Automatic Enumeration of Gold Nanomaterials at the Single-Particle Level

High Confidence Single Particle Analysis with Machine Learning

Single particle analysis can effectively determine the heterogeneity between particles based on the local information on a single particle, which is utilized extensively for monitoring chemical reactions and biological activities. However, the study of obtaining ensemble reaction information at the single particle level, which can obtain both the structural and functional heterogeneity of particles as well as the ensemble reaction information, is challenging because the selection of a single particle mainly depends on experience, which will lead to a certain randomness when analyzing the ensemble reaction with single particles. Using machine learning, it is demonstrated that the proposed intelligent single particle analysis strategy can provide single particle and ensemble analyses with high confidence. Convolutional neural network and Gaussian mixture model were utilized to develop a machine learning model for resonance scattering imaging analysis of plasmonic nanoparticles. It can identify the scattered light of single particles and select representative or diverse particles. When single particle scattering imaging is used to obtain ensemble information on the reaction, the error caused by the selection of individual particles can be significantly reduced by selecting representative particles. In addition, the real situation of the reaction can be better revealed by selecting diverse particles. These results indicate that the intelligent single particle analysis strategy has great potential for imaging analysis and biological sensing.

Single-microbead space-confined digital quantification strategy (SMSDQ) for counting microRNAs at the single-molecule level

Quantification of microRNAs (miRNAs) at the single-molecule level is of great significance for clinical diagnostics and biomedical research. The challenges lie in the limits to transforming single-molecule measurements into quantitative signals. To address these limits, here, we report a new approach called a Single Microbead-based Space-confined Digital Quantification (SMSDQ) to measure individual miRNA molecules by counting gold nanoparticles (AuNPs) with localized surface plasmon resonance (LSPR) light-scattering imaging. One miRNA target hybridizes with the alkynyl-modified capture DNA probe immobilized on a microbead (60 μm) and the azide-modified report DNA probe anchored on AuNP (50 nm), respectively. Through the click reaction between the alkynyl and azide group, a single microbead can covalently link the AuNPs in the confined space within the view of the microscope. By digitally counting the light-scattering spots of AuNPs, we demonstrated the proposed approach with single-molecule detection sensitivity and high specificity of single-base discrimination. Taking the advantages of ultrahigh sensitivity, specificity, and the digital detection manner, the approach is suitable for evaluating cell heterogeneity and small variations of miRNA expression and has been successfully applied to direct quantification of miRNAs in one-tenth single-cell lysates and serum samples without RNA-isolated and nucleic acid amplification steps.

Nucleic Acids Detection for Mycobacterium tuberculosis Based on Gold Nanoparticles Counting and Rolling-Circle Amplification

Tuberculosis (TB) is a common infectious disease caused by Mycobacterium tuberculosis, which usually disturbs the lungs, and remains the second leading cause of death from an infectious disease worldwide after the human immunodeficiency virus. Herein, we constructed a simple and sensitive method for Mycobacterium tuberculosis-specific DNA detection with the dark-field microscopic imaging of gold nanoparticles (AuNPs) counting strategy and rolling-circle amplification (RCA). Taking advantage of RCA amplification, one target molecule produced hundreds of general oligonucleotides, which could form the sandwich structure with capture-strand-modified magnetic beads and AuNPs. After magnetic separation, AuNPs were released and detected by dark-field imaging; about 10 fM Mycobacterium tuberculosis-specific DNA target can still be differentiated from the blank. No significant change of the absorbance signals was observed when the target DNA to genomic DNA ratio (in mass) was from 1:0 to 1:10⁶. The spike recovery results in genomic DNA from human and Klebsiella pneumoniae suggested that the proposed method has the feasibility for application with biological samples. This proposed method is performed on an entry-level dark-field microscope setup with only a 6 μL detection volume, which creates a new, simple, sensitive, and valuable tool for pathogen detection.

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.

An algorithm-assisted automated identification and enumeration system for sensitive hydrogen sulfide sensing under dark field microscopy

A sensitive H2S sensing strategy has been developed based on the automated identification and enumeration algorithm.

Transpeptidation-mediated single-particle imaging assay for sensitive and specific detection of sortase with dark-field optical microscopy

Transpeptidation of surface proteins catalyzed by the transpeptidase sortase plays a critical role in the infection process of Gram-positive pathogen. Monitoring sortase activity and screening its inhibitors are of great significance to fundamental understanding of the infection mechanism and pharmaceutical development. Herein, we developed a digital single-particle imaging method to quantify sortase A (SrtA) activity based on transpeptidation-mediated assembly and enumeration of gold nanoparticles (GNPs). The assay utilizes two peptide stands, in which one has the SrtA recognition sequence LPXTG motif while the other carries an oligoglycine nucleophile at the one end and a biotin group at the other. The presence of SrtA enables the ligation of two peptides and allows for the immobilization of streptavidin-functionalized GNPs. Thus, SrtA activity can be quantified by imaging and enumeration of the surface-assembled GNPs at the single-particle level via dark-field microscopy. The single-particle method was highly sensitive to SrtA activity with a low detection limit of 7.9 pM and a wide linear dynamic range from 0.05 to 50 nM. Besides detection of SrtA in complex biological samples such as Gram-positive pathogen lysates, the proposed method was also successfully applied to estimate the half-maximal inhibitory concentration (IC₅₀) values of SrtA inhibitors (curcumin, berberine hydrochloride and quercetin). The present method, combining single-GNP counting and dark-field imaging, provides a facile and novel analytical tool for SrtA activity and its inhibitor screening.

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.

Colocalized Particle Counting Platform for Zeptomole Level Multiplexed Quantification

For multiplexed detection, it is important yet challenging to simultaneously meet the requirement of sensitivity, throughput, and implementation convenience for practical applications. Using the detection of DNAs and miRNAs for illustration, we present a colocalized particle counting platform that can realize the separation-free multiplexed detection of 6 nucleic acid targets with a zeptomole sensitivity and a dynamic range of up to 5 orders of magnitude. The presence of target induces the formation of a sandwich nanostructure via hybridization; thus, there is an occurrence of colocalization of two microbeads with two different colors. The sequence specific coding is realized by an arbitrary combination of two fluorescence channels with different emitting colors. The platform presents robustness in detecting multiple nucleic acid targets with a minimal cross talk and matrix effect as well as the ability to distinguish the specific miRNA from members of the same family. The results of simultaneous detection of 3 miRNAs in 3 different cell lines present straight consistency with that of the standard qRT-PCR. This platform can be adapted to other multiplexing designs such as the "turn-off" mode, in which the proportion of colocalized microbeads is decreased due to the strand-displacement reaction initiated by the specific target. This separation-free platform offers the possibility to achieve the on-site multiplexed detection with compatibility to different experimental designs and extensibility to other signal sources for enumeration.

Automated Nanoplasmonic Analysis of Spherical Nucleic Acids Clusters in Single Cells

Spherical nucleic acids (SNAs) have been extensively used in the field of biosensing, drug delivery, and theranostics. Precise engineering of SNAs and their clinical application require better understanding of their cellular internalization process. We demonstrate a colorimetry-based algorithm that can analyze the aggregation states of SNAs clusters on the basis of the changes of plasmonic colors of SNAs. The dark-field microscopy (DFM) images of cytoplasmic region of single cells are imported as raw data. All the image spots are analyzed in the interference reduction process, and the clustering states of target image spots are assigned on the basis of the distribution of coordinates of all the pixels in the CIE map. This method provides faster analysis on clustering states of extracellular and intracellular SNAs with good accuracy. Moreover, the clustering states of SNAs in 20 single cells (generally >1000) can be efficiently distinguished within 200 s. Therefore, our method provides an automatic, quantitative, objective, and repeatable way to analyze SNAs aggregations, and shows good application potential in robust and quantitative nanoplasmonic analysis in single cells.

Optical aptasensing of mercury(II) by using salt-induced and exonuclease I-induced gold nanoparticle aggregation under dark-field microscope observation

An optical method for determination of Hg(II) is described that exploits the aggregation of gold nanoparticles (AuNPs) under dark-field microscope (DFM) observation. This assay is based on the use of a Hg(II)-specific aptamer, AuNPs modified with complementary DNA strands, and exonuclease I (Exo I). In the absence of Hg(II), the added dsDNA prevents salt-induced aggregation of the green-colored AuNPs. If Hg(II) is added, the aptamer will capture it to form T-Hg(II)-T pairs, and the complementary strand is digested by Exo I. On addition of a solution of NaCl, the AuNPs will aggregate. This is accompanied by a color change from green to orange/red) in the dark-field image. By calculating the intensity of the orange/red dots in the dark-field image, concentration of Hg(II) can be accurately determined. The limit of detection is as low as 36 fM, and response is a linear in the 83 fM to 8.3 μM Hg(II) concentration range. Graphical abstract Schematic representation of a colorimetric assay for Hg(II) based on the use of a mercury(II)-specific aptamer, gold nanoparticles modified with complementary DNA strands, and exonuclease I.

Colorimetric ultrasensitive detection of DNA based on the intensity of gold nanoparticles with dark-field microscopy

We present an ultrasensitive colorimetric nucleic acid assay based on the intensity of gold nanoparticles (Au NPs) using dark field microscopy. In the absence of target DNA, two hairpin-like DNA strands with protruding single-stranded DNA (ssDNA) can be absorbed onto the Au NP surface via non-covalent interactions between the exposed nitrogen bases of ssDNA and Au NPs, which inhibits Au NPs from aggregating in a high concentration of salt media, while in the presence of target DNA, two hairpin DNA strands hybridize with target DNA to form double-stranded DNA (dsDNA). After hybridization chain reaction (HCR) amplification, rigid dsDNA polymers are formed, which results in serious Au NP aggregation in the salt environment. By measuring the intensity change of yellow and red dots on a dark-field image, the concentration of target DNA can be accurately quantified with a limit of detection (LOD) of 66 fM.

Time-resolved visual detection of heparin by accelerated etching of gold nanorods

Plasmonic gold nanorods are promising and sensitive light scattering probes, which can reach the single particle level. Herein, we present the light scattering properties of gold nanorods for time-resolved visual detection of heparin based on the rapid etching of gold nanorods under dark-field microscopy.

A galvanic exchange process visualized on single silver nanoparticles via dark-field microscopy imaging

The study of the galvanic exchange (GE) mechanism is beneficial for designing and developing new bimetallic nanocrystal structures with excellent bifunctional catalytic properties. Herein, we have visually demonstrated a GE process by real-time monitoring of the reaction between silver nanoparticles (AgNPs) and Au3+ at the single nanoparticle level using light scattering dark-field microscopy imaging. The localized surface plasmon resonance (LSPR) scattering spectral shifts of the AgNPs which reveal the Ag removal rate and Au deposition rate on the surface of the AgNPs can be observed. Furthermore, a pixel meta three color channel method has been introduced for analyzing the scattering light color changes of plasmonic particles to reveal the kinetics of the atomic deposition process on a single AgNP during GE, thus making the reaction kinetics of the GE process directly observable. Therefore, this study provides an efficient and promising approach for understanding the GE mechanism and exploiting its reaction kinetics.

The aptamer-thrombin-aptamer sandwich complex-bridged gold nanoparticle oligomers for high-precision profiling of thrombin by dark field microscopy

We present a simple and efficient colorimetric assay strategy for ultrasensitive visual detection of human α-thrombin, which is essentially based on the formation of the DNA1-thrombin-DNA2 sandwich complex-bridged gold nanoparticle (Au NP) oligomers. Unlike the traditional colorimetric sensing strategies which induced the nanoparticle aggregates with uncontrolled aggregate size. In this work, the DNA1with rich G bases was firstly conjugated on the surfaces of Au NPs fixed on the hexadecyl trimethylammonium bromide (CTAB)-coated glass slide, and thrombin was captured by the DNA1. Then, the other DNA2 with rich G bases interacted with the former DNA1-thrombin complex and formed a DNA1-thrombin-DNA2 sandwich complex. The subsequently added Au NPs can be bound to the Au NP-DNA1-thrombin-DNA2 via Au-S bond to trigger the formation of Au NP oligomers, an apparent color change of the single Au NPs from green to yellow and red was observed under dark field microscopy. By measuring the intensity change of the yellow and red Au NPs, the concentration of target thrombin could be accurately quantified. As a proof of concept experiment, the formation of Au NP oligomers resulted in significantly improved sensitivity (10 fM of limit of detection and 20 fM of limit of quantity) and wider linear dynamic range of thrombin detection (20 fM-20 nM), the relative standard deviation (RSD) was less than 5.73% (n = 5). In addition, in order to validate the potential application in clinical diagnosis, the content of thrombin in a human serum samples was also quantified.

Digital quantification of DNA by mapping polarization degree related with coding gold nanorods

The development of highly sensitive and low-cost methods for detecting DNA is of critical importance. Here, we describe a strategy for the highly sensitive and low-cost digital detection of target DNA. Individual DNA molecules were encoded with a single gold nanorod (Au NR), which was then separated and enriched using the magnetic immune-separation process, followed by dehybridization and dispersion into a buffer solution and immobilization on glass slides for polarized dark-field microscopic imaging. With the imaging we can get the first three data sets of the Stokes vector, and the experimental degree of the linear polarization of the light scattered by the Au NR was obtained. Using the Monte Carlo simulation program, the Muller matrix of the Au NRs was simulated and the simulated degree of the linear polarization was calculated to be 0.58. Based on the experimental and simulated degree of the linear polarization, the Au NRs were identified and quantified with an in-house Matlab program, and the concentration of the target DNA at the femtomolar level was therefore achieved.

Color resolution improvement of the dark-field microscopy imaging of single light scattering plasmonic nanoprobes for microRNA visual detection

Imaging of light scattering plasmonic nanoparticles (PNPs) with the aid of the dark-field microscopy imaging (iDFM) technique has attracted wide attention owing to its high signal-to-noise ratio, but to improve the color resolution and contrast of dark-field microscopy (DFM) images of single light scattering PNPs in a small spectral variation environment is still a challenge. In this study, a new color analytical method for resolving the resolution and contrast in DFM images has been developed and further applied for colorimetric analysis using the digital image processing technique. The color of single light scattering PNP images is automatically coded at first with the hue values of the HSI color model, and then amplified using the MATLAB program even for marginal spectral changes, leading to significant improvement of the color resolution of DFM images and easy detection with the naked eye. As a proof of concept, this method is then applied to distinguish single PNPs with various sizes and to visually detect hepatocellular carcinoma-associated microRNA. As it greatly improved the color resolution of iDFM and its detection sensitivity, this method shows promise to serve as a better alternative for sensitive visual analysis and spectrometer-based spectral analysis.

Analytical methods based on the light-scattering of plasmonic nanoparticles at the single particle level with dark-field microscopy imaging

This minireview summarizes analytical methods based on the light-scattering of gold nanoparticles with the dark-field microscopy imaging technique at the single particle level.

A universal and enzyme-free immunoassay platform for biomarker detection based on gold nanoparticle enumeration with a dark-field microscope

We develop a universal and enzyme-free magnetic bead-based sandwich-format immunoassay platform for biomarker detection by combining secondary antibody functionalized AuNPs and automatic AuNP counting.

Noise Reduction Method for Quantifying Nanoparticle Light Scattering in Low Magnification Dark-Field Microscope Far-Field Images

Nanoparticles have become a powerful tool for cell imaging and biomolecule, cell and protein interaction studies, but are difficult to rapidly and accurately measure in most assays. Dark-field microscope (DFM) image analysis approaches used to quantify nanoparticles require high-magnification near-field (HN) images that are labor intensive due to a requirement for manual image selection and focal adjustments needed when identifying and capturing new regions of interest. Low-magnification far-field (LF) DFM imagery is technically simpler to perform but cannot be used as an alternate to HN-DFM quantification, since it is highly sensitive to surface artifacts and debris that can easily mask nanoparticle signal. We now describe a new noise reduction approach that markedly reduces LF-DFM image artifacts to allow sensitive and accurate nanoparticle signal quantification from LF-DFM images. We have used this approach to develop a "Dark Scatter Master" (DSM) algorithm for the popular NIH image analysis program ImageJ, which can be readily adapted for use with automated high-throughput assay analyses. This method demonstrated robust performance quantifying nanoparticles in different assay formats, including a novel method that quantified extracellular vesicles in patient blood sample to detect pancreatic cancer cases. Based on these results, we believe our LF-DFM quantification method can markedly decrease the analysis time of most nanoparticle-based assays to impact both basic research and clinical analyses.

Plasmon-induced light concentration enhanced imaging visibility as observed by a composite-field microscopy imaging system

The plasmon-induced light concentration (PILC) effect, which has been supposed to be responsible for lots of linear and nonlinear enhanced optical signals such as Raman and high-harmonic generation, is hard to directly observe. Herein, we developed a scattered light based composite-field microscopy imaging (iCFM) system by coupling the oblique and vertical illumination modes, which were adopted in dark- and bright-field microscopy imaging systems, respectively, and through which iCFM system monochromatic background (MCB) images are available, to directly observe the PILC effect in far-field scattering microscopy imaging. Owing to the PILC effect, the scattering signal gain of plasmonic nanoparticles was found to be larger than that of the background, and the imaging visibility of plasmonic nanoparticles was improved by 2.4-fold for silver nanoparticles (AgNPs) and 1.6-fold for gold nanorods (AuNRs). Successful observation of the PILC effect visually together with application in enhanced visibility in cancer cell imaging by this composite illumination system might open an exciting prospect of light scattering microscopy imaging techniques with largely increased visibility.

HSI colour-coded analysis of scattered light of single plasmonic nanoparticles

Single plasmonic nanoparticles (PNPs) analysis with dark-field microscopic imaging (iDFM) has attracted much attention in recent years. The ability for quantitative analysis of iDFM is critical, but cumbersome, for characterizing and analyzing the scattered light of single PNPs. Here, a simple automatic HSI colour coding method is established for coding dark-field microscopic (DFM) images of single PNPs with localized surface plasmon resonance (LSPR) scattered light, showing that hue value in the HSI system can realize accurate quantitative analysis of iDFM and providing a novel approach for quantitative chemical and biochemical imaging at the single nanoparticle level.

Precision improvement in dark-field microscopy imaging by using gold nanoparticles as an internal reference: a combined theoretical and experimental study

Low accuracy is a big obstacle in the dark-field microscopy imaging (iDFM) technique in practical applications. In order to reduce the deviations and fluctuations in the observed or snapped scattered light in the iDFM technique caused by unavoidable measurement errors, bare gold nanoparticles (AuNPs) were introduced as an internal reference (IR). The feasibility of using AuNPs as the IR in iDFM in theory was verified. The function of the IR in improving the precision of the acquired data through post data analysis was identified by three kinds of experiments: monitoring the oxidation process of silver nanoparticles (AgNPs) at room temperature, quantifying the level of glucose with AgNPs used as probes and quantifying the change in the light intensity of AuNPs after the plasmon resonance energy transfer (PRET) between AuNPs and tetramethylrhodamine (TAMRA).