Extracellular vesicles (EVs), including exosomes, are promising circulating biomarkers for disease diagnosis. Conventional EVs analysis requires multiple instrumentations to obtain their phenotypic features, which limits its wide applications. Here, we present a plasmonic biosensor technology for multifunctional analysis of EVs. The system is based on a functionalized surface plasmon resonance (SPR) biosensor and an advanced plasmonic microscopy to capture and image EVs at single-particle level. SPR images are processed with a home-developed deep learning algorithm to identify EVs and quantify image intensity automatically. By combining immunosensing and single particle analysis, this approach enables both physical and chemical characterization of EVs. As a proof-of-concept, we applied it to analyze EVs secreted from human lung cancer A549 cell lines. Results show the capabilities in the detection of size, concentration and affinity constant. Due to the single particle imaging and multifunctional analysis capability, we anticipate that this technology will find use in clinical and scientific applications.

Plasmonic microscopy is a powerful tool for nanoscopic bio- and chemical sample analysis due to its high sensitivity. Phase quantification in plasmonic microscopy would provide inherent information, i.e., refractive index, for identification of nanomaterials. However, it usually relies on complex optics to acquire quantitative phase images. Here, we demonstrated the quantitative amplitude and phase imaging capabilities through holographical reconstructions of the plasmonic patterns recorded in the interferometric plasmonic microscopy. Operating the plasmonic microscopy over the surface plasmon resonance angle separates the twin images and allows for accurate mapping of the amplitude and phase distribution of surface plasmon near fields. Results show that the imaging capabilities enable direct visualization of complex surface plasmon fields arising from interactions with nanoparticles and nanowires, without the need for nanoscopic scanning probes. Theoretical and experimental analysis also suggests future applications in the identification of nanoparticles and super-resolution imaging. The proposed technology is thus promising for nanoplasmonic study and various sensing purposes.

Significance The detection of low-abundance molecular biomarkers is key to the liquid–biopsy-based disease diagnosis. Existing methods are limited by the affinity and specificity of recognition probes and the mass transportation of analyte molecules onto the sensor surfaces, resulting in insufficient sensitivity and long assay time. This work establishes a rapid and ultrasensitive approach by actively tuning binding kinetics and accelerating the mass transportation via nanoparticle micromanipulations. This is significant because it permits extremely sensitive measurements within clinically acceptable assay time. It is incubation-free, washing-free, and compatible with low- and high-affinity probes.

PslG attracted a lot of attention recently due to its great potential abilities in inhibiting biofilms of However, how PslG affects biofilm development still remains largely unexplored. Here, we focused on the surface motility of bacterial cells, which is critical for biofilm development. We studied the effects of PslG on bacterial surface movement in early biofilm development at a single-cell resolution by using a high-throughput bacterial tracking technique. The results showed that compared with no exogenous PslG addition, when PslG was added to the medium, bacterial surface movement was significantly (4 to 5 times) faster and proceeded in a more random way with no clear preferred direction. A further study revealed that the fraction of walking mode increased when PslG was added, which then resulted in an elevated average speed. The differences of motility due to PslG addition led to a clear distinction in patterns of bacterial surface movement and retarded microcolony formation greatly. Our results provide insight into developing new PslG-based biofilm control techniques. Biofilms of are a major cause for hospital-acquired infections. They are notoriously difficult to eradicate and pose serious health hazards to human society. So, finding new ways to control biofilms is urgently needed. Recent work on PslG showed that PslG might be a good candidate for inhibiting/disassembling biofilms of through Psl-based regulation. However, to fully explore PslG functions in biofilm control, a better understanding of PslG-Psl interactions is needed. Toward this end, we examined the effects of PslG on the surface movement of in this work. The significance of our work is in greatly enhancing our understanding of the inhibiting mechanism of PslG on biofilms by providing a detailed picture of bacterial surface movement at a single-cell level, which will allow a full understanding of PslG abilities in biofilm control and thus present potential applications in biomedical fields.

Surface plasmon resonance microscopy (SPRM) has been widely used as a sensitive imaging platform for chemical and biological analysis. The SPRM system inevitably suffers from focus inhomogeneity and drifts, especially in long-term recordings, leading to distorted images and inaccurate quantification. Traditional focus correction approaches require additional optical parts to detect and adjust focal conditions. Herein, we propose a deep-learning-based image processing method to gain autofocused SPRM images, without increasing the complexity of the optical systems. We trained a generative adversarial network (GAN) model with thousands of SPRM images of nanoparticles acquired at different focal distances. The trained model was able to directly generate focused SPRM images from single-shot defocused images, with no prior knowledge of the focus conditions during recording. Experiments using Au nanoparticles show that this method is effective in both static and time-lapse monitoring. The proposed autofocus technique thus provides an approach for improving the consistency among SPRM studies and for long-term monitoring.