Dynamic single-molecule sensing by actively tuning binding kinetics for ultrasensitive biomarker detection.

SignificanceThe 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.

Codetection of Proteins and RNAs on Extracellular Vesicles for Pancreatic Cancer Early Diagnosis.

Tumor-derived extracellular vesicles (EVs) carry tumor-specific proteins and RNAs, thus becoming prevalent targets for early cancer diagnosis. However, low expression of EV cargos and insufficient diagnostic power of individual biomarkers hindered EVs application in clinical practice. Herein, we propose a multiplex Codetection platform of proteins and RNAs (Co-PAR) for EVs. Co-PAR adopted a pair of antibody-DNA probes to recognize the same target protein, which in turn formed a double-stranded DNA. Thus, the target protein could be quantified by detecting the double-stranded DNA via qPCR. Meanwhile, qRT-PCR simultaneously quantified the target RNAs. Thus, with a regular qPCR instrument, Co-PAR enabled the codetection of multiplex proteins and RNAs, with the sensitivity of 102 EVs/µL (targeting CD63) and 1 EV/µL (targeting snRNA U6). We analyzed the coexpressions of three protein markers (CD63, GPC-1, HER2) and three RNA markers (snRNA U6, GPC-1 mRNA, miR-10b) on EVs from three pancreatic cell lines and 30 human plasma samples using Co-PAR. The diagnostic accuracy of the 6-biomarker combination reached 92.9%, which was at least 6.2% higher than that of 3-biomarker combinations and at least 13.5% higher than that of 6 single biomarkers. Co-PAR, as a multiparameter detection platform for EVs, has great potential in early disease diagnosis.

Heterogeneous Analysis of Extracellular Vesicles for Osteosarcoma Diagnosis.

Osteosarcoma (OS) is the most prevalent primary tumor of bones, often diagnosed late with a poor prognosis. Currently, few effective biomarkers or diagnostic methods have been developed for early OS detection with high confidence, especially for metastatic OS. Tumor-derived extracellular vesicles (EVs) are emerging as promising biomarkers for early cancer diagnosis through liquid biopsy. Here, we report a plasmonic imaging-based biosensing technique, termed subpopulation protein analysis by single EV counting (SPASEC), for size-dependent EV subpopulation analysis. In our SPASEC platform, EVs are accurately sized and counted on plasmonic sensor chips coated with OS-specific antibodies. Subsequently, EVs are categorized into distinct subpopulations based on their sizes, and the membrane proteins of each size-dependent subpopulation are profiled. We measured the heterogeneous expression levels of the EV markers (CD63, BMP2, GD2, and N-cadherin) in each of the EV subsets from both OS cell lines and clinical plasma samples. Using the linear discriminant analysis (LDA) model, the combination of four markers is applied to classify the healthy donors (n = 37), nonmetastatic OS patients (n = 13), and metastatic patients (n = 12) with an area under the curve of 0.95, 0.92, and 0.99, respectively. SPASEC provides accurate EV sensing technology for early OS diagnosis.

A Single-Shot Autofocus Approach for Surface Plasmon Resonance Microscopy.

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.

Multifunctional Detection of Extracellular Vesicles with Surface Plasmon Resonance Microscopy.

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.

Effects of PslG on the Surface Movement of Pseudomonas aeruginosa.

PslG attracted a lot of attention recently due to its great potential abilities in inhibiting biofilms of Pseudomonas aeruginosa 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.IMPORTANCE Biofilms of Pseudomonas aeruginosa 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 Pseudomonas aeruginosa 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 Pseudomonas aeruginosa 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.

The application of SVR model in the improvement of QbD: a case study of the extraction of podophyllotoxin.

In order to make a further optimization of process design via increasing the stability of design space, we brought in the model of Support Vector Regression (SVR). In this work, the extraction of podophyllotoxin was researched as a case study based on Quality by Design (QbD). We compared the fitting effect of SVR and the most used quadratic polynomial model (QPM) in QbD, and an analysis was made between the two design spaces obtained by SVR and QPM. As a result, the SVR stayed ahead of QPM in prediction accuracy, the stability of model and the generalization ability. The introduction of SVR into QbD made the extraction process of podophyllotoxin well designed and easier to control. The better fitting effect of SVR improved the application effect of QbD and the universal applicability of SVR, especially for non-linear, complicated and weak-regularity problems, widened the application field of QbD.

Correlation between membrane proteins and sizes of extracellular vesicles and particles: A potential signature for cancer diagnosis.

Extracellular vesicles and particles (EVPs) are recognized as ideal liquid biopsy tools for cancer detection, and membrane proteins are commonly used EVP biomarkers. However, bulk analysis of EVP membrane protein biomarkers typically fails to meet the clinical requirement for diagnostic accuracy. We investigated the correlation between the membrane protein expression level, the binding kinetics to aptamers and the sizes of EVPs with interferometric plasmonic microscopy (iPM), and demonstrated the implementation of the correlative signature to determine cancer types. Using EVPs collected from both cell model and clinical plasma samples with liver, lung, breast, or prostate cancer, we found that the selective set of membrane protein expression levels of five protein markers and their binding kinetics were highly heterogeneous across various sizes of EVPs, resulting in the low overall accuracy (<50%) in cancer classification with bulk analysis of all populations. By grouping the EVPs into three subpopulations according to their sizes, the overall accuracy could be increased to about 70%. We further grouped the EVPs into subpopulations with a 10 nm interval in sizes and analysed the correlation between the membrane proteins and sizes with a machine learning algorithm. The results show that the overall accuracy to discriminate cancer types could be improved to 85%. Therefore, this work highlights the significance of size-dependent subtyping of EVPs and suggests that the correlation between the selective set of membrane proteins and sizes of EVP can serve as a signature for clinical cancer diagnosis.

Precise Identification and Profiling of Surface Proteins of Ultra Rare Tumor Specific Extracellular Vesicle with Dynamic Quantitative Plasmonic Imaging.

Specific detection of tumor-derived EVs (tEVs) in plasma is complicated by nontumor EVs and non-EV particles. To accurately identify tEVs and profile their surface protein expression at single tEV resolution directly with clinical plasma is still an unmet need. Here, we present a Dynamic Immunoassay for Single tEV surface protein Profiling (DISEP), a kinetic assay based on surface plasmon resonance microscopy (SPRM) for specific single tEV profiling. DISEP adopts a pair of low-affinity oligonucleotide probes to respectively label EV surface proteins and functionalize an SPRM biosensor interface. tEVs labeled with the oligonucleotide probes possess distinctive binding kinetics from nonspecific particles in plasma, which permits accurate digital plasmonic counting of single EVs. We demonstrate DISEP for recognizing target EVs among 350-fold background plasma particles with high sensitivity (4677 EVs per µL). Clinical plasma samples were analyzed to discriminate between pancreatic cancer patients (n = 40) and healthy donors (n = 45). With a panel of biomarker signatures (EpCAM, HER2, and GPC1), DISEP only requires 10 µL primary sample from each donor to classify tumor patients with an area under the curve of 0.98. DISEP provides a highly specific EV detection and surface protein profiling strategy for early cancer diagnosis.

Profiling extracellular vesicle surface proteins with 10 µL peripheral plasma within 4 h.

Extracellular vesicle (EV) surface proteins, expressed by primary tumours, are important biomarkers for early cancer diagnosis. However, the detection of these EV proteins is complicated by their low abundance and interference from non-EV components in clinical samples. Herein, we present a MEmbrane-Specific Separation and two-step Cascade AmpLificatioN (MESS2CAN) strategy for direct detection of EV surface proteins within 4 h. MESS2CAN utilises novel lipid probes (long chains linked by PEG2K with biotin at one end, and DSPE at the other end) and streptavidin-coated magnetic beads, permitting a 49.6% EV recovery rate within 1 h. A dual amplification strategy with a primer exchange reaction (PER) cascaded by the Cas12a system then allows sensitive detection of the target protein at 10 EV particles per microliter. Using 4 cell lines and 90 clinical test samples, we demonstrate MESS2CAN for analysing HER2, EpCAM and EGFR expression on EVs derived from cells and patient plasma. MESS2CAN reports the desired specificity and sensitivity of EGFR (AUC = 0.98) and of HER2 (AUC = 1) for discriminating between HER2-positive breast cancer, triple-negative breast cancer and healthy donors. MESS2CAN is a pioneering method for highly sensitive in vitro EV diagnostics, applicable to clinical samples with trace amounts of EVs.

Quantitative Amplitude and Phase Imaging with Interferometric Plasmonic Microscopy.

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.

Overshadow Effect of Psl on Bacterial Response to Physiochemically Distinct Surfaces Through Motility-Based Characterization.

Biofilms of Pseudomonas aeruginosa are ubiquitously found on surfaces of many medical devices, which are the major cause of hospital-acquired infections. A large amount of work has been focused on bacterial attachment on surfaces. However, how bacterial cells evolve on surfaces after their attachment is the key to get better understanding and further control of biofilm formation. In this work, by employing both single-cell- and collective-motility of cells, we characterized the bacterial surface movement on physiochemically distinct surfaces. The measurement of cell surface motility showed consistent results that gold and especially platinum surfaces displayed a stronger capability in microcolony formation than polyvinyl chloride and polycarbonate surfaces. More interestingly, we found that overproduction of Psl led to a narrower variance in cell surface motility among tested surfaces, indicating an overshadow effect of Psl for bacteria by screening the influence of physicochemical properties of solid surfaces. Our results provide insights into how Pseudomonas aeruginosa cells adapt their motion to physiochemically distinct surfaces, and thus would be beneficial for developing new anti-biofouling techniques in biomedical engineering.