Single-Particle Assay of Poly(ADP-ribose) Polymerase-1 Activity with Dark-Field Optical Microscopy

Innovative Progress of LSPR-Based Dark-Field Scattering Spectral Imaging in the Biomedical Assay at the Single-Particle Level

The growing demand for detection and sensing in the biomedical field is placing higher demands on technology. In clinical testing, it is expected to be able to realize both rapid large-field imaging and analysis of single particles (or single molecules or single cells), and it is expected to be able to grasp both the unique individuality of single particles in time and space during the complex reaction process, as well as the regular correlation between single particles in the same population distribution. Supported and promoted by the theory of localized surface plasmon resonance (LSPR), dark-field microscopy, as a single-particle optical imaging technique with a very high signal-to-noise ratio, provides a powerful new means to address the above clinical detection needs. This review will focus on the innovative applications of dark-field microscopy in biomedical-related assays in the past five years, introducing the basic principles and listing the impressing works. We also summarize how dark-field microscopy has been combined with other techniques, including surface-enhanced Raman scattering, fluorescence, colorimetry, electrochemistry, etc., to witness the joint progress and promotion of detection methods in the future. It also provides an outlook on the current challenges and future trends in this field.

A label-free electrochemical biosensor for sensitive analysis of the PARP-1 activity

Early diagnosis of tumors is becoming increasingly important in modern healthcare. As studies have demonstrated, Poly(ADP)ribose polymerase-1 (PARP-1) is overexpressed in more aggressive tumors. Consequently, sensitive detection of PARP-1 activity holds significant practical importance in clinical diagnostics and biomedical research. Herein, an electrochemical biosensor for the sensitive monitoring of the PARP-1 activity have been proposed. The presence of target PARP-1 firstly triggers enzyme-initiated auto-PARylation and formed negatively charged polymer consisting of a few to 200 ADP-ribose units. Due to electrostatic adsorption, negatively charged PAR will bind with a large number of positively charged methylene blue (MB) electroactive molecules. By detecting the electrochemical signal of MB on the indium tin oxide (ITO) electrode, PARP-1 activity detection was achieved with a linear detection range of 0-1.0 U and a detection limit as low as 0.003 U. The proposed biosensor shows great prospects of clinical application.

Ultrasensitive Detection of Cancer Biomarkers Using Photonic-Crystal-Enhanced Single-Molecule Imaging

The rapid and sensitive quantification of low-abundance protein markers holds immense significance in early disease diagnosis and treatment. Single-molecule fluorescence imaging exhibits very high detection sensitivity and thus has great application potential in this area. The single-molecule signal, however, is often susceptible to interference from background noise due to its inherently weak intensity. A variety of signal amplification techniques based on cascading reactions have been developed to improve the signal-to-noise ratio of single-molecule imaging. Nevertheless, the operation of these methods is typically complicated and time-consuming, which limits the clinical application. Herein, we introduce an enzyme-free, photonic-crystal-based single-molecule (PC-SM) biochip for cost-effective, time-efficient, and ultrasensitive detection of disease markers. The PC-SM biochip can enhance the signal-to-noise ratio of single molecules by nearly 3-fold compared with unamplified samples, through coupling of the single-molecule photon energy with the optical band gap of the photonic crystal. We used the PC-SM biochip to detect the low-abundance leukemia inhibitory factor in the blood of pancreatic cancer patients and healthy people and achieved a detection limit of 2.0 pg/L and an AUC of 0.9067. The method exhibits exceptional sensitivity and specificity, showing great application potential in various clinical settings.

Streptavidin-Conjugated DNA for the Boronate Affinity-Based Detection of Poly(ADP-Ribose) Polymerase-1 with Improved Sensitivity

This work reports the development of a fluorescence method for the detection of poly(ADP-ribose) polymerase-1 (PARP1), in which a phenylboronic acid-modified fluorescein isothiocyanate dye (FITC-PBA) was used to recognize the formed poly(ADP-ribose) (PAR) polymer. The detection system was designed by conjugating recombinant streptavidin (rSA) with PARP1-specific double-stranded DNA (dsDNA) through streptavidin-biotin interaction. Capture of PARP1 via rSA-biotin-dsDNA allowed for the poly-ADP-ribosylation (PARylation) of both rSA and PARP1 in a homogeneous solution. The resulting rSA-biotin-dsDNA/PAR conjugates were then captured and separated via the commercialized nitrilotriacetic acid-nickel ion-modified magnetic bead (MB-NTA-Ni) through the interaction between NTA-Ni on MB surface and oligohistidine (His₆) tag in rSA. The PAR polymer could capture the dye of FITC-PBA through the borate ester interaction between the boronic acid moiety in PBA and the cis-diol group in ribose, thus causing a decrease in fluorescence signal. The PARylation of streptavidin and the influence of steric hindrance on PARylation efficiency were confirmed using reasonable detection strategies. The method showed a wide linear range (0.01~20 U) and a low detection limit (0.01 U). This work should be valuable for the development of novel biosensors for the detection of poly(ADP-ribose) polymerases and diol-containing species.

Plasmonic Nanomaterials in Dark Field Sensing Systems

Plasma nanoparticles offer promise in data storage, biosensing, optical imaging, photoelectric integration, etc. This review highlights the local surface plasmon resonance (LSPR) excitation mechanism of plasmonic nanoprobes and its critical significance in the control of dark-field sensing, as well as three main sensing strategies based on plasmonic nanomaterial dielectric environment modification, electromagnetic coupling, and charge transfer. This review then describes the component materials of plasmonic nanoprobes based on gold, silver, and other noble metals, as well as their applications. According to this summary, researchers raised the LSPR performance of composite plasmonic nanomaterials by combining noble metals with other metals or oxides and using them in process analysis and quantitative detection.

Plasmonic scattering imaging of single Cu2-xSe nanoparticle for Hg2+ detection

The development of new efficient contrast nanoprobe has been greatly concerned in the field of scattering imaging for sensitive and accurate detection of trace analytes. In this work, the non-stoichiometric Cu_2-xSe nanoparticle with typical localized surface plasmon resonance (LSPR) properties originating from their copper deficiency as a plasmonic scattering imaging probe was developed for sensitive and selective detection of Hg²⁺ under dark-field microscopy. Hg²⁺ can compete with Cu(I)/Cu(II) which were sources of optically active holes coexisting in these Cu_2-xSe nanoparticles for its higher affinity with Se^2-. The plasmonic properties of Cu_2-xSe were adjusted effectively. Thus, the color scattering images of Cu_2-xSe nanoparticles was changed from blue to cyan, and the scattering intensity was obviously enhanced with the dark-field microscopy. There was a linear relationship between the scattering intensity enhancement and the Hg²⁺ concentration in the range of 10-300 nM with a low detection limit of 1.07 nM. The proposed method has good potential for Hg²⁺ detection in the actual water samples. This work provides a new perspective on applying new plasmonic imaging probe for the reliable determination of trace heavy metal substances in the environment at a single particle level.

Ag NPs/PMMA nanocomposite as an efficient platform for fluorescence regulation of riboflavin

The fluorescence detection platform has broad application in many fields. In this paper, we report a simple and efficient fluorescence detection platform based on the synergistic effects of Ag nanoparticles (Ag NPs) and polymethylmethacrylate (PMMA). Ag NPs were introduced to realize the plasmon enhancement fluorescence and a thin PMMA layer was used to adjust the distance between Ag NPs and riboflavin. The thin PMMA layer not only enhances the fluorescence by enhancing adhesion of substrate, but also optimizes the plasmon enhancement fluorescence effect by serving as the spacer. The fluorescence enhancement factor based on this platform shows a trend of increasing with the decrease of the concentration of riboflavin, and the detection of riboflavin is realized based on this feature, the lowest detectable concentration is as low as 0.27 µM. In addition to the detection based on plasmon enhancement fluorescence, the detection of riboflavin at low concentrations can also be realized by the shift and broadening of the fluorescence peak due to the Ag NPs. The combination of the two ways of plasmon enhancement fluorescence and shift of the fluorescence spectra is used for the detection of riboflavin. These results show that the platform has great potential applications in the field of detection and sensing.

Highly sensitive visual colorimetric sensor for xanthine oxidase detection by using MnO2-nanosheet-modified gold nanoparticles

In this study, a highly sensitive colorimetric assay has been constructed for the determination of xanthine oxidase (XOD) activity by the GNP@MnO₂ core-shell nanoparticles as probe. In the presence of XOD, xanthine can be oxidized to produce H₂O₂, which makes the MnO₂ shell fallen off. With the single particle detection (SPD) based dark field microscopy (DFM), the scattering color of GNP@MnO₂ NP probe shows obvious change before and after etching process. At the single particle level, noticeable color change of the single probe can be easily detected in the existence of trace XOD. This SPD-based colorimetric strategy displays broad linear dynamic range (0.02-4 mU/mL) and low detection limit of 7.82 μU/mL, which is more sensitive than the results from ensemble sample measurement. In addition, we tested the inhibitory effect of quercetin on the activity of XOD and obtained good inhibition effect. As a consequence, this SPD-based colorimetric strategy provides new perception for the ultrasensitive detection of molecules in complex system.

High Resolution of Plasmonic Resonance Scattering Imaging with Deep Learning

The dark-field microscopy (DFM) imaging technology has the advantage of a high signal-to-noise ratio, and it is often used for real-time monitoring of plasmonic resonance scattering and biological imaging at the single-nanoparticle level. Due to the limitation of the optical diffraction limit, it is still a challenging task to accurately distinguish two or more nanoparticles whose distance is less than the diffraction limit. Here, we propose a computational strategy based on a deep learning framework (NanoNet), which will realize the effective segmentation of the scattered light spots in diffraction-limited DFM images and obtain high-resolution plasmonic light scattering imaging. A small data set of DFM and the corresponding scanning electron microscopy (SEM) image pairs are used to learn for obtaining a highly resolved semantic imaging model using NanoNet, and thus highly resolved DFM images matching the resolution of those acquired using SEM can be obtained. Our method has the ability to transform diffraction-limited DFM images to highly resolved ones without adding a complex optical system. As a proof of concept, a highly resolved DFM image of living cells through the NanoNet technique is successfully made, opening up a new avenue for high-resolution optical nanoscopic imaging.

Weak Reaction Scatterometry of Plasmonic Resonance Light Scattering with Machine Learning

Weak reactions are usually overlooked due to weak detectable features and susceptibility to interference from noise signals. Strategies for detecting weak reactions are essential for exploring reaction mechanisms and exploiting potential applications. Machine learning has recently been successfully used to identify patterns and trends in the data. Here, it is demonstrated that machine learning-based techniques can offer accurate local surface plasmon resonance (LSPR) scatterometry by improving the precision of the plasmonic scattering imaging in weak chemical reactions. Dark-field microscopy (DFM) imaging technique is the most effective method for high-sensitivity plasmonic nanoparticles LSPR scatterometry. Unfortunately, deviations caused by the instrument and operating errors are inevitable, and it is difficult to effectively detect the presence of weak reactions. Thus, introducing a machine learning calibration model to automatically calibrate the scattering signal of the nanoprobe in the reaction process can greatly improve the confidence of LSPR scatterometry under DFM imaging and allow DFM imaging to effectively monitor unobvious or weak reactions. By this approach, the weak oxidation of silver nanoparticles (AgNPs) in water by dissolved oxygen was successfully monitored. Moreover, a trivial reaction between AgNPs and mercury ions was detected in a dilute mercury solution with a concentration greater than 1.0 × 10^-10 mol/L. These results suggest the great potential of the combination of LSPR scatterometry and machine learning as a method for imaging analysis and intelligent sensing.

Automated Plasmonic Resonance Scattering Imaging Analysis via Deep Learning

Plasmonic nanoparticles, which have excellent local surface plasmon resonance (LSPR) optical and chemical properties, have been widely used in biology, chemistry, and photonics. The single-particle light scattering dark-field microscopy (DFM) imaging technique based on a color-coded analytical method is a promising approach for high-throughput plasmonic nanoparticle scatterometry. Due to the interference of high noise levels, accurately extracting real scattering light of plasmonic nanoparticles in living cells is still a challenging task, which hinders its application for intracellular analysis. Herein, we propose an automatic and high-throughput LSPR scatterometry technique using a U-Net convolutional deep learning neural network. We use the deep neural networks to recognize the scattering light of nanoparticles from background interference signals in living cells, which have a dynamic and complicated environment, and construct a DFM image semantic analytical model based on the U-Net convolutional neural network. Compared with traditional methods, this method can achieve higher accuracy, stronger generalization ability, and robustness. As a proof of concept, the change of intracellular cytochrome c in MCF-7 cells under UV light-induced apoptosis was monitored through the fast and high-throughput analysis of the plasmonic nanoparticle scattering light, providing a new strategy for scatterometry study and imaging analysis in chemistry.