A large-scale diagnosis of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is essential to downregulate its spread within as well as across communities and mitigate the current ...outbreak of the pandemic novel coronavirus disease 2019 (COVID-19). Herein, we report the development of a rapid (less than 5 min), low-cost, easy-to-implement, and quantitative paper-based electrochemical sensor chip to enable the digital detection of SARS-CoV-2 genetic material. The biosensor uses gold nanoparticles (AuNPs), capped with highly specific antisense oligonucleotides (ssDNA) targeting viral nucleocapsid phosphoprotein (N-gene). The sensing probes are immobilized on a paper-based electrochemical platform to yield a nucleic-acid-testing device with a readout that can be recorded with a simple hand-held reader. The biosensor chip has been tested using samples collected from Vero cells infected with SARS-CoV-2 virus and clinical samples. The sensor provides a significant improvement in output signal only in the presence of its targetSARS-CoV-2 RNAwithin less than 5 min of incubation time, with a sensitivity of 231 (copies μL–1)−1 and limit of detection of 6.9 copies/μL without the need for any further amplification. The sensor chip performance has been tested using clinical samples from 22 COVID-19 positive patients and 26 healthy asymptomatic subjects confirmed using the FDA-approved RT-PCR COVID-19 diagnostic kit. The sensor successfully distinguishes the positive COVID-19 samples from the negative ones with almost 100% accuracy, sensitivity, and specificity and exhibits an insignificant change in output signal for the samples lacking a SARS-CoV-2 viral target segment (e.g., SARS-CoV, MERS-CoV, or negative COVID-19 samples collected from healthy subjects). The feasibility of the sensor even during the genomic mutation of the virus is also ensured from the design of the ssDNA-conjugated AuNPs that simultaneously target two separate regions of the same SARS-CoV-2 N-gene.
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The current outbreak of the pandemic coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) demands its rapid, convenient, and large-scale ...diagnosis to downregulate its spread within as well as across the communities. But the reliability, reproducibility, and selectivity of majority of such diagnostic tests fail when they are tested either to a viral load at its early representation or to a viral gene mutated during its current spread. In this regard, a selective “naked-eye” detection of SARS-CoV-2 is highly desirable, which can be tested without accessing any advanced instrumental techniques. We herein report the development of a colorimetric assay based on gold nanoparticles (AuNPs), when capped with suitably designed thiol-modified antisense oligonucleotides (ASOs) specific for N-gene (nucleocapsid phosphoprotein) of SARS-CoV-2, could be used for diagnosing positive COVID-19 cases within 10 min from the isolated RNA samples. The thiol-modified ASO-capped AuNPs agglomerate selectively in the presence of its target RNA sequence of SARS-CoV-2 and demonstrate a change in its surface plasmon resonance. Further, the addition of RNaseH cleaves the RNA strand from the RNA–DNA hybrid leading to a visually detectable precipitate from the solution mediated by the additional agglomeration among the AuNPs. The selectivity of the assay has been monitored in the presence of MERS-CoV viral RNA with a limit of detection of 0.18 ng/μL of RNA having SARS-CoV-2 viral load. Thus, the current study reports a selective and visual “naked-eye” detection of COVID-19 causative virus, SARS-CoV-2, without the requirement of any sophisticated instrumental techniques.
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The global pandemic of coronavirus disease 2019 (COVID-19) highlights the shortcomings of the current testing paradigm for viral disease diagnostics. Here, we report a stepwise protocol for an ...RNA-extraction-free nano-amplified colorimetric test for rapid and naked-eye molecular diagnosis of COVID-19. The test employs a unique dual-prong approach that integrates nucleic acid (NA) amplification and plasmonic sensing for point-of-care detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with a sample-to-assay response time of <1 h. The RNA-extraction-free nano-amplified colorimetric test utilizes plasmonic gold nanoparticles capped with antisense oligonucleotides (ASOs) as a colorimetric reporter to detect the amplified nucleic acid from the COVID-19 causative virus, SARS-CoV-2. The ASOs are specific for the SARS-CoV-2 N-gene, and binding of the ASOs to their target sequence results in the aggregation of the plasmonic gold nanoparticles. This highly specific agglomeration step leads to a change in the plasmonic response of the nanoparticles. Furthermore, when tested using clinical samples, the accuracy, sensitivity and specificity of the test were found to be >98.4%, >96.6% and 100%, respectively, with a detection limit of 10 copies/μL. The test can easily be adapted to diagnose other viral infections with a simple modification of the ASOs and primer sequences. It also provides a low-cost, rapid approach requiring minimal instrumentation that can be used as a screening tool for the diagnosis of COVID-19 at point-of-care settings in resource-poor situations. The colorimetric readout of the test can even be monitored using a handheld optical reader to obtain a quantitative response. Therefore, we anticipate that this protocol will be widely useful for the development of biosensors for the molecular diagnostics of COVID-19 and other infectious diseases.
It is imperative to draw attention to the intrinsic properties of copper and and other metal nanoparticles and why they can be used in diagnostic imaging. An advantage of metallic nanoparticles is ...their unique ability to conduct plasma absorption, enhance Rayleigh scattering, enhance Raman scattering on surfaces, etc.
As an emerging class of carbon nanomaterials, carbon dots (CDs) have garnered many researchers’ interests in the past decade due to their excellent biocompatibility, replete surface functional ...groups, water dispersibility, and unique photoluminescence. These extraordinary properties have opened new avenues for their advanced application in cell labeling, bioimaging, drug delivery, sensors, and energy‐related devices. In this paper, we critically review recent advances in the synthetic strategies and the application of CDs for biological purposes, specifically, imaging and therapy. Finally, a perspective has been given on the potential challenges facing the translation of these materials from the bench to the market. WIREs Nanomed Nanobiotechnol 2017, 9:e1436. doi: 10.1002/wnan.1436
This article is categorized under:
Diagnostic Tools > In Vitro Nanoparticle-Based Sensing
Excellent biocompatibility, water dispersibility, and unique photoluminescence properties of carbon dots have made them a promising candidate for versatile biomedical applications at nanoscale.
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Various cancer cells have been demonstrated to have the capacity to form plasmonic gold nanoparticles when chloroauric acid is introduced to their cellular microenvironment. But their biomedical ...applications are limited, particularly considering the millimolar concentrations and longer incubation period of ionic gold. Here, we describe a simplistic method of intracellular biomineralization to produce plasmonic gold nanoparticles at micromolar concentrations within 30 min of application utilizing polyethylene glycol as delivery vector for ionic gold. We have characterized this process for intracellular gold nanoparticle formation, which progressively accumulates proteins as the ionic gold clusters migrate to the nucleus. This nano-vectorized application of ionic gold emphasizes its potential biomedical opportunities while reducing the quantity of ionic gold and required incubation time. To demonstrate its biomedical potential, we further induce in-situ biosynthesis of gold nanoparticles within MCF7 tumor mouse xenografts which is followed by its photothermal remediation.
Fluorescent array-based sensing is an emerging differential sensing platform for sensitive detection of analytes in a complex environment without involving a conventional “lock and key” type-specific ...interaction. These sensing techniques mainly rely on different optical pattern generation from a sensor array and their pattern recognition to differentiate analytes. Currently emerging, compelling pattern-recognition method, Machine Learning (ML), enables a machine to “learn” a pattern by training without having the recognition method explicitly programmed into it. Thus, ML has an enormous potential to analyze these sensing data better than widely used statistical pattern-recognition methods. Here, an array-based sensor using easy-to-synthesize carbon dots with varied surface functionality is reported, which can differentiate between eight different proteins at 100 nM concentration. The utility of using machine learning algorithms in pattern recognition of fluorescence signals from the array has also been demonstrated. In analyzing the array-based sensing data, Machine Learning algorithms like “Gradient-Boosted Trees” have achieved a 100% prediction efficiency compared to inferior-performing classical statistical method “Linear Discriminant Analysis”.
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Cortisol has been identified as a biomarker in saliva to monitor psychological stress. In this work, we report a label-free paper-based electrical biosensor chip to quantify salivary cortisol at a ...point-of-care (POC) level. A high specificity of the sensor chip to detect cortisol with a detection limit of 3 pg/mL was achieved by conjugating anticortisol antibody (anti-CAB) on top of gold (Au) microelectrodes using 3,3′-dithiodipropionic acid di(N-hydroxysuccinimide ester (DTSP) as a self-assembled monolayer (SAM) agent. The electrode design utilized poly(styrene)-block-poly(acrylic acid) (PS67-b-PAA27) polymer and graphene nanoplatelets (GP) suspension coated on filter paper to increase the sensitivity of the immune response. A biosensor chip was then integrated with a lab-built low-cost miniaturized printed circuit board (PCB) to provide an electrical connection and to wirelessly transmit/receive electrical signals using MATLAB. This fully integrated proposed hand-held device successfully exhibited a wide cortisol-detection range from 3 pg/mL to 10 μg/mL, with a sensitivity of 50 Ω (pg mL–1)−1. The performance of the proposed cortisol sensor chip was validated using an enzyme-linked immunosorbent assay (ELISA) technique with a regression value of 0.9951. The advantages of the newly developed cortisol immune biosensor over previously reported chips include an improved limit of detection, no need for additional redox medium for electron exchange, faster response to achieve stable data, excellent shelf life, and its economical production.
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In the field of theranostics, diagnostic nanoparticles are designed to collect highly patient-selective disease profiles, which is then leveraged by a set of nanotherapeutics to improve the ...therapeutic results. Despite their early promise, high interpatient and intratumoral heterogeneities make any rational design and analysis of these theranostics platforms extremely problematic. Recent advances in deep-learning-based tools may help bridge this gap, using pattern recognition algorithms for better diagnostic precision and therapeutic outcome. Triple-negative breast cancer (TNBC) is a conundrum because of the complex molecular diversity, making its diagnosis and therapy challenging. To address these challenges, we propose a method to predict the cellular internalization of nanoparticles (NPs) against different cancer stages using artificial intelligence. Here, we demonstrate for the first time that a combination of machine-learning (ML) algorithm and characteristic cellular uptake responses for individual cancer cell types can be successfully used to classify various cancer cell types. Utilizing this approach, we can optimize the nanomaterials to get an optimum structure–internalization response for a given particle. This methodology predicted the structure–internalization response of the evaluated nanoparticles with remarkable accuracy (Q 2 = 0.9). We anticipate that it can reduce the effort by minimizing the number of nanoparticles that need to be tested and could be utilized as a screening tool for designing nanotherapeutics. Following this, we have proposed a diagnostic nanomaterial-based platform used to assemble a patient-specific cancer profile with the assistance of machine learning (ML). The platform is composed of eight carbon nanoparticles (CNPs) with multifarious surface chemistries that can differentiate healthy breast cells from cancerous cells and then subclassify TNBC cells vs non-TNBC cells, within the TNBC group. The artificial neural network (ANN) algorithm has been successfully used in identifying the type of cancer cells from 36 unknown cancer samples with an overall accuracy of >98%, providing potential applications in cancer diagnostics.
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