Beating the reaction limits of biosensor sensitivity with dynamic tracking of single binding events

A Label-free Optical Biosensor-Based Point-of-Care Test for the Rapid Detection of Monkeypox Virus

Diagnostic approaches that combine the high sensitivity and specificity of laboratory-based digital detection with the ease of use and affordability of point-of-care (POC) technologies could revolutionize disease diagnostics. This is especially true in infectious disease diagnostics, where rapid and accurate pathogen detection is critical to curbing the spread of disease. We have pioneered an innovative label-free digital detection platform that utilizes Interferometric Reflectance Imaging Sensor (IRIS) technology. IRIS leverages light interference from an optically transparent thin film, eliminating the need for complex optical resonances to enhance the signal by harnessing light interference and the power of signal averaging in shot-noise-limited operation to achieve virtually unlimited sensitivity. In our latest work, we have further improved our previous 'Single-Particle' IRIS (SP-IRIS) technology by allowing the construction of the optical signature of target nanoparticles (whole virus) from a single image. This new platform, 'Pixel-Diversity' IRIS (PD-IRIS), eliminated the need for z-scan acquisition, required in SP-IRIS, a time-consuming and expensive process, and made our technology more applicable to POC settings. Using PD-IRIS, we quantitatively detected the Monkeypox virus (MPXV), the etiological agent for Monkeypox (Mpox) infection. MPXV was captured by anti-A29 monoclonal antibody (mAb 69-126-3) on Protein G spots on the sensor chips and were detected at a limit-of-detection (LOD) - of 200 PFU/ml (~3.3 attomolar). PD-IRIS was superior to the laboratory-based ELISA (LOD - 1800 PFU/mL) used as a comparator. The specificity of PD-IRIS in MPXV detection was demonstrated using Herpes simplex virus, type 1 (HSV-1), and Cowpox virus (CPXV). This work establishes the effectiveness of PD-IRIS and opens possibilities for its advancement in clinical diagnostics of Mpox at POC. Moreover, PD-IRIS is a modular technology that can be adapted for the multiplex detection of pathogens for which high-affinity ligands are available that can bind their surface antigens to capture them on the sensor surface.

Classification of Molecular Binding Traces for Dynamic Single-Molecule Sensing

Interference from nonspecific binding imposes a fundamental limit in the sensitivity of biosensors that is dependent on the affinity and specificity of the available sensing probes. The dynamic single-molecule sensing (DSMS) strategy allows ultrasensitive detection of biomarkers at the femtomolar level by identifying specific binding according to molecular binding traces. However, the accuracy in classifying binding traces is not sufficient from separate features, such as the bound lifetime. Here, we establish a DSMS workflow to improve the sensitivity and linearity by classifying molecular binding traces in surface plasmon resonance microscopy with multiple kinetic features. The improvement is achieved by correlation analysis to select key features of binding traces, followed by unsupervised k-clustering. The results show that this unsupervised classification approach improves the sensitivity and linearity in microRNA (hsa-miR155-5p, hsa-miR21-5p, and hsa-miR362-5p) detection to achieve a limit of detection at the subfemtomolar level.

A high-dimensional microfluidic approach for selection of aptamers with programmable binding affinities

Aptamers are being applied as affinity reagents in analytical applications owing to their high stability, compact size and amenability to chemical modification. Generating aptamers with different binding affinities is desirable, but systematic evolution of ligands by exponential enrichment (SELEX), the standard for aptamer generation, is unable to quantitatively produce aptamers with desired binding affinities and requires multiple rounds of selection to eliminate false-positive hits. Here we introduce Pro-SELEX, an approach for the rapid discovery of aptamers with precisely defined binding affinities that combines efficient particle display, high-performance microfluidic sorting and high-content bioinformatics. Using the Pro-SELEX workflow, we were able to investigate the binding performance of individual aptamer candidates under different selective pressures in a single round of selection. Using human myeloperoxidase as a target, we demonstrate that aptamers with dissociation constants spanning a 20-fold range of affinities can be identified within one round of Pro-SELEX.

Machine Learning Color Feature Analysis of a High Throughput Nanoparticle Conjugate Sensing Assay

Plasmonic nanoparticles are finding applications within the single molecule sensing field in a "dimer" format, where interaction of the target with hairpin DNA causes a decrease in the interparticle distance, leading to a localized surface plasmon resonance shift. While this shift may be detected using spectroscopy, achieving statistical relevance requires the measurement of thousands of nanoparticle dimers and the timescales required for spectroscopic analysis are incompatible with point-of-care devices. However, using dark-field imaging of the dimer structures, simultaneous digital analysis of the plasmonic resonance shift after target interaction of thousands of dimer structures may be achieved in minutes. The main challenge of this digital analysis on the single-molecule scale was the occurrence of false signals caused by non-specifically bound clusters of nanoparticles. This effect may be reduced by digitally separating dimers from other nanoconjugate types. Variation in image intensity was observed to have a discernible impact on the color analysis of the nanoconjugate constructs and thus the accuracy of the digital separation. Color spaces wherein intensity may be uncoupled from the color information (hue, saturation, and value (HSV) and luminance, a* vector, and b* vector (LAB) were contrasted to a color space which cannot uncouple intensity (RGB) to train a classifier algorithm. Each classifier algorithm was validated to determine which color space produced the most accurate digital separation of the nanoconjugate types. The LAB-based learning classifier demonstrated the highest accuracy for digitally separating nanoparticles. Using this classifier, nanoparticle conjugates were monitored for their plasmonic color shift after interaction with a synthetic RNA target, resulting in a platform with a highly accurate yes/no response with a true positive rate of 88% and a true negative rate of 100%. The sensor response of tested single stranded RNA (ssRNA) samples was well above control responses for target concentrations in the range of 10 aM-1 pM.

Digital detection of proteins

This paper reviews methods for detecting proteins based on molecular digitization, i.e., the isolation and detection of single protein molecules or singulated ensembles of protein molecules. The single molecule resolution of these methods has resulted in significant improvements in the sensitivity of immunoassays beyond what was possible using traditional "analog" methods: the sensitivity of some digital immunoassays approach those of methods for measuring nucleic acids, such as the polymerase chain reaction (PCR). The greater sensitivity of digital protein detection has resulted in immuno-diagnostics with high potential societal impact, e.g., the early diagnosis and therapeutic intervention of Alzheimer's Disease. In this review, we will first provide the motivation for developing digital protein detection methods given the limitations in the sensitivity of analog methods. We will describe the paradigm shift catalyzed by single molecule detection, and will describe in detail one digital approach - which we call digital bead assays (DBA) - based on the capture and labeling of proteins on beads, identifying "on" and "off" beads, and quantification using Poisson statistics. DBA based on the single molecule array (Simoa) technology have sensitivities down to attomolar concentrations, equating to ∼10 proteins in a 200 μL sample. We will describe the concept behind DBA, the different single molecule labels used, the ways of analyzing beads (imaging of arrays and flow), the binding reagents and substrates used, and integration of these technologies into fully automated and miniaturized systems. We provide an overview of emerging approaches to digital protein detection, including those based on digital detection of nucleic acids labels, single nanoparticle detection, measurements using nanopores, and methods that exploit the kinetics of single molecule binding. We outline the initial impact of digital protein detection on clinical measurements, highlighting the importance of customized assay development and translational clinical research. We highlight the use of DBA in the measurement of neurological protein biomarkers in blood, and how these higher sensitivity methods are changing the diagnosis and treatment of neurological diseases. We conclude by summarizing the status of digital protein detection and suggest how the lab-on-a-chip community might drive future innovations in this field.

A high-throughput single-particle imaging platform for antibody characterization and a novel competition assay for therapeutic antibodies

Monoclonal antibodies (mAbs) play an important role in diagnostics and therapy of infectious diseases. Here we utilize a single-particle interferometric reflectance imaging sensor (SP-IRIS) for screening 30 mAbs against Ebola, Sudan, and Lassa viruses (EBOV, SUDV, and LASV) to find out the ideal capture antibodies for whole virus detection using recombinant vesicular stomatitis virus (rVSV) models expressing surface glycoproteins (GPs) of EBOV, SUDV, and LASV. We also make use of the binding properties on SP-IRIS to develop a model for mapping the antibody epitopes on the GP structure. mAbs that bind to mucin-like domain or glycan cap of the EBOV surface GP show the highest signal on SP-IRIS, followed by mAbs that target the GP1-GP2 interface at the base domain. These antibodies were shown to be highly efficacious against EBOV infection in non-human primates in previous studies. For LASV detection, 8.9F antibody showed the best performance on SP-IRIS. This antibody binds to a unique region on the surface GP compared to other 15 mAbs tested. In addition, we demonstrate a novel antibody competition assay using SP-IRIS and rVSV-EBOV models to reveal the competition between mAbs in three successful therapeutic mAb cocktails against EBOV infection. We provide an explanation as to why ZMapp cocktail has higher efficacy compared to the other two cocktails by showing that three mAbs in this cocktail (13C6, 2G4, 4G7) do not compete with each other for binding to EBOV GP. In fact, the binding of 13C6 enhances the binding of 2G4 and 4G7 antibodies. Our results establish SP-IRIS as a versatile tool that can provide high-throughput screening of mAbs, multiplexed and sensitive detection of viruses, and evaluation of therapeutic antibody cocktails.

Label-free single-particle imaging approach for ultra-rapid detection of pathogenic bacteria in clinical samples

Rapid detection of pathogenic bacteria within a few minutes is the key to control infectious disease. However, rapid detection of pathogenic bacteria in clinical samples is quite a challenging task due to the complex matrix, as well as the low abundance of bacteria in real samples. Herein, we employ a label-free single-particle imaging approach to address this challenge. By tracking the scattering intensity variation of single particles in free solution, the morphological heterogeneity can be well identified with particle size smaller than the diffraction limit, facilitating the morphological identification of single bacteria from a complex matrix in a label-free manner. Furthermore, the manipulation of convection in free solution enables the rapid screening of low-abundance bacteria in a small field of view, which significantly improves the sensitivity of single-particle detection. As a proof of concept demonstration, we are able to differentiate the group B streptococci (GBS)-positive samples within 10 min from vaginal swabs without using any biological reagents. This is the most rapid and low-cost method to the best of our knowledge. We believe that such a single-particle imaging approach will find wider applications in clinical diagnosis and disease control due to its high sensitivity, rapidity, simplicity, and low cost.

Attomolar sensitivity microRNA detection using real-time digital microarrays

MicroRNAs (miRNAs) are a family of noncoding, functional RNAs. With recent developments in molecular biology, miRNA detection has attracted significant interest, as hundreds of miRNAs and their expression levels have shown to be linked to various diseases such as infections, cardiovascular disorders and cancers. A powerful and high throughput tool for nucleic acid detection is the DNA microarray technology. However, conventional methods do not meet the demands in sensitivity and specificity, presenting significant challenges for the adaptation of miRNA detection for diagnostic applications. In this study, we developed a highly sensitive and multiplexed digital microarray using plasmonic gold nanorods as labels. For proof of concept studies, we conducted experiments with two miRNAs, miRNA-451a (miR-451) and miRNA-223-3p (miR-223). We demonstrated improvements in sensitivity in comparison to traditional end-point assays that employ capture on solid phase support, by implementing real-time tracking of the target molecules on the sensor surface. Particle tracking overcomes the sensitivity limitations for detection of low-abundance biomarkers in the presence of low-affinity but high-abundance background molecules, where endpoint assays fall short. The absolute lowest measured concentration was 100 aM. The measured detection limit being well above the blank samples, we performed theoretical calculations for an extrapolated limit of detection (LOD). The dynamic tracking improved the extrapolated LODs from femtomolar range to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}∼ 10 attomolar (less than 1300 copies in 0.2 ml of sample) for both miRNAs and the total incubation time was decreased from 5 h to 35 min.

Boosting the detection performance of severe acute respiratory syndrome coronavirus 2 test through a sensitive optical biosensor with new superior antibody

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus emerged in late 2019 leading to the COVID-19 disease pandemic that triggered socioeconomic turmoil worldwide. A precise, prompt, and affordable diagnostic assay is essential for the detection of SARS-CoV-2 as well as its variants. Antibody against SARS-CoV-2 spike (S) protein was reported as a suitable strategy for therapy and diagnosis of COVID-19. We, therefore, developed a quick and precise phase-sensitive surface plasmon resonance (PS-SPR) biosensor integrated with a novel generated anti-S monoclonal antibody (S-mAb). Our results indicated that the newly generated S-mAb could detect the original SARS-CoV-2 strain along with its variants. In addition, a SARS-CoV-2 pseudovirus, which could be processed in BSL-2 facility was generated for evaluation of sensitivity and specificity of the assays including PS-SPR, homemade target-captured ELISA, spike rapid antigen test (SRAT), and quantitative reverse transcription polymerase chain reaction (qRT-PCR). Experimentally, PS-SPR exerted high sensitivity to detect SARS-CoV-2 pseudovirus at 589 copies/ml, with 7-fold and 70-fold increase in sensitivity when compared with the two conventional immunoassays, including homemade target-captured ELISA (4 × 103 copies/ml) and SRAT (4 × 104 copies/ml), using the identical antibody. Moreover, the PS-SPR was applied in the measurement of mimic clinical samples containing the SARS-CoV-2 pseudovirus mixed with nasal mucosa. The detection limit of PS-SPR is calculated to be 1725 copies/ml, which has higher accuracy than homemade target-captured ELISA (4 × 104 copies/ml) and SRAT (4 × 105 copies/ml) and is comparable with qRT-PCR (1250 copies/ml). Finally, the ability of PS-SPR to detect SARS-CoV-2 in real clinical specimens was further demonstrated, and the assay time was less than 10 min. Taken together, our results indicate that this novel S-mAb integrated into PS-SPR biosensor demonstrates high sensitivity and is time-saving in SARS-CoV-2 virus detection. This study suggests that incorporation of a high specific recognizer in SPR biosensor is an alternative strategy that could be applied in developing other emerging or re-emerging pathogenic detection platforms.

Determination of the Highly Sensitive Carboxyl-Graphene Oxide-Based Planar Optical Waveguide Localized Surface Plasmon Resonance Biosensor

This study develops a highly sensitive and low-cost carboxyl-graphene-oxide-based planar optical waveguide localized surface plasmon resonance biosensor (GO-OW LSPR biosensor), a system based on measuring light intensity changes. The structure of the sensing chip comprises an optical waveguide (OW)-slide glass and microfluidic-poly (methyl methacrylate) (PMMA) substrate, and the OW-slide glass surface-modified gold nanoparticle (AuNP) combined with graphene oxide (GO). As the GO has an abundant carboxyl group (–COOH), the number of capture molecules can be increased. The refractive index sensing system uses silver-coated reflective film to compare the refractive index sensitivity of the GO-OW LSPR biosensor to increase the refractive index sensitivity. The result shows that the signal variation of the system with the silver-coated reflective film is 1.57 times that of the system without the silver-coated reflective film. The refractive index sensitivity is 5.48 RIU⁻¹ and the sensor resolution is 2.52 ± 0.23 × 10⁻⁶ RIU. The biochemical sensing experiment performs immunoglobulin G (IgG) and streptavidin detection. The limits of detection of the sensor for IgG and streptavidin are calculated to be 23.41 ± 1.54 pg/mL and 5.18 ± 0.50 pg/mL, respectively. The coefficient of variation (CV) of the repeatability experiment (sample numbers = 3) is smaller than 10.6%. In addition, the affinity constants of the sensor for anti-IgG/IgG and biotin/streptavidin are estimated to be 1.06 × 10⁷ M⁻¹ and 7.30 × 10⁹ M⁻¹, respectively. The result shows that the GO-OW LSPR biosensor has good repeatability and very low detection sensitivity. It can be used for detecting low concentrations or small biomolecules in the future.

The quantum dot vs. organic dye conundrum for ratiometric FRET-based biosensors: which one would you chose?

Förster resonance energy transfer (FRET) is a widely used and ideal transduction modality for fluorescent based biosensors as it offers high signal to noise with a visibly detectable signal. While intense efforts are ongoing to improve the limit of detection and dynamic range of biosensors based on biomolecule optimization, the selection of and relative location of the dye remains understudied. Herein, we describe a combined experimental and computational study to systematically compare the nature of the dye, i.e., organic fluorophore (Cy5 or Texas Red) vs. inorganic nanoparticle (QD), and the position of the FRET donor or acceptor on the biomolecular components. Using a recently discovered transcription factor (TF)-deoxyribonucleic acid (DNA) biosensor for progesterone, we examine four different biosensor configurations and report the quantum yield, lifetime, FRET efficiency, IC50, and limit of detection. Fitting the computational models to the empirical data identifies key molecular parameters driving sensor performance in each biosensor configuration. Finally, we provide a set of design parameters to enable one to select the fluorophore system for future intermolecular biosensors using FRET-based conformational regulation in in vitro assays and new diagnostic devices.

Highly Sensitive Exosome Detection for Early Diagnosis of Pancreatic Cancer Using Immunoassay Based on Hierarchical Surface-Enhanced Raman Scattering Substrate

Exosomes have emerged as potential biomarkers for pancreatic cancer (PaC). However, it is still challenging to get quantitative detection of exosomes with the specific surface receptors. In this study, a highly sensitive detection system is first constructed for the direct quantitation of specific exosomes in real samples using hierarchical surface-enhanced Raman scattering substrate (H-SERS substrate) and rapid enrichment strategy magnetic beads @ exosomes @ SERS detection probes (MEDP). It is found that the detection system (MEDP @ H-SERS substrate) could provide a 3.5 times higher SERS intensity compared with MEDP sandwich immunocomplex only. Moreover, LRG1-positive exosomes (LRG1-Exos) and GPC1-positive exosomes (GPC1-Exos) are chosen to distinguish PaC through exosome proteomics and database screening. The lower limit of detection (LOD) is 15 particles µL-1 using the MEDP @ H-SERS substrate. Significantly, the detection in clinical samples shows that the innovative combination of LRG1-Exos and GPC1-Exos could improve the diagnostic efficiency of PaC, with an area under the operating characteristic curve (AUC) of 0.95. Even for the early-stage PaC, the diagnostic accuracy is still high (AUC = 0.95). Collectively, the findings indicate that the MEDP @ H-SERS substrate has great potential for the early diagnosis of PaC.

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.

Microscopies Enabled by Photonic Metamaterials

In recent years, the biosensor research community has made rapid progress in the development of nanostructured materials capable of amplifying the interaction between light and biological matter. A common objective is to concentrate the electromagnetic energy associated with light into nanometer-scale volumes that, in many cases, can extend below the conventional Abbé diffraction limit. Dating back to the first application of surface plasmon resonance (SPR) for label-free detection of biomolecular interactions, resonant optical structures, including waveguides, ring resonators, and photonic crystals, have proven to be effective conduits for a wide range of optical enhancement effects that include enhanced excitation of photon emitters (such as quantum dots, organic dyes, and fluorescent proteins), enhanced extraction from photon emitters, enhanced optical absorption, and enhanced optical scattering (such as from Raman-scatterers and nanoparticles). The application of photonic metamaterials as a means for enhancing contrast in microscopy is a recent technological development. Through their ability to generate surface-localized and resonantly enhanced electromagnetic fields, photonic metamaterials are an effective surface for magnifying absorption, photon emission, and scattering associated with biological materials while an imaging system records spatial and temporal patterns. By replacing the conventional glass microscope slide with a photonic metamaterial, new forms of contrast and enhanced signal-to-noise are obtained for applications that include cancer diagnostics, infectious disease diagnostics, cell membrane imaging, biomolecular interaction analysis, and drug discovery. This paper will review the current state of the art in which photonic metamaterial surfaces are utilized in the context of microscopy.

A Review on Applications of CFD Modeling in COVID-19 Pandemic

COVID-19 pandemic has started a big challenge to the world health and economy during recent years. Many efforts were made to use the computation fluid dynamic (CFD) approach in this pandemic. CFD was used to understanding the airborne dispersion and transmission of this virus in different situations and buildings. The effect of the different conditions of the ventilation was studied by the CFD modeling to discuss preventing the COVID-19 transmission. Social distancing and using the facial masks were also modeled by the CFD approach to study the effect on reducing dispersion of the microdroplets containing the virus. Most of these recent applications of the CFD were reviewed for COVID-19 in this article. Special applications of the CFD modeling such as COVID-19 microfluidic biosensors, and COVID-19 inactivation using UV radiation were also reviewed in this research. The main findings of each research were also summarized in a table to answer critical questions about the effectiveness levels of applying the COVID-19 health protocols. CFD applications for modeling of COVID-19 dispersion in an airplane cabin, an elevator, a small classroom, a supermarket, an operating room of a hospital, a restaurant, a hospital waiting room, and a children's recovery room in a hospital were discussed briefly in different scenarios. CFD modeling for studying the effect of social distancing with different spaces, using and not using facial masks, difference of sneezing and coughing, different inlet/outlet ventilation layouts, combining air-conditioning and sanitizing machine, and using general or local air-conditioning systems were reviewed.

Sensitive and real-time detection of IgG using interferometric reflecting imaging sensor system

Considering the limitations of well-known traditional detection techniques, innovative research studies have focused on the development of new sensors to offer label-free, highly sensitive, real-time, low-cost, and rapid detection for biomolecular interactions. In this study, we demonstrate immunoglobulin G (IgG) detection in aqueous solutions by using real-time and label-free kinetic measurements of the Interferometric Reflectance Imaging Sensor (IRIS) system. By performing kinetic characterization experiments, the sensor's performance is comprehensively evaluated and a high correlation coefficient value (>0.94) is obtained in the IgG concentration range of 1-50 μg/mL with a low detection limit (0.25 μg/mL or 1.67 nM). Moreover, the highly sensitive imaging system ensures accurate quantification and reliable validation of recorded binding events, offering new perspectives in terms of direct biomarker detection for clinical applications.

Evaluation of Polytyramine Film and 6-Mercaptohexanol Self-Assembled Monolayers as the Immobilization Layers for a Capacitive DNA Sensor Chip: A Comparison

The performance of a biosensor is associated with the properties of an immobilization layer on a sensor chip. In this study, gold sensor chips were modified with two different immobilization layers, polytyramine film and 6-mercaptohexanol self-assembled monolayer. The physical, electrochemical and analytical properties of polytyramine film and mercaptohexanol self-assembled monolayer modified gold sensor chips were studied and compared. The study was conducted using atomic force microscopy, cyclic voltammetry and a capacitive DNA-sensor system (CapSenze™ Biosystem). The results obtained by atomic force microscopy and cyclic voltammetry indicate that polytyramine film on the sensor chip surface possesses better insulating properties and provides more spaces for the immobilization of the capture probe than a mercaptohexanol self-assembled monolayer. A capacitive DNA sensor hosting a polytyramine single-stranded DNA-modified sensor chip displayed higher sensitivity and larger signal amplitude than that of a mercaptohexanol single-stranded DNA-modified sensor chip. The linearity responses for polytyramine single-stranded DNA- and mercaptohexanol single-stranded DNA-modified sensor chips were obtained at log concentration ranges, equivalent to 10^-12 to 10^-8 M and 10^-10 to 10^-8 M, with detection limits of 4.0 × 10^-13 M and 7.0 × 10^-11 M of target complementary single-stranded DNA, respectively. Mercaptohexanol single-stranded DNA- and polytyramine single-stranded DNA-modified sensor chips exhibited a notable selectivity at an elevated hybridization temperature of 50 °C, albeit the signal amplitudes due to the hybridization of the target complementary single-stranded DNA were reduced by almost 20% and less than 5%, respectively.

Scattering-based Light Microscopy: From Metal Nanoparticles to Single Proteins

Our ability to detect, image, and quantify nanoscopic objects and molecules with visible light has undergone dramatic improvements over the past few decades. While fluorescence has historically been the go-to contrast mechanism for ultrasensitive light microscopy due to its superior background suppression and specificity, recent developments based on light scattering have reached single-molecule sensitivity. They also have the advantages of universal applicability and the ability to obtain information about the species of interest beyond its presence and location. Many of the recent advances are driven by novel approaches to illumination, detection, and background suppression, all aimed at isolating and maximizing the signal of interest. Here, we review these developments grouped according to the basic principles used, namely darkfield imaging, interferometric detection, and surface plasmon resonance microscopy.

Gradient-Based Rapid Digital Immunoassay for High-Sensitivity Cardiac Troponin T (hs-cTnT) Detection in 1 μL Plasma

Rapid and sensitive detection of biomarkers is the key to the diagnosis of acute diseases. One example is the detection of troponin in myocardial infarction. Here, we report a gradient-based digital immunoassay method, which can achieve high-sensitivity cardiac troponin T (hs-cTnT) detection with only 1 μL of plasma sample. We designed a multizone microfluidic channel functionalized with capture antibody specific to troponin. Taking advantage of limited sample volume, a troponin concentration gradient is created along the channel because of binding induced depletion. We quantified the concentration gradient by counting the detection antibody conjugated gold nanoparticles bound to different test zones with optical imaging. Differential counting between the zones removes most common noises and nonspecific bindings. The total analytical time is about 30 min, and the limit of quantification is 6.2 ng/L. We examined 41 clinical plasma samples from 15 patients and the change in hs-cTnT concentration in serial samples showed good linear correlation with clinical results (R² = 0.98). Therefore, this simple and sensitive gradient-based digital immunoassay method is a promising technology for clinical hs-cTnT detection and could be adapted for detection of other biomarkers.

Enhancement of COVID-19 detection time by means of electrothermal force

The rapid spread and quick transmission of the new ongoing pandemic coronavirus disease 2019 (COVID-19) has urged the scientific community to looking for strong technology to understand its pathogenicity, transmission, and infectivity, which helps in the development of effective vaccines and therapies. Furthermore, there was a great effort to improve the performance of biosensors so that they can detect the pathogenic virus quickly, in reliable and precise way. In this context, we propose a numerical simulation to highlight the important role of the design parameters that can significantly improve the performance of the biosensor, in particular the sensitivity as well as the detection limit. Applied alternating current electrothermal (ACET) force can generate swirling patterns in the fluid within the microfluidic channel, which improve the transport of target molecule toward the reaction surface and, thus, enhance the response time of the biosensor. In this work, the ACET effect on the SARS-CoV-2 S protein binding reaction kinetics and on the detection time of the biosensor was analyzed. Appropriate choice of electrodes location on the walls of the microchannel and suitable values of the dissociation and association rates of the binding reaction, while maintaining the same affinity, with and without ACET effect, are also, discussed to enhance the total performance of the biosensor and reduce its response time. The two-dimensional equations system is solved by the finite element approach. The best performance of the biosensor is obtained in the case where the response time decreased by 61% with AC applying voltage.

A computational simulation platform for designing real-time monitoring systems with application to COVID-19

With the aim of contributing to the fight against the coronavirus disease 2019 (COVID-19), numerous strategies have been proposed. While developing an effective vaccine can take months up to years, detection of infected patients seems like one of the best ideas for controlling the situation. The role of biosensors in containing highly pathogenic viruses, saving lives and economy is evident. A new competitive numerical platform specifically for designing microfluidic-integrated biosensors is developed and presented in this work. Properties of the biosensor, sample, buffer fluid and even the microfluidic channel can be modified in this model. This feature provides the scientific community with the ability to design a specific biosensor for requested point-of-care (POC) applications. First, the validation of the presented numerical platform against experimental data and then results and discussion, highlighting the important role of the design parameters on the performance of the biosensor is presented. For the latter, the baseline case has been set on the previous studies on the biosensors suitable for SARS-CoV, which has the highest similarity to the 2019 nCoV. Subsequently, the effects of concentration of the targeted molecules in the sample, installation position and properties of the biosensor on its performance were investigated in 11 case studies. The presented numerical framework provides an insight into understanding of the virus reaction in the design process of the biosensor and enhances our preparation for any future outbreaks. Furthermore, the integration of biosensors with different devices for accelerating the process of defeating the pandemic is proposed.

Biosensors-on-Chip: An Up-to-Date Review

Generally, biosensors are designed to translate physical, chemical, or biological events into measurable signals, thus offering qualitative and/or quantitative information regarding the target analytes. While the biosensor field has received considerable scientific interest, integrating this technology with microfluidics could further bring significant improvements in terms of sensitivity and specificity, resolution, automation, throughput, reproducibility, reliability, and accuracy. In this manner, biosensors-on-chip (BoC) could represent the bridging gap between diagnostics in central laboratories and diagnostics at the patient bedside, bringing substantial advancements in point-of-care (PoC) diagnostic applications. In this context, the aim of this manuscript is to provide an up-to-date overview of BoC system development and their most recent application towards the diagnosis of cancer, infectious diseases, and neurodegenerative disorders.

Plasmonic Assemblies for Real-Time Single-Molecule Biosensing

Their tunable optical properties and versatile surface functionalization have sparked applications of plasmonic assemblies in the fields of biosensing, nonlinear optics, and photonics. Particularly, in the field of biosensing, rapid advances have occurred in the use of plasmonic assemblies for real-time single-molecule sensing. Compared to individual particles, the use of assemblies as sensors provides stronger signals, more control over the optical properties, and access to a broader range of timescales. In the past years, they have been used to directly reveal single-molecule interactions, mechanical properties, and conformational dynamics. This review summarizes the development of real-time single-molecule sensors built around plasmonic assemblies. First, a brief overview of their optical properties is given, and then recent applications are described. The current challenges in the field and suggestions to overcome those challenges are discussed in detail. Their stability, specificity, and sensitivity as sensors provide a complementary approach to other single-molecule techniques like force spectroscopy and single-molecule fluorescence. In future applications, the impact in real-time sensing on ultralong timescales (hours) and ultrashort timescales (sub-millisecond), time windows that are difficult to access using other techniques, is particularly foreseen.

Extended Kalman Filtering Projection Method to Reduce the 3σ Noise Value of Optical Biosensors

Optical biosensors have experienced a rapid growth over the past decade because of their high sensitivity and the fact that they are label-free. Many optical biosensors rely on tracking the change in a resonance signal or an interference pattern caused by the change in refractive index that occurs upon binding to a target biomarker. The most commonly used method for tracking such a signal is based on fitting the data with an appropriate mathematical function, such as a harmonic function or a Fano, Gaussian, or Lorentz function. However, these functions have limited fitting efficiency because of the deformation of data from noise. Here, we introduce an extended Kalman filter projection (EKFP) method to address the problem of resonance tracking and demonstrate that it improves the tolerance to noise, reduces the 3σ noise value, and lowers the limit of detection (LOD). We utilize the method to process the data of experiments for detecting the binding of C-reactive protein in a urine matrix with a chirped guided mode resonance sensor and are able to improve the LOD from 10 to 1 pg/mL. Our method reduces the 3σ noise value of this measurement compared to a simple Fano fit from 1.303 to 0.015 pixels. These results demonstrate the significant advantage of the EKFP method to resolving noisy data of optical biosensors.

Numerical framework for simulating bio-species transport in microfluidic channels with application to antibody biosensors

Diagnosis is a fundamental stage in health care and medical treatment. Microfluidic biosensors and lab-on-a-chip devices are amongst the few practical tools for achieving this goal. A new computational code, specifically for designing microfluidic-integrated biosensors is developed, the details of which is presented in this work. This new approach is developed using control-volume based finite-element (CVFEM) method and solves bio-recognition chemical reactions and full Navier–Stokes equations. The results of the proposed platform are validated against the experimental data for a microfluidic based biosensor, where excellent agreement is achieved. The properties of the biosensor, sample, buffer fluid and even the microfluidic channel can easily be modified in this platform. This feature provides the scientific community with the ability to design a specific biosensor for requested point-of-care applications.•

A new approach is developed using control-volume based finite-element (CVFEM) method for investigating flow inside a microfluidic-integrated biosensor. It is also used to study the influence of surface functionalization on binding cycle.

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The proposed model solves bio-recognition chemical reactions as well as full Navier–Stokes and energy equations. Experimental-based or personalized equations of the chemical reactions and flow behaviour are adoptable to this code.

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The developed model is Fortran-based and has the potential to be used in both industry and academia for biosensing technology.

Critical Review: digital resolution biomolecular sensing for diagnostics and life science research

One of the frontiers in the field of biosensors is the ability to quantify specific target molecules with enough precision to count individual units in a test sample, and to observe the characteristics of individual biomolecular interactions. Technologies that enable observation of molecules with "digital precision" have applications for in vitro diagnostics with ultra-sensitive limits of detection, characterization of biomolecular binding kinetics with a greater degree of precision, and gaining deeper insights into biological processes through quantification of molecules in complex specimens that would otherwise be unobservable. In this review, we seek to capture the current state-of-the-art in the field of digital resolution biosensing. We describe the capabilities of commercially available technology platforms, as well as capabilities that have been described in published literature. We highlight approaches that utilize enzymatic amplification, nanoparticle tags, chemical tags, as well as label-free biosensing methods.

Dynamic Tracking Biosensors: Finding Needles in a Haystack

Accurate detection of target molecules at low concentrations in the presence of undesired molecules in abundance is a major challenge for biosensors. Nonspecific binding of undesired molecules to receptors limits the minimum detectable concentration of the target significantly. Dynamic tracking (DT) of binding and unbinding events allows us to overcome this challenge and provides a remarkable improvement in the minimum detectable target concentration. Through a combination of theoretical analysis and detailed statistical simulations, here we show that, with aggressive scaling, DT sensors are capable of fM detection limits even if the undesired molecules are present at nM concentrations, which is several orders of magnitude better than traditional endpoint (EP) biosensors. In addition, we propose a novel unconstrained detection scheme that does not rely on a priori knowledge of the dissociation constants and also allows facile back-extraction of critical parameters. Indeed, this work provides a theoretical basis for DT sensors and demonstrates its suitability to usher in a new paradigm on biosensing in hostile environments.

How Nanoparticles Transform Single Molecule Measurements into Quantitative Sensors

Single molecule measurements are revolutionizing the understanding of the stochastics of behavior of single molecules. There is a common theme referred to as a near-field approach, in how many single molecule measurements are being performed in assays. The term near field is used because the measurement volume is typically very small such that a single molecule, or a single molecule binding pair, within that volume is of an appreciable concentration. The next development in detection will be performing many single molecule measurements at one time such that single molecule measurements can be used as the basis for quantitative analysis. There have already been some notable developments in this direction. Again, all have a common theme in that nanoparticles are used to create many near-field volumes that can be measured simultaneously. Herein, the coupled developments in nanoparticles and measurement strategies that allow nanoparticles to be the backbone of the next generation of sensing technologies are discussed.

Single-molecule biosensors: Recent advances and applications

Single-molecule biosensors serve the unmet need for real time detection of individual biological molecules in the molecular crowd with high specificity and accuracy, uncovering unique properties of individual molecules which are hidden when measured using ensemble averaging methods. Measuring a signal generated by an individual molecule or its interaction with biological partners is not only crucial for early diagnosis of various diseases such as cancer and to follow medical treatments but also offers a great potential for future point-of-care devices and personalized medicine. This review summarizes and discusses recent advances in nanosensors for both in vitro and in vivo detection of biological molecules offering single-molecule sensitivity. In the first part, we focus on label-free platforms, including electrochemical, plasmonic, SERS-based and spectroelectrochemical biosensors. We review fluorescent single-molecule biosensors in the second part, highlighting nanoparticle-amplified assays, digital platforms and the utilization of CRISPR technology. We finally discuss recent advances in the emerging nanosensor technology of important biological species as well as future perspectives of these sensors.