Carbon Dot Blinking Fingerprint Uncovers Native Membrane Receptor Organizations via Deep Learning

Super-Resolution Microscopic Imaging of Lipid Droplets in Living Cells via Carbonized Polymer Dot-Based Polarity-Responsive Nanoprobe

Lipid droplets (LDs) are dynamic subcellular organelles that participate in various physiological processes, and their abnormality can also lead to various diseases. Tracing the dynamics of LDs in living cells will be valuable for understanding cell physiological states. Here, we employed a structured light illumination super-resolution imaging assisted with a carbonized polymer dot (CPD)-based fluorescence nanoprobe to track the travel paths of LDs and other organelles. The CPDs we developed are highly biocompatible with living cells and exhibit a highly sensitive response to solvent polarity, allowing for high specificity in staining LDs in living cells. Aided by these nanoprobes, we successfully observed many real-time LD-involved dynamics in living cells, such as intracellular LD interactions, communications with other organelles, and dynamic behaviors under external stimuli (oxidative stress inducer). These studies deepen our comprehension of the physiological role of LDs and drive the advancement of super-resolution fluorescent probes.

Carbon Dots: A Versatile Platform for Cu2+ Detection, Anti-Counterfeiting, and Bioimaging

Carbon dots (CDs) have garnered extensive interest in basic physical chemistry as well as in biomedical applications due to their low cost, good biocompatibility, and great aqueous solubility. However, the synthesis of multi-functional carbon dots has always been a challenge for researchers. Here, we synthesized novel CDs with a high quantum yield of 28.2% through the straightforward hydrothermal method using Diaminomaleonitrile and Boc-D-2, 3-diaminopropionic acid. The size, chemical functional group, and photophysical properties of the CDs were characterized by TEM, FTIR, XPS, UV, and fluorescence. It was demonstrated in this study that the prepared CDs have a high quantum yield, excellent photostability, and low cytotoxicity. Regarding the highly water-soluble property of CDs, they were proven to possess selective and sensitive behavior against Cu2+ ions (linear range = 0-9 μM and limit of detection = 1.34 μM). Moreover, the CDs were utilized in fluorescent ink in anti-counterfeiting measures. Because of their low cytotoxicity and good biocompatibility, the CDs were also successfully utilized in cell imaging. Therefore, the as-prepared CDs have great potential in fluorescence sensing, anti-counterfeiting, and bioimaging.

The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery

The application of artificial intelligence (AI) approaches to drug discovery for G protein-coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand-GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process "faster, smarter and cheaper," we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery. LINKED ARTICLES: This article is part of a themed issue Therapeutic Targeting of G Protein-Coupled Receptors: hot topics from the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists 2021 Virtual Annual Scientific Meeting. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.14/issuetoc.

Blinking Carbon Dots as a Super-resolution Imaging Probe

Single-molecule localization microscopy (SMLM) emerges as a powerful approach for super-resolution imaging that provides unprecedented resolution at the nanometer length scale. However, the development of appropriate probes with specific photophysical traits and characteristics is crucial for this approach. This study demonstrates two different fluorescent carbon dots (CDs) derived from the same molecular precursor─one emitting in red and the other in green─as a SMLM-based super-resolution imaging probe for different applications with an average localization precision of 20 nm and a resolution of 60 nm. Both the CDs exhibit spontaneous blinking with high photon count and low duty cycle but with different blinking cycles. The red emissive CDs with a lower blinking cycle are ideal for quantitative analysis, whereas green emissive CDs with a higher blinking cycle are ideal for high-resolution imaging. We show that the difference in blinking features is linked to their chemical compositions, and the presence of much denser trap states in red emitting CDs is responsible for the reduction of its blinking cycle. This study shows that CDs can be designed as a potential probe for SMLM-based super-resolution imaging for diverse bioimaging applications.

Deep Learning-Assisted Automated Multidimensional Single Particle Tracking in Living Cells

The translational and rotational dynamics of anisotropic optical nanoprobes revealed in single particle tracking (SPT) experiments offer molecular-level information about cellular activities. Here, we report an automated high-speed multidimensional SPT system integrated with a deep learning algorithm for tracking the 3D orientation of anisotropic gold nanoparticle probes in living cells with high localization precision (<10 nm) and temporal resolution (0.9 ms), overcoming the limitations of rotational tracking under low signal-to-noise ratio (S/N) conditions. This method can resolve the azimuth (0°-360°) and polar angles (0°-90°) with errors of less than 2° on the experimental and simulated data under S/N of ∼4. Even when the S/N approaches the limit of 1, this method still maintains better robustness and noise resistance than the conventional pattern matching methods. The usefulness of this multidimensional SPT system has been demonstrated with a study of the motions of cargos transported along the microtubules within living cells.

Deep-Learning-Enhanced Diffusion Imaging Assay for Resolving Local-Density Effects on Membrane Receptors

G-protein-coupled receptor (GPCR) density at the cell surface is thought to regulate receptor function. Spatially resolved measurements of local-density effects on GPCRs are needed but technically limited by density heterogeneity and mobility of membrane receptors. We now develop a deep-learning (DL)-enhanced diffusion imaging assay that can measure local-density effects on ligand-receptor interactions in the plasma membrane of live cells. In this method, the DL algorithm allows the transformation of 100 ms exposure images to density maps that report receptor numbers over any specified region with ∼95% accuracy by 1 s exposure images as ground truth. With the density maps, a diffusion assay is further established for spatially resolved measurements of receptor diffusion coefficient as well as to express relationships between receptor diffusivity and local density. By this assay, we scrutinize local-density effects on chemokine receptor CXCR4 interactions with various ligands, which reveals that an agonist prefers to act with CXCR4 at low density while an inverse agonist dominates at high density. This work suggests a new insight into density-dependent receptor regulation as well as provides an unprecedented assay that can be applicable to a wide variety of receptors in live cells.

Deep learning in single-molecule imaging and analysis: recent advances and prospects

Single-molecule microscopy is advantageous in characterizing heterogeneous dynamics at the molecular level. However, there are several challenges that currently hinder the wide application of single molecule imaging in bio-chemical studies, including how to perform single-molecule measurements efficiently with minimal run-to-run variations, how to analyze weak single-molecule signals efficiently and accurately without the influence of human bias, and how to extract complete information about dynamics of interest from single-molecule data. As a new class of computer algorithms that simulate the human brain to extract data features, deep learning networks excel in task parallelism and model generalization, and are well-suited for handling nonlinear functions and extracting weak features, which provide a promising approach for single-molecule experiment automation and data processing. In this perspective, we will highlight recent advances in the application of deep learning to single-molecule studies, discuss how deep learning has been used to address the challenges in the field as well as the pitfalls of existing applications, and outline the directions for future development.

Quantification of mRNA in Single Cells Based on Dimerization-Induced Photoluminescence Nonblinking of Quantum Dots

Photoluminescence (PL) intermittency (or "blinking") is a unique characteristic of single quantum dot (QD) emission. Here, we report a novel single-molecule detection strategy for the intracellular mRNA of interest using the mRNA-induced nonblinking QD dimers as probes. The working principle of the method is that the DNA hybrid of the target DNA (or mRNA) with a biotin-modified ssDNA probe can induce two blinking streptavidin-modified QDs (SAV-QDs) conjugated. The formed QD dimer as a bright spot showed a nonblinking emission property, observed with total inner reflection fluorescence microscopy (TIRFM). In theory, one nonblinking spot indicated a target DNA (or mRNA). The experimental results from single-spot fluorescence trajectory analysis and single-particle brightness analysis based on TIRFM and fluorescence correlation spectroscopy (FCS) techniques verified this dimerization process of QDs or its induced nonblinking emission. Employing a target DNA with the same base sequences to Survivin mRNA as a model, the detection strategy was used to detect the target DNA concentration based on the linear relationship between the percentage of the nonblinking spots and the target DNA concentration. This single-molecule detection strategy was also successfully used for determining Survivin mRNA in a single HeLa cell. The method can simplify the hybridization steps, eliminate self-quenching and photobleaching of fluorophores, and reduce the influence of unspecific binding on the detection.