorescence imaging on ordinary inverted microscopes.
Microbiota–host interaction has attracted more and more attentions in recent years. The association between microbiota and host health is largely attributed to its influence on host immune system. Microbial-derived antigens and metabolites play a critical role in shaping the host immune system, including regulating its development, activation, and function. However, during various diseases the microbiota–host communication is frequently found to be disordered. In particular, gut microbiota dysbiosis associated with or led to the occurrence and progression of infectious diseases, autoimmune diseases, metabolic diseases, and neurological diseases. Pathogenic microbes and their metabolites disturb the protective function of immune system, and lead to disordered immune responses that usually correlate with disease exacerbation. In the other hand, the immune system also regulates microbiota composition to keep host homeostasis. Here, we will discuss the current advances of our knowledge about the interactions between microbiota and host immune system during health and diseases.
The voltage-dependent cardiac sodium channel plays a key role in cardiac excitability and conduction and it is the drug target of medically important. However, its atomic- resolution structure is still lack. Here, we report a modeled structure of Nav1.5 pore domain in closed state. The structure was constructed by Rosetta-membrane homology modeling method based on the template of eukaryotic Nav channel NavPaS and selected by energy and direct coupling analysis (DCA). Moreover, this structure was optimized through molecular dynamical simulation in the lipid membrane bilayer. Finally, to validate the constructed model, the binding energy and binding sites of closed-state local anesthetics (LAs) in the modeled structure were computed by the MM-GBSA method and the results are in agreement with experiments. The modeled structure of Nav1.5 pore domain in closed state may be useful to explore molecular mechanism of a state-dependent drug binding and helpful for new drug development.
