High-throughput Label-free Detection Research Articles

Molybdenum Disulfide-Assisted Spontaneous Formation of Multistacked Gold Nanoparticles for Deep Learning-Integrated Surface-Enhanced Raman Scattering.

Several fabrication methods have been developed for label-free detection in various fields. However, fabricating high-density and highly ordered nanoscale architectures by using soluble processes remains a challenge. Herein, we report a biosensing platform that integrates deep learning with surface-enhanced Raman scattering (SERS), featuring large-area, close-packed three-dimensional (3D) architectures of molybdenum disulfide (MoS2)-assisted gold nanoparticles (AuNPs) for the on-site screening of coronavirus disease (COVID-19) using human tears. Some AuNPs are spontaneously synthesized without a reducing agent because the electrons induced on the semiconductor surface reduce gold ions when the Fermi level of MoS2 and the gold electrolyte reach equilibrium. With the addition of polyvinylpyrrolidone, a two-dimensional large-area MoS2 layer assisted in the formation of close-packed 3D multistacked AuNP structures, resembling electroless plating. This platform, with a convolutional neural network-based deep learning model, achieved outstanding SERS performance at subterascale levels despite the microlevel irradiation power and millisecond-level acquisition time and accurately assessed susceptibility to COVID-19. These results suggest that our platform has the potential for rapid, low-damage, and high-throughput label-free detection of exceedingly low analyte concentrations.

Rapid enrichment detection of patulin and alternariol in apple using surface enhanced Raman spectroscopy with coffee-ring effect

Patulin (PAT) and alternariol (AOH) are the main mycotoxin contaminants in fruits and their products, which have great toxic effects on human body due to their teratogenicity and carcinogenicity. This study proposed a surface enhanced Raman spectroscopy (SERS) technology combining chemometrics and coffee-ring effect to build high-throughput label-free detection models for PAT and AOH. A stable coffee ring structure was built by optimizing the drying temperature and droplet volume. Comparing the partial least squares (PLS) models grounded on variables selection method, the best performance was obtained by using synergy interval (Si) and genetic algorithm (GA) for PAT (Rc = 0.9905, Rp = 0.9759) and AOH (Rc = 0.9829, Rp = 0.9808), respectively. The limits of detection (LOD) for PAT and AOH were as low as 1 μg L−1, and the recovery rates were 92.80%–114.83% with relative standard deviation (RSD) ≤ 4.86 for PAT and 82.06%–108.13% with RSD ≤2.28% for AOH. The SERS technology combined with chemometrics and coffee-ring effect holds promise for high-throughput label-free detection of PAT and AOH in fruits and their products.

Multiplexed and High-Throughput Label-Free Detection of RNA/Spike Protein/IgG/IgM Biomarkers of SARS-CoV-2 Infection Utilizing Nanoplasmonic Biosensors.

To tackle the COVID-19 outbreak, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an unmet need for highly accurate diagnostic tests at all stages of infection with rapid results and high specificity. Here, we present a label-free nanoplasmonic biosensor-based, multiplex screening test for COVID-19 that can quantitatively detect 10 different biomarkers (6 viral nucleic acid genes, 2 spike protein subunits, and 2 antibodies) with a limit of detection in the aM range, all within one biosensor platform. Our newly developed nanoplasmonic biosensors demonstrate high specificity, which is of the upmost importance to avoid false responses. As a proof of concept, we show that our detection approach has the potential to quantify both IgG and IgM antibodies directly from COVID-19-positive patient plasma samples in a single instrument run, demonstrating the high-throughput capability of our detection approach. Most importantly, our assay provides receiving operating characteristics, areas under the curve of 0.997 and 0.999 for IgG and IgM, respectively. The calculated p-value determined through the Mann–Whitney nonparametric test is <0.0001 for both antibodies when the test of COVID-19-positive patients (n = 80) is compared with that of healthy individuals (n = 72). Additionally, the screening test provides a calculated sensitivity (true positive rate) of 100% (80/80), a specificity (true negative rate) >96% (77/80), a positive predictive value of 98% at 5% prevalence, and a negative predictive value of 100% at 5% prevalence. We believe that our very sensitive, multiplex, high-throughput testing approach has potential applications in COVID-19 diagnostics, particularly in determining virus progression and infection severity for clinicians for an appropriate treatment, and will also prove to be a very effective diagnostic test when applied to diseases beyond the COVID-19 pandemic.

High-throughput label-free detection of DNA-to-RNA transcription inhibition using brightfield microscopy and deep neural networks

Drug discovery is in constant evolution and major advances have led to the development of in vitro high-throughput technologies, facilitating the rapid assessment of cellular phenotypes. One such phenotype is immunogenic cell death, which occurs partly as a consequence of inhibited RNA synthesis. Automated cell-imaging offers the possibility of combining high-throughput with high-content data acquisition through the simultaneous computation of a multitude of cellular features. Usually, such features are extracted from fluorescence images, hence requiring labeling of the cells using dyes with possible cytotoxic and phototoxic side effects. Recently, deep learning approaches have allowed the analysis of images obtained by brightfield microscopy, a technique that was for long underexploited, with the great advantage of avoiding any major interference with cellular physiology or stimulatory compounds. Here, we describe a label-free image-based high-throughput workflow that accurately detects the inhibition of DNA-to-RNA transcription. This is achieved by combining two successive deep convolutional neural networks, allowing (1) to automatically detect cellular nuclei (thus enabling monitoring of cell death) and (2) to classify the extracted nuclear images in a binary fashion. This analytical pipeline is R-based and can be easily applied to any microscopic platform.

High-throughput label-free detection of Ochratoxin A in wine using supported liquid membrane extraction and Ag-capped silicon nanopillar SERS substrates

Mycotoxins are toxic secondary metabolites produced by fungi, found in a variety of food sources and cause diseases and death in both humans and animals. On-site and easy to use detection would be significant improvement considering the currently used methods. We present an approach, based on surface-enhanced Raman spectroscopy (SERS), where label-free detection of Ochratoxin A (OTA) was achieved in a multiwell SERS detection platform. When quantifying OTA in wine samples the SERS-based detection was used in combination with high throughput supported liquid membrane (SLM) extraction. The obtained detection limit in white wine was 115 ppb and we found that the SERS-based quantification is comparable with LC-MS. However, we also found that the matrix effect from red wine was interfering with both SERS and LC-MS detection. The SLM-SERS method provides a time and cost effective approach for OTA detection, which is a clear advantage over other extraction and direct detection approaches.

High-Throughput Label-Free Detection Using a Gold Nanoslit Array With 2-D Spectral Images and Spectral Integration Methods

We fabricated gold nanoslit arrays and experimentally compared the sensing capabilities using the single-polarization and multipolarization spectral integration methods. The signal-to-noise ratio and refractive index detection limit using the multipolarization method were increased to three times larger than the simple integration method. Combining 2-D spectral images and the spectral integration method, we demonstrated real time and multiple detections of 30-nm-gold nanoparticles. The detection limit of the system is down to 0.147 particles/μm2.

Towards a high-throughput label-free detection system combining localized-surface plasmon resonance and microfluidics

This work reports an integrated platform combining localized-surface plasmon resonance (LSPR) and microfluidic chips to carry out multiplexed and label-free protein analysis. We fabricated an optical bench to enable detection using only a laboratory UV-Vis spectrophotometer. This assay not only improves throughput, but also allows quantitative analysis.

CSI–FID: High throughput label-free detection of DNA binding molecules

Determining the sequence specifity of DNA binding molecules is a non-trivial task. Here we describe the development of a platform for assaying the sequence specificity of DNA ligands using label free detection on high density DNA microarrays. This is achieved by combining Cognate Site Identification (CSI) with Fluorescence Intercalation Displacement (FID) to create CSI–FID. We use the well-studied small molecule DNA ligand netropsin to develop this high throughput platform. Analysis of the DNA binding properties of protein- and small molecule-based libraries with CSI–FID will advance the development of genome-anchored molecules for therapeutic purposes.

Label-free and dynamic detection of biomolecular interactions for high-throughput microarray applications

Direct monitoring of primary molecular-binding interactions without the need for secondary reactants would markedly simplify and expand applications of high-throughput label-free detection methods. A simple interferometric technique is presented that monitors the optical phase difference resulting from accumulated biomolecular mass. As an example, 50 spots for each of four proteins consisting of BSA, human serum albumin, rabbit IgG, and protein G were dynamically monitored as they captured corresponding antibodies. Dynamic measurements were made at 26 pg/mm(2) SD per spot and with a detectable concentration of 19 ng/ml. The presented method is particularly relevant for protein microarray analysis because it is label-free, simple, sensitive, and easily scales to high-throughput.

A novel high-throughput scanning microscope for label-free detection of protein and small-molecule chemical microarrays.

We describe a novel scanning optical microscope based on a polarization-modulated nulling ellipsometry. The new microscope employs a combination of scanning mirror and sample translation and thus enables high-throughput label-free detection of biomolecular microarrays with more than 10 000 protein or small-molecule targets. For illustration, we show the image of a 2760-spot protein microarray on a functionalized glass slide obtained with such a microscope. The new scanning microscope is also capable of determining, in parallel, the real-time binding kinetics of multiple molecular species under aqueous conditions.