Robust Multichannel Encoding for Highly Multiplexed Quantitative PCR

New Approaches to Plant Pathogen Detection and Disease Diagnosis

Detecting plant pathogens and diagnosing diseases are critical components of successful pest management. These key areas have undergone significant advancements driven by breakthroughs in molecular biology and remote sensing technologies within the realm of precision agriculture. Notably, nucleic acid amplification techniques, with recent emphasis on sequencing procedures, particularly next-generation sequencing, have enabled improved DNA or RNA amplification detection protocols that now enable previously unthinkable strategies aimed at dissecting plant microbiota, including the disease-causing components. Simultaneously, the domain of remote sensing has seen the emergence of cutting-edge imaging sensor technologies and the integration of powerful computational tools, such as machine learning. These innovations enable spectral analysis of foliar symptoms and specific pathogen-induced alterations, making imaging spectroscopy and thermal imaging fundamental tools for large-scale disease surveillance and monitoring. These technologies contribute significantly to understanding the temporal and spatial dynamics of plant diseases.

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

Next generation multiplexing for digital PCR using a novel melt-based hairpin probe design

Digital PCR (dPCR) is a powerful tool for research and diagnostic applications that require absolute quantification of target molecules or detection of rare events, but the number of nucleic acid targets that can be distinguished within an assay has limited its usefulness. For most dPCR systems, one target is detected per optical channel and the total number of targets is limited by the number of optical channels on the platform. Higher-order multiplexing has the potential to dramatically increase the usefulness of dPCR, especially in scenarios with limited sample. Other potential benefits of multiplexing include lower cost, additional information generated by more probes, and higher throughput. To address this unmet need, we developed a novel melt-based hairpin probe design to provide a robust option for multiplexing digital PCR. A prototype multiplex digital PCR (mdPCR) assay using three melt-based hairpin probes per optical channel in a 16-well microfluidic digital PCR platform accurately distinguished and quantified 12 nucleic acid targets per well. For samples with 10,000 human genome equivalents, the probe-specific ranges for limit of blank were 0.00%-0.13%, and those for analytical limit of detection were 0.00%-0.20%. Inter-laboratory reproducibility was excellent (r 2 = 0.997). Importantly, this novel melt-based hairpin probe design has potential to achieve multiplexing beyond the 12 targets/well of this prototype assay. This easy-to-use mdPCR technology with excellent performance characteristics has the potential to revolutionize the use of digital PCR in research and diagnostic settings.

A rapid, multiplex digital PCR assay to detect gene variants and fusions in non-small cell lung cancer

Digital PCR (dPCR) is emerging as an ideal platform for the detection and tracking of genomic variants in cancer due to its high sensitivity and simple workflow. The growing number of clinically actionable cancer biomarkers creates a need for fast, accessible methods that allow for dense information content and high accuracy. Here, we describe a proof-of-concept amplitude modulation-based multiplex dPCR assay capable of detecting 12 single-nucleotide and insertion/deletion (indel) variants in EGFR, KRAS, BRAF, and ERBB2, 14 gene fusions in ALK, RET, ROS1, and NTRK1, and MET exon 14 skipping present in non-small cell lung cancer (NSCLC). We also demonstrate the use of multi-spectral target-signal encoding to improve the specificity of variant detection by reducing background noise by up to an order of magnitude. The assay reported an overall 100% positive percent agreement (PPA) and 98.5% negative percent agreement (NPA) compared with a sequencing-based assay in a cohort of 62 human formalin-fixed paraffin-embedded (FFPE) samples. In addition, the dPCR assay rescued actionable information in 10 samples that failed to sequence, highlighting the utility of a multiplexed dPCR assay as a potential reflex solution for challenging NSCLC samples.

Analytical Performance and Concordance with Next-Generation Sequencing of a Rapid, Multiplexed dPCR Panel for the Detection of DNA and RNA Biomarkers in Non-Small-Cell Lung Cancer

FDA approval of targeted therapies for lung cancer has significantly improved patient survival rates. Despite these improvements, barriers to timely access to biomarker information, such as nucleic acid input, still exist. Here, we report the analytical performance and concordance with next-generation sequencing (NGS) of a highly multiplexed research-use-only (RUO) panel using digital PCR (dPCR). The panel's analytical sensitivity and reactivity were determined using contrived DNA and RNA mixes. The limit of blank was established by testing FFPE curls classified as negative by pathology. Concordance was established on 77 FFPE samples previously characterized using the Oncomine Precision Assay®, and any discordant results were resolved with Archer Fusionplex® and Variantplex® panels. The analytical sensitivity, reported as the estimated mutant allele fraction (MAF), for DNA targets ranged from 0.1 to 0.9%. For RNA targets (ALK, RET, ROS, NTRK 1/2/3 Fusions, and MET Exon 14 skipping alteration), the analytical sensitivity ranged from 23 to 101 detected counts with 5 ng of total RNA input. The population prevalence-based coverage ranged from 89.2% to 100.0% across targets and exceeded 99.0% in aggregate. The assay demonstrated >97% concordance with respect to the comparator method.

New realm of precision multiplexing enabled by massively-parallel single molecule UltraPCR

PCR has been a reliable and inexpensive method for nucleic acid detection in the past several decades. In particular, multiplex PCR is a powerful tool to analyze many biomarkers in the same reaction, thus maximizing detection sensitivity and reducing sample usage. However, balancing the amplification kinetics between amplicons and distinguishing them can be challenging, diminishing the broad adoption of high order multiplex PCR panels. Here, we present a new paradigm in PCR amplification and multiplexed detection using UltraPCR. UltraPCR utilizes a simple centrifugation workflow to split a PCR reaction into ∼34 million partitions, forming an optically clear pellet of spatially separated reaction compartments in a PCR tube. After in situ thermocycling, light sheet scanning is used to produce a 3D reconstruction of the fluorescent positive compartments within the pellet. At typical sample DNA concentrations, the magnitude of partitions offered by UltraPCR dictate that the vast majority of target molecules occupy a compartment uniquely. This single molecule realm allows for isolated amplification events, thereby eliminating competition between different targets and generating unambiguous optical signals for detection. Using a 4-color optical setup, we demonstrate that we can incorporate 10 different fluorescent dyes in the same UltraPCR reaction. We further push multiplexing to an unprecedented level by combinatorial labeling with fluorescent dyes - referred to as "comboplex" technology. Using the same 4-color optical setup, we developed a 22-target comboplex panel that can detect all targets simultaneously at high precision. Collectively, UltraPCR has the potential to push PCR applications beyond what is currently available, enabling a new class of precision genomics assays.

Micro- and nanosystems for the detection of hemorrhagic fever viruses

Hemorrhagic fever viruses (HFVs) are virulent pathogens that can cause severe and often fatal illnesses in humans. Timely and accurate detection of HFVs is critical for effective disease management and prevention. In recent years, micro- and nano-technologies have emerged as promising approaches for the detection of HFVs. This paper provides an overview of the current state-of-the-art systems for micro- and nano-scale approaches to detect HFVs. It covers various aspects of these technologies, including the principles behind their sensing assays, as well as the different types of diagnostic strategies that have been developed. This paper also explores future possibilities of employing micro- and nano-systems for the development of HFV diagnostic tools that meet the practical demands of clinical settings.

Smart-Plexer: a breakthrough workflow for hybrid development of multiplex PCR assays

Developing multiplex PCR assays requires extensive experimental testing, the number of which exponentially increases by the number of multiplexed targets. Dedicated efforts must be devoted to the design of optimal multiplex assays ensuring specific and sensitive identification of multiple analytes in a single well reaction. Inspired by data-driven approaches, we reinvent the process of developing and designing multiplex assays using a hybrid, simple workflow, named Smart-Plexer, which couples empirical testing of singleplex assays and computer simulation to develop optimised multiplex combinations. The Smart-Plexer analyses kinetic inter-target distances between amplification curves to generate optimal multiplex PCR primer sets for accurate multi-pathogen identification. In this study, the Smart-Plexer method is applied and evaluated for seven respiratory infection target detection using an optimised multiplexed PCR assay. Single-channel multiplex assays, together with the recently published data-driven methodology, Amplification Curve Analysis (ACA), were demonstrated to be capable of classifying the presence of desired targets in a single test for seven common respiratory infection pathogens.

Melt-Encoded-Tags for Expanded Optical Readout in Digital PCR (METEOR-dPCR) Enables Highly Multiplexed Quantitative Gene Panel Profiling

Digital PCR (dPCR) is an important tool for precise nucleic acid quantification in clinical setting, but the limited multiplexing capability restricts its applications for quantitative gene panel profiling. Here, this work describes melt-encoded-tags for expanded optical readout in digital PCR (METEOR-dPCR), a simple two-step assay that enables simultaneous quantification of a large panel of arbitrary genes in a dPCR platform. Target genes are quantitatively converted into DNA tags with unique melting temperatures through a ligation approach. These tags are then counted and distinguished by their melt-curve profiles on a dPCR platform. A multiplexing capacity of M^N, where M is the number of resolvable melting temperature and N is the number of fluorescence channel, can be achieved. This work validates METEOR-dPCR with simultaneous DNA copy number profiling of 60 targets using dPCR in cancer cells, and demonstrates its sensitivity for estimating tumor fraction in mixed tumor and normal DNA samples. The rapid, quantitative, and highly multiplexed METEOR-dPCR assay will have wide appeal for many clinical applications.

Deep Domain Adaptation Enhances Amplification Curve Analysis for Single-Channel Multiplexing in Real-Time PCR

Data-driven approaches for molecular diagnostics are emerging as an alternative to perform an accurate and inexpensive multi-pathogen detection. A novel technique called Amplification Curve Analysis (ACA) has been recently developed by coupling machine learning and real-time Polymerase Chain Reaction (qPCR) to enable the simultaneous detection of multiple targets in a single reaction well. However, target classification purely relying on the amplification curve shapes faces several challenges, such as distribution discrepancies between different data sources (i.e., training vs testing). Optimisation of computational models is required to achieve higher performance of ACA classification in multiplex qPCR through the reduction of those discrepancies. Here, we proposed a novel transformer-based conditional domain adversarial network (T-CDAN) to eliminate data distribution differences between the source domain (synthetic DNA data) and the target domain (clinical isolate data). The labelled training data from the source domain and unlabelled testing data from the target domain are fed into the T-CDAN, which learns both domains' information simultaneously. After mapping the inputs into a domain-irrelevant space, T-CDAN removes the feature distribution differences and provides a clearer decision boundary for the classifier, resulting in a more accurate pathogen identification. Evaluation of 198 clinical isolates containing three types of carbapenem-resistant genes (blaNDM, blaIMP and blaOXA-48) illustrates a curve-level accuracy of 93.1% and a sample-level accuracy of 97.0% using T-CDAN, showing an accuracy improvement of 20.9% and 4.9% respectively. This research emphasises the importance of deep domain adaptation to enable high-level multiplexing in a single qPCR reaction, providing a solid approach to extend qPCR instruments' capabilities in real-world clinical applications.

Bare glassy nanopore for length-resolution reading of PCR amplicons from various pathogenic bacteria and viruses

In this study, it is confirmed that without addition of organic solvent and embedding polymer hydrogel into glass nanopore, bare glass nanopore can faithfully measure various lengths of DNA duplexes from 200 to 3000 base pairs with 200 base pairs resolution, showing well-separated peak amplitudes of blockage currents. Furthermore, motivated by this readout capability of duplex DNA, amplicons from Polymerase Chain Reaction (PCR) amplification are straightforwardly discriminated by bare glassy nanopore without fluorescent labeling. Except simultaneous discrimination of up to 7 different segments of the same lambda genome, various pathogenic bacteria and viruses including SARS-CoV-2 and its mutants in clinical samples can be discriminated at high resolution. Moreover, quantitative measurement of PCR amplicons is obtained with detection range spanning from 0.75 aM to 7.5 pM and detection limit of 7.5 aM, which reveals that bare glass nanopore can faithfully disclose PCR results without any extra labeling.

Next-generation molecular diagnostics: Leveraging digital technologies to enhance multiplexing in real-time PCR

Real-time polymerase chain reaction (qPCR) enables accurate detection and quantification of nucleic acids and has become a fundamental tool in biological sciences, bioengineering and medicine. By combining multiple primer sets in one reaction, it is possible to detect several DNA or RNA targets simultaneously, a process called multiplex PCR (mPCR) which is key to attaining optimal throughput, cost-effectiveness and efficiency in molecular diagnostics, particularly in infectious diseases. Multiple solutions have been devised to increase multiplexing in qPCR, including single-well techniques, using target-specific fluorescent oligonucleotide probes, and spatial multiplexing, where segregation of the sample enables parallel amplification of multiple targets. However, these solutions are mostly limited to three or four targets, or highly sophisticated and expensive instrumentation. There is a need for innovations that will push forward the multiplexing field in qPCR, enabling for a next generation of diagnostic tools which could accommodate high throughput in an affordable manner. To this end, the use of machine learning (ML) algorithms (data-driven solutions) has recently emerged to leverage information contained in amplification and melting curves (AC and MC, respectively) - two of the most standard bio-signals emitted during qPCR - for accurate classification of multiple nucleic acid targets in a single reaction. Therefore, this review aims to demonstrate and illustrate that data-driven solutions can be successfully coupled with state-of-the-art and common qPCR platforms using a variety of amplification chemistries to enhance multiplexing in qPCR. Further, because both ACs and MCs can be predicted from sequence data using thermodynamic databases, it has also become possible to use computer simulation to rationalize and optimize the design of mPCR assays where target detection is supported by data-driven technologies. Thus, this review also discusses recent work converging towards the development of an end-to-end framework where knowledge-based and data-driven software solutions are integrated to streamline assay design, and increase the accuracy of target detection and quantification in the multiplex setting. We envision that concerted efforts by academic and industry scientists will help advance these technologies, to a point where they become mature and robust enough to bring about major improvements in the detection of nucleic acids across many fields.

Rapid SARS-CoV-2 Variants Enzymatic Detection (SAVED) by CRISPR-Cas12a

The continuous and rapid surge of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with high transmissibility and evading neutralization is alarming, necessitating expeditious detection of the variants concerned. Here, we report the development of rapid SARS-CoV-2 variants enzymatic detection (SAVED) based on CRISPR-Cas12a targeting of previously crucial variants, including Alpha, Beta, Gamma, Delta, Lambda, Mu, Kappa, and currently circulating variant of concern (VOC) Omicron and its subvariants BA.1, BA.2, BA.3, BA.4, and BA.5. SAVED is inexpensive (US$3.23 per reaction) and instrument-free. SAVED results can be read out by fluorescence reader and tube visualization under UV/blue light, and it is stable for 1 h, enabling high-throughput screening and point-of-care testing. We validated SAVED performance on clinical samples with 100% specificity in all samples and 100% sensitivity for the current pandemic Omicron variant samples having a threshold cycle (C_T) value of ≤34.9. We utilized chimeric CRISPR RNA (crRNA) and short crRNA (15-nucleotide [nt] to 17-nt spacer) to achieve single nucleotide polymorphism (SNP) genotyping, which is necessary for variant differentiation and is a challenge to accomplish using CRISPR-Cas12a technology. We propose a scheme that can be used for discriminating variants effortlessly and allows for modifications to incorporate newer upcoming variants as the mutation site of these variants may reappear in future variants. IMPORTANCE Rapid differentiation and detection tests that can directly identify SARS-CoV-2 variants must be developed in order to meet the demands of public health or clinical decisions. This will allow for the prompt treatment or isolation of infected people and the implementation of various quarantine measures for those exposed. We report the development of the rapid SARS-CoV-2 variants enzymatic detection (SAVED) method based on CRISPR-Cas12a that targets previously significant variants like Alpha, Beta, Gamma, Delta, Lambda, Mu, and Kappa as well as the VOC Omicron and its subvariants BA.1, BA.2, BA.3, BA.4, and BA.5 that are currently circulating. SAVED uses no sophisticated instruments and is reasonably priced ($3.23 per reaction). As the mutation location of these variations may reoccur in subsequent variants, we offer a system that can be applied for variant discrimination with ease and allows for adjustments to integrate newer incoming variants.

CRISPR-Based Diagnostics: Challenges and Potential Solutions toward Point-of-Care Applications

The COVID-19 pandemic has challenged the conventional diagnostic field and revealed the need for decentralized Point of Care (POC) solutions. Although nucleic acid testing is considered to be the most sensitive and specific disease detection method, conventional testing platforms are expensive, confined to central laboratories, and are not deployable in low-resource settings. CRISPR-based diagnostics have emerged as promising tools capable of revolutionizing the field of molecular diagnostics. These platforms are inexpensive, simple, and do not require the use of special instrumentation, suggesting they could democratize access to disease diagnostics. However, there are several obstacles to the use of the current platforms for POC applications, including difficulties in sample processing and stability. In this review, we discuss key advancements in the field, with an emphasis on the challenges of sample processing, stability, multiplexing, amplification-free detection, signal interpretation, and process automation. We also discuss potential solutions for revolutionizing CRISPR-based diagnostics toward sample-to-answer diagnostic solutions for POC and home use.

Adaptive Filtering Framework to Remove Nonspecific and Low-Efficiency Reactions in Multiplex Digital PCR Based on Sigmoidal Trends

Real-time digital polymerase chain reaction (qdPCR) coupled with machine learning (ML) methods has shown the potential to unlock scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One promising application of this emerging field explores single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves, also known as data-driven multiplexing. However, accurate target classification is compromised by the presence of undesired amplification events and not ideal reaction conditions. Therefore, here, we proposed a novel framework to identify and filter out nonspecific and low-efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported data-driven multiplexing method called amplification curve analysis (ACA), using available published data where the ACA is demonstrated to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named adaptive mapping filter (AMF), to adjust the percentage of outliers removed according to the number of positive counts in qdPCR. From an overall total of 152,000 amplification events, 116,222 positive amplification reactions were evaluated before and after filtering by comparing against melting peak distribution, proving that abnormal amplification curves (outliers) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to assess classification performance before and after AMF, showing an improved sensitivity of 1.2% when using inliers compared to a decrement of 19.6% when using outliers (p-value < 0.0001), removing 53.5% of all wrong melting curves based only on the amplification shape. This work explores the correlation between the kinetics of amplification curves and the thermodynamics of melting curves, and it demonstrates that filtering out nonspecific or low-efficient reactions can significantly improve the classification accuracy for cutting-edge multiplexing methodologies.

Similar color analysis based on deep learning (SCAD) for multiplex digital PCR via a single fluorescent channel

Digital PCR (dPCR) has recently attracted great interest due to its high sensitivity and accuracy. However, the existing dPCR depends on multicolor fluorescent dyes and multiple fluorescent channels to achieve multiplex detection, resulting in increased detection cost and limited detection throughput. Here, we developed a deep learning-based similar color analysis method, namely SCAD, to achieve multiplex dPCR in a single fluorescent channel. As a demonstration, we designed a microwell chip-based diplex dPCR system for detecting two genes (bla_NDM and bla_VIM) with two kinds of green fluorescent probes, whose emission colors are difficult to discriminate by traditional fluorescence intensity-based methods. To verify the possibility of deep learning algorithms to distinguish the similar colors, we first applied t-distributed stochastic neighbor embedding (tSNE) to make a clustering map for the microwells with similar fluorescence. Then, we trained a Vision Transformer (ViT) model on 10 000 microwells with two similar colors and tested it with 262 202 microwells. Lastly, the trained model was proven to have highly accurate classification ability (>98% for both the training set and the test set) and precise quantification ability on both bla_NDM and bla_VIM (ratio difference <0.10). We envision that the developed SCAD method would significantly expand the detection throughput of dPCR without the need for other auxiliary equipment.

Multiplexed CRISPR-based microfluidic platform for clinical testing of respiratory viruses and identification of SARS-CoV-2 variants

The coronavirus disease 2019 (COVID-19) pandemic has demonstrated a clear need for high-throughput, multiplexed and sensitive assays for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory viruses and their emerging variants. Here, we present a cost-effective virus and variant detection platform, called microfluidic Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (mCARMEN), which combines CRISPR-based diagnostics and microfluidics with a streamlined workflow for clinical use. We developed the mCARMEN respiratory virus panel to test for up to 21 viruses, including SARS-CoV-2, other coronaviruses and both influenza strains, and demonstrated its diagnostic-grade performance on 525 patient specimens in an academic setting and 166 specimens in a clinical setting. We further developed an mCARMEN panel to enable the identification of 6 SARS-CoV-2 variant lineages, including Delta and Omicron, and evaluated it on 2,088 patient specimens with near-perfect concordance to sequencing-based variant classification. Lastly, we implemented a combined Cas13 and Cas12 approach that enables quantitative measurement of SARS-CoV-2 and influenza A viral copies in samples. The mCARMEN platform enables high-throughput surveillance of multiple viruses and variants simultaneously, enabling rapid detection of SARS-CoV-2 variants.

Virtual-Partition Digital PCR for High-Precision Chromosomal Counting Applications

Digital PCR (dPCR) is the gold-standard analytical platform for rapid high-precision quantification of genomic fragments. However, current dPCR assays are generally limited to monitoring 1-2 analytes per sample, thereby limiting the platform's ability to address some clinical applications that require the simultaneous monitoring of 20-50 analytes per sample. Here, we present virtual-partition dPCR (VPdPCR), a novel analysis methodology enabling the detection of 10 or more target regions per color channel using conventional dPCR hardware and workflow. Furthermore, VPdPCR enables dPCR instruments to overcome upper quantitation limits caused by partitioning error. While traditional dPCR analysis establishes a single threshold to separate negative and positive partitions, VPdPCR establishes multiple thresholds to identify the number of unique targets present in each positive droplet based on fluorescence intensity. Each physical partition is then divided into a series of virtual partitions, and the resulting increase in partition count substantially decreases partitioning error. We present both a theoretical analysis of the advantages of VPdPCR and an experimental demonstration in the form of a 20-plex assay for noninvasive fetal aneuploidy testing. This demonstration assay─tested on 432 samples contrived from sheared cell-line DNA at multiple input concentrations and simulated fractions of euploid or trisomy-21 "fetal" DNA─is analyzed using both traditional dPCR thresholding and VPdPCR. VPdPCR analysis significantly lowers the variance of the chromosomal ratio across replicates and increases the accuracy of trisomy identification when compared to traditional dPCR, yielding > 98% single-well sensitivity and specificity. VPdPCR has substantial promise for increasing the utility of dPCR in applications requiring ultrahigh-precision quantitation.

Multiplex Nucleic Acid Assay of SARS-CoV-2 via a Lanthanide Nanoparticle-Tagging Strategy

Early diagnosis, early isolation, and early treatment are efficient solutions to control the COVID-19 pandemic. To achieve the accurate early diagnosis of SARS-CoV-2, a multiplex detection strategy is required for the cross-validation to solve the problem of "false negative" of the existing gold standard assay. Here, we present a multicomponent nucleic acid assay platform for SARS-CoV-2 detection based on lanthanide nanoparticle (LnNP)-tagging strategy. For targeting SARS-CoV-2's RNA fragments ORF1ab gene, RdRp gene, and E gene, three LnNP probes can be used simultaneously to identify three sites in one sample through elemental mass spectrometry detection with limits of detection of 1.2, 1.3, and 1.3 fmol, respectively. With the multisite cross-validation, we envision that this multiplex and sensitive detection platform may provide an effective strategy for SARS-CoV-2 fast screening with a high accuracy.