生物物理学报:英文版最新文献

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.

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.

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.

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 Na_v1.5 pore domain in closed state. The structure was constructed by Rosetta-membrane homology modeling method based on the template of eukaryotic Na_v channel Na_vPaS 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 Na_v1.5 pore domain in closed state may be useful to explore molecular mechanism of a state-dependent drug binding and helpful for new drug development.

Nanomaterials-based artificial enzymes (nanozymes) with valuable enzyme-like catalytic properties have been booming during the past few years. Promoted by the advances in biological medicine and nanotechnology, nanozymes possess the potential to serve as an emerging agent for biosensing, immunoassays, detection and diagnosis, catalytic therapeutics, and other applications in the biomedicine field. Two-dimensional (2D) nanomaterials are of considerable interest in biomedical applications due to their ultrathin layered structure and unique physiochemical properties. Inspired by the diversified catalytic performance of 2D nanomaterials, scientists extensively have developed 2D materials as bioactive nanozymes for theranostic nanomedicine. Here, recent advances in enzyme-like 2D nanomaterials design and construction are comprehensively presented. Additionally, we exhibit that, with the synergistic effect of catalytic activities and desirable physicochemical performances, 2D nanozymes can serve as versatile platforms with extensive applications from target detection to in vivo theranostic. It is believed that such promising alternatives towards natural enzymes will be of vital significance in the field of nanotechnology and biomedicine.

Antimicrobial peptides (AMPs) are integral components of the innate immune defence system of all complex organisms including plants, insects, and mammals. They have wide range of antibacterial, antifungal, antiviral, and even anticancer activities, therefore AMPs are attractive candidates for developing novel therapeutic approaches. Cationic α-helical membrane disrupting peptides are perhaps the most widely studied subclass of AMPs due to their common fundamental characteristics that allow for detailed structure-function analysis and therefore offer a promising solution to the threat of multidrug resistant strains of bacteria. The majority of the studies of AMP activity focused on the biological and biophysical aspects of membrane disruption; the understanding of the molecular mechanism of action from the physicochemical point of view forms a relatively small subfield. This review will provide an overview of these works, focusing on the empirical and thermodynamic models of AMP action.

High-throughput proteomics based on mass spectrometry (MS) analysis has permeated biomedical science and propelled numerous research projects. pFind 3 is a database search engine for high-speed and in-depth proteomics data analysis. pFind 3 features a swift open search workflow that is adept at uncovering less obvious information such as unexpected modifications or mutations that would have gone unnoticed using a conventional data analysis pipeline. In this protocol, we provide step-by-step instructions to help users mastering various types of data analysis using pFind 3 in conjunction with pParse for data pre-processing and if needed, pQuant for quantitation. This streamlined pParse-pFind-pQuant workflow offers exceptional sensitivity, precision, and speed. It can be easily implemented in any laboratory in need of identifying peptides, proteins, or post-translational modifications, or of quantitation based on ¹⁵N-labeling, SILAC-labeling, or TMT/iTRAQ labeling.

Ischemic stroke results in cerebral tissue hypoxia and increased expression of hypoxia-inducible factor (HIF), which is critically implicated in ischemic brain injury. Understanding the mechanisms of HIF-1alpha regulation in the ischemic brain has been an important research focus. The generation of both nitric oxide (NO) and reactive oxygen species (ROS) is increased under hypoxic/ischemic conditions and each of them has been independently shown to regulate HIF-1alpha expression. In this study, we investigated the cross-effects of NO and ROS on the expression of HIF-1alpha in hypoxic astrocytes. Exposure of astrocytes to 2 h-hypoxia remarkably increased HIF-1alpha protein levels, which was accompanied by increased NO and ROS production. Decreasing ROS with NAC, NADPH oxidase inhibitor DPI, or SOD mimetic MnTMPyP decreased hypoxia-induced HIF-1alpha protein accumulation and increased NO level in hypoxic astrocytes. The NO synthase (NOS) inhibitor L-NAME inhibited ROS generation, which led to a reduction in hypoxia-induced HIF-1alpha protein expression. Although NOS inhibitor or ROS scavengers alone reduced HIF-1alpha protein levels, the reduction was reversed when NOS inhibitor and ROS scavenger present together. The NO scavenger PTIO increased hypoxia-induced HIF-1alpha protein expression and ROS production, while HIF-1alpha protein level was decreased in the presence of NO scavenger and ROS scavenger together. These results suggest that ROS, NO, and their interaction critically contribute to the regulation of hypoxia-induced HIF-1alpha protein accumulation under hypoxic condition. Furthermore, our results indicate that hypoxia-induced NO generation may represent an endogenous mechanism for balancing ROS-mediated hypoxic stress, as reflected by inhibiting hypoxia-induced HIF-1alpha protein accumulation.

Interleukin 6 (IL-6) is a cytokine with dual functions of pro-inflammation and anti-inflammation. It is mainly produced by mononuclear macrophages, Th2 cells, vascular endothelial cells and fibroblasts. IL-6 binds to glycoprotein 130 and one of these two receptors, membrane-bound IL-6R or soluble IL-6R, forming hexamer (IL-6/IL-6R/gp130), which then activates different signaling pathways (classical pathway, trans-signaling pathway) to exert dual immune-modulatory effects of anti-inflammation or pro-inflammation. Abnormal levels of IL-6 can cause multiple pathological reactions, including cytokine storm. Related clinical studies have found that IL-6 levels in severe COVID-19 patients were much higher than in healthy population. A large number of studies have shown that IL-6 can trigger a downstream cytokine storm in patients with COVID-19, resulting in lung damages, aggravating clinical symptoms and developing excessive inflammation and acute respiratory distress syndrome (ARDS). Monoclonal antibodies against IL-6 or IL-6R, such as tocilizumab, sarilumab, siltuximab and olokizumab may serve as therapeutic options for COVID-19 infection.

Breast cancer ranks second in the list of most common cancers among women and brings the double burden of economy and health to women. Therefore, it is an urgent and necessary task to study the pathogenic mechanism and the treatment of breast cancer. Glycoprotein hormone is a kind of hormones to promote the growth and the development of cell and stanniocalcin 2 (STC2) is one of them. Research has shown us a various expression of SCT2 in organs and tissues and it can regulate many different pathological and physiological processes. In addition, there are a lot of previous studies that indicated a close correlation between STC2 and the development and metastasis of many cancers, which infers STC2 can serve as biomarker of certain cancers. Until now, the effects of STC2 on breast cancer have been studied widely, but research findings demonstrated two different views, one view is that STC2 plays an oncogenic role and the other is the opposite. In this paper, it will summarize and evaluate the research data and results about mammalian STC2 on breast cancer.