Abstract
The recent emergence of human coronaviruses (CoVs) causing severe acute respiratory syndrome (SARS) is posing a great threat to global public health. Therefore, the rapid and accurate identification of pathogenic viruses plays a vital role in selecting appropriate treatments, saving people's lives and preventing epidemics. Nucleic acids, including deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), are natural biopolymers composed of nucleotides that store, transmit, and express genetic information. Applications of nucleic acid detection range from genotyping and genetic prognostics, to expression profiling and detection of infectious disease. The nucleic acid detection for infectious diseases is widely used, as evidenced by the widespread use of COVID-19 tests for the containment of the pandemic. Nanotechnology influences all medical disciplines and has been considered as an essential tool for novel diagnostics, nanotherapeutics, vaccines, medical imaging, and the utilization of biomaterials for regenerative medicine. In this review, the recent advances in the development of nanotechnology-based diagnostic methods for coronavirus, and their applications in nucleic acid detection are discussed in detail. The techniques for the amplification of nucleic acids are summarized, as well as the use of magnetic nanoparticles for nucleic acid extraction. Besides, current challenges and future prospects are proposed, along with the great potential of nanotechnology for the effective diagnosis of coronavirus.
Keywords: Coronaviruses, Nucleic acid extraction, Nucleic acid amplification, Magnetic nanoparticles, CRISPR-Cas
1. Introduction
Coronaviruses (CoVs) belongs to the subfamily Coronavirinae in the family of Coronaviridae of the order Nidovirales, which are enveloped and spherical viruses with a single-stranded RNA genome(Haake et al., 2020). The first coronavirus was identified in the 1960s. Coronaviruses are classified into four genera including alpha-coronavirus(α-CoV), beta-coronavirus(β-CoV), gamma-coronavirus(γ-CoV), and delta-coronavirus(δ-CoV)(Chen et al., 2020a, Chen et al., 2020b), of which α-CoV and β-CoV are reported to infect humans(de Wilde et al., 2018). Coronaviruses can cause respiratory and neurological diseases(Corman et al., 2012; Lim et al., 2019; Wang et al., 2019). The emergence of CoVs causing severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) with global spread represents a significant threat to public health(Noh et al., 2017). Recently, the emergence of ribonucleic acid (RNA) enveloped human β-CoV named SARS-CoV-2 which is etiologically related to the well-known severe acute respiratory syndrome coronavirus (SARS-CoV), has been challenging the global public health community to confront a novel infectious disease (coronavirus disease 2019, COVID-19)(Shen et al., 2020). COVID-19 that affects the lower respiratory tract and manifests as pneumonia in humans(Sohrabi et al., 2020), remains a significant issue on the global health. They can infect respiratory, gastrointestinal, hepatic, and central nervous system of human(Ge et al., 2013). The pandemic of COVID-19 has threatened the public health worldwide, with the living and working conditions of billions of people globally severely disrupted due to various forms of social distancing and lockdowns in many cities.
Nucleic acids (DNA and RNA) are natural biopolymers composed of nucleotides which play crucial biological roles in all forms of living organisms, such as storing, encoding, and transmitting genetic information for cellular function maintaining and genetic information relaying. DNA stores genetic information and encodes the amino acid sequences of proteins responsible for cellular function. RNA plays various important roles in the coding, decoding, regulation, and expression of genes. Consequently, nucleic acids are used as important biomarkers for biological studies and medical diagnostics(Breaker, 2004). For example, nucleic acids are used for clinical diagnostics for infectious disease and cancer as well as for monitoring epidemics and outbreaks of new diseases(Hartman et al., 2013). Quantitative and qualitative determination of nucleic acids is of great significance in modern biology and medicine. Detection of DNA/RNA of pathogenic bacteria and viruses is beneficial for making an appropriate strategy for the treatment. During the past several decades, many approaches have been developed for the detection of nucleic acids that hold great promise for clinical translation. The classic nucleic acid test (NAT) process includes nucleic acid extraction, amplification, and detection. Molecular diagnostics is widely adapted in various fields such as disease detection and health monitoring, which evaluates the nucleic acids (e.g., DNA, RNA, or a variation of both) of bacteria or virus in human samples as for diseases diagnosis(Park et al., 2018). The polymerase chain reaction (PCR) has become one of the most important tools in molecular diagnostics, providing exquisite sensitivity and specificity for detection of nucleic acids. Real-time quantitative PCR (RT-qPCR) is a rapid, specific, and sensitive TaqMan PCR method for detection, subgrouping, and quantitation of pathogens. This assay increases the sensitivity of conventional PCR(Zhang et al., 2020). Owing to its broad applicability, high sensitivity, and high sequence specificity, the PCR-based method has become a routine and reliable technique for detecting coronaviruses(Shen et al., 2020).
Nanotechnology is broadly defined as the application of materials and devices where at least one dimension is less than 100 nm, which has already been employed in the diagnosis and treatment of viral diseases. Nanoscience and nanotechnology deal with very small particles in many disciplines such as biology, chemistry, materials science, physics, engineering and so on. Materials science is important in all areas of antiviral research, including viral structural and biological studies, detection, treatment and vaccination(Tang et al., 2020, Tang et al., 2020). Nanoparticles are small size particles with a large surface-to-volume ratio(Kaushik, 2020; Saravanan et al., 2021), which offer many applications in a range of fields from chemistry to biology and biomedicine(Zhu et al., 2015). Over the decades, nanoparticles have been widely used and studied for their unique properties, such as small size, surface adaptability, improved solubility and multifunctionality, resulting in the development of better and safer drugs, tissue-targeted treatments, personalized nanomedicine and early diagnosis and prevention of diseases(Fornaguera and García-Celma, 2017; Soares et al., 2018). A variety of nanomaterials, including metallic nanoparticles, magnetic nanoparticles, silica nanoparticles, polymeric nanoparticles, carbon nanotubes, and quantum dots, have already been used for virus detections that may open a new area of potential applications (Draz and Shafiee, 2018; Halfpenny and Wright, 2010; Lee et al., 2013). Nanotechnology has already proven its value through its diagnostic, vaccine, and therapeutic applications that have expanded into clinical applications(Varahachalam et al., 2021). Nanomaterials-based nucleic acid detection of viral infectious diseases has various advantages in the diagnostic field(Cheng et al., 2008). Moreover, nanomaterials are powerful tools for the diagnosis, prevention, and treatment of COVID-19(Tang et al., 2020, Tang et al., 2020).
Molecular diagnostics based on nucleic acid detection follows four basic steps: sample collection, nucleic acid extraction from the collected sample, nucleic acid amplification, and analysis. The process examines the nucleic acids to investigate the pathogen responsible for the infections. Efficient and robust nucleic acid extraction from complex clinical samples is one of the most fundamental steps. The nucleic acid extraction is the first step in the molecular analysis of nucleic acids (Wu et al., 2019). It is crucial for the entire detection and analysis as it directly influences the subsequent steps(Liu et al., 2010).Nucleic acid extraction generally involves cell (or virion) lysis, nucleic acid isolation and purification(Ali et al., 2017). However, traditional nucleic acid extraction methods are time-consuming and labor-intensive(Stray and Zimmermann, 2019). To address this issue, magnetic nanomaterials have offered a promising and efficient platform for the extraction and purification of nucleic acids. For the nucleic acid amplification, different techniques have been developed for the amplification for enhanced sensitivity. In this review, the recent advances in the development of nanotechnology-based diagnostic methods for coronavirus, and their applications in COVID-19 detection are discussed in detail. The use of magnetic nanomaterials for nucleic acid extraction and techniques for the amplification of nucleic acids are summarized (Scheme 1 ). Besides, current challenges and possible solutions are proposed, along with the great potential of nanotechnology for the effective diagnosis of coronavirus.
Scheme 1.
An overview of nucleic acid extraction and nucleic acid amplification for COVID-19 detection.
2. Nucleic acid extraction based on magnetic nanomaterials
2.1. Classification and characteristics of MNPs
Magnetic nanoparticles (MNPs) with a diameter of 10–100 nm are preferred for use in vivo, as this reduces particle opsonization and their subsequent clearance, allowing to overcome most of the physiological barriers (Canfarotta and Piletsky, 2014; Martinelli et al., 2019; Shubayev et al., 2009). The properties of MNPs strongly derive from their physicochemical characteristics, mean size, and morphology(Farinha et al., 2021; Manescu et al., 2021). As shown in Fig. 1 , MNPs are usually categorized into three groups: (1) single metal MNPs, (2) metal oxide MNPs, and (3) metal alloy MNPs. Single metal MNPs present a core composed of one single pure metal structure such as iron (Fe), Co, and nickel (Ni). Metal oxides essentially include iron oxides (FexOy) and ferrites, such as CoFe2O4, MgFe2O4 or MnFe2O4. Alloy MNPs consist of a combination of two or more different pure metals, for example, iron cobalt alloys (FeCo) and iron platinum alloys (FePt)(Farinha et al., 2021). MNPs have high surface area-to-volume ratio, high binding affinity to the detection targets, and can be magnetically controlled for aggregation and dispersion, making the pre-concentration, purification and separation of nucleic acids simple and easy. Active substances attached to the surface of MNPs can be combined with specific biomolecules, such as DNA/RNA, enzymes, proteins, and can be separated under the action of an external magnetic field that has rapid separation, high specificity, and good reproducibility. Therefore, MNPs are widely used for the rapid automatic detection of nucleic acids, which is of great significance in the medical field(Ngo et al., 2016; Tang et al., 2018; Tran and Piro, 2021).
Fig. 1.
The classification and characteristics of MNPs.
In MNPs-based methods, nucleic acids in lysed samples can be specifically absorbed on MNPs due to various surface-modified functional groups. Therefore, nanomaterials with magnetic properties could be customized with unique virus receptors, allowing viral molecules to attach to MNPs, which would facilitate their magnetic extraction using an external magnetic field. In the presence of magnetic fields, nucleic acids are rapidly separated from most impurities in the supernatant. After quick washing steps to eliminate trace impurities, purified nucleic acids can be further released from the surface of MNPs by elution buffer with altered ionic strength, which is much simpler and faster than spin column-based methods.
2.2. Magnetic beads technology
Magnetic beads technology is one of the emerging strategies for the extraction of genomic and RNA, plasmid, and mitochondrial DNA, and it is one of the common methods to alleviate the centrifugation requirements and has been widely used in the genomics and proteomics(Váradi et al., 2014). During the past few years, specifically functionalized magnetic particles have been developed. Together with an appropriate buffer system, they allow for the rapid and efficient purification directly after extraction from crude cell extracts(Berensmeier, 2006). Furthermore, they allow for simple automation of the entire process and the isolation of nucleic acids from larger sample volumes. Several studies have shown that magnetic particles can isolate as few as tens of copies of target sequences from 1 mL of serum, which could then be amplified and detected by conventional means. The positive aspect of this technique is that it avoids the centrifugation step and provides an alternative way for automation of extraction procedures from a large number of samples. Magnetic particle or beads are the first choice to eliminate centrifuge-dependent steps during the extraction process. Magnetic beads utilize different ligands such as antigens, antibodies, oligonucleotides, or aptamers, which bind specifically to its target in the sample. This extraction technique can be used in batch processes with a large number of samples (blood, tissues, etc.) and is relatively easy to perform, making it one of the best options for automation, high-throughput applications, and high sample processing capabilities(Franzreb et al., 2006). This method is also suitable for resource-limited environments as it is virtually equipment-free(Ali et al., 2017). Magnetic bead separation presents many advantages over centrifuge-dependent extraction process by allowing an equipment-free process. Magnetic bead-based extraction realizes the collection of nucleic acids through the binding of nucleic acids to a silicon-based matrix with moderate operation difficulty, high product purity, and proper automatic level.
2.3. MNPs-based nucleic acid extraction
The inherent properties of MNPs, such as high surface-to-volume ratio, magnetically controlled particle aggregation and dispersion(Ma et al., 2019) and their ability to bind to a large number of different biomolecules (DNA, RNA, enzymes), offer a promising platform for the isolation of DNA and RNA from complex samples as well as enriching nucleic acids that facilitates their detection(Farinha et al., 2021). In recent years, MNPs have been applied for nucleic acid separation and purification, which could bind nucleic acid by the free chemical groups modified on the surface(Bhati et al., 2021; Oberacker et al., 2019; Ota et al., 2006). Using magnetic separation methods, nucleic acids can be directly isolated from crude biological samples without any restrictions with respect to the sample volumes. Typically, MNPs allow the nucleic acids to be gathered by magnets, and the nucleic acid binding on MNPs could be rapidly eluted. The heterogeneous extraction process is greatly simplified and has the potential for the development of “universal” nucleic acid extraction methods for the detection of pathogens in various samples(Shih et al., 2016; Zhang et al., 2019). By developing functional magnetic materials and suitable buffers, it is possible to extract target nucleic acids from crude cell extracts and purify them directly and efficiently(Chen et al., 2021). The centrifugal steps that may lead to degradation of nucleic acids are avoided in the magnetic separation process(Smerkova et al., 2013). The entire separation process does not require centrifugation or column separation, and multiple samples can be processed simultaneously, which is easy to realize automatic operation. Due to its excellent efficiency, this method is particularly suitable for nucleic acid extraction of micro samples.
Fig. 2 illustrates a schematic diagram of the extraction process of nucleic acids based on MNPs. The whole process consists of five major steps: lysis, MNPs-nucleic acids binding, washing, elution, and collection. In this process, MNPs bind to the nucleic acids by functionalizing MNPs with ligands that specifically bind to DNA and RNA, and are then separated from the rest of sample matrix by applying a magnetic field through the magnetophoretic phenomenon. During this step, the MNPs, which are still bound to their target, are collected towards the magnet, making it easy to discard the unwanted material. The MNPs are then washed in elution buffer to facilitate the release of the DNA/RNA molecules from the nanoparticles. Afterwards, the MNPs are separated from the supernatant containing the free nucleic acid molecules using an external magnetic field(Chircov et al., 2019).
Fig. 2.
The extraction process of nucleic acids based on MNPs including lysis, binding, washing, elution, and collection.
2.4. Applications of MNPs in DNA/RNA extraction
Over the past few years, MNPs have been widely used in the biomedical fields including magnetic biosensing, magnetic imaging, magnetic separation, drug and gene delivery, and hyperthermia therapy. Ma et al. analyzed and compared genomic DNA extraction based on MNPs from E.coli JM109, yeast, whole blood, and serum respectively, and found that all these genomic nucleic acids extracted using the MNPs-based method from different species can be applicable for the molecular biology research(Ma et al., 2013). Kaur et al. demonstrated a rapid and highly sensitive detection of S. typhi, in which they employed a MNPs-based pathogen enrichment protocol, followed by loop-mediated nucleic acid amplification and simultaneous detection by an in-situ optical system(Kalendar et al., 2018). Won et al. developed a simple and fast bacteria isolation method using magnet nanoparticle-embedded silica nanotube (MNSNT). Under certain ionic conditions, bacteria in the sample were simply bound on the outside wall of MNSNT, which were further collected with a magnet. The bacteria separated with MNSNT were then detected using PCR after heat-induced cell lysis without the need of washing and elution steps(Won et al., 2013). Kang et al. described an approach of conducting MNPs-based nucleic acid extraction procedure in an ordinary plastic Pasteur pipette with no vortex mixer, centrifuge, or dry bath, and applied this approach to extract nucleic acid of various pathogens, including RNA virus, DNA virus, gram-negative bacteria, and gram-positive bacteria from a broad range of samples(Kang et al., 2021). Bhati et al. developed an improved method for DNA extraction from human saliva using bare MNPs, and they found that the yield and purity of DNA was higher compared to other methods. In addition, this method requires no centrifugation step while it is mandatory for the spin column-based techniques (Bhati et al., 2021). MNPs have been used in RT-qPCR diagnosis for the extraction of viral RNA from SARS-CoV-2. Studies have shown that silica-coated MNPs can be used to rapidly extract RNA from the virus in patient samples for the further detection by RT-PCR that avoided the needs for lengthy RNA extraction while also making the method more sensitive(Campos et al., 2020). Zhao et al. developed a carboxyl polymer-coated MNPs, namely pcMNPs, and established a simple but efficient viral RNA extraction system for the sensitive detection of SARS-CoV-2 RNA via RT-PCR. This method merges the lysis and binding steps, and the pcMNPs-RNA complexes can be directly introduced into RT-qPCR reactions(Yoo et al., 2021). As compared with traditional column-based extraction methods, the pcMNPs-based method has several advantages. Firstly, pcMNPs-based method combines the virus lysis and RNA binding into one step, and the pcMNPs-RNA complexes can be directly introduced into subsequent RT- PCR reactions without elution step, which dramatically reduces the operation time and risk of contamination. Secondly, pcMNPs have excellent viral RNA binding properties, which results in high sensitivity and linearity for the detection of SARS-CoV-2 viral RNA using RT-PCR. Thirdly, since no centrifugation steps are required, MNPs-based methods allow fully automated nucleic acid purification, which is highly important in current SARS-CoV-2 diagnosis. Furthermore, the pcMNPs-RNA complexes obtained by this method are also compatible with various isothermal amplification methods, and thus could be used in the development of point-of-care devices. In conclusion, benefitting from its simplicity, robustness, and excellent performances, this new extraction method may provide a promising alternative to solve the time-consuming and laborious viral RNA extraction operations, and thus exhibits a great potential in current molecular diagnosis of COVID-19, especially for the early clinical diagnosis.
One of the greatest advantages of the MNPs-based extraction methods is that their aggregation require neither centrifugation nor column separation, thus they can shorten the time for the separation the components, reduce the risk of cross-contamination, and eliminate the need for costly equipment, such as centrifuges and liquid chromatography systems(Laurent et al., 2008). Besides, MNPs-based methods can also serve for a variety of automated low- to high-throughput procedures that can help save time and money. In addition, magnetic separation allows for recycling of the magnetic beads that is useful for large-scale use. These advantages enable faster, more efficient and cheaper MNPs-based methods for nucleic acid separation and purification.
3. Nucleic acid detection based on CRISPR and amplification techniques
The clustered regularly interspaced short palindromic repeats (CRISPR) system, first discovered in bacteria and capable of removing viral genes incorporated into bacterial genes, has received a great deal of attention in the nucleic acid detection(Bolotin et al., 2005; Mojica et al., 2005). The working mechanism is that the CRISPR-Cas protein can locate and cut out the target nucleic acid sequence with the aid of an RNA called crRNA(Shmakov et al., 2017). Particularly, the CRISPR-Cas system shows outstanding gene-editing capabilities(Cong et al., 2013; Gilbert et al., 2013), and it also exhibits enormous promise for the rapid and sensitive detection of nucleic acid. At present, class 2 CRISPR-Cas system, including type II, type V and type VI, has been widely used to detect nucleic acids(van Dongen et al., 2020). Among the CRISPR-Cas system, the CRISPR-Cas12a has drawn a lot of attention since it not only can cleave the target nucleic acid (cis cleavage) but also exhibit strong collateral cleavage activity (trans-cleavage) for single-stranded DNA (ssDNA). Specifically, when the specific DNA targets are recognized by the Cas12a/crRNA duplex, the collateral cleavage activity of the system is activated to nonspecifically and indiscriminately cleave non-target ssDNA strands(Janice S. Chen et al., 2021). Recently, the trans-cleavage activity of Cas12a has been reported as 3–17 turnovers per second (Janice S. Chen et al., 2021). In CRISPR-Cas12a-based biosensing, a range of amplification techniques have been exploited for SARS-CoV-2 detection that allow for highly sensitive and specific detection of nucleic acids including recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP), and PCR (Scheme 2 ).
Scheme 2.
Schematic illustration of the techniques for nucleic acid amplification including RPA, LAMP, and PCR.
3.1. Recombinase polymerase amplification (RPA)
RPA is an isothermal nucleic acid amplification method that mimics the process of nucleic acid replication in cells(Olaf Piepenburg et al., 2006). RPA is free of thermal cycling process, making it simple to operate without the need of expensive, high-tech equipment. The reaction of RPA can be completed under 37–42 °C in 3–10 min, which can well integrate with the CRISPR-Cas12a system in an assay. Several RT-RPA-assisted CRISPR-Cas12a-based detection techniques have been developed for the sensitive detection of SARS-CoV-2. Using RT-RPA and Cas12a, Wang et al. developed a highly efficient detection platform able to detect less than 5 viral copies per reaction for the SARS-CoV-2 genes (Fig. 3 A)(Wang et al., 2021, Wang et al., 2021, Wang et al., 2021). Briefly, the target RNA was extracted from clinical samples and amplified by RT-RPA reaction, and the target amplicons were injected into the CRISPR-Cas12a system. Specifically, the target amplicon was recognized by the Cas12a/crRNA duplex, thus activating the collateral activity of CRISPR-Cas12a to cut the reporter probes effectively, leading to the quencher moving away from the fluorophore. Consequently, the fluorescence intensity increased. Huang et al. also combined RT-RPA with the CRISPR-Cas12a system for the detection of SARS-CoV-2 (Fig. 3B)(Huang et al., 2020). In this work, the trans-cleavage activity of the CRISPR-Cas12a system and a fluorescent signal probe was utilized to detect SARS-CoV-2. The detection process took ∼50 min and the limit of detection was 2 copies per sample. However, it still remains an issue that the target amplification and detection steps are separate from each other where the nucleic acid-rich sample (e.g., RPA amplicons) may expose to the environment that potentially increases the risk of contamination. To meet this challenge, Sun et al. developed a one-pot method based on RT-RPA and CRISPR-Cas12a to detect SARS-CoV-2 (Fig. 3C)(Sun et al., 2021). In this assay, the target RNA was first extracted from the clinical samples and then added to the bottom of the tube that contained the RT-RPA mix. Simultaneously, the CRISPR-Cas12a reagents were placed on the tube lid. During this amplification process, the target amplification regents and other components were physically separated. The CRISPR-Cas12a reagents were centrifuged to transfer them to the tube bottom after the amplification. At this time, the target amplicons served as activators to activate the Cas12a/crRNA duplex to cleave the reporter probe and increase the fluorescence intensity of the solution. The assay had excellent sensitivity and selectivity toward SARS-CoV-2, with a low detection limit of 2.5 copies/μl input (RNA standard), as well as a rapid process within 50 min.
Fig. 3.
CRISPR and RPA for the fluorescent detection of nucleic acid. (A) Schematic of the detection method based on RPA and CRISPR-Cas12a for the determination of SARS-CoV-2(Wang et al., 2021, Wang et al., 2021, Wang et al., 2021) (Copyright, 2021 American Chemical Society). (B) A CRISPR-fluorescent assay for detection of SARS-CoV-2 RNA in clinical samples(Huang et al., 2020) (Copyright, 2020 Elsevier, reproduced with permission from Elsevier Ltd.). (C) A RT-RPA-based detection method for SARS-CoV-2(Sun et al., 2021) (Copyright, 2021 Springer Nature, reproduced with permission from Springer Nature Ltd.).
Other than fluorescent reporters, colorimetric probes such as gold nanoparticles (AuNPs) are convenient and cost-effective for the detection of SARS-CoV-2 in clinical samples. Jiang et al. proposed an effective way to visually detect the SARS-CoV-2 genome by utilizing the magnetic pull-down to capture AuNPs (Fig. 4 A)(Jiang et al., 2021). In this assay, a biotinylated ssDNA probe served as a substrate for CRISPR-Cas12a, and AuNPs modified with complementary DNA strands were used to visually detect viral RNA. In the absence of the target RNA, the biotinylated ssDNA probes were kept intact and DNA-AuNPs would bind with biotinylated ssDNA probes through the DNA hybridization reaction. The complex was further immobilized on streptavidin-coated magnetic beads through biotin–streptavidin interaction. Consequently, the red DNA-AuNPs were magnetically enriched at the bottom of the tube and the supernatant presented colorless. When the target RNA was present, the CRISPR-Cas12 system was activated to cleave biotinylated ssDNA. At this point, the DNA-AuNPs would be in the supernatant, so the solution still showed a red color. In this assay, the detection limit is 50 RNA copies per reaction. The colorimetric detection of SARS-CoV-2 can be also achieved by modulating the dispersion state of AuNPs in solution. For instance, Zhang et al. developed a colorimetric assay based on RT-RPA and CRISPR-Cas12a to detect the SARS-CoV-2 genome (Fig. 4B)(Zhang et al., 2021, Zhang et al., 2021, Zhang et al., 2021). When the target was absent, the CRISPR-Cas12a system would not be activated and ssDNA strands modified on AuNPs were kept intact. Thus, AuNPs remained dispersed in the solution. However, when the target was present, the CRISPR-Cas12a system was activated to cleave ssDNA strands modified on AuNPs, leading to the aggregation of AuNPs. As a consequence, a peak shift and color change of the solution was observed. The detection limit of this approach is 1 copy of the viral genome sequence per test.
Fig. 4.
CRISPR and RPA for the colorimetric detection of nucleic acid. (A) CRISPR-Cas12a-based colorimetric technique for the visual detection of SARS-CoV-2 (Jiang et al., 2021) (Copyright, 2021 American Chemical Society). (B) RT-RPA-coupled CRISPR-Cas12a for the colorimetric detection of SARS-CoV-2(Zhang et al., 2021) (Copyright, 2021 American Chemical Society). (C) A wearable diagnostic approach for the visual detection of SARS-CoV-2 (Nguyen et al., 2021) (Copyright, 2021 Springer Nature, reproduced with permission from Springer Nature Ltd.).
AuNPs-based lateral flow assays (LFAs) can be also used to detect SARS-CoV-2 with naked-eye readout. Li et al. developed a microfluidic platform based on RT-RPA, CRISPR-Cas12a, and LFA for the contamination-free and visual detection of the SARS-CoV-2 genome(Li et al., 2022). In this method, the viral RNA was first extracted from swab samples. Subsequently, the extracted RNA was amplified by the RT-RPA reaction in a microfluidic chip. The detection results could be directly read by the naked eye against an LFA dipstick. In this LFA dipstick, fluorescein (FAM)-ssDNA-biotin probe acted as the reporter probe, and FAM would bind with anti-FITC antibody-AuNPs to achieve a visible detection. In the absence of the target, the reporter probe would not be cleaved, thus anti-FITC antibody-AuNPs would be anchored on the control band. Conversely, in the presence of the target, the reporter was cleaved by the activated CRISPR-Cas12a. At this time, anti-FITC antibody-AuNPs bound with the FAM and the complex was anchored on the test band. It accomplished the detection of 100 copies of SARS-CoV-2 RNA target. In addition, a novel detection platform was also reported which combined LFA and microfluidic chip with wearable materials (Fig. 4C)(Nguyen et al., 2021). This designed face-mask sensor contained three reaction zones, including the lysis reagents zone, RT-RPA reaction zone, and CRISPR-Cas12a reaction zone. When the SARS-CoV-2-derived amplicons were present, the Cas12a/crRNA duplex was activated to cut the FAM-ssDNA-biotin probe. At this point, the LFA strip was utilized to achieve the visual detection of the target. The whole assay took ∼1.5 h and the limit of detection for this face-mask sensor was 500 copies of the SARS-CoV-2 in vitro transcribed RNA.
3.2. Loop-mediated isothermal amplification (LAMP)
LAMP is an isothermal nucleic acid amplification technique in which a DNA polymerase and four DNA primers are used including two inner primers and two outer ones. LAMP can generally amplify a few copies of DNA targets to detectable amounts under isothermal conditions in less than an hour. LAMP only needs one enzyme for the exponential amplification, thus it holds great promise for point-of-care detections. LAMP can combine with the CRISPR-Cas system for the detection of nucleic acid. Specifically, the Cas12a/crRNA complex can recognize the LAMP product with a large amount of the repeat target-specific dsDNA. The CRISPR-Cas12a system is activated to cut the signal probes that provides an ideal way for the specific detection of dsDNA. At present, a variety of RT-LAMP-assisted CRISPR-Cas12a system-based sensing strategies have been designed for the sensitive detection of SARS-CoV-2.
Alfredo et al. proposed a direct approach based on LAMP and the CRISPR-Cas12a for the rapid detection of SARS-CoV-2 without RNA extraction (Fig. 5 A)(Garcia-Venzor et al., 2021). Firstly, the inactivated clinical sample was amplified through a LAMP reaction. Meanwhile, Cas12a protein and N-gene specific crRNA were preincubated and the reporter probe was added. The CRISPR-Cas12a system was activated by the target amplicons, leading to the cleavage of the reporter probe effectively. Consequently, the fluorophore was released and emitted fluorescence. The limit of detection for this method is 16 copies/μL. Despite the great amplification efficiency of LAMP, false-positive results inevitably affect the detection results. To meet this challenge, Zhang et al. proposed a detection method for the specific detection of SARS-CoV-2(Zhang et al., 2021, Zhang et al., 2021, Zhang et al., 2021). In this assay, a uracil-DNA-glycosylase (UDG) and the reverse transcription-LAMP were utilized to reduce the aerosol contamination. Based on this detection mechanism, the wild-type or spike mutant SARS-CoV-2 spike N501Y could be detected with a limit of detection of 10 copies/μL (wild-type). To reduce the risk of carryover contaminations, researchers developed a one-pot method for SARS-CoV-2 detection (Fig. 5B)(Pang et al., 2020). In this assay, the CRISPR-Cas12a reagents and RT-LAMP reagents were put inside the top and bottom of the tube, respectively. The target RNA was then put in the bottom where it was amplified based on RT-LAMP. The CRISPR-Cas12a reagents were then mixed with the target amplicon produced by RT-LAMP at the bottom of the tube by simply inverting the tube and flicking the wrist. As a consequence, the CRISPR-Cas12a system was activated to cleave the signal probe, leading to the generation of bright green fluorescence. The whole process took 40 min and showed 100% clinical specificity. Wang et al. also accomplished one-pot SARS-CoV-2 detection using a similar approach (Fig. 5C)(Wang et al., 2021). The whole process took 45 min and the diagnostic results accorded with the quantitative method authorized by the Centers for Disease Control and Prevention. To achieve the point-of-care detection of SARS-CoV-2 in clinical samples, Chen et al. utilized a smartphone and portable 3D printing instrument to realize contamination-free and visual SARS-CoV-2 detection(Wang et al., 2020a, Wang et al., 2020b). In this detection platform, the whole process takes 40 min, and the limit detection is 20 copies of RNA of SARS-CoV-2.
Fig. 5.
CRISPR and LAMP for the fluorescent detection of nucleic acid. (A) A fluorescent method based on LAMP and CRISPR-Cas12 for SARS-CoV-2 detection (Garcia-Venzor et al., 2021) (Copyright, 2021 Frontiers, reproduced with permission from Frontiers Ltd.). (B) A one-pot fluorescent method for COVID-19 detection(Pang et al., 2020) (Copyright, 2020 American Chemical Society). (C) Working principle of one-pot and visual COVID-19 detection based on RT-LAMP-CRISPR(Wang et al., 2021) (Copyright, 2021 Elsevier, reproduced with permission from Elsevier Ltd.).
Other than fluorescent readouts, colorimetric assays are also developed with a convenient and cost-effective readout for the detection of SARS-CoV-2. Zhang et al. designed a AuNPs-based visual assay that combined CRISPR-Cas12a with RT-LAMP for the rapid and sensitive determination of COVID-19 (Fig. 6 A)(Zhang et al., 2021). When the viral RNA was present, the Cas12a/crRNA duplex was activated to cleave linker-ssDNA. Hence, AuNP probe pairs (AuNP-DNA1 and AuNP-DNA2) could not be cross-linked and the solution displayed a red color. Conversely, when the SARS-CoV-2 RNA was absent, the CRISPR-Cas12a was kept inactivated and the linker-ssDNA was kept intact. Consequently, AuNP probe pairs were cross-linked with linker-ssDNA, accompanied by a color change from red to purple. The whole assay time was 40 min and it could detect down to 4 copies/μL of SARS-CoV-2 RNA. Apart from AuNPs, other colorimetric substrates could also be applied for the visual detection of viral nucleic acids. For instance, Xie et al. evaluated the performance of 16 types of fluorophore-ssDNA-quencher reporters that served as colorimetric substrates in CRISPR-Cas12a-based assays (Fig. 6B)(Xie et al., 2022). Among them, 9 fluorophore-ssDNA-quencher reporters were suitable for colorimetric detection, with an excellent performance using ROX-labeled reporters. Particularly, in this colorimetric method, a convolutional neural network algorithm was developed to standardize and automate the colorimetric analysis of images and integrated this into the MagicEye mobile phone software. The sensitivity of this technique reached 40 total copies.
Fig. 6.
CRISPR and LAMP for the detection of nucleic acid. (A) Schematic diagram for COVID-19 detection based on CRISPR-Cas12a and the LAMP(Zhang et al., 2021) (Copyright, 2021 Elsevier, reproduced with permission from Elsevier Ltd.). (B) A colorimetric method for the visual detection of SARS-CoV-2 (Xie et al., 2022) (Copyright, 2022 American Chemical Society). (C) A LFA strip for the portable and visual detection of SARS-CoV-2 (Broughton et al., 2020) (Copyright, 2020 Springer Nature, reproduced with permission from Springer Nature Ltd.).
For the quick and self-inspection of viral nucleic acids, LFA can be utilized as a portable, easy-to-operate tool. Broughton et al. reported a rapid RT-LAMP-assisted CRISPR-Cas12a system-based LFA for the detection of SARS-CoV-2 (Fig. 6C)(Broughton et al., 2020). To achieve the visual detection of SARS-CoV-2, the FAM-biotin reporter was used as a substrate and LFA strips were designed to capture the labeled reporter. FAM-biotin molecules were captured at the control line, but a signal was produced at the test line due to the collateral cleavage activity of CRISPR-Cas12a. Based on this detection mechanism, the whole assay took ∼45 min and the limit of detection is 10 copies per μL. Furthermore, Yi et al. designed a DNA capture probe-based LFA strip for the detection of SARS-CoV-2 (Yi et al., 2021). Compared to conventional LFA strips, streptavidin-modified AuNPs instead of anti-FAM antibody-AuNPs acted as signal probes for the naked-eye readout. When the target RNA was absent, the biotinylated-ssDNA reporter was kept intact and then hybridized with an ssDNA probe immobilized on the test line of the LFA strip. Subsequently, the streptavidin-modified AuNPs were captured by the test line through biotin-streptavidin interaction. The excess streptavidin-modified AuNPs were anchored on the control line by biotinylated-antibody. Conversely, when the target RNA was present, the biotinylated-ssDNA reporter was cleaved by the activated CRISPR-Cas12a system. At this time, streptavidin-modified AuNPs would not aggregate on the test line of the LFA strip due to a lack of biotinylated-ssDNA reporter, thus only showing a visible red signal on the control line. The approach achieved ultra-sensitivity of 1 copy/μL in ∼60 min. To improve the portability, Rezaei et al. proposed a portable and semi-automated device for the detection of COVID-19 (Rezaei et al., 2021). The device contained four parts, including a heater, a cooler fan, a proportional integral derivative controller to regulate the temperature, and designated areas for 0.2 mL Eppendorf® PCR tubes. Notably, up to 500 samples could be processed simultaneously in under 35 min using this multiplexing portable equipment that could increase the number of wells in the reaction zone.
3.3. Polymerase chain reaction (PCR)
For the molecular diagnosis of coronavirus infections, PCR is regarded as the gold standard due to its great sensitivity and specificity (Chan et al., 2015). A variety of biosensors have been proposed based on PCR and CRISPR-Cas12a. Specifically, by combining PCR pre-amplification with the CRISPR-Cas12a system, a nucleic acid detection approach named HOLMES (one-hour low-cost multipurpose highly efficient system) was proposed by Wang et al.(Li et al., 2018). Recently, Liang et al. proposed a RT-PCR-assisted CRISPR-Cas12a-based assay to specifically detect the Omicron variant (Fig. 7 A)(Liang et al., 2022). In this assay, the target RNA was extracted from patient samples and amplified by RT-PCR. Subsequently, the CRISPR-Cas12a system was activated by the target amplicons, resulting in an effective cleavage of the signal probe and an increase in the fluorescence signal. In contrast, no significant fluorescence signal was observed in the absence of the target RNA. Notably, this method had good specificity and sensitivity that could detect as low as 2 copies/μL of target RNA. To further improve portability, Li et al. constructed an automated microfluidic system to distinguish the variant of SARS-CoV-2 (Fig. 7B)(Li et al., 2021). By detecting 30 clinical samples, the practicability and accuracy of the assay were validated. This approach showed 100% consistency with the next-generation sequencing technology, which could act as a potential and portable tool to distinguish the delta variant of SARS-CoV-2.
Fig. 7.
CRISPR and PCR for the detection of nucleic acid. (A) RT-PCR and CRISPR-Cas12a for the detection of SARS-CoV-2 Omicron variant(Liang et al., 2022) (Copyright, 2022 Elsevier, reproduced with permission from Elsevier Ltd.). (B) Principle of the automated Cas12a-microfluidic assay(Li et al., 2021) (Copyright, 2021 Royal Society of Chemistry, reproduced with permission from Royal Society of Chemistry Ltd.). (C) A visual biosensor for SARS-CoV-2 detection(Ma et al., 2022) (Copyright, 2022 Elsevier, reproduced with permission from Elsevier Ltd.). (D) Schematic illustration of a solid-state nanopore sensor based on the CRISPR-Cas12a system (Nouri et al., 2021) (Copyright, 2021 American Chemical Society).
Besides fluorescence, colorimetric sensing strategies can be also developed for the detection of SARS-CoV-2. For the ultrasensitive detection of SARS-CoV-2, Ma et al. developed an RT-PCR-assisted CRISPR-Cas12a-powered detection device with a smartphone readout (Fig. 7C)(Ma et al., 2022). In the absence of SARS-CoV-2, the CRISPR-Cas12a system would not be activated and the linker ssDNA would not be cleaved. AuNPs-DNA would aggregate through complementary hybridization between the linker ssDNA and the ssDNA modified on AuNPs. At this point, the color of the solution changed from red to purple. Conversely, in the presence of target RNA, dsDNA amplicons would activate the Cas12a/crRNA complex to cut the linker ssDNA, resulting in the dispersion of AuNPs-DNA and a red color of the solution, achieving a limit of detection of 1 copy/μL.
Apart from the above sensing strategies, Nouri et al. proposed a solid-state CRISPR-Cas12a-assisted nanopore detection method for the specific determination of SARS-CoV-2(Fig. 7D)(Nouri et al., 2021). This work contained three streamlined steps: RT-PCR, CRISPR-Cas12a assay, and nanopore-based molecule classification and counting. The circular M13mp18 ssDNA with an excellent signal-to-noise ratio was selected as the reporter in the nanopore measurement. In this assay, the target RNA was first amplified by one-step RT-PCR. After amplification, the dsDNA amplicons served as activators to activate the CRISPR-Cas12 system to cleave the signal reporters, leading to a signal change. Conversely, in the absence of the target RNA, the CRISPR-Cas12 system would not be activated and the circular ssDNA reporter would not be degraded. The whole process took 65 min with a sensitivity of 13.5 copies/μL of viral RNA.
3.4. Other techniques for nucleic acid amplification
In addition to conventional RPA, LAMP, and PCR amplification, other amplification techniques have been constructed for the detection of nucleic acid. Recombinase-aided amplification (RAA), primer exchange reaction (PER), dual-priming isothermal amplification (DAMP), enzymatic recombinase amplification (ERA), multiple cross displacement amplification (MCDA), exonuclease III cleavage reaction, and entropy-driven reaction system, can combine with CRISPR-Cas12a to detect SARS-CoV-2.
Similar to RPA, RAA is an isothermal amplification technique without the need for a sophisticated thermal cycler in which recombinase uvsX (E. coli), ssDNA binding protein, and DNA polymerase were used. Wang et al. introduced an RT-RAA-assisted detection method to advance the point-of-care diagnosis of COVID-19 (Fig. 8 A)(Wang et al., 2020a, Wang et al., 2020b). Briefly, the target RNA was extracted from clinical samples and amplified by RT-RAA. Then, target amplicons were injected into the CRISPR-Cas12a reaction solution. The RT-RAA products would be recognized by the Cas12a/crRNA duplex, thus activating the trans-cleavage activity of the CRISPR-Cas12a system to cut the reporter probe. Consequently, it generated green fluorescence that could be seen with the naked eye under 485 nm light. The limit of detection for this method was 10 copies of SARS-CoV-2.
Fig. 8.
CRISPR and other amplification techniques for the detection of nucleic acid. (A) A detection approach based on the CRISPR-Cas12a system for naked-eye readout of COVID-19(Wang et al., 2020a, Wang et al., 2020b) (Copyright, 2020 Elsevier, reproduced with permission from Elsevier Ltd.). (B) Principle of the PER-based assay for the detection of SARS-CoV-2 RNA (Li et al., 2022) (Copyright, 2022 Royal Society of Chemistry, reproduced with permission from Royal Society of Chemistry Ltd.). (C) Overview of the digital warm-start-CRISPR assay(Ding et al., 2021) (Copyright, 2021 Elsevier, reproduced with permission from Elsevier Ltd.).
Another isothermal nucleic acid amplification technique known as PER was developed by Yin et al.(Kishi et al., 2017), which has received a great deal of interest in the fields of DNA nanotechnology and bio-imaging. Li et al. developed a technique for the detection of SARS-CoV-2 RNA by combining PER with CRISPER-Cas12a (Fig. 8B)(Li et al., 2022). When the target RNA was present, the PER cascade reaction was triggered to produce a significant amount of ssDNAs. The CRISPR-Cas12a system was activated by these ssDNAs to cut the signal reporter and generate a fluorescent signal. This detection approach could achieve a detection limit as low as 1.3 pM in 40 min at 37 °C.
RT-DAMP was proposed by Liu et al.(Ding et al., 2019), which was a variant of RT-LAMP with a new primer design strategy. Ding et al. chose RT-DAMP to develop a digital warm-start CRISPR assay for the quantitative detection of SARS-CoV-2 (Fig. 8C)(Ding et al., 2021). In this work, the detection was established by partitioning the first warm-start CRISPR-Cas12a-based reaction into sub-nanoliter aliquots within a Quant Studio 3D digital chip. In this assay, the target RNA was amplified by the RT-DAMP reaction to generate a lot of target amplicons. Meanwhile, the Cas12a/crRNA duplex was specifically bound to target amplicons to activate the cleavage activity of the CRISPR-Cas12a system. Consequently, the reporter probe was cleaved which generated a fluorescent signal. The assay was able to detect down to 5 copies/μl SARS-CoV-2 RNA in the chip.
MCDA, an isothermal nucleic acid amplification method, allows nucleic acid amplification using a simple instrument for COVID-19 diagnosis. Zhu et al. combined RT-MCDA with CRISPR-Cas12a to design an approach for the detection of SARS-CoV-2 RNA(Zhu et al., 2021). In this assay, the viral RNA was first converted to cDNA by RT reaction. Subsequently, the cDNA served as the template for MCDA amplification. Through MCDA amplification, a lot of amplicons with the TTTT PAM site were generated. Then, the CRISPR-Cas12a system was activated by amplicons to cleave the signal probes effectively. In particular, a LFA strip was applied for the signal readout to achieve visual detection. The detection could be completed within 1 h and the sensitivity of LFA was 7 copies (for each of the target templates) per test.
4. Conclusion and future perspective
The SARS-CoV-2 is a newly emerged virus that causes mild to severe pneumonia. COVID-19 is still a major issue worldwide after its global super-spread, and emerging viral diseases have not got specific and reliable treatments. The pivot of an effective human response to this COVID-19 pandemic is early, rapid and accurate testing of clinical samples of suspected and probable cases. Molecular diagnostics such as nucleic acid detection can achieve early and rapid detection of targets and are considered as an ideal approach for the detection of pathogens in infectious diseases. The nucleic acid detection for infectious diseases are widely used, as evidenced by the widespread use of COVID-19 tests for the containment of the pandemic(Zhou et al., 2020).
The ongoing SARS-CoV-2 pandemic highlights the importance of nanotechnology in providing tools and technologies for diagnostic and antiviral research. Nanotechnology is one of the most promising tools for the development of simple and fast sample preparation methods that require no multiple steps and complex systems for sample preparation because of the increased surface-to-volume ratio of nanostructures (Lundqvist et al., 2008). To date, considerable efforts have been made to improve the detection of coronavirus and a variety of improved or new methods have been developed. Obtaining high-quality nucleic acid is a key factor for pathogen detection since nucleic acid extraction is the first step. Accordingly, various studies have focused on methods to improve the efficiency of gene extraction, including the MNPs-based nucleic acid extraction. Nowadays, the idea of using magnetic separation techniques to purify biologically active compounds (nucleic acids and proteins) from the cells and cell organelles has attracted rapidly growing interest. Comparing with other nucleic acid separation techniques that are generally time-consuming and require expensive equipment, magnetic separation has several advantages. MNPs have exhibited superior properties such as larger surface-to-volume ratio, excellent reactivity and unique magnetic response(Hajba and Guttman, 2016; Haun et al., 2010), which make the pre-concentration, purification and separation of nucleic acids easy and feasible(Tang et al., 2020). MNPs shorten the purification stage and eliminate pre-treatment and pre-enrichment steps. Overall, MNPs are expected to facilitate the development of improved analysis protocols that are faster, cheaper and simpler than currently existing ones.
Accurate and early detection of SARS-CoV-2 infection is critical for minimizing spread and initiating treatment(Espy et al., 2006). Nucleic acid amplification has been considered the gold standard for diagnosis of many viral infections. The nucleic acid detection for infectious diseases are widely used, as evidenced by the widespread use of COVID-19 tests for the containment of the pandemic. In this review, we summarize the nucleic acids detection methods based on the combination of CRISPR-Cas and amplification techniques such as RPA, LAMP, PCR and other nucleic acid amplification methods. The performances of these detection methods based on amplification techniques with CRISPR-Cas12a technology for coronavirus detection are listed in Table 1 , and the comparison in the reaction conditions of different amplification techniques is listed in Table 2 . In summary, technical breakthroughs have been reported in viral detection on molecular levels. Many of these breakthroughs have taken advantage of recent advances in rapidly evolving micro and nanotechnology to bring improvements to the speed, sensitivity, operability, and portability of viral diagnostics. Nanomaterials can provide new opportunities such as more efficient, convenient, and safer applications. However, challenges still remain such as costs, toxicities to the environment and humans, and regulatory issues before being introduced to the market. Although the nanotechnology-based approaches for the nucleic acid extraction and detection of coronavirus have attracted considerable attentions, there are still some challenges that should be addressed in the future works. Firstly, the magnetic separation performance of MNPs should be improved to meet the application requirements in fast response and accurate positioning under external magnetic field(Liu et al., 2018; Wu et al., 2016). Secondly, the stability of MNPs could be improved that directly affects their real-world application (Defaei et al., 2018). Thirdly, the amplification techniques can be simplified and optimized to well integrate with other platforms such as microfluidics to better serve as point-of-care testing tools for the detection of SARS-CoV-2. The fight against infectious diseases caused by SARS-CoV-2 remains challenging despite the tremendous efforts and significant advances in public healthcare. As shown in this review, nanotechnology has already been shown to enhance the diagnostics in coronavirus infections. To tackle future challenges, the collaboration between different scientific fields, clinicians and industry is required. With the rapid development of new technologies and methods, we believe that more excellent and efficient detection methods will be developed in the future.
Table 1.
A summary of the detection performance based on amplification techniques with CRISPR-Cas12a technology for coronavirus detection.
| Signal amplification | Sensitivity | Detection time | Real sample | Signal output | Ref |
|---|---|---|---|---|---|
| RPA | <5 viral copies per reaction | – | Nasal swab, oropharyngeal swab, anal swab, sputum, stool, and sputum supernatant | Fluorescence | Wang et al. (2021c) |
| 2 copies per sample | ∼50 min | Nasal swab | Fluorescence | (Huang et al., 2020b) | |
| 2.5 copies/μl input (RNA standard); 1 copy/μl input (pseudovirus) |
∼50 min | Pharyngeal swab | Fluorescence | Sun et al. (2021) | |
| 2 copies/μL of full-length COVID-19 genome; 0.5 copy/μL of DNA fragment of N gene | – | – | Fluorescence | Malci et al. (2022) | |
| 50 RNA copies per reaction | – | Nasopharyngeal and throat swab | Colorimetry | (Jiang et al., 2021b) | |
| 1 copy of viral genome sequence per test | – | Clinical standard sample | Colorimetry | (Zhang et al., 2021d) | |
| 100 copies | – | Nasopharyngeal swab | LFA | Li et al. (2022b) | |
| 500 copies | ∼90 min | Clinical samples | LFA | Nguyen et al. (2021) | |
| LAMP | 16 copies/μL | 40 min | Clinical samples | Fluorescence | (Alfredo Garcia-Venzor et al., 2021) |
| 10 copies/μL (wild-type) | – | – | Fluorescence | Zhang et al. (2021b) | |
| 30 copies/μL (150 copies) | 40 min | Respiratory swab | Fluorescence | (Pang et al., 2020b) | |
| 5 copies | 45 min | Clinical samples | Fluorescence | (Wang et al., 2021a) | |
| 20 copies | 40 min | Respiratory swab | Fluorescence | Chen et al. (2020b) | |
| 4 copies/μL | 40 min | – | Colorimetry | (Zhang et al., 2021e) | |
| 58 copies | – | Clinical samples | Colorimetry | Xie et al. (2022) | |
| 10 copies per μl input | <40 min | Respiratory swab | LFA | Broughton et al. (2020) | |
| 1 copy/μL | ∼60/32 min | Nasopharyngeal swab | LFA | (Yi et al., 2021b) | |
| 35 copies/μL | 35 min | Nasopharyngeal or oropharyngeal swab | LFA | Rezaei et al. (2021) | |
| PCR | 2 copies per reaction | – | Oropharyngeal swab | Fluorescence | (Yuanhao Liang et al., 2022) |
| 1 copy/μL | – | Clinical samples | Fluorescence | Li et al. (2021) | |
| 1 copy/μL | ∼90 min | Throat swab | Colorimetry | (Ma et al., 2022a) | |
| 13.5 copies/μL | 65 min | – | Electrochemistry | (Nouri et al., 2021a) | |
| RAA | 10 copies | – | Clinical samples | Fluorescence | Wang et al. (2020b) |
| PER | 1.3 pM | 40 min | Complex biological samples | Fluorescence | (Li et al., 2022a) |
| DAMP | 5 copies/μL | – | Swab and saliva samples | Fluorescence | (Ding et al., 2021a) |
| ERA | 0.25/0.5 copies/μL | 40 min | Clinical samples | LFA | Liu et al. (2021) |
| MCDA | ∼60 min | 7 copies/test | LFA | Zhu et al. (2021) |
Table 2.
A comparison of the reaction conditions of different amplification techniques.
| Methods | Target | Primers | Required enzymes | Reaction time (h) | Temperature (°C) | Amplicon |
|---|---|---|---|---|---|---|
| RPA | DNA | 2 | DNA polymerase and recombinase | 0.5–1.5 | 37–42 | DNA |
| LAMP | DNA | 4–6 | DNA polymerase | <1 | 60–65 | DNA |
| PCR | DNA | 2 | Taq DNA polymerase | 1.5–2 | 95/55/72 | DNA |
| RAA | DNA | 2 | DNA polymerase and recombinase | 0.5 | 37 | DNA |
| PER | RNA | 1 | DNA polymerase | 0.5 | 37 | DNA |
| DAMP | DNA | 6 | DNA polymerase | 2 | 60–65 | DNA |
| ERA | DNA | 2 | DNA polymerase and recombinase | 20–30 | 37–42 | DNA |
| MCDA | DNA | 6 | DNA polymerase | 35 | 63 | DNA |
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors are grateful for the financial support from National Key Research and Development Program of China (2020YFA0909100), National Natural Science Foundation of China (22104128), Zhejiang Provincial Natural Science Foundation of China (LR22C200003), and the Fundamental Research Funds for the Central Universities (226-2022-00169).
Data availability
No data was used for the research described in the article.
References
- Alfredo Garcia-Venzor B.R.-Z., Marquez-Garcia Eduardo, Maldonado Vilma, Moncada-Morales Angelica, Olivera Hiram, Lopez Irma, Zuñiga Joaquin, Melendez-Zajgla Jorge. Front. Med. 2021;8 doi: 10.3389/fmed.2021.627679. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ali N., Rampazzo R.d.C.P., Costa A.D.T., Krieger M.A. BioMed Res. Int. 2017;2017:1–13. doi: 10.1155/2017/9306564. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Berensmeier S. Appl. Microbiol. Biotechnol. 2006;73(3):495–504. doi: 10.1007/s00253-006-0675-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bhati A., Varghese A., Rajan G., Sridhar V., Mohan Y., Pradeep S., Babu S., Kaikkolante N., Sarma M., Arun S., Sekar A.P., Iype T., Santhosh S., Ramchand C.N. Anal. Biochem. 2021;624 doi: 10.1016/j.ab.2021.114182. [DOI] [PubMed] [Google Scholar]
- Bolotin A., Quinquis B., Sorokin A., Ehrlich S.D. Microbiol. 2005;151(8):2551–2561. doi: 10.1099/mic.0.28048-0. [DOI] [PubMed] [Google Scholar]
- Breaker R.R. Nature. 2004;432(7019):838–845. doi: 10.1038/nature03195. [DOI] [PubMed] [Google Scholar]
- Broughton J.P., Deng X., Yu G., Fasching C.L., Servellita V., Singh J., Miao X., Streithorst J.A., Granados A., Sotomayor-Gonzalez A., Zorn K., Gopez A., Hsu E., Gu W., Miller S., Pan C.-Y., Guevara H., Wadford D.A., Chen J.S., Chiu C.Y. Nat. Biotechnol. 2020;38(7):870–874. doi: 10.1038/s41587-020-0513-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Campos E.V.R., Pereira A.E.S., de Oliveira J.L., Carvalho L.B., Guilger-Casagrande M., de Lima R., Fraceto L.F. J. Nanobiotechnol. 2020;18(1) doi: 10.1186/s12951-020-00685-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Canfarotta F., Piletsky S.A. Adv. Healthc. Mater. 2014;3(2):160–175. doi: 10.1002/adhm.201300141. [DOI] [PubMed] [Google Scholar]
- Chan J.F., Choi G.K., Tsang A.K., Tee K.M., Lam H.Y., Yip C.C., To K.K., Cheng V.C., Yeung M.L., Lau S.K., Woo P.C., Chan K.H., Tang B.S., Yuen K.Y. J. Clin. Microbiol. 2015;53(8):2722–2726. doi: 10.1128/JCM.01224-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chen T.Y., Vorsino A.E., Kosinski K.J., Romero-Weaver A.L., Buckner E.A., Chiu J.C., Lee Y. JoVE-J Vis. 2021;170 doi: 10.3791/62354. [DOI] [PubMed] [Google Scholar]
- Chen Y., Liu Q., Guo D. J. Med. Virol. 2020;92(4):418–423. doi: 10.1002/jmv.25681. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chen Y., Shi Y., Chen Y., Yang Z., Wu H., Zhou Z., Li J., Ping J., He L., Shen H., Chen Z., Wu J., Yu Y., Zhang Y., Chen H. Biosens. Bioelectron. 2020;169 doi: 10.1016/j.bios.2020.112642. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cheng X., Chen G., Rodriguez W.R. Anal. Bioanal. Chem. 2008;393(2):487–501. doi: 10.1007/s00216-008-2514-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chircov C., Grumezescu A.M., Holban A.M. Materials. 2019;12(13) doi: 10.3390/ma12132158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cong L., Ran F.A., Cox D., Lin S., Barretto R., Habib N., Hsu P.D., Wu X., Jiang W., Marraffini L.A., Zhang F. Science. 2013;339(6121):819–823. doi: 10.1126/science.1231143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Corman V.M., Eckerle I., Bleicker T., Zaki A., Landt O., Eschbach-Bludau M., van Boheemen S., Gopal R., Ballhause M., Bestebroer T.M., Muth D., Müller M.A., Drexler J.F., Zambon M., Osterhaus A.D., Fouchier R.M., Drosten C. Euro Surveill. : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin. 2012;17(39) doi: 10.2807/ese.17.39.20285-en. [DOI] [PubMed] [Google Scholar]
- de Wilde A.H., Snijder E.J., Kikkert M., van Hemert M.J. Curr. Top. Microbiol. 2018;419:1–42. doi: 10.1007/82_2017_25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Defaei M., Taheri-Kafrani A., Miroliaei M., Yaghmaei P. Int. J. Biol. Macromol. 2018;113:354–360. doi: 10.1016/j.ijbiomac.2018.02.147. [DOI] [PubMed] [Google Scholar]
- Ding X., Xu Z., Yin K., Sfeir M., Liu C. Anal. Chem. 2019;91(20):12852–12858. doi: 10.1021/acs.analchem.9b02582. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ding X., Yin K., Li Z., Sfeir M.M., Liu C. Biosens. Bioelectron. 2021;184 doi: 10.1016/j.bios.2021.113218. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Draz M.S., Shafiee H. Theranostics. 2018;8(7):1985–2017. doi: 10.7150/thno.23856. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Espy M.J., Uhl J.R., Sloan L.M., Buckwalter S.P., Jones M.F., Vetter E.A., Yao J.D., Wengenack N.L., Rosenblatt J.E., Cockerill F.R., 3rd, Smith T.F. Clin. Microbiol. Rev. 2006;19(1):165–256. doi: 10.1128/CMR.19.1.165-256.2006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Farinha P., Coelho J.M.P., Reis C.P., Gaspar M.M. Nanomaterials. 2021;11(12):3432. doi: 10.3390/nano11123432. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fornaguera C., García-Celma M.J. J. Personalized Med. 2017;7(4) doi: 10.3390/jpm7040012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Franzreb M., Siemann-Herzberg M., Hobley T.J., Thomas O.R. Appl. Microbiol. Biotechnol. 2006;70(5):505–516. doi: 10.1007/s00253-006-0344-3. [DOI] [PubMed] [Google Scholar]
- Ge X.Y., Li J.L., Yang X.L., Chmura A.A., Zhu G., Epstein J.H., Mazet J.K., Hu B., Zhang W., Peng C., Zhang Y.J., Luo C.M., Tan B., Wang N., Zhu Y., Crameri G., Zhang S.Y., Wang L.F., Daszak P., Shi Z.L. Nature. 2013;503(7477):535–538. doi: 10.1038/nature12711. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gilbert Luke A., Larson Matthew H., Morsut L., Liu Z., Brar Gloria A., Torres Sandra E., Stern-Ginossar N., Brandman O., Whitehead Evan H., Doudna Jennifer A., Lim Wendell A., Weissman Jonathan S., Qi Lei S. Cell. 2013;154(2):442–451. doi: 10.1016/j.cell.2013.06.044. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Haake C., Cook S., Pusterla N., Murphy B. Viruses. 2020;12(9) doi: 10.3390/v12091023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hajba L., Guttman A. Biotechnol. Adv. 2016;34(4):354–361. doi: 10.1016/j.biotechadv.2016.02.001. [DOI] [PubMed] [Google Scholar]
- Halfpenny K.C., Wright D.W. WIREs Nanomed. Nanobiotechn. 2010;2(3):277–290. doi: 10.1002/wnan.83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hartman M.R., Ruiz R.C.H., Hamada S., Xu C., Yancey K.G., Yu Y., Han W., Luo D. Nanoscale. 2013;5(21) doi: 10.1039/c3nr04015a. [DOI] [PubMed] [Google Scholar]
- Haun J.B., Yoon T.J., Lee H., Weissleder R. WIREs Nanomed. Nanobiotechn. 2010;2(3):291–304. doi: 10.1002/wnan.84. [DOI] [PubMed] [Google Scholar]
- Huang Z., Tian D., Liu Y., Lin Z., Lyon C.J., Lai W., Fusco D., Drouin A., Yin X., Hu T., Ning B. Biosens. Bioelectron. 2020;164 doi: 10.1016/j.bios.2020.112316. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Janice S., Chen E.M., Harrington Lucas B., Da Costa Maria, Tian Xinran, Palefsky Joel M., Doudna Jennifer A. Science. 2021;360:436–439. doi: 10.1126/science.aar6245. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jiang Y., Hu M., Liu A.-A., Lin Y., Liu L., Yu B., Zhou X., Pang D.-W. ACS Sens. 2021;6(3):1086–1093. doi: 10.1021/acssensors.0c02365. [DOI] [PubMed] [Google Scholar]
- Kalendar R., Kaur A., Kapil A., Elangovan R., Jha S., Kalyanasundaram D. PLoS One. 2018;13(3) doi: 10.1371/journal.pone.0194817. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kang J., Li Y., Zhao Y., Wang Y., Ma C., Shi C. Anal. Biochem. 2021;635 doi: 10.1016/j.ab.2021.114445. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kaushik A. Expet Opin. Drug Deliv. 2020;18(5):531–534. doi: 10.1080/17425247.2021.1860938. [DOI] [PubMed] [Google Scholar]
- Kishi J.Y., Schaus T.E., Gopalkrishnan N., Xuan F., Yin P. Nat. Chem. 2017;10(2):155–164. doi: 10.1038/nchem.2872. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Laurent S., Forge D., Port M., Roch A., Robic C., Vander Elst L., Muller R.N. Chem. Rev. 2008;108(6):2064–2110. doi: 10.1021/cr068445e. [DOI] [PubMed] [Google Scholar]
- Lee J.H., Kim J.W., Cheon J. Mol. Cell. 2013;35(4):274–284. doi: 10.1007/s10059-013-0103-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li D., Duan C., Cheng W., Gong Y., Yao Y., Wang X., Wang Z., Xiang Y. Chem. Commun. 2022;58(28):4484–4487. doi: 10.1039/d2cc00488g. [DOI] [PubMed] [Google Scholar]
- Li P., Zhang J., Lin Q., Kong J., Fang X. Chem. Commun. 2021;57(92):12270–12272. doi: 10.1039/d1cc04874k. [DOI] [PubMed] [Google Scholar]
- Li S.Y., Cheng Q.X., Wang J.M., Li X.Y., Zhang Z.L., Gao S., Cao R.B., Zhao G.P., Wang J. Cell Discov. 2018;4:20. doi: 10.1038/s41421-018-0028-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li Z., Ding X., Yin K., Avery L., Ballesteros E., Liu C. Biosens. Bioelectron. 2022;199 doi: 10.1016/j.bios.2021.113865. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Liang Y., Lin H., Zou L., Deng X., Tang S. Biosens. Bioelectron. 2022;205 doi: 10.1016/j.bios.2022.114098. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lim X.F., Lee C.B., Pascoe S.M., How C.B., Chan S., Tan J.H., Yang X., Zhou P., Shi Z., Sessions O.M., Wang L.F., Ng L.C., Anderson D.E., Yap G. J. Gen. Virol. 2019;100(10):1363–1374. doi: 10.1099/jgv.0.001307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Liu H., Li S., Liu L., Tian L., He N. Biosens. Bioelectron. 2010;26(4):1442–1448. doi: 10.1016/j.bios.2010.07.078. [DOI] [PubMed] [Google Scholar]
- Liu S., Huang M., Xu Y., Kang J., Ye S., Liu S., Wang Z., Liu H., Yu J., Hu K., Wang T. Virol. Sin. 2021;36(5):1083–1087. doi: 10.1007/s12250-021-00406-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Liu Z., Liu Y., Shen S., Wu D. J. Mater. Chem. B. 2018;6(3):366–380. doi: 10.1039/c7tb02941a. [DOI] [PubMed] [Google Scholar]
- Lundqvist M., Stigler J., Elia G., Lynch I., Cedervall T., Dawson K.A. Proc. Natl. Acad. Sci. U.S.A. 2008;105(38):14265–14270. doi: 10.1073/pnas.0805135105. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ma C., Li C., Wang F., Ma N., Li X., Li Z., Deng Y., Wang Z., Xi Z., Tang Y., He N. J. Biomed. Nanotechnol. 2013;9(4):703–709. doi: 10.1166/jbn.2013.1566. [DOI] [PubMed] [Google Scholar]
- Ma L., Yin L., Li X., Chen S., Peng L., Liu G., Ye S., Zhang W., Man S. Biosens. Bioelectron. 2022;195 doi: 10.1016/j.bios.2021.113646. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ma Y., Chen T., Iqbal M.Z., Yang F., Hampp N., Wu A., Luo L. Electrophoresis. 2019;40(16-17):2011–2028. doi: 10.1002/elps.201800401. [DOI] [PubMed] [Google Scholar]
- Malci K., Walls L.E., Rios-Solis L. ACS Synth. Biol. 2022;11(4):1555–1567. doi: 10.1021/acssynbio.1c00617. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Manescu V., Paltanea G., Antoniac I., Vasilescu M. Materials. 2021;14(20):5948. doi: 10.3390/ma14205948. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Martinelli C., Pucci C., Ciofani G. APL Bioengin. 2019;3(1) doi: 10.1063/1.5079943. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mojica F.J., Díez-Villaseñor C., García-Martínez J., Soria E. J. Mol. Evol. 2005;60(2):174–182. doi: 10.1007/s00239-004-0046-3. [DOI] [PubMed] [Google Scholar]
- Ngo H.T., Wang H.N., Fales A.M., Vo-Dinh T. Anal. Bioanal. Chem. 2016;408(7):1773–1781. doi: 10.1007/s00216-015-9121-4. [DOI] [PubMed] [Google Scholar]
- Nguyen P.Q., Soenksen L.R., Donghia N.M., Angenent-Mari N.M., de Puig H., Huang A., Lee R., Slomovic S., Galbersanini T., Lansberry G., Sallum H.M., Zhao E.M., Niemi J.B., Collins J.J. Nat. Biotechnol. 2021;39(11):1366–1374. doi: 10.1038/s41587-021-00950-3. [DOI] [PubMed] [Google Scholar]
- Noh J.Y., Yoon S.-W., Kim D.-J., Lee M.-S., Kim J.-H., Na W., Song D., Jeong D.G., Kim H.K. Arch. Virol. 2017;162(6):1617–1623. doi: 10.1007/s00705-017-3281-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Nouri R., Jiang Y., Tang Z., Lian X.L., Guan W. Nano Lett. 2021;21(19):8393–8400. doi: 10.1021/acs.nanolett.1c02974. [DOI] [PubMed] [Google Scholar]
- Oberacker P., Stepper P., Bond D.M., Höhn S., Focken J., Meyer V., Schelle L., Sugrue V.J., Jeunen G.J., Moser T., Hore S.R., von Meyenn F., Hipp K., Hore T.A., Jurkowski T.P. PLoS Biol. 2019;17(1) doi: 10.1371/journal.pbio.3000107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Olaf Piepenburg C.H.W., Stemple Derek L., Armes Niall A. PLoS Biol. 2006;4(7) doi: 10.1371/journal.pbio.0040204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ota H., Lim T.K., Tanaka T., Yoshino T., Harada M., Matsunaga T. J. Biotechnol. 2006;125(3):361–368. doi: 10.1016/j.jbiotec.2006.03.007. [DOI] [PubMed] [Google Scholar]
- Pang B., Xu J., Liu Y., Peng H., Feng W., Cao Y., Wu J., Xiao H., Pabbaraju K., Tipples G., Joyce M.A., Saffran H.A., Tyrrell D.L., Zhang H., Le X.C. Anal. Chem. 2020;92(24):16204–16212. doi: 10.1021/acs.analchem.0c04047. [DOI] [PubMed] [Google Scholar]
- Park C.Y., Park Y.H., Kim Y.S., Song H.J., Kim J.D. Biomed. Eng. Online. 2018;17(S2) [Google Scholar]
- Rezaei M., Razavi Bazaz S., Morshedi Rad D., Shimoni O., Jin D., Rawlinson W., Ebrahimi Warkiani M. Biosensors. 2021;11(10) doi: 10.3390/bios11100369. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Saravanan M., Mostafavi E., Vincent S., Negash H., Andavar R., Perumal V., Chandra N., Narayanasamy S., Kalimuthu K., Barabadi H. Micro. Pathogens. 2021;156 doi: 10.1016/j.micpath.2021.104908. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shen M., Zhou Y., Ye J., Abdullah Al-maskri A.A., Kang Y., Zeng S., Cai S. J. Pharm. Anal. 2020;10(2):97–101. doi: 10.1016/j.jpha.2020.02.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shih C.L., Chong K.Y., Hsu S.C., Chien H.J., Ma C.T., Chang J.W., Yu C.J., Chiou C.C. New biotechnol. 2016;33(1):116–122. doi: 10.1016/j.nbt.2015.09.003. [DOI] [PubMed] [Google Scholar]
- Shmakov S., Smargon A., Scott D., Cox D., Pyzocha N., Yan W., Abudayyeh O.O., Gootenberg J.S., Makarova K.S., Wolf Y.I., Severinov K., Zhang F., Koonin E.V. Nat. Rev. Microbiol. 2017;15(3):169–182. doi: 10.1038/nrmicro.2016.184. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shubayev V.I., Pisanic T.R., 2nd, Jin S. Adv. Drug Deliv. Rev. 2009;61(6):467–477. doi: 10.1016/j.addr.2009.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Smerkova K., Dostalova S., Vaculovicova M., Kynicky J., Trnkova L., Kralik M., Adam V., Hubalek J., Provaznik I., Kizek R. J. Pharmaceut. Biomed. 2013;86:65–72. doi: 10.1016/j.jpba.2013.07.039. [DOI] [PubMed] [Google Scholar]
- Soares S., Sousa J., Pais A., Vitorino C. Front. Chem. 2018;6:360. doi: 10.3389/fchem.2018.00360. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sohrabi C., Alsafi Z., O'Neill N., Khan M., Kerwan A., Al-Jabir A., Iosifidis C., Agha R. Int. J. Surg. 2020;76:71–76. doi: 10.1016/j.ijsu.2020.02.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Stray J., Zimmermann B. Methods Mol. Biol. 2019;1885:309–323. doi: 10.1007/978-1-4939-8889-1_21. [DOI] [PubMed] [Google Scholar]
- Sun Y., Yu L., Liu C., Ye S., Chen W., Li D., Huang W. J. Transl. Med. 2021;19(1):74. doi: 10.1186/s12967-021-02741-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tang C., He Z., Liu H., Xu Y., Huang H., Yang G., Xiao Z., Li S., Liu H., Deng Y., Chen Z., Chen H., He N. J. Nanobiotechnol. 2020;18(1) doi: 10.1186/s12951-020-00613-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tang S., Gu Y., Lu H., Dong H., Zhang K., Dai W., Meng X., Yang F., Zhang X. Anal. Chim. Acta. 2018;1004:1–9. doi: 10.1016/j.aca.2017.12.004. [DOI] [PubMed] [Google Scholar]
- Tang Z., Kong N., Zhang X., Liu Y., Hu P., Mou S., Liljeström P., Shi J., Tan W., Kim J.S., Cao Y., Langer R., Leong K.W., Farokhzad O.C., Tao W. Nat. Rev. Mater. 2020;5(11):847–860. doi: 10.1038/s41578-020-00247-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tran H.V., Piro B. Mikrochim. Acta. 2021;188(4):128. doi: 10.1007/s00604-021-04784-3. [DOI] [PubMed] [Google Scholar]
- van Dongen J.E., Berendsen J.T.W., Steenbergen R.D.M., Wolthuis R.M.F., Eijkel J.C.T., Segerink L.I. Biosens. Bioelectron. 2020;166 doi: 10.1016/j.bios.2020.112445. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Váradi C., Lew C., Guttman A. Anal. Chem. 2014;86(12):5682–5687. doi: 10.1021/ac501573g. [DOI] [PubMed] [Google Scholar]
- Varahachalam S.P., Lahooti B., Chamaneh M., Bagchi S., Chhibber T., Morris K., Bolanos J.F., Kim N.-Y., Kaushik A. Int. J. Nanomed. 2021;16:539–560. doi: 10.2147/IJN.S283686. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang N., Luo C., Liu H., Yang X., Hu B., Zhang W., Li B., Zhu Y., Zhu G., Shen X., Peng C., Shi Z. Viruses. 2019;11(4) doi: 10.3390/v11040379. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang R., Qian C., Pang Y., Li M., Yang Y., Ma H., Zhao M., Qian F., Yu H., Liu Z., Ni T., Zheng Y., Wang Y. Biosens. Bioelectron. 2021;172 doi: 10.1016/j.bios.2020.112766. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang X., Chen X., Chu C., Deng Y., Yang M., Ji Z., Xu F., Huo D., Luo Y., Hou C. Sensor. Actuator. B Chem. 2020;323 [Google Scholar]
- Wang X., Zhong M., Liu Y., Ma P., Dang L., Meng Q., Wan W., Ma X., Liu J., Yang G., Yang Z., Huang X., Liu M. Sci. Bull. 2020;65(17):1436–1439. doi: 10.1016/j.scib.2020.04.041. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang Y., Guo Y., Zhang L., Yang Y., Yang S., Yang L., Chen H., Liu C., Li J., Xie G. Sensor. Actuator. B Chem. 2021;334 [Google Scholar]
- Wang Y., Liu D., Lin H., Chen D., Sun J., Xie Y., Wang X., Ma P., Nie Y., Mei H., Zhao B., Huang X., Jiang G., Jiang X., Qu J., Zhao J., Liu J. ACS Chem. Biol. 2021;16(3):491–500. doi: 10.1021/acschembio.0c00840. [DOI] [PubMed] [Google Scholar]
- Won J.Y., Son S.J., Um S.H., Choi J.-W., Min J. J. Biomed. Nanotechnol. 2013;9(5):886–890. doi: 10.1166/jbn.2013.1499. [DOI] [PubMed] [Google Scholar]
- Wu K., Su D., Liu J., Saha R., Wang J.-P. Nanotechnology. 2019;30(50) doi: 10.1088/1361-6528/ab4241. [DOI] [PubMed] [Google Scholar]
- Wu L., Mendoza-Garcia A., Li Q., Sun S. Chem. Rev. 2016;116(18):10473–10512. doi: 10.1021/acs.chemrev.5b00687. [DOI] [PubMed] [Google Scholar]
- Xie S., Tao D., Fu Y., Xu B., Tang Y., Steinaa L., Hemmink J.D., Pan W., Huang X., Nie X., Zhao C., Ruan J., Zhang Y., Han J., Fu L., Ma Y., Li X., Liu X., Zhao S. ACS Synth. Biol. 2022;11(1):383–396. doi: 10.1021/acssynbio.1c00474. [DOI] [PubMed] [Google Scholar]
- Yi Z., de Dieu Habimana J., Mukama O., Li Z., Odiwuor N., Jing H., Nie C., Hu M., Lin Z., Wei H., Zeng L. Biosensors. 2021;12(1):11. doi: 10.3390/bios12010011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yoo H.M., Kim I.-H., Kim S. Int. J. Mol. Sci. 2021;22(11):6150. doi: 10.3390/ijms22116150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yuanhao Liang H.L., Zou Lirong, Deng Xiaoling, Tang Shixing. Biosens. Bioelectron. 2022;205 doi: 10.1016/j.bios.2022.114098. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhang M., Li L., Li B., Tian N., Yang M., Zhang H., You C., Zhang J. Anal. Biochem. 2019;579:9–17. doi: 10.1016/j.ab.2019.05.004. [DOI] [PubMed] [Google Scholar]
- Zhang N., Wang L., Deng X., Liang R., Su M., He C., Hu L., Su Y., Ren J., Yu F., Du L., Jiang S. J. Med. Virol. 2020;92(4):408–417. doi: 10.1002/jmv.25674. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhang T., Zhao W., Zhao W., Si Y., Chen N., Chen X., Zhang X., Fan L., Sui G. Anal. Chem. 2021;93(48):16184–16193. doi: 10.1021/acs.analchem.1c04065. [DOI] [PubMed] [Google Scholar]
- Zhang W.S., Pan J., Li F., Zhu M., Xu M., Zhu H., Yu Y., Su G. Anal. Chem. 2021;93(8):4126–4133. doi: 10.1021/acs.analchem.1c00013. [DOI] [PubMed] [Google Scholar]
- Zhang Y., Chen M., Liu C., Chen J., Luo X., Xue Y., Liang Q., Zhou L., Tao Y., Li M., Wang D., Zhou J., Wang J. Sensor. Actuator. B Chem. 2021;345 doi: 10.1016/j.snb.2021.130411. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhou F., Yu T., Du R., Fan G., Liu Y., Liu Z., Xiang J., Wang Y., Song B., Gu X., Guan L., Wei Y., Li H., Wu X., Xu J., Tu S., Zhang Y., Chen H., Cao B. Lancet (London, England) 2020;395(10229):1054–1062. doi: 10.1016/S0140-6736(20)30566-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhu X., Li J., He H., Huang M., Zhang X., Wang S. Biosens. Bioelectron. 2015;74:113–133. doi: 10.1016/j.bios.2015.04.069. [DOI] [PubMed] [Google Scholar]
- Zhu X., Wang X., Li S., Luo W., Zhang X., Wang C., Chen Q., Yu S., Tai J., Wang Y. ACS Sens. 2021;6(3):881–888. doi: 10.1021/acssensors.0c01984. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
No data was used for the research described in the article.










