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Functional Nucleic Acid Based Biosensor for Microorganism Detection

  • Yunbo Luo
Chapter

Abstract

Food safety especially the problems of microorganism pollution is always a nonnegotiable attribute in food trade, sales, and consumption. It is significant to detect the microorganisms including themselves, their crude excretion or their toxin, and so on that are possible to decrease the quality of food and increase the food safety risks. With the contributions of the progress in analytical chemistry and molecular biology, many kinds of technology satisfy the rapid detections of microorganisms with high specification and sensitivity. However, nucleic acid is a typical factor in these hazard exposures. In this review, we have reached up to a comprehensive representation of functional nucleic acid biosensors for detecting microorganism. Functional nucleic acid is one of the most vital biological micromolecules, indispensable for almost every life events of microorganisms and rich in all organisms. As for the research idea, highlight, and superiority of the functional nucleic acid biosensor for microorganisms, the sequence of nucleic acid is the important part where the information is taken from. From another point of view, DNA can be utilized as recognizing element and enzyme upon the specific structure to detect microorganisms. Therefore, it is shown obviously that functional nucleic acid biosensors can be efficient for detecting microorganisms, and research on it is becoming profound in microorganism detection. And this chapter will be nearly the most comprehensive description about functional nucleic acid-based biosensor for the microorganism detection.

Keywords

Functional nucleic acid Biosensor microorganisms Detection 

2.1 Introduction

Foods can be affected by many kinds of microorganisms, such as harmful microorganisms to people in the programs of production, packing, as well as transportation. The microorganisms can easily make people feel uncomfortable, catch disease, and even die. It is also an important factor which causes sudden food safety accident. As we know, waterborne diseases are that caused by waterborne pathogens [1]. In developing countries, waterborne diseases cause thousands of people’s death every year [2]. In addition, drinking water polluted by pathogens may also be harmful to various organisms including animals and plants [3]. It can be seen that infectious diseases affected by microorganisms have been becoming increasingly severe all around the world. Therefore, to control the issue of pathogenic infection, there is an expanding need to detect the microorganisms related to health of people in the air, water, and soil rapidly and accurately.

Isolating and culturing techniques are basic ways of traditional methods for microorganism detecting. After that, microorganisms will be identified by biochemical method or direct microscopy which is accurate. However, there are many disadvantages at the same time, such as that it cannot detect microorganisms in a short while.

To make the detection of microorganism more convenient, instrumental analytical methods are developed such as HPLC and GC which are utilized for analysis of the biochemical composition of various bacteria. So, the microorganisms can be identified [4, 5]. The mass-spectrometric technique and capillary electrophoresis (CE) are also used to identify microorganisms [6, 7]. In addition, the gas of various microorganisms is also a typical signal for assay of microorganisms, which is the basic principle of electronic nose [8]. In conclusion, the instrumental analytic technique holds some advantages such as simple, convenient, and easy to learn, but it also has many shortcomings including high cost and low efficiency which makes it hard to be adopted to food safety detection widely.

Immunological methods are techniques that have high specificity because antigens and antibodies can bind each other with high specificity which can be utilized to qualitatively or quantitatively detection of microorganisms. A great variety of immunological methods are developed [9, 10, 11, 12, 13, 14]. There are a lot of strong points of immunological methods including relatively simple machines, easily storing of samples, high specificity, and quantitative detection. But, some shortcomings of this type of method cannot be ignored such as the fact that it is hard to detect several types of microorganisms, false-positive result, and limited sensitivity related to molecular biology methods. Thus, it is obvious that immunological methods are not perfect enough for microorganism detection.

Functional nucleic acid based biosensors have been extensively adopted in microbe assay. This method can detect microorganism in short time specifically and sensitively. Besides, it provides a rapid and simple method to differentiate viable and nonviable microbes. The biggest advantage over antibodies is probably that aptamers are amenable to SELEX. Unlike the isolation of antibodies, aptamer selection can be carried out at designated conditions, and counter selections can be performed to remove cross activity, which is difficult to achieve in antibodies. Functional nucleic acid based biosensors overcome the disadvantages of microorganism detections by the general molecular methods.

Functional nucleic acids (FNAs) describe a family of molecules whose function goes beyond the recognitions of complementary nucleic acids [15]. We can define FNAs in microorganisms assay from two major classes in microorganism detection. The first class is FNAs for signal transduction in microorganisms assay. Using FNAs for signal transduction means that FNAs can transduct microorganism signal to nucleic acids signal. For non-culturable method, which means that nucleic acids of microorganisms can be detected directly, the most common FNAs are probes. For non-extraction method, in which the microorganisms will be recognized by specific binding in whole cells, the aptamers are adopted in general. The second class of FNAs is used for signal amplification. For example, RCA, LAMP, SDP, and HCR are all signal amplification methods based on FNAs. In addition, FNAs combined with nanometer materials or fluorophores can be used for signal output.

In this review, we introduce the FNA-based biosensors for microorganisms. In next part, the construction of functional nucleic acid biosensors (FNABs) for microorganism detection and its technological elements will be reviewed to supplying basic information of biosensors. Then, the various biosensors will be introduced in aspects of principles, correlation technique, related applications, and properties in third part. Finally, future perspectives on FNABs for microorganism detection with rapid, accurate, and multiplexing capability are provided.

2.2 The Construction of Functional Nucleic Acid Based Biosensors for Microorganism Detection and Its Technological Element

Functional nucleic acid based biosensor detecting microorganism consists of signal recognition, transduction and signal amplification components. Many kinds of targets can be identified by target recognition elements. Molecular recognition events can be converted into other signals easy to detect by transduction elements. There are different structural DNA motifs such as fluorescence probe, hairpin, quadruplex, crossover, DNAzyme, aptamer–substrate complex, which make detection specific, sensitive, and rapid. Signal amplification elements are dependent for some amplification technologies such as PCR, RT-PCR, Real-Time PCR, LAMP, RCA, EXPAR.

As an analytical tool, biosensor has been utilized in many fields such as environmental monitoring, medical detections, safety testing of food, and disease diagnosis. The adoption of biosensors for microorganism assay is of great significance. FNABs have advantages over other classes of biosensors because of high selectivity and sensitivity including the fact that the FNAs are easy to immobilize, prepare and label different signals. In the following parts, various elements of functional nucleic acids will be introduced as well as their applications. Novel synthetic probes (e.g., PNAs, aptamers) that are low costing and flexible fabrication have been adopted to make point-of-care FNABs for rapid and credible detection of microorganism.

There are many kinds of NABs including DNA, RNA, PNA, and aptamers. The working principle of NABs except for aptamers is Chargaff’s rules of base pairing, and the combinations of aptamers and target such as proteins, cells, and small organic molecules are similar to the reaction between antibodies and antigens.

The functional nucleic acid biosensor for microorganism detection can not only detect the microorganisms but also infectious agents including genetic polymorphisms, viruses, and point mutations (SNPs) [16]. A lot of researchers take advantage of DNA-based probes to amplify the signal using PCR. Compared with antibodies or enzymes, DNA has more simple recognition principle because antibodies and enzymes are not stable after reaction with targets [17]. DNA is utilized more widely than other kinds of nucleic acid probes that were adopted in sensing progress (Fig. 2.1).
Fig. 2.1

Proportion of publications of various types of NABs in clinical applications from 1998 to 2013 (Reproduced from [18], with permission from Elsevier)

To make rapid and low-cost detection of microorganisms, it is expected that there are more reactions in evolution to detect multiple analytes. Hybridization is one of typical principle to detect target counterpart, which is designed to react with a known DNA. In addition to DNA–DNA hybridization, there are RNA–RNA and DNA–RNA hybridizations at the same time. The basic NABs’ pattern is introduced in Fig. 2.2.
Fig. 2.2

Schematic representation of NABs (Reproduced from [19], with permission from Elsevier)

There are some chemical methods to compound DNA probe, such as PCR. Differently, RNA probe is obtained using the reverse transcription (RT) of messenger RNA (mRNA) or the approach of utilizing the order of amino acids of relative protein to forecast nucleotide sequence. This method has some shortcomings because of codon degeneracy although its feasibility is proved. Typical nucleic acids hybridization methods need more labor force and time than hybridization procedure of a physical biosensor.

FNABs are physicochemical transducer because its carrier is immobilized by nucleic acids [20]. There are a large number of methods that can be utilized for immobilization. What is more, the process can be promoted by immobilization tenor [21].

2.2.1 Signal Recognization and Transduction of Functional Nucleic Acid Based Biosensor

The information about structures and functional properties of this biopolymer can be taken from the nucleic acids base sequence. The structural information is about the formation of primes, probes, double-strand, G-quadruplexes, supramolecular crossover tiles, complex of base and metal ions, triplex DNA structures, artificial nucleic acid probe, which reflect functional information [22]. Primes and probes are the most usual and typical functional nucleic acids in the detection of microorganisms which can recognize or capture target sequence or play a significant role in application process.
  1. (a)

    Aptamers

     
Aptamers are the single-stranded DNA or RNA ligands which can bind target selectively. In general, people utilize a random nucleic acids library of 1013–1016 sequences to isolate the aptamers, and the most common method is SELEX. Utilizing the specific interaction between aptamers and target, a variety of high-affinity and high-specificity aptamers are isolated to combine to targets such as small molecular, peptide, and protein of microorganisms, as well as supramolecular complex, such as virus or whole bacteria. Because of high specificity, low cost, and high affinity, many aptamers are adopted to microorganism detection. To combine with the target, great majority of aptamers need to form a G-quadruplex structure (Fig. 2.3).
Fig. 2.3

Schematic of the intramolecular G-quadruplex structure formed by TBA (Reproduced from [18, 23], with permission from Elsevier)

  1. (b)

    A DNAzyme with Peroxidase Activity

     
DNAzyme is specific sequence with catalytic activities, and the most utilized catalytic activities are peroxidase activities [24]. DNAzyme was found by accident. Sen et al. intended to select one aptamer of N-methyl mesoporphyrin IX (NMM) and got a lot of guanine-rich aptamers which were found that they have catalytic activities [25]. After the foundation, the researches further proved the peroxidase activity of G-rich sequence/hemin complex [26]. And it is also founded that the addition of hemin can enhance the peroxidase activity of the DNAzyme greatly [24]. Thus, people can use the changes of peroxidase substrates to know the activity of the DNAzyme. For example, when H2O2, exists the DNAzyme can catalyze ABTS to ABTS•+, resulting in the green color (Fig. 2.4) [24, 27].
Fig. 2.4

Principle of a peroxidase DNAzyme for signal transformation (Reproduced from [24, 27] with permission from American Chemical Society)

  1. (c)

    A DNAzyme with Cleavage Activity

     
In addition to the DNAzyme with peroxidase activity, other kinds of nucleic acid enzymes (NAEs) were also isolated via vitro selections [28, 29, 30]. This DNAzyme has demonstrated a high specificity for crude extracellular mixture of target bacteria. The fluorophores and quenchers are commonly used to combine with DNAzyme, and the principles of one example are shown in Fig. 2.5. The feature of these DNAzymes is that they cleave alone RNA linkage (R; Fig. 2.5), and a fluorophore (F) and a quencher (Q) are labeled on a DNA chain. When the target exists, the DNAzyme with cleavage activity will separate the fluorophore and quencher, resulting in enhanced fluorescence (Fig. 2.5).
Fig. 2.5

Principles of DNAzyme with cleavage activity to transform the CEM to fluorescence signal. F fluorescein–dT, Q dabcyl–dT, R adenosine ribonucleotide (Reproduced from [31] with permission from Wiley)

  1. (d)

    Triplex DNA Structures

     
Triplex DNA structures were first found by Felsenfeld et al. [32]. The high affinity and specificity combination of pyridine/polyadenosine duplexes and single-stranded pyridine oligonucleotides was proved by triplex DNA structure. Hoogsteen or reverse Hoogsteen hydrogen bond is built with bases of the purine-rich strand if a DNA or RNA oligonucleotide combined to the major DNA groove (Fig. 2.6) and the triplex structure was set up. There are two types of structure of triplex DNA as shown in Fig. 2.6. In recent years, triplex DNA has been applied in detection.
Fig. 2.6

Structures of two kinds of triplex DNA (Reproduced from [32] with permission from Annual Reviews)

2.2.2 Signal Transduction Elements

Fluorescence Resonance Energy Transfer (FRET)

When two chromophores are close to each other, the energy of a chromophore called donor can transfer to the other called accepter, which is FRET. And the distance must range from 10 to 100 A. In addition to the distance, effective FRET also needs an adequate overlap of the emission spectrum of the donor and the excitation spectrum of the accepter [33, 34]. FRET increases the fluorescence intensity of accepter chromophore and decreases the energy of donor chromophore at the same time.

Gold Nanoparticles

Gold nanoparticles (AuNPs) are the most widely used nanoparticles in the detection of microorganism. For example, gold nanoparticles can be utilized in localized surface plasmon resonance (LSPR), and extinction coefficients of them are much higher than those of organic chromophores [35]. Due to the good features of AuNPs, they are extensively adopted as signal transduction elements [36]. When the distances between AuNPs change, the color will change. This principle can be used in the DNA detection because the DNA can change the distance. The combination of DNA and AuNPs makes the detection simpler and easier to observe with no need of advanced instruments [37].

2.2.3 Signal Amplification of Functional Nucleic Acid Based Biosensor

DNA can act as transduction as well due to the amplification method. In general, the amplifications of nucleic acids are classified into two groups: thermocycling and isothermal amplification methods. The most important difference of the two groups is the temperature: the temperature of thermocycling is changing in the reaction process, but the temperature of isothermal amplification is same during the process. There are many advantages of thermocycling amplification such as the high efficiency of amplification, but the drawbacks exist as well, such as high probability of sequence mismatches, high cost, and susceptibility to contamination.

In order to overcome the weaknesses of thermocycling amplification, isothermal methods are developed. In this part, we describe several isothermal amplification methods including LAMP, RCA, and SDA as well as some thermal amplification methods, such as PCR, RT-PCR, which have been applied widely in nucleic acid amplification.
  1. (a)

    Isothermal Amplification Technology

     

Rolling Circle Amplification (RCA)

RCA was developed by Paul’s group to detecting mutation and counting single molecule. This method is based on circularized oligonucleotide probes replicated by either linear or geometric amplification driven by DNA polymerase under isothermal conditions [38]. In the presence of circular primer and corresponding linear oligonucleotide, they will bond to each other and started to copy the sequence, and more than 109 copies will be taken within 90 min (Fig. 2.7). The efficiency of RCA is high because the sequences have a variety of repetitive parts.
Fig. 2.7

The processes of RCA. (a) Circularizable probe with a small gap. (b) The combination of ssDNA target and primer. (c) RCA interaction with DNA polymerase (Reproduced from [37] with permission from Nature)

Loop-Mediated Isothermal Amplification (LAMP)

Notomi et al. utilized LAMP to amplify nucleic acid for the first time (Fig. 2.8) [34]. Due to the four primers that are utilized, LAMP can rapidly recognize the target, which makes it an attractive approach [40]. In addition, the temperature of system does not need to change [41]. What is more, this kind of technology is simpler and more efficient than other methods for pathogen detection. And it has been utilized in building biosensor.
Fig. 2.8

Mechanism of LAMP (Reproduced from [38] with permission from Oxford)

Strand Displacement Amplification (SDA)

SDA is another isothermal amplification. In the process of SDA, the DNA was nicked at the recognition site at first. Then, with the presence of DNA polymerase, the 3′ end of DNA was extended, and the downstream DNA strand was displaced. The SDA can amplify target DNA in exponential amplification due to the displaced DNA strand that acts as a template for an antisense reaction [42]. In the reported studies, SDA is often combined with HRP-like DNAzymes.

Hybridization Chain Reaction (HCR)

HCR has been widely used to amplification due to the strong comings including low cost, high selection, and no need of enzyme. In HCR reaction, there are three components: ssDNA sequence and two hairpins. And the ssDNA can be designed as target, and it is amplified via designed oligonucleotide hairpin probes. The key point is that HCR reaction occurs only when initiator is present; otherwise, the DNA strands are in hairpin structure and HCR cannot happen. (Fig. 2.9) [43]. Because of free energy and free enzyme, HCR as a simple and convenient approach has been extensively utilized in the built of biosensors for microorganism detection [57].
Fig. 2.9

The mechanism of HCR (Reproduced from [33] with permission from Elsevier)

  1. (b)

    Thermal Amplification Technology

     

There are several advantages of PCR which is effective, specific, and widely used in sample with complex and shortcomings, for example, qualitative detection cannot be satisfied using traditional PCR. To overcome this shortcoming, the real-time PCR is developed, which consists of two types: fluorescence probes and specific nucleic acid dye system. Due to the advantages of real-time PCR, a lot of researchers utilized it into microorganism detection and analysis [44, 45].

In addition to the traditional PCR and RT-PCR, digital PCR was also developed and studied as the third generation of PCR technology [46]. In digital PCR, the reaction volume was divided into thousands of reaction cells at first, and the copy number of target can be taken from dilution percentage and Poisson distribution. Digital PCR was designed in the 1990s for the first time, and after that, it has been developed to several fields. Due to the function of quantitative detection, digital PCR is regarding as the improved technology of real-time PCR [47]. Now, Bio-Rad QX100 digital PCR system made by Bio-Rad Company has been developed since about one decade ago and been regarded as the best digital PCR system which is accurate, stable, and cost-effective, which has been utilized in bacteria detection [48].

2.2.4 Signal Output Systems of Functional Nucleic Acid Based Biosensor

  1. (a)

    Fluorescence Signal

     
Fluorescence signal is one of the most commonly used methods to label the nucleic acids and to satisfy the real-time analysis. In general, there are two kinds of strategies adopted to enable aptamer to make fluorescent signal. One kind is dual labeling aptamers (Fig. 2.10) [49] of which the principle is FRET and there are more than one reporter that used optical signal transduction. Other methods are illustrated in Fig. 2.10.
Fig. 2.10

Signal-on fluorescence NABs. (a) There are a fluorophore (F) and a quencher (Q) that labeled on the each end of hairpin structure. When target exists, it will bind to hairpin and increase the distance of F and Q which increase the fluorescence signal. (b) Aptamers replace the ssDNA with Q. Thus, F and Q are separated causing the enhancement of fluorescence signal. (c) The combination of target and sequence cause the two pyrene monomer close to each other, and the fluorescence signal is increased (Reproduced from [49] with permission from Elsevier)

Another strategy is modified aptamers by organic fluorophores in conformationally labile regions of aptamers. When the target combines to the aptamer, the structure of aptamer will change and the fluorescence characteristics such as intensity and anisotropy are changed as well. This kind of signaling aptamer has been built by modified DNA aptamers with a fluorophore adjacent. And the addition of fluorophore makes the Kd much higher than the aptamers without modification. However, signaling aptamers that based on the conformational transduction lose the affinity which is the common shortcoming of this method. The features and binding site can affect the signal as well, and the ways of combinations of aptamers and labels are various. In addition, the changes of local environment also change the signal.

In addition to labeled aptamers with fluorescent dyes, replacing nonfluorescent nucleotides with fluorescent nucleotide analogs can also satisfy the requirements of signal transduction. Many studies about introducing labels to the original random sequences have been reported to select the binding part.
  1. (b)

    Colorimetric Signal

     

AuNP-Based Colorimetric Signal

The combination of nanomaterial science and biology can bring a variety of advantages of both technologies and promote the development of bionanotechnology. And the typical example is the combination of gold nanoparticles and DNA. In solution, the distance between AuNPs can change the surface plasmon properties and large extinction coefficients, and if the AuNPs are close to each other, the color is blue, and if not, the color will turn red. There are two kinds of colorimetric functional nucleic acids with the function of gold nanoparticles.

One class is utilized to detect the unfolded DNA according to the features of AuNPs which provides a novel method to analyze the process of recognization of target. In solution, ssDNA such as unbound aptamers can bind on the surface of AuNPs, which makes AuNPs not closed to each other in which condition the color is red. But when the targets exist, the aptamers can bind to the target and be folded that cannot bind to AuNPs. Without the ssDNA, the nanoparticles become close to others, and the color turns blue. This assay format has been utilized for targets detection, such as ions and the proteins [50] (Fig. 2.11).
Fig. 2.11

Principles of AuNP-based biosensor to detect thrombin (Reproduced from [50] with permission from The Royal Society of Chemistry)

Another type method utilizes aptamer-modified AuNP probes to control the distance of AuNPs. In this method, the nanoparticles are linked by ssDNA and aptamer. If the targets exist, the aptamers will bind to the target instead of ssDNA which isolates the AuNPs (Fig. 2.12a, b) [51]. Due to the change of distance of AuNPs, the blue color turns to red and can be observed by naked eyes. This signal-on assay format has been utilized to detect various molecules, such as cocaine and adenosine in serum. Moreover, signal-off assay is also developed. At first, aptamers are binding to the ssDNA that is labeled on the surface of AuNPs, and AuNPs are isolated. While the targets are present, aptamers will combined with targets and be dissociate from the AuNPs. Thus, the color turns to purple in short time (Fig. 2.12c) [49].
Fig. 2.12

(a) The principle of signal-on colorimetric AuNP-based NAB. (b) The principle of signal-on colorimetric AuNP-based NAB. (c) The principle of signal-off colorimetric AuNP-based NAB (Reproduced from [49] with permission from Elsevier)

DNAzyme-Based Colorimetric Signal

The most commonly used DNAzyme is that with peroxide activity in bioanalytical chemistry. To guarantee the high peroxide activity, a hemin is needed to combine with oligonucleotides in order to form G-quadruplex structure.

In recent years, DNAzymes with peroxide activity have been utilized into bioanalytical assay of microorganisms via colorimetric changes. G-rich oligonucleotides were added to the hairpin structure to detect the OTA by Yang et al. [40]. The hairpin includes aptamer of OTA, DNAzyme sequence, and blocking tail. When target exists, the hairpin will be opened, and in the presence of hemin, the DNAzyme sequence will turn into G4 structure with peroxide activity to catalyze reaction with color changed (Fig. 2.13) [52].
Fig. 2.13

The schematic of OTA detecting based on G-quadruplex (Reproduced from [52] with permission from Elsevier)

  1. (c)

    Chemiluminescence Signal

     
Willner et al. developed a chemiluminescence method to detecting DNA (Fig. 2.14) [53]. The probe A is modified on the surface of AuNP, and probe B acts as reporter. Besides, probe B is formed by two parts: the sequence of one part is G-riched which can form G-quadruplex, and the other can hybridize with the target DNA. When the target sequence exists, it will bind probe A and probe B. Thus, adding luminol and H2O2, a chemiluminescent signal can be observed.
Fig. 2.14

The principle of chemiluminescence method of detecting DNA utilizing a sandwich assay on an Au surface (Reproduced from [53] with permission from American Chemical Society)

In addition, a simpler method based on similar principle was designed by the same group (Fig. 2.15) [54]. In this method, there are both aptamer sequence and DNAzyme sequence. When target DNA is present, hairpin structure will be destroyed, and the hemin can bind to the DNAzyme part to guarantee the catalytic activity. Upon adding ABTS and H2O2, the system then produces a colorimetric signal.
Fig. 2.15

The principle of a colorimetric DNA detection using G-quadruplet (Reproduced from [54] with permission from American Chemical Society)

  1. (d)

    Mass-Sensitive Signal

     
Mass-sensitive biosensor can analyze mass-related properties utilizing surface with high sensitivity and modified DNA. There is no need of label in aptamer-based, mass-sensitive biosensors which can be defined into two groups. The first type is evanescent wave-based sensors including plasmon resonance (SPR) sensors and others. And another type is acoustic wave-based sensors consisting of quartz crystal microbalances (QCMs), surface acoustic wave (SAW) devices, and micromechanical cantilever-based sensors.
  1. (e)

    Electrochemical Signal

     

When aptamers bind to the target molecular, they will be folded, and the structure of aptamer will turn to three-dimensional (3D) shapes. If we labeled the aptamers on the conductive support, the part of redox-active moieties can be tethered to the aptamers in 3D structure. Thus, the presence of target can be analyzed via analyzing the change of electron transfer features of the redox moieties. Until now, many biosensors based on this theory have been developed by researchers.

2.3 Functional Nucleic Acid Based Biosensors for Microorganism Detection

2.3.1 Aptamer Based Biosensors for Microorganism Detection

The most special feature of aptamers is that it can be combined with different targets including small molecules, drugs, proteins, and other non-nucleic acid targets. And because of that, aptamers are applied widely in microorganisms, metal, and cancer cell assay. Besides, there are many advantages of aptamers such as low costs which is selected in vitro, less batch-to-batch variation, and selection without a good immune response. With so many advantages, aptamer is utilized extensively in microorganism detection.
  1. (a)

    Aptamer Selection Strategies

     

In general, there are two classes of selection (SELEX strategies and SELEX variant strategies) which will be introduced in this part.

Ellington and Szostak designed the SELEX process for aptamers selection for the first time [55]. The basic process of general SELEX is constructing random oligonucleotide libraries, separating the target nucleic acid complex and amplification by RT-PCR (for RNA selection) or PCR (for DNA selection). The first aptamers selected by SELEX are against organic dyes and T4 DNA polymerase. After that, many other biomolecules are also targeted which can be found in the aptamer database written by Ellington et al. Except for this database, some other publications summarized the kinds of target molecules as well [28, 56]. Many aptamers isolated by SELEX can combine to whole cell, crude extracellular mixture, intracellular proteins, and purified molecules.

Apart from SELEX, there are several SELEX variant strategies. The non-SELEX selection of aptamer is developed by Maxim Berezovski’s research group for the first time, which is without amplification progress compared with SELEX (Fig. 2.16) [57]. To separate, capillary electrophoresis (CE) is used in SELEX progress. The affinity of a DNA library to a target protein of CE-based progress is higher than that of four orders of magnitude.
Fig. 2.16

The schematic of SELEX and non-SELEX (Reproduced from [57] with permission from American Chemical Society)

Toggle SELEX was reported by Rebekah White’s group, and the most special feature is that aptamers can combine with human as well as porcine thrombin. According to their study, “toggling” SELEX can select RNA aptamers that can be applied in humans and animal samples (Fig. 2.17) [58].
Fig. 2.17

Toggle SELEX (Reproduced from [58] with permission from Elsevier)

Recently, monoclonal surface display SELEX (MSD-SELEX) was developed by Zhu et al. to enrich and identify aptamers from a library simply, rapidly, and efficiently (Fig. 2.18) [59].
Fig. 2.18

Scheme of the MSD-SELEX (Reproduced from [59] with permission from American Chemical Society)

The SELEX experiment will be hard to program utilizing a purified, soluble target if it needs the cell membrane or a coreceptor to fold properly. The most typical advantage of complex-target SELEX is that it can be utilized in complex protein mixtures. Besides, although the information about the cell membrane is not known, aptamers can identify and bind the target [60, 61].

The function of affinity chromatography is the separation and purity of the constituents of a biochemical mixture (Fig. 2.19). The targets can be modified on the beads surface through chemical bonds if targets are organic molecules [62]. A variety of people attempt to select aptamers because the magnets can isolate target-bound aptamers simply and easily. Some aptamers are selected utilizing beads that are labeled by target [62, 63, 64]. Except for the above types of SELEX, there are other kinds of SELEX for aptamer selection [65, 66], which are not introduced in detail.
Fig. 2.19

(a) A schematic illustration of the selection step utilizing an affinity column, (b) the actions of selecting aptamers utilizing magnetic beads, (c) several kinds of functional group-activated beads (Reproduced from [62] with permission from Nature)

  1. (b)

    Properties and Categories of Aptamers

     
The interactions between aptamers and targets are extremely specific. What is more, aptamers can be synthesized and labeled. In addition, some of aptamers also have catalytic activity called aptazymes which can bind target and play catalytic activity.
  1. (c)

    The Application of Aptamer Biosensors for Microorganism Detection

     

After obtaining aptamers with high binding affinity and specificity, the next step is to design a signaling mechanism so that a sensor can be produced. Aptamers can be combined to deferent signal transduction technologies to construct biosensors to detect microorganisms, such as fluorescent biosensor, flow cytometry, electrochemical sensor.

In order to increase the sensitivity of the test, the aptamer often combined with other signal amplification technologies, such as gold nanoparticles. Chang’s group built the structure of the aptamer-AuNP-binding capacity to detect Staphylococcus [67]. Interestingly, the DNAzyme and aptamer were combined in one sequence which acted as detection probe in Yang’s approach, which has both specific binding function and catalytic activity [68] (Fig. 2.20).
Fig. 2.20

Flowchart of S. aureus detection using aptamer-conjugated AuNPs (Reproduced from [67] with permission from Nature)

Lipopolysaccharides (LPS) are integral components of the outer membrane of all Gram-negative bacteria which can be used to detect microorganisms. So, we utilized HCR and aptamer to detect the E. coli O111:B4 using LPS as target [69]. In detail, biotinylated monomer DNA building blocks were mixed together but did not hybridize on an experimental time scale. Except that the probe can be used to detect target sequence, it can open the hairpins in the solution and triggered a chain reaction of hybridization reaction. Afterward, streptavidin-horseradish peroxidase (SA-HRP) combined with DNA. After that, the aptamer binds LPS which is captured by probe. If LPS exist, a visible optical signal appears. Otherwise, the production of HCR will be washed by washing buffer. In order to detect microorganisms quantitatively, we optimized the conditions of progress including the reaction time of HCR and the amount of the capture probe and detection probes. So, the concentration of LPS can be detected according to the optical density value, and a relatively low detection limit (1.73 ng/mL) was obtained, with a linear response range of 1–105 ng/mL. Obviously, there are a variety of advantages of this method such as short reaction time, simple operation, and high sensitivity (Fig. 2.21).
Fig. 2.21

Schematic representation of HCR-based aptasensor for the sensitive detection of LPS (Reproduced from [69] with permission from American Chemical Society)

2.3.2 Functional Nucleic Acid Based Colorimetric Biosensors for Microorganism Detection

Functional nucleic acid based colorimetric biosensor is a significant technology for microorganism detection. Colorimetric biosensor is convenient in microorganism detection because the results can be taken without proper instruments. Besides, AuNPs are commonly used in this type of biosensor, the color of which can be changed with the change of nanoparticles’ distance. Moreover, a nucleic acid–protein nano-polymer included SA-HRP and TMB has been made as signal output. In addition, G-quadruplex DNAzyme is also applied in the visual sensing because of their optical properties and great specificity. We have done some researches on the above colorimetric biosensors.
  1. (a)

    Gold Nanoparticles-Functional Nucleic Acid Based Colorimetric Biosensors for Microorganism Detection

     

The intersection between molecular and nanomaterials science offers fertile ground to advance the development of versatile biomaterials and bionanotechnology [70]. A successful example is the interaction between gold nanoparticles (AuNPs) and DNA. AuNPs are typical optical materials that display distance-dependent surface plasmon properties, resulting in strong color changes that rival or even exceed the most intense organic dyes [71]. Nucleic acid has been used as a programmable molecule to tune the distance between AuNPs. In addition to tunable properties from conventional hybridization between one single-stranded sequence and its complementary sequence, functional nucleic acids that can perform specific binding features with conformational changes or catalytic reactions in the presence of specific non-DNA molecules [72, 73], bacteria [74], cells [75], or even viruses [76] have been reported.

We reported a colorimetric biosensor to detect OTA (a fungal toxin that belongs to microorganism detection) which is based on a switchable double-stranded DNA jugate and unmodified gold nanoparticles [77]. Hairpin probes (H1, H2&FH) were designed in this experiment. Hairpin (H1&H2) can make the provided dsDNA concatemers by the process of hybridization chain reaction (HCR), and the functional hairpin (FH) can recognize the target. In the presence of target sequence, the initiator probe will be released, which can start the HCR to form the dsDNA concatemers. Then, the unmodified gold nanoparticles aggregated together, and their color turned from purple to blue, termed as light-off sensing way. Furthermore, H1 inserted an aptamer sequence to generate dsDNA concatemers with multiple small molecule binding sites. In the presence of small molecule targets, concatemers cannot form because of the ssDNA sticky ends. Then, we can find a blue-to-purple color variation due to the regeneration of the ssDNA, which is termed as light-on way. Both nucleic acid and small molecule targets can be detected by one sensing device of the two-way biosensor, whose detection limits achieved nM, and the elements of the biosensor is label-free, enzyme-free, and sophisticated instrumentation-free.
  1. (b)

    G-Quadruplex-Functional Nucleic Acid Based Colorimetric DNAzyme Biosensors for Microorganism Detection

     

There are some short, single-stranded DNA molecules called DNAzymes, which consist of a special G-quadruplex structure within an intercalated hemin. This kind of DNAzymes can oxidize the ABTS2− by H2O2 to cause a green-colored radical ion (ABTS•+, = 3.6 × 104 M−1 cm−1) [27]. Because of the great colorimetric property, DNAzymes perform a significant role in the analytical assays as a simple colorimetric format, and they also will be great molecular tools in the biosensors and nanodevices design. In addition, DNAzymes have a lot of advantages, such as low cost, high stability against heat [78], and easy labeling, and can be utilized to detect different targets microorganisms DNA [79].

We developed a colorimetric G-quadruplex LAMP sensor that combines the isothermal amplification and the DNAzyme for the ultrasensitive detection of Salmonella (Fig. 2.22) [80]. This is an example of functional nucleic acid colorimetric biosensors for microorganism detection. First of the research, there are primers within a signal inner primer of a 17-nt DNAzyme complementary sequence (a signal precursor), which were designed for the amplification process and colorimetric detection. The target DNA can initiate LAMP amplification, and the amplification results conclude a larger number of DNAzyme sequences. After adding the hemin, the free DNAzyme fragments combine it and form G-quadruplex-hemin conjugates which perform as colorimetric signal readouts for the naked eye observation. The novel colorimetric strategy does not need any forces or other apparatus, and the detection limit can achieve less than 0.5 pg. Moreover, the reported sensor showed high foreground in the DNA visual detection and may even pave the way for other amplification-based colorimetric detection and the point-of-care determination.
Fig. 2.22

The principle of the facile cascade signal amplification strategy using DNAzyme loop-mediated isothermal amplification for the ultrasensitive colorimetric detection of Salmonella (Reproduced from [80] with permission from Elsevier)

  1. (c)

    Nano-polymer-Based Functional Nucleic Acid Based Colorimetric Biosensor for Microorganism Detection

     

Streptavidin-horseradish peroxidase modified hybridization chain reaction (HCR-HRP) nanocomposites is another signal amplifier and colorimetric signal conversion element, which catalyzed hydrogen peroxide (H2O2) via TMB to generate an obvious green color and turned yellow after sulfuric acid termination with optical absorption at 450 nm. Bulb-like triplex turn-on switch (BTTS) acts as a novel selective molecular recognition and signal transduction element and was designed as bulb-like and was composed of a bulb-like microorganisms aptamer (BLA) in the center to capture target microorganisms flanked by mirror sequences to hybridize with the bridge probe (BP) to form a triplex nucleic acid stem by Watson-Crick base pairing and Hoogsteen base pairing.

We developed an ingenious structure-switching aptasensor for LPS (a crude extracellular element of target bacteria) detection based on the (HCR-HRP) nanostructures as the signal amplifier and colorimetric signal report element and bulb-like triplex turn-on switch (BTTS) as the effective molecular recognition (Fig. 2.23) [81]. Upon LPS introduction, the bulb-like LPS aptamer (BLA) prefers to bind with LPS, and the bridge probe is isolated to aptamer to signal transduction. In the presence of BP, the HCR reaction is able to be promoted which can amplify signal greatly. Upon the adding of streptavidin-horseradish peroxidase (SA-HRP), HCR-HRP nanostructures formed, and the colorimetric signals can be observed by naked eyes. Within 4 h, as low as 50 pg/mL of LPS can be detected by spectrophotometer, and as low as 20 ng/mL of LPS can be detected by naked eyes. This method is a novel design that has potential for LPS detection in future clinical diagnosis, food security, and environment monitoring.
Fig. 2.23

Principles of the method of LPS detection utilizing HCR-HRP nanostructures (Reproduced from [81] with permission from Elsevier)

2.3.3 Lateral Flow Nucleic Acid Based Biosensors (LFNABs) for Microorganism Detection

A variety of approaches can be utilized in analysis of microorganisms. The detection methods need to be easy to learn for people, and lateral flow biosensors (LFB) can fulfill the requirement. In addition, there are also some other advantages of LFNABs including short detecting time, no need of equipment, sensitivity, and low cost. Therefore, lateral flow biosensors have been becoming more and more popular in the area of food safety monitoring, biological detection, and environmental science and so forth.
  1. (a)

    The Development of Lateral Flow Biosensor

     
The first LFB was developed for the detection of glucose by Free et al. [82]. Due to a variety of advantages, LFB was widely used in many fields. After that, immune chromatographic was combined with LFB which enhances the specificity greatly. Thus, the developed LFB was adapted to the environment monitor, veterinary diagnostics, and food security supervisory control [83]. In recent years, nucleic acids have been utilized to LFB. In fact, the utilization of nuclei acid makes the LFB more sensitive and specific and decreases the cost greatly compared to antibody.
  1. (b)

    The Structures of Lateral Flow Biosensors

     
In general, there are five elements of lateral flow nucleic acid biosensors: sample pad, conjugation pad, nitrocellulose membrane, absorbent pad, and backing pad (Fig. 2.24).
Fig. 2.24

The structure of lateral flow nucleic acid biosensors (Reproduced from [84] with permission from Springer)

Sample Pad

The sample pad acted as a platform to ensure the best analytical status of preparation and delivery sample as well as buffer salts, proteins, detergents, and viscosity enhancers. Thus, the materials of sample pad are porous materials in general including cellulose fiber or glass fiber [85]. The porous of materials can isolate the coarse molecular and whole cells.

Conjugate Pad

Biorecognition molecules, e.g., aptamer, are in the conjugate pad. Conjugate pad would better to liberate recognition molecules rapidly to promote the reaction of liquid sample and molecules. The lack of preparation of a labeled conjugate may bring the bad effect to the susceptibility of the test.

Nitrocellulose Membrane

Nitrocellulose membrane is the most important part of LFA because the test line and control line are there. It is better that the nitrocellulose membrane can bind the seized molecules but does not bind the molecules that are detected. In addition to nitrocellulose membrane, many other types of membranes are also utilized [86].

Adsorbent Pad

The adsorbent pad is used to supply traveling power to guarantee the liquid sample traveling through the strip in suitable flow rate. Absorbent capacity is important because it affects the background of results. Besides, cellulose filter is wildly utilized.

Backing Pad

The function of backing pad is supporting the strip and making the test easier. In addition, its materials are not strict.

  • Signal Amplification, Recognition, and Output Elements of Lateral Flow Biosensor

Signal Amplification Systems

Amplification can increase the sensitivity greatly and let down the detection limit. PCR and isothermal amplification such as NASBA, HDA, RPA, LAMP, and SDA are commonly used in this process. Recently, in order to decrease the detection time, the amplification process is canceled sometimes.

Signal Recognition Systems

Sandwich reaction is utilized in lateral flow biosensor to recognize targets. There are three formats of recognition principles of LFNABs: binding of antibodies and antigens, hybridization, and FNA-based reaction.

Signal Output Systems

There are many kinds of reporter materials that can be utilized in lateral flow biosensor for signal output, such as AuNPs, fluorophores, quantum dots, and so on.

Different Signaling Systems of LFNABs for Microorganism Detection

There are a series of labels in LFNAB including textile dyes, carbon nanoparticles, gold nanoparticles, selenium nanoparticles, colored latex beads, liposomes, p-converting phosphors, magnetic particles, quantum dots, organic fluorophores, and so on. Any material which is utilized in the detection should maintain its superiority upon compound with biorecognition molecules. A good signal label should have some great character such as high affinity with biomolecules. Recently, several reviews whose focus is signal systems which are applied in the LFB were reported [87, 88, 89, 90].

2.3.4 PCR-Functional Nucleic Acid Based Biosensors for Microorganisms Quantitative Detection

  1. (a)

    RT-PCR-Based Functional Nucleic Acid Based Biosensors for Microorganisms Quantitative Detection

     
The alternative real-time PCR technique was developed by Higuchi for the first time [91]. In RT-PCR reaction, the key point is the fluorophore which can show the process by observing the fluorescence intensity. The fluorescence is an important factor of this method because it can show the amplification information and help the quantitative detection. Real-time PCR holds some advantages over traditional PCR including high sensitivity, automation, and efficiency. (Fig. 2.25).
Fig. 2.25

The principle of three types of qPCR. (a) TaqMan-based qPCR. (b) SYBR Green I-based qPCR. (c) The molecular beacon-based qPCR (Reproduced from [91] with permission from Nature)

SYBR Green I which is most extensively utilized among fluorescent dye can link to double-stranded DNA [92]. In addition to fluorescent dye, detection probes that are modified by fluorophore can be used in PCR progress. The principle of it is FRET: when there is overlap of excitation spectrum of a fluorophore and that of quencher, the quencher can quench the fluorophore, and if not, fluorophore will emit low fluorescence but high-density fluorescence [34]. In order to make specificity higher, specific probe will be not closed during the PCR progress which makes it can be captured by products of PCR [93, 94, 95, 96].

A variety of microorganisms can be detected by RT-PCR such as Actinobacillus actinomycetemcomitans, Bacteroides forsythus, Porphyromonas gingivalis, Treponema denticola, and Treponema socranskii, in saliva and subgingival plaque samples [97]. Moreover, a real-time PCR using the TaqMan system (PElABI) can be utilized for quantitative detection of B. forsythus [98] and P. gingivalis [99]. Except for bacteria, RT-PCR has been adopted to detect virus, such as potato virus Y [100] and rose rosette virus [101].
  1. (b)

    Digital-PCR-Functional Nucleic Acid Based Biosensors for Microorganisms Quantitative Detection

     

“Digital PCR” was reported in 1999 by Kinzler and Vogelstein [102]. Digital PCR (dPCR) reported in 1999 is the PCR type that achieves quantification detection without reference material.

The analysis of target locus of individual molecules is the key point of digital PCR. At first, the sample was separated into a variety of droplets, and some of them have at least one target that is “positive,” but other droplets are “negative.” Then, PCR will measure the number of positive aliquots.

Digital PCR has been applied extensively over a wide range of fields to detect microorganisms and developed to be more efficient. For example, using Escherichia coli as a target, Dong-Ku Kang demonstrates that the IC3D can achieve quantification detection of both stock and clinical isolates of E. coli in spiked blood [103].

Droplet digital PCR is the third generation of PCR techniques, which achieves the absolute quantification of molecular target without the utilization of standard curves, due to the recent advent of compartmentalization. Droplet digital PCR (ddPCR) is an efficient technique for quantitative detection of microorganisms. Here, Davide Porcellato reported a new ddPCR assay for the quantitative detection of the Bacillus cereus group in milk. The main advantage of ddPCR is low detection limit compared to dPCR. The new ddPCR technique is a promising method for the quantification of target bacteria in low concentration in milk [104].

Aurélie Hennebique developed digital PCR (dPCR) assays allowing rapid and accurate detection and quantification of these resistant mutants in respiratory samples, especially when the proportion of mutants in a wild-type background is low. There are three dPCR gyrA assays designed to detect and differentiate the wild-type and one of the three gyrA mutations previously described as associated with FQ resistance in L. pneumophila: 248C>T (T83I), 259G>A (D87N), and 259G>C (D87H). These results demonstrate that dPCR is a highly sensitive alternative to quantify FQ resistance in L. pneumophila, and it could be used in clinical practice to detect patients that could be at higher risk of therapeutic failure [105].

In digital PCR, any targets will be detected when the efficiency of the reaction is high enough which is different from real-time PCR. Thus, it is not an important thing that whether a response is more effective than another, because the target can be detected if they are fully amplified.

2.3.5 Isothermal Amplification-Functional Nucleic Acid Based Biosensors for Microorganism Detection

PCR-based methods have been applied widely into various samples detection due to their high sensitivity and specificity. It has many drawbacks such as instrument dependence, long reaction time and the need of agarose gel electrophoresis to observe. Therefore, there are several isothermal amplification methods that have now been explored to overcome the drawbacks of PCR-based methods. And it is becoming an efficient and fast tool in the areas for detecting bacteria and other microorganisms after many practical proof.
  1. (a)

    LAMP-Based Functional Nucleic Acid Based Biosensors for Microorganism Detection

     
Loop-mediated isothermal amplification was first explored by Notomi in 2000, and it is the most mature isothermal amplification method. The process can be finished in the preparation of four or six independent primes in a stable temperature. The most significant superiorities of this strategy are its high efficiency and sensitivity because after the amplification process there will be a large amount of products produced in the programs, and the results can be analyzed by a lot of means including a turbid meter, SYBR Green I, gel electrophoresis, and so on [106]. LAMP attracts the interest of many investigators in microorganism detection fields. For an instance, LAMP has been employed for the Shigella assay [107], Salmonella determination [108], and E. coli analysis [109]. More recently, we also described a novel quantitative strategy based on LAMP and EvaGreen dye for L. monocytogenes detection [110].
  1. (b)

    SDA-Based Functional Nucleic Acid Based Biosensors for Microorganism Detection

     
There is no need to change the reaction temperature in the process of SDA. In the presence of exonuclease, the unmodified strand will be nicked at recognition site of which the 3′ end will be extended and the former strand will be taken place. After many cycles, the specific strand can be amplified. Walker et al. developed a new approach called multiplex SDA which is used to co-amplify both a gene (16S ribosomal gene) of relevant Mycobacterium species and a target sequence (IS6110) of Mycobacterium tuberculosis [111]. RT-SDA system also used this approach for RNA templates by reverse transcriptase for the initial procedure [112].
  1. (c)

    RCA-Based Functional Nucleic Acid Based Biosensors for Microorganism Detection

     
Rolling circle amplification (RCA) is another isothermal amplifying method. In this technology, except for the traditional primer sequences, a new circle DNA replication and a new enzyme (phi29 DNA polymerase) have been applied [113]. Then two or more primers were utilized in the process of RCA. Those approaches can achieve 10,000-fold amplification of circular DNA. The products of RCA can be analyzed by gel electrophoresis and so on [112]. This method for amplification has attracted concentration of researches in fungal and bacteria detection fields [114, 115].
  1. (d)

    NASBA-Based Functional Nucleic Acid Based Biosensors for Microorganism Detection

     
The target of the amplication of NASBA is RNA. In the process of NASBA, the RNA templates, primer, and RNA polymerase are utilized, and a reverse transcriptase will be used to produce DNA. Each of the synthesized RNAs can be utilized in amplification process for the next time which produces RNAs more and more. The amplicons can be analyzed by colorimetric assay, gel electrophoresis, and other detection technologies [112]. Many researchers have applied the NASBA on the pathogens detection such as Salmonella enterica [116], S. aureus [117], and Vibrio cholerae [118].
  1. (e)

    HDA-Based Functional Nucleic Acid Based Biosensors for Microorganism Detection

     
Another DNA isothermal amplification method is helicase-dependent amplification (HDA). HDA can generate single-stranded templates through a helicase not heat to achieve the goal of amplification. Vincent described a primer which can hybrid to ssDNA template to extend by DNA polymerases [119]. HDA has great advantages of a simple reaction scheme and a constant reaction temperature in the whole amplification time. The products are detected by gel electrophoresis, real-time format, and ELISA [112]. HDA has been utilized in microorganism detection by many research [120].
  1. (f)

    NEMA-Based Functional Nucleic Acid Based Biosensors for Microorganism Detection

     

NEMA has multiple advantages and has been developed in wide fields. This is an amplification technology which is based on the theory of strand displacement. In the reaction process, one strand of duplex DNA can be first cleaved only, and then the amplicons will be amplified greatly through a nicking endonuclease activity. NEMA attracts more interests from scientists because it just needs only two pairs of general primers and the simple need can easy the design work of primer [106]. Besides, NEMA produces less pollution by aerosol than LAMP. In addition, NEMA can produce approximately 400 bp product, which makes it more universal than other isothermal amplification strategies [113, 116, 119]. Kong et al. developed the detection of M. tuberculosis, which is a typical example of NEME application [121].

2.3.6 Functional Nucleic Acid Based High-Throughput Biosensors for Microorganism Detection

  1. (a)

    Multiplex PCR-Based Functional Nucleic Acid Based Biosensors for Microorganism Detection

     

Multiplex PCR (M-PCR) is similar to traditional PCR because the principles of them are same to each other, but there is more than one pair of primers in multiple PCR. Because of that, there are more than one DNA templates that are combined by primers, and more than one DNA fragments are amplified in one progress [122].

M-PCR has a variety of advantages, but there are several disadvantages of it including the primers’ inhibition, various efficiencies of different templates, and so on. Because of these disadvantages, M-PCR cannot be developed and adopted to wider uses, especially in high-throughput detection.

Kalyan D. Chavda utilized M-PCR to detect Enterobacteriaceae with a wide range of β-lactamases directly from perianal swabs, and CTX-M-, pAmpC-, and KPC-producing Enterobacteriaceae can be detected sensitively using this assay [123].
  1. (b)

    Universal Primer-Multiplex PCR-Functional Nucleic Acid Based Biosensors for Microorganism Detection

     

In order to overcome the shortcomings of traditional M-PCR, universal primer (UP) was designed in the reaction [124]. UP is the most important element in the progress. In this method, the primers consist of complementary sequence of templates and UP sequence. Thus, the amplicons of primers can be amplified by UP.

The UP-M-PCR method was firstly developed by us [125] which was first utilized to detect stacked GM maize Bt11×GA21. After that, we adopted it to simultaneous detection of the three organisms (Fig. 2.26) [108]. UP-M-PCR makes the traditional multiplex-PCR simpler. And at the same time, disparities between various primers can be overcome with a high specificity and sensitivity (85, 155, and 104 copies/reaction for E. coli O157, L. monocytogenes, and Salmonella spp., respectively). According to our results of tests, UP-M-PCR is more impossible to cross contaminate, and it has a relative accuracy of 91.77% among 36 food samples. So, UP-M-PCR is a rapid-screening approach of microorganism analyzing and target gene detecting.
Fig. 2.26

Schematic representation of universal primer-multiplex PCR approach (Reproduced from [108] with permission from Elsevier)

In 2012, we have established a useful multiplex PCR method called UP-M-PCR for the detection of five significantly important exotoxin genes and one internal control gene in P. aeruginosa (Fig. 2.27) [126]. In UP-M-PCR, the use of UP makes amplified efficiency of different targeted products identical, and the compatibility of primers in a reaction greatly increased. Therefore, various virulence genes can be identified at the same time more rapidly and sensitively than conventional methods. The results of this study show that the UP-M-PCR method can be used to determine the presence of exotoxin genes of drinking water and environmental P. aeruginosa in outbreak situations or in routine surveillance studies to judge virulence potential and investigate pathogenesis. It can serve as an efficient method in the practical detection of various virulence genes of other pathogens simultaneously.
Fig. 2.27

Schematic representation of UP-M-PCR. UP-SP primer is the compound primer that contains a specific primer at the 3′ end and UP sequence at the 5′ end (in bold) (Reproduced from [126] with permission from Springer)

Based on UP-M-PCR, there are a variety of new techniques developed including multiplex dPCR, microemulsion PCR, and so on. According to that research, UP-M-PCR can amplify various targets in one process which can be utilized in high-throughput technology.
  1. (c)

    Multiplex LAMP and Multiplex Lateral Flow Nucleic Acid Biosensor

     
Multiple targets can be detected in one process by LFNAB. The multiplex lateral flow assay could detect all tested E. coli strains from serogroups O157 (22/22), O26 (17/17), and O111 (7/7), and the detection limit was 104 CFU/mL [127]. In order to achieve the rapid detection of P. aeruginosa genes, we successfully designed multiplex LFNAB which is the combination of the LFNAB and the mLAMP. Differentiation of the internal standard gene ecfX and toxin genes (ExoS and ExoU) in P. aeruginosa was detected using FITC-, hex- and digoxin-modified primers in the mLAMP process (Fig. 2.28). In the presence of targets, AuNPs accumulate in the text line which produced a red band and that is visual without instrumentation. The detection limit of P. aeruginosa or its toxin gene utilizing this method is 20 CFU/mL in 50 min [128]. The advantages of this method are low-cost, pollution-free, and time-saving.
Fig. 2.28

Principle of multiplex LAMP coupled with lateral flow nucleic acid biosensor (Reproduced from [128] with permission from Springer)

  1. (d)

    Multiplex Fluorescence-Functional Nucleic Acid Based Biosensors for Microorganism Detection

     

Adapting different fluorescence elements to detect various microorganisms in a progress is a convenient and rapid method. A variety of molecular targets can be detected in one reaction which is the typical advantage and make high-throughput analysis of multiple samples from large study groups or longitudinal studies technically feasible.

In Duan’s study, they developed a novel and high sensitivity fluorescence bioassay for the detection of S. typhimurium and S. aureus in one process [129]. Because of the high affinity of aptamer to the relative bacteria, the aptamer 1-MNPs-S.typhimurium complex subsequently binds to NaY0.78F4:Yb0.2, Tm0.02 UCNP-modified aptamer 1, and the aptamer 2-MNPs-S.aureus binds to NaY0.28F4:Yb0.70, Er0.02 UCNP-modified aptamer 2 (Fig. 2.29) [129]. This is the first utilization of combination of aptamer and magnetic nanoparticles to bind target.
Fig. 2.29

Fabrication processes of bio-functionalized nanoparticles and principle of the performed bioassay (Reproduced from [129] with permission from American Chemistry Society)

Except for species detection, direct quantification of bacteria can be analyzed with multiple fluorescence elements. Enumeration of live bacteria by flow cytometer is a more suitable rapid method with the use of dual staining with SYBR I Green nucleic acid gel stain and propidium iodide (SYBR-I/PI) [130].
  1. (e)

    Gene Chip-Functional Nucleic Acid Based Biosensors for Microorganism Detection

     

There are two types of methods which are developed for arraying a variety of DNA molecules in a very small space. One type is cDNA-sized fragments which are the production of PCR and spotted onto poly lysine-coated glass slides [131]. The other one is short (∼25 nucleotide) oligonucleotides that are modified on a glass surface [132]. This kind of production of both methods is called “chips.” In order to increase specificity, it is important that each gene has to be represented by several (typically 20) different oligonucleotides. What is more, there is only one different central base between oligonucleotide and partner adjacent on the chips.

Liquid chip is commonly used in detection of microorganisms. For example, Staphylococcus aureus can be detected rapidly using liquid chip with the detection limit of 103 CFU/ml which is developed by Wang et al. [133]. What is more, there is a novel method, liquid chip xMAP, that can detect seven different microorganisms in one time developed by Lü et al. [134]. This method can detect 140 strains of microorganisms and 56 types of food samples without cross-reaction or false result, and as low as 1–100 pg and 105–106 CFU/ml can be detected.
  1. (f)

    Multiplex Ligation-Dependent Probe Amplification-Functional Nucleic Acid Based Biosensors for Microorganism Detection

     

It is difficult for multiplex PCR to achieve high specificity which is really significant. So, multiplex ligation-dependent probe amplification (MLPA) was developed by Schouten et al. to make up for deficiencies [135]. Capillary electrophoresis (CE) was always adopted to analyze the products according to the small differences among the lengths of the amplicons. In addition, fluorescence is often labeled to the ligation probes in order to achieve quantitation. MLPA was extensively utilized in medical diagnostics and clinical applications since the development of it [136, 137].

Zhang et al. adopted MLPA to diagnose P. marneffei infection which can detect P. marneffei DNA in cultured cells and paraffin-embedded tissue samples in a short time and with high specificity [138]. There are three pairs of probes that were adopted for amplifying the internally (intergenic) transcribed spacer (ITS) region of P. marneffei rRNA utilizing a systematic phylogenetic analysis. MLPA can be used to detect P. marneffei that is isolated from human patients, bamboo rat, and the local environment which is proved.
  1. (g)

    Multiplex Digital PCR-Functional Nucleic Acid Based Biosensors for Microorganism Detection

     

Multiplex digital PCR is an innovative PCR technology developed in the 2000s, based in the partition of the sample to be analyzed in thousands to millions of individual PCR reaction. A major advantage of it is an increased sensitivity for detection of a few mutants mixed with wild-type DNA sequences. Digital PCR has wide clinical applications in the oncology field and ongoing applications in noninvasive prenatal diagnosis and organ transplant rejection monitoring.

There is a new method of multiplex digital PCR that was developed by Zhong et al. [139]. In a droplet, only one target allele exists even though there are multiple primers/probes. So, although the labels are in the same color, various reactions with different efficiencies can also be discriminated. What is more, the probe concentration can regulate the efficiencies, resulting in a convenient and general purpose method for multiplexing. Due to advantages, digital PCR is extensively utilized in GMO quantification [140], microorganism detection [48], as well as other fields.
  1. (h)

    The Second-Generation Sequencing Technology-Functional Nucleic Acid Based Biosensors for Microorganism Detection

     
Second-generation sequencing is sequencing by synthesis. Four types of dNTPs are labeled by different fluorophores. When dNTPs are added to strand by DNA polymerase, corresponding fluorescence will be enhanced. Thus, the sequence can be obtained by recording the fluorescence changes continuously [141]. This technology can be utilized to large-scale sequence. Sequencing techniques can be utilized in microbial assay, consisting of comparison of microbial genomics, taxonomy, macrogenomics, and single-celled bacteria sequencing [142].
  1. (i)

    TaqMan-Functional Nucleic Acid Based Biosensors for Microorganism Detection

     

Fluorogenic PCR-based (TaqMan) technology has been reported to detect quantities of organisms, such as clinical bacteria [143, 144, 145] and plant pathogenic potato leaf roll virus [146]. Besides, a TaqMan assay for detection of the potato ring rot bacterium, Clavibacter michiganensis subsp. sepedonicus, is shown, too [147]. TaqMan PCR exploits the 59 nuclease activity of Taq DNA polymerase [148] in conjunction with fluorogenic DNA probes [149]. There are one fluorescent reporter dye and one quencher dye that are labeled on probe which hybridized specifically to the target PCR product. In PCR amplification process, the reporter fluorescence is increasing because fluorescent reporter dyes and quencher dyes are separated by Taq DNA polymerase. Repeated PCR cycles result in exponential amplification of the PCR product and a corresponding increase in fluorescence intensity.

S. A. Weller et al. reported the design of a fluorogenic PCR-based assay which utilizes a probe-primer set (RS) to detect all known strains of R. solanacearum and another set (B2) specific for the biovar 2A genotype [150]. Because each probe is labeled with a different reporter dye, both tests can be achieved in a single tube.

2.3.7 Functional Nucleic Acid Based Biosensor for Living or Dead Bacteria Detection

The food quality can be affected by both of dead and living bacteria. Recently, microbial detection has gone from traditional approaches to molecular biological detection technology. At first, the traditional approach can detect living microorganisms only. However, living microorganisms and dead microorganisms can all be detected by molecular approaches, and the methods of identifying living and dead microorganisms are developed nowadays. The novel technologies of detecting living and dead microorganisms seemed like being developed as more sensitive, precise, and comprehensive in the future.
  1. (a)

    The Development of Functional Nucleic Acid Based Biosensor for Living or Dead Bacteria Detection

     

There are advantages and shortcomings of culture, immunology, and nucleic acid-based approaches. As for culturing method, it can analyze living microorganism only, but the detection time is about 3–7 days [151]. Although the detection time of immunological diagnostic method is short, it cannot identify the living and dead microorganisms [152]. Nucleic acid-based approaches have been utilized to microbe tests [153]. Due to the development of molecular methods, the detection of microorganisms has become more sensitive, specific, and rapid. Unfortunately, these approaches cannot be the rapid and simplistic measure to distinguish viable and nonviable bacteria. Viable bacteria can cause food corruption and pathogenicity rather than nonviable bacteria. Thus, distinguishing living and dead microorganisms is important, which is a challenge.

In recent years, the studies of identifying the living and dead microorganisms are becoming research focus. These technologies can overcome the shortcomings of traditional molecular methods [154]. In general, the prospect of the nucleic acid molecular tests for viable and nonviable microorganism is moving the more sensitive, precise, and comprehensive.
  1. (b)

    Functional Nucleic Acid Based Biosensor for Living or Dead Bacteria Detection

     

Reverse Transcription PCR-Functional Nucleic Acid Based Biosensor for Living or Dead Bacteria Detection

According to the studies, mRNA is an excellent referent of animate cells. And it is used in reverse transcription-PCR (RT-PCR) to analyze the condition of gene expression in cells. RT-PCR was adopted to detect living Legionella pneumophila and Vibrio cholerae based on that mRNA only exists in the sample that contains living microorganisms.

In addition, real-time RT-PCR (RRT-PCR) method was also designed. The common feature of these two types of methods is that they both need purified mRNA which is difficult to obtain because the half-lives of most mRNAs are 1.5–2 min. At the same time, there are also some problems that are to extract and preserve intact RNA in the physiological or the environmental condition.

Viability PCR-Functional Nucleic Acid Based Biosensors for Living or Dead Bacteria Detection

There are many viability dyes that have been utilized in microorganism detection combined with qPCR in general, which is called v-PCR. DNA-binding dyes such as ethidium monoazide (EMA) or propidium monoazide (PMA) can only get through membrane of dead cell which is damaged and combined to DNA which makes it unable to be amplified by qPCR. And this is the pretreatment of qPCR.

The membrane integrity is one of the most commonly used differences between living and dead cells. Upon adding DNA-binding dyes, the dead cells can be easily infiltrated, but living cells are not. EMA-qPCR is a simple approach to analyze living and dead cells. Besides, PMA is also utilized in this field because PMA can enter the dead cells and bind to DNA irreversibly.

Nuclease PCR-Based Functional Nucleic Acid Biosensors for Living or Dead Bacteria Detection

Another sample pretreatment for distinguish viable and nonviable cells is utilizing nuclease to nick the exposed DNA. The membranes of living cells are stable to protect nucleic acid. But the DNA of dead cells cannot be protected due to damaged membrane and can be affected easily by outside contamination. Deoxyribonuclease I (DNase I) can cleave ssDNA and dsDNA. Thus, it is usually adopted to remove DNA in sample for analyzing RNA. Due to the membrane of dead cells that cannot protect the DNA inside, the DNA exists only in living cells with the adding of DNase. In Villarreal’s research, it was reported about a DNase I- and Proteinase K-based treatment protocol developed and optimized for the detection, characterization, and analysis of live populations of bacteria present in drinking water biofilms.

Nanoparticles and Spectroscopy Technology-Functional Nucleic Acid Based Biosensors for Living or Dead Bacteria Detection

It is an important challenge to characterize whether the cell is alive or not especially in severe conditions. The nucleic acid dyes can be utilized to enter cells with imperfect membrane to solve this problem. Nevertheless, the problem still exists. Thus, nanoparticles and spectroscopy technology is adapted to solve this problem.

Fourier transform infrared (FTIR) and Raman spectroscopy are the technologies that can analyze the reaction on the surface. The Raman micro-spectrometer is utilized in situ rapid discriminating the viable cells as well. 58S substrates, 45S5 Bioglass, and bioinert silica can be labeled to the viable microorganisms, which makes them able to be detected. These two methods can distinguish microorganisms with high specification. However, nanostructures are needed to combine with SERS to achieve high sensitivity. According to the report, the silver nanoparticles are used to characterize the living and dead microorganisms.

In recent years, studies had shown the strongpoints of laser-induced breakdown spectroscopy (LIBS) in differentiating microorganism. Escherichia coli are characterized, which have divalent cation in the outer membrane. With its function of characterizing microorganisms, it can also be utilized to analyze living and dead cells.

Dielectrophoresis Technology-Functional Nucleic Acid Based Biosensors for Living or Dead Bacteria Detection

Dielectrophoresis (DEP) had been utilized to differentiate living and dead microorganisms by nonuniform AC electric fields based on the principle that frequency responses of cells on the different states are different. Several novel types of DEP have been developed such as iDEP, cEDP, and rEDP. For example, the biggest difference of iDEP is the utilization of insulators to overcome the problems of electrodes. Although the living and dead microorganisms cannot be differentiated using electrokinetic mobility, it can be differentiated via analyzing dielectrophoretic mobility based on the principle that dielectrophoretic mobility of viable microorganism is higher than that of nonviable microorganism.

2.3.8 Artificial Nucleic Acid Based Biosensors for Microorganism Detection

The artificial nucleic acid, peptide nucleic acid (PNA), which is a special nucleic acid with a peptide-like backbone, can also be utilized in nucleic acid hybridization. Compared to DNA, PNA has similar structure with DNA but different backbone [155]. This difference between DNA and PNA makes the PNA more stable and easier to combine with DNA. In the utilization of PNA, fluorescent dye is commonly used.

We describe the construction of an all-in-one biosensor that combines the amplification features of Universal Blocking Linker RPA (UBLRPA) with visual detection on a PNA-based lateral flow device (PLFD). The design of this innovative cascade is shown in Fig. 2.30. The strategy for the UBLRPA is used to generate numerous single linker-attached duplex DNAs. The primer includes a nucleic acid segment (specific primer, dark/light blue) complementary to the invA gene of Salmonella and is linked by a C3 spacer blocker (abasic site, red) and a universal nucleic acid sequence. Recombinase proteins assemble along the oligonucleotide primers to form a stable helical filament (protein–DNA complex). This complex is highly invasive to homologous sequences and forms a D-loop structure at the cognate site, which exposes the 3′-end of the primer. Subsequent to polymerization, continuous strand displacement amplifications occur in the presence of the polymerase, and the polymerization is always terminated at a basic site. Thus, free single-stranded nucleic acid residues that yield, at both ends of duplex products, the complementary DNA of PNA containing probe are generated. Subsequently, the visual detection of the generated single-duplex DNA complexes was performed on a PLFD, with the principle shown in Scheme 1B. By being linked to streptavidin, the biotin-CCP was directly sprayed on the nitrocellulose membrane of the control line. Another biotin-TCP is also immobilized on the test line by means of the biotin-streptavidin linkage. The ligation of Cys-P and AuNPs led to the formation of the AuNPs-P complex that was loaded onto the conjugate pad. While the solution was applied on the sample pad, the complex migrates forward with the solution by capillary action. When positive products are present, the hybridization among the immobilized TCP, the universal linker single-double strand products, and the AuNPs-P complex all occurs on the test line. With the accumulation of AuNPs, the test line is then visualized as a characteristic red band. The excess AuNPs-P complexes can be captured by the immobilized UL on the control line, forming a second red band. Typically, in a sample solution without positive products, there would be no AuNPs-P complexes accumulated on the test line, meaning that only one red band would be observed on the control line. The biosensor has high selectivity and extraordinary repeatability using S. typhimurium, and the limit of detection was 4 CFU mL−1. Furthermore, when milk samples that were artificially contaminated with S. typhimurium were analyzed, the results were obtained within 30 min without complicated instrumentation and exhibited good precision and recovery. Therefore, the portable and all-in-one biosensor for the detection of food pathogens has excellent prospective use for the in situ screening of food and environmental samples.
Fig. 2.30

Design strategy of the all-in-one nucleic acid biosensor system. (a) The pre-amplification strategy for the Universal Blocking Linker RPA (UBLRPA). (b) The signal output principle of the PNA-based lateral flow device (PLFD)

There are also some other researches on the PNA biosensors for microorganism detection. Some examples are described below. In 2014, Bingjie Cai et al. reported an ultrasensitive label-free biosensor based on PNA–DNA hybridization and graphene oxide [156]. In this study, the probe is PNA instead of DNA. The detection limit achieved as low as 100 fM, which is 1 order of magnitude lower than that of the similar DNA-DNA hybridization biosensor. The R-GO FET biosensor can distinguish the complementary DNA from noncomplementary DNA and even one-base mismatched DNA. The novel DNA biosensor has a regeneration capability, interestingly. The developed R-GO FET DNA biosensor has a great property of ultrasensitivity and high specificity and shows its good prospect in disease diagnostics as a point-of-care tool. In 2016, Luzia Mendes reported a novel method to detect the PNA-FISH technique [157]. In their research, microorganisms can hybrid to their PNA probes simultaneously (PNA-FISH multiplex). Except for being tested on existing strains of A. actinomycetemcomitans and P. gingivalis, the PNA-FISH method can also be applied to detect microorganisms in the gingival and subgingival plaque samples which are from some patients who suffer from severe periodontitis. In 2017, Susana P. et al. develop labeled multiplex PNA-FISH method for the specific identification and localization of the CF-associated traditional organism P. aeruginosa and the emergent species I. limosus [158] (Fig. 2.31).
Fig. 2.31

Schematic illustration of the R-GO FET biosensor for detection of DNA based on PNA-DNA hybridization (Reproduced from [157] with permission from Elsevier)

2.3.9 DNAzyme Based Biosensors for Microorganism Detection

DNAzymes are short synthetic oligonucleotide molecules, which are also called deoxyribozymes, catalytic DNAs, or DNA enzymes. They have catalytic activity which can be isolated in a kind of in vitro method called SELEX. After it was first found, DNAzymes have been utilized for the many kinds of biosensors development. Herein, we developed a colorimetric G-quadruplex LAMP sensor that combine the isothermal amplification and the DNAzyme for the ultrasensitive detection of Salmonella [80]. This is an example of functional nucleic acid colorimetric biosensor for microorganism detection. First of the research, there are primers within a signal inner primer of a 17-nt DNAzyme complementary sequence (a signal precursor), which were designed for the amplification process and colorimetric detection. The target DNA can initiate LAMP amplification, and the amplification results conclude a larger number of DNAzyme sequences. After adding the hemin, the free DNAzyme fragments combined with each other and formed G-quadruplex-hemin conjugates which perform as colorimetric signal readouts for the naked eye observation. The novel colorimetric strategy does not need any forges or other apparatus, and the detection limit can achieve less than 0.5 pg. Moreover, the reported sensor showed high foreground in the DNA visual detection and may even pave the way for other amplification-based colorimetric detection and the point-of-care determination.

Recently, DNAzyme has been applied in various chemiluminescent or colorimetric determinations [159, 160]. For example, Willner and his coworkers reported a method based on this DNAzyme for single-stranded DNA and telomerase activity detection [54]. They also explored the same DNAzyme-containing primer to detect PCR product detection [161]. However, there are false-positive signals because of primer dimer formation in this method like all other primer probes. More recently, Feng Du and Zhuo Tang reported a facile technology based on the advantage of the 5′-nuclease activity of Taq DNA polymerase to free a DNAzyme inserted in the sensor for PCR product colorimetric analysis [162]. This DNAzyme, discharged as a byproduct of a specific DNA target PCR, can fold up and form G-quadruplex structure with hemin and then oxidize ABTS into a green condition in the presence of hydrogen peroxide. In this research, the detection limit could reach as low as 100 A. hydrophila cells.

Except for the above DNAzyme, there are some other DNAzymes in bacteria detection, which can catalyze the cleavage of another nucleic acid molecule [163, 164, 165]. Because the catalysis is carried out with multiple turnovers, the DNAzyme introduces an amplification step into the experimental setup [166]. This amplification does not need protein components, which make the process costly and have low thermal stability. Two main approaches were used to detect genetic targets. In 2011, M. Monsur Ali et al. demonstrated a facile method based on isolating fluorogenic DNAzymes for a specific bacterium detection, which is from random-sequence DNA library by utilizing the unpurified complex extracellular mixture extracted from the target microbe [31]. In 2013, Kyryl Zagorovsky and Warren C. W. Chan developed a plasmonic DNAzyme strategy combined with AuNPs for point-of-care genetic detection of infectious pathogens which can be used in remote settings (Fig. 2.32) [167]. In 2016, Ping-Yao Hsieh et al. have engineered a simple paper sensor device containing a bacteria-detecting RNA-cleaving DNAzyme, which exhibits unchanged detection capability after regular storage at room temperature for 6 months [168]. More recently, Fang Yu et al. explored a new method called DNAzyme-integrated plasmonic nanosensor (DIPNs) that can specifically detect target sample of microorganisms, which is an inexpensive and culture-free process. This method also incorporates with enzyme-dependent nanoplasmonic biosensor and real-time DNAzyme-based sensor in 2017 [169].
Fig. 2.32

(a) Conceptual design of MNAzyme catalysis. (b) AuNP aggregate formation. (c) Outline of the MNAzyme assay. (d) Scheme depicting how the assay can be conducted at the point-of-care to analyze multiple targets in parallel (Reproduced from [167] with permission from Wiley)

2.3.10 Gold Nanoparticle (AuNP)-Functional Nucleic Acid Based Biosensors for Microorganism Detection

AuNPs perform a significant role as excellent labels in many diagnosis because of their great properties including easy functionalization, biocompatibility, good stability, a characteristic surface plasmon resonance, a strong red color, easy manipulation, and prominent enhancement of signal in nanoscale morphology. The gold nanoparticles can make the detection result observed by naked eyes, and they have been applied into many areas consisting of the analysis of nucleic acid, proteins, small molecules, ions, and even cancer cells. The size and shape change causes the optical properties of gold nanoparticles, which varies from 2 to 150 nm, but 15–40 nm sizes were used generally. However, AuNPs still have some drawbacks: the sensitivity is dependent on the amount of the targets, and amplification of signal is costly.

We developed a gold nanoparticles visual strategy for P. aeruginosa detection as well as its toxin genes. This approach can recognize the internal standard gene ecfX and toxin genes (ExoS and ExoU) in P. aeruginosa by modifying primers using different labels in multiple LAMP including hex, FITC, and digoxin. In the presence of primers with different labels consist of FITC (hex, digoxin) and biotin, Bst DNA polymerase and dntps, the mLAMP can start and generate a lot of duplex DNA products with the label of biotin and FITC (hex, digoxin). Then the lateral flow biosensor recognizes the label of the product by the different antibody fixed on the strip. Anti-biotin antibodies were labeled on the AuNP. Anti-FITC (hex, digoxin) antibodies were labeled on the test line of lateral flow biosensor. The accumulation of AuNP generated a significant red band, enabling visual detection of P. aeruginosa and its toxin genes by naked eyes. After systematic optimization of LFNAB preparation and detecting conditions, the limit sensitivity detection of current approach can reach as low as 20 CFU/mL P. aeruginosa and its toxin. At the meantime the limit time of the detection can be achieved in 50 min, which is more sensitive than PCR. Therefore, this strategy offered a fast, easy-operating, sensitive, low-cost, and pollution-free method for the determination of P. aeruginosa and its toxin genes. Except for the above, many other chemical substances were also synthesized to detect microorganism.

AuNPs were also prepared for synthetic DNA and applied into bacterial 16S rRNA detection of Escherichia coli (DH5α) in cell cultures [170]. AuNP–DNA molecular beacon conjugates produced a sensitivity provident by three orders of magnitude, and the detection limit achieved 100 CFU/mL of E. coli within 1 h, which is much more sensitive than molecular beacons alone. In another example, Donmez et al. reported a nanosensor based on Tb3+ and Eu3+ chelated AuNP to detect dipicolinic acid, which is a unique biomarker of bacterial spores [171]. Also, another strategy based on gold nanoparticles has been demonstrated to detect E. coli in real water or food samples by Jin et al. This method established platform based on FRET and used the conjunction of lanthanide-doped up conversion nanoparticles functionalized with complementary DNA and aptamer functionalized gold nanoparticles [172].

2.3.11 Silver Nanoparticle (AgNP)-Functional Nucleic Acid Based Biosensors for Microorganism Detection

Nano-biotechnology is a novel technology that developed in recent years and has potential in wide fields. In the area of metal nanoparticles, silver nanoparticles are widely utilized in microorganisms due to the toxicity against a broad spectrum of pathogenic microbes. There are some typical applications of silver nanoparticles as follow.

Ghinwa et al. explored a protein-A-antibody-modified AgNP biosensor based on Raman for the detection of bacteria [173]. Specificity was reached by incubating bacteria with abundant polyclonal antibodies. And the surface-enhanced Raman spectroscopy selectivity of bacteria was superior to bulk Raman spectroscopy selectivity. It is found that the surface charge of the cell wall was widely dependent of the Raman spectra of microorganisms upon in situ synthesis of AgNPs directly on the surface of bacteria (Fig. 2.33) [174]. This method presented that these bacteria provided approximately 30 times higher Raman signal than those bacteria obtained by mixing colloid and bacterial suspensions. The detection can be completed wholly in 10 min, and the required total volume of reactant bacteria was 1 mL. In addition, as little as 3 μL of sample can perform the SERS determination greatly. Moreover, this new approach suggested by the researchers can distinguish between three E. coli strains and one Staphylococcus epidermidis strain by means of a hierarchy cluster analysis. SERS mapping detected 250 CFU/mL on hydrophobic glass slides.
Fig. 2.33

The principle diagram of the Zhou’s methods (Reproduced from [174] with permission from American Chemistry Society)

2.3.12 Nanozyme-Functional Nucleic Acid Based Biosensors for Microorganism Detection

We developed a continual cascade nanozyme biosensor for the detection of viable Enterobacter sakazakii (ES) based on propidium monoazide (PMA), loop-mediated isothermal amplification (LAMP), and nanozyme-strip (Fig. 2.34) [175]. Recent outbreaks of ES life-threatening neonatal infections linked to Enterobacter sakazakii heightened the need to develop rapid and ultrasensitive detection methods, particularly those capable of determining cell viability. As we know, conventional culture-based protocols are considered as a gold standard for detection of ES, but they are time-consuming, taking up to 7 days, and show relatively low sensitivity. The use of immunoassays such as enzyme-linked immunosorbent assay (ELISA) also has been limited because of low specificity and low sensitivity. Recently, many biomolecular methods have been reported for the detection of ES, such as PCR and real-time PCR. However, they are not routinely used due to the requirement for an expensive thermal cycle. More recently, loop-mediated isothermal amplification (LAMP) has emerged as a promising alternative gene amplification method that combines rapidity, simplicity, high specificity, and efficiency. 109 copies of target DNA can be obtained utilizing LAMP within 1 h. However, it is not able to distinguish the viability of the detected bacteria. However, working with RNA is technically demanding, and some mRNA molecules which persist in dead cells may lead to false-positive results. Ethidium monoazide (EMA) or propidium monoazide (PMA) sample treatment has been combined with LAMP to distinguish viable from dead cells in Listeria monocytogenes, Salmonella, and Vibrio parahaemolyticus. These DNA-binding dyes selectively penetrate compromised membranes and intercalate into DNA of dead cells but not viable cells recently. Upon exposure to intense visible light, the photoreactive azide group on the dye is converted to a highly reactive nitrene radical and crosses with dead cell DNA. So it is unavailable for subsequent LAMP amplifications, and PMA was demonstrated to be advantageous over EMA in terms of dead cell exclusivity. However, LAMP combined with PMA typically does not immediately report the results on-site testing which is unable to realize visual detection.
Fig. 2.34

The design of the continual cascade nanozyme biosensor (Reproduced from [175] with permission from American Chemistry Society)

In order to meet the need of the detection, nanozyme-strip has attracted significant attention in recent years because of its stability and ability to be reused. The nanozyme-strip is based on the peroxidase-like activity of MNPs and has been successfully applied to biomedical detection of Ebola and environmental analysis. The main objective of this study was to develop a continual cascade nanozyme biosensor for the detection of viable ES. The ompA gene of ES was determined using FITC-modified and BIO-modified primers in LAMP process. In the presence of BIO-and FITC-modified primers and Bst DNA polymerase large fragments, the LAMP produced 109 dual-labeled DNA products. LAMP combined with PMA treatment was applied for differentiation from viable and dead state of ES. Then, by using Fe3O4 magnetic as a nanozyme probe, a MNP-based immunochromatographic strip (nanozyme strip) was further employed for amplified signal to allow visual detection and quantification by strip reader. Owing to the catalytic properties of the probe, the detection sensitivity was improved compared with colloidal gold strip. And the 10 CFU/mL lower limit of biosensor is improved compared with previously reported techniques and is much faster (within 1 h) and simpler (without specialist facilities). Hence, the developed continual cascade nanozyme biosensor provides a rapid, ultrasensitive, and simple tool for on-site detection of viable ES.

There are some other research on the nanozyme-based biosensor for microorganism detection. Demin Duan et al. generated a novel strip test based on nanozyme for the glycoprotein (GP) of EBOV (EBOV-GP) detection and the sensitivity limit reached at 1 ng/mL by the naked eye, which is more than 100-fold higher the step based on colloidal gold [176]. This method can make it true that as low as 240 pfu/mL, pseudo-EBOV can be rapidly detected within 30 min. Demin duan et al. previous focused on the intrinsic peroxidase-like activity of MNPs which is fundamental in this research. In this research, they let the nanozyme take place of colloidal gold nanoparticles in the lateral flow biosensor because of the catalyzing peroxidase activity of the nanozyme which can produce an obvious color reaction. This novel probe can recognize, separate, and visualize EBOV on the strip after labeling with anti-EBOV antibody. Owing to the catalytic properties of the probe, the detection sensitivity of the nanozyme-strip has been improved significantly without any need of special equipment. This novel assay represents a suitable technology for Ebola-stricken areas due to the high sensitivity and simplicity. In addition, the nanozyme also has been applied in the cancer therapy. ShiyanFu did a research on the structural effect of Fe3O4 nanoparticles on peroxidase-like activity for cancer therapy [177]. More recently, the nanozyme is applied into the molecule detection, such as malathion [178], glucose and antioxidant [179], mercury(II) ions [180], multiple DNAs [181], and so on.

2.3.13 Magnetic Nanoparticle (MNP)-Functional Nucleic Acid Based Biosensors for Microorganism Detection

Nanoparticle-based platforms have been applied into many detection areas so as to reach real-time detection for better foodborne pathogen monitor. Magnetic nanoparticles have performed a significant role in much diagnosis because of their great properties including easy functionalization, biocompatibility, and high stability to reach fast, accurate, simple, and cheap analysis of detection. There are some typical applications of magnetic nanoparticles in biology detection.

In 2006, Gao et al. employed a method based on vancomycin-modified MNPs which could generate multivalent interactions and have an analysis of bacteria in blood samples [182]. The enrichment culture was then belonged to fluorescent vancomycin staining. The detection process lasted 2 h, and the limit of the detection is 10 CFU/mL. The bacterial count was confirmed by back titration method. In 2011, Chung et al. formed hydrogen bonds with the terminal d-alanyl-d-alanine (d-Ala-d-Ala) moieties of N-acetyl glucosamine (NAG) peptide and the N-acetylmuramic acid (NAM) subunits to bind the antibiotic vancomycin modified with trans-cyclooctene (Vanc-TCO) and the cell wall of Gram-positive bacteria [183]. The MNPs can be attached to tetrazine (MFNP-Tz), which were applied to label bacterial by bioorthogonal chemistry. As a result, there are several dose-dependent complexes of Vanc-TCO to S. aureus, S. pneumoniae, S. epidermidis, and E. faecalis cell walls, thus the Gram-positive bacteria. The achievement also makes magnetic detection of bacteria come true. Chung et al. also concentrated on dependent binding of nanoparticles to bacteria and the difference according to the type of bacteria and inhibited with unmodified antibiotics. In 2014, Wu et al. developed a multiplexed fluorescence resonance energy transfer aptasensor based on multicolor up conversion nanoparticles coupled with magnetic nanoparticles to detect the target of S. aureus, S. typhimurium, and Vibrio parahaemolyticus (Fig. 2.35). The strategy using different rare-earth-doped up conversion nanoparticle labels which have independent emission peaks to achieve the bacteria detection. Autofluorescence of biomolecules was prevented using a 980 nm infrared diode laser. The magnetic nanoparticles can separate the targets from interferences efficiently without any other pretreatments of the samples. In addition, this approach resulted in stable and target-specific aptamers, which is superior to the susceptibility of traditional antibodies. The novel aptamer-based detection assay provided a benefit of high sensitivity due to using a stable bioassay platform [184].
Fig. 2.35

Schematic diagram of aptamer-based detection assay for bacterial detection (Reproduced from [184] with permission from American Chemistry Society)

2.3.14 Functional Nucleic Acid Based Fluorescence Biosensors for Microorganism Detection

Fluorescence is a highly sensitive and versatile technique that has been used extensively in biosensor development. In addition to fluorescence intensity, many other properties such as fluorescence lifetime, polarization, energy transfer, and emission wavelength can be measured. Fluorescence’s light-emitting mechanisms are not same, which will be introduced in this review.
  1. (a)

    F/Q

     

In general, the fluorophore and quencher are mostly used in biosensor to result fluorescence because it is convenient and easy to be modified to other substances. Because the fluorophore and quencher are positioned right next to each other, the sensor background is extremely low, leading to a large signal enhancement in the presence of the target.

The principle of fluorophore is that when they absorb energy from light, they will transfer this energy and emit it as light of a characteristic wavelength. Briefly, after absorpting the energy from the light, a fluorophore at ground state will be raised to a higher vibrational level of an excited singlet state, which takes about one femtosecond (10−15 s). In the next process, the fluorophore will return to the lowest vibrational level of an excited singlet state because some energy is lost as heat. And about 1 ps (10−12 s) will be taken in this process. And because of the energy is lost, the energy of the absorbed light is higher than the energy of the emitted fluorescence light, and therefore emission occurs at a longer wavelength than absorption. Fluorescence is the light that is emitted from the excited singlet state [185].

If the fluorophore react with other fluorophore or nonfluorescent molecules and produce a nonfluorescent complex, the fluorophore is quenched. This is the principle of contact quenching, static quenching, or ground-state complex formation. In contact quenching, two molecules interact by proton-coupled electron transfer through the formation of hydrogen bonds. When the complex absorbs energy from light, the excited state immediately does not emit a photon, and the molecules do not emit fluorescent light and just return to the ground state. The key point of contact quenching is that the complex of two molecules changes in the absorption spectra.

“Collisional quenching” or “dynamic quenching” is another kind of decrease the fluorescence intensity. When a fluorophore at excited state contact with another molecule in the one solution, the fluorophore is deactivated and that is called collisional quenching. After contraction, the fluorophore returns to the ground state without emission of fluorescence light. The features and structures of the fluorophore and the manner of its interaction with the other molecule can all affect the extent of quenching. Oxygen, halogens, and amines can quench the fluorophore in collisional quenching.

Dr. M. Sunbul’s group utilized fluorophore to form fluorescent turn-on probes, and they are nonfluorescent in solutions in the absence of target but become highly fluorescent when binding to target RNA aptamers [186]. In principle, fluorophore were quenched by conjugating to small molecules that can efficiently quench the fluorescence. Nonfluorescent complex of fluorophore and quencher give the information that there is no target (OFF state). But, when there is any aptamer, the fluorophore would combine with the aptamer instead of quencher, and the fluorescence will be enhanced (ON state).
  1. (b)

    QDs

     

QDs have a variety of advantages over the organic fluorophore and fluorescent proteins, which are extensively adopted in biological labeling [187]: broad excitation spectra and narrow emission spectra can be excited and detected at the same time. These features make it possible that the multiple targets can be detected at the same time utilizing multicolor QD systems [188, 189]. Compared with other fluorescent dyes, QDs are more stable which can be utilized in real-time monitor [190].

QDs can also be combined with aptamers to analyze the target microorganisms with high selectivity and sensitivity (Fig. 2.36) [191]. In this method, the high photostability of QDs can enhance the signal intensity which results in improved sensitivity over approach utilizing individual dye-labeled probes.
Fig. 2.36

Conceptual scheme of the flow cytometric assay for Vibrio parahaemolyticus and S. typhimurium detection (Reproduced from [191] with permission from American Chemistry Society)

  1. (c)

    Other Fluorescent Labels

     
Except for F/Q and QOs, there are some other fluorescent labels for microorganism detection. For example, Jin Huang developed a DNA-detection system which adopted pyrene molecules to the amplification capability of HCR and makes fluorescence signal amplified (Fig. 2.37) [192]. Pyrene moieties are labeled at the DNA hairpins H1* and H2* via a six-carbon-atom spacer at each end. When there is no target DNA, both probes (H1* and H2*) are in the closed form, and the distance between two pyrene moieties are not short enough. But in the presence of target, target can pair with the sticky end of H1*, which can open the hairpin and HCR occurs. In this case, a pyrene moiety on one probe is close to a pyrene moiety on the neighboring probe. Therefore, a lot of pyrene excimers are formed, each of which emits at approximately 485 nm. According to the emission intensities of the pyrene monomer and the excimer, the target DNA can be analyzed sensitively (Fig. 2.37).
Fig. 2.37

Working principle behind the detection of DNA on the basis of HCR amplification and the formation of pyrene excimer. Py = pyrene. (Reproduced from [192] with permission from Wiley)

2.3.15 Functional Nucleic Acid Based Electrochemical Biosensors for Microorganism Detection

The electrochemical biosensors have many advantages over other biosensors such as good results in a turbid media, high sensitivity, miniaturization potential, and so on [193]. When the sample and electrode interact, the changes can be measured by electrochemical sensors. According to the measured parameter, the techniques are classified as amperometric (current), potentiometric (potential), and impedimetric (impedance) [194]. Electrochemical sensors can be produced with high reproducible mass by utilizing technologies such as thick-film technology. Besides, the electrode sensor can be designed to high-throughput sensors using screen printing electrodes [193, 194, 195].
  1. (a)

    Functional Nucleic Acid Based Electrochemical Biosensors for Virus Detection

     

Although avian influenza virus shows rapid evolutionary dynamics, consistent with a high background mutation rate and rapid replication [196], electrochemical biosensors for its detection are highly recommended. Such strategy allows for making simple and rapid changes of specific recognition elements and solves the problem associated with high mutation rate of the avian influenza virus [197].

Kamila Malecka designed an electrochemical genosensor to detect target DNA and RNA sequences originated from avian influenza virus H5N1 [198]. The ion-channel mechanism is the basic principle of it. When the redox active marker [Fe(CN)6]3−/4− exists in the sample solution, electrochemical technique–Oster young square wave voltammetry can get signals upon hybridization processes.
  1. (b)

    Functional Nucleic Acid Based Electrochemical Biosensors for Pathogen Detection

     

Pathogen detection is another field of electrochemical biosensors applications which is rapid method for microorganism detection and is researched in the past two decades.

Moreover, the electrochemical biosensors can be made into a simple device for low cost because of the presented state-of-the-art techniques utilized in the fabrication of electronics [193, 199, 200, 201, 202]. However, some disadvantages restrict electrochemical biosensors to pathogens detection which are needed to be overcome. Particularly, the complex food sample is the most concerned factor which causes the most difficulties because the bacteria are highly unlikely distributed in/on foods in the pattern of uniformity. Thus, collections and pretreatments of sample are needed to satisfy the requirement of direct use of electrochemical biosensors [203].

Electrochemical biosensors can be easily combined with other technologies. In order to detect S. typhimurium, E. Sheikhzadeh developed a label-less electrochemical biosensor which combined poly [pyrrole-co-3-carboxyl-pyrrole] copolymer and aptamer [204]. What is more, there is a methodology with high specification and sensitivity that was developed for quantitative detection of Enterobacteriaceae bacteria. This method combined exonuclease III-assisted target recycling amplification with a simple electrochemical DNA biosensor. In addition, Zahra Izadi used a DNA-based Au-nanoparticle-modified pencil graphite electrode (PGE) biosensor to detect Bacillus cereus [205].
  1. (c)

    Functional Nucleic Acid Based Electrochemical Biosensors for Toxin Detection

     

In recent years, the nanotechnology has been extensively utilized in bioanalytical devices. Moreover, in the detection of toxins, it also shows a lot of advantages for food safety and environmental applications. Because of high sensitivity and design versatility of electrochemical biosensors, the toxins of low levels and the small size can be detected by electrochemical biosensors. The nanomaterials will be developed further to higher sensitivity and short detection time [206].

2.3.16 Surface-Enhanced Raman Spectroscopy-Functional Nucleic Acid Based Biosensors for Microorganism Detection

  1. (a)

    Principles of Surface-Enhanced Raman Spectroscopy

     
The Surface-Enhanced Raman Spectroscopy (SERS) effect can amplify Raman signals (most of them barely coming from molecules) by several orders of magnitude. In the process of SERS, the light and metals react which is the reason of signal amplification. The key point of SERS is that the molecules have to be close to the metal surface within 10 nm. The denomination surface-enhanced Raman scattering or SERS sums up particularly well these three cornerstones of their effect [207]:
  • Surface (S): SERS is a surface spectroscopy technique, and the molecules must be on (or close to) the surface which is an important factor in adoptions of SERS. Before the utilization of SERS, it is must be guaranteed that the molecules can attach to (or at least be very close to) the surface of the metal substrate.

  • Enhanced (E): The signal enhancement is provided by plasmon resonances in the metal substrate. In fact, the term “plasmon resonances” means a family of effects in related to the interaction of electromagnetic radiation with metals.

  • Raman (R): The technique consists in measuring the Raman signals of molecules (the SERS probes or analytes). Raman spectroscopy is the study of inelastic light scattering, and, when applied to molecules, it provides an insight into their chemical structure (in particular their vibrational structure).

  1. (b)

    The Application of Surface-Enhanced Raman Spectroscopy-Functional Nucleic Acid Based Biosensorsfor Microorganism Detection

     

Surface-enhanced Raman spectroscopy as a sensitive and specific method has been utilized in many fields of biology. Besides, the SERS technique can analyze molecules even in trace amounts [208]. Resonance Raman at different excitation wavelengths can be widely used to analyze different microorganisms. Gold nanoparticles, silver nanoparticles, and other heavy metal nanomaterials and various core–shell nanoparticles are widely utilized as SERS-enhanced substrates [209, 210, 211].

L. Zeiri developed a SERS-based biosensor for the quantitative detection of S. typhimurium and S. aureus simultaneously using aptamers and nanoparticles [212]. The signal probe consists of AuNPs, Raman signal molecule, and aptamers. And the combinations of GNPs and aptamers were utilized to capture. In the range of 102–107 cfu mL−1, S. typhimurium and S. aureus concentration exhibited a good linear relationship, and the detection limit was 35 cfu mL−1 for S. aureus and 15 cfu mL−1 for S. typhimurium. This method is sensitive, selective, and rapid.

In 2011, Sandeep P. Ravindranath demonstrated a cross-platform method to detect three different pathogens at the same time utilizing Raman and UV–vis absorption spectroscopy. Gold (Au), silver (Ag), and Ag–Au core–shell nanoparticles were modified with anti-Salmonella typhimurium aptamers, anti-Staphylococcus aureus, and anti-Escherichia coli O157:H7 antibodies. In order to signal output, Raman reporter molecules were also labeled to them. A microfiltration step was utilized to achieve a detection platform with high selection and good specification, with total detection time under 45 min for both species (E. coli O157:H7 vs. S. typhimurium) and strain (E. coli O157:H7 vs. E. coli K12) level sensing at a limit of a detection ranging between 102 and 103 CFU/ml [213].

The multiple target detection method using SER technology was also developed by Hui Zhang [214]. The Raman signal probes are built using AuNPs labeled by Raman molecules (mercaptobenzoic acid and 5, 5′-Dithiobis (2-nitrobenzoic acid)) and aptamer. This method with short detection time, high sensitivity, and specificity was widely utilized to analyze the microorganisms in actual samples.

2.3.17 Surface Plasmon Resonance (SPR)-Functional Nucleic Acid Based Biosensors for Microorganism Detection

  1. (a)

    Principles and Advantages of SPR

     

According to the studies, surface plasmon resonance (SPR) is an important technology in monitoring the real-time reaction. Two mechanisms have been considered to explain the SERS effect. The main contribution arises from a huge enhancement of the local electromagnetic field close to surface roughness, due to the excitation of a localized surface plasmon, while a further enhancement can be observed for molecules adsorbed onto specific sites when resonant charge transfer occurs. If there are any molecules that bonds to the conducting surface, the oscillations will be changed with high sensitivity [215].

According to the principle of SPR, we can sum up the advantages of it. For example, using this method can achieve real-time detection without modifying the samples. In addition, there is no need to use a lot of samples, and fewer samples fulfill requirements. Besides, this method is rapid without sample pretreatment. At last, it is sensitive and easy to combine with high throughput and quality [97].
  1. (b)

    The Application of Surface Plasmon Resonance (SPR)-Functional Nucleic Acid Based Biosensors for Microorganism Detection

     

In 2005, Lee et al. developed a sensor that combined the DNA microarrays and surface plasmon resonance (SPR) to measure the single-stranded DNA (ssDNA) [216]. When ExoIII and target DNA are modified to a 3′-terminated ssDNA microarray, hybridization adsorption of the target ssDNA leads to the direction-dependent ExoIII hydrolysis of probe ssDNA strands and the release of the intact target ssDNA back into the solution. The targets bonded to probe and it caused the change of SPR signal. The detection limit is 10–100 pM.

Tan Tai Nguyen developed surface plasmon resonance (SPR) optical fiber sensor to analyze PCR amplification without fluorophore [217]. The integrated device was comprised of the microfluidic PCR reactor and the optical fiber SPR sensor with bimetallic (Ag/Al) coating. This sensor can amplify the DNA of Salmonella spp. within 30 min. Besides, the SPR device can measure the DNA amplicon. Thus, it is an all-in-one device that can serve as a DNA amplification-to-detection instrument.

2.3.18 Flow Cytometry-Functional Nucleic Acid Based Biosensors for Microorganism Detection

Flow cytometry (FCM) can analyze the single cell according to the size and granularity by using light-scattering features when the cell flows through a measuring device [218].

The main components of flow cytometers and cell sorters are basically fluidics, optics (excitation and collection), an electronic network (detectors), and a computer. The fluidics is responsible for directing liquid containing particles to the focused light source. The excitation optic focuses the light source on the cells/particles, while collection optics transmits the light scatter or fluorescent light of the particle to an electronic network. The electronic network detects the signal and converts the signals to a digital data that is proportional to light intensity, and the computer is also required to analyze data [219, 220] .

Flow cytometry is used in various applications based on the detection of the membrane and cytoplasmic and nuclear antigens. Additionally, whole cells and cellular components such as organelles, DNA, RNA, chromosomes, cytokines, hormones, and protein content can also be investigated by flow cytometry. Analysis of cell proliferation and cell cycle and measurements of calcium flux and membrane potentials are the commonly used examples of methods developed for flow cytometry [221].

Flow cytometry was first used in the 1970s as a common method for cellular biology. In recent years, it is utilized in microorganisms although they are hardly characterized because of small size. In addition, FCM can achieve high-throughput detection as well as single cell distinguishment. Nucleic acid-binding dyes especially SYTO dyes and PI have been extensively adopted to characterize cell viability. If DNA of living and dead microorganisms is binding to DNA dyes, it is easy to distinguish by UVA. In addition, EMA, annexin V, and amine reactive viability dyes (ViDs) are dead cell dyes in general. FCM can be adopted for the live/dead microorganism detection. According to the report, FCM has been utilized to detect E. coli, S. enterica serovar Typhimurium, Shigella flexneri, and a community of freshwater bacteria [222].

FCM-FACS enables us to isolate single cells directly from water samples prior to incubation in a medium. This single-cell isolation reflects bacterial population diversity. This is in contrast to most of the conventional isolation procedures, in which cells are grown in a medium prior to isolation. However, FCM-FACS has not been used to isolate pathogens that are present in low concentrations in environmental samples without preincubation. Therefore, Ozawa S. developed FCM-FACS method to detect and isolate pathogens that are present in low concentrations in water environment. As a result, specific isolation was achieved even when the target was present at 0.01% of the total population in pure culture study and when the target was present at 10 cells/mL in spiked water sample. As a result of comparison with conventional methods, the bacterial proportion was almost preserved by FCM-FACS method better than the result of by dynabeads separation technique [223].

2.3.19 Gene Chip-Functional Nucleic Acid Based Biosensors for Microorganism Detection

  1. (a)

    Detection Techniques Based on Solid Arrays

     

A DNA microarray, also called DNA chip or biochip, has so many microscopic DNA that modified a solid surface. In order to make sure that the PCR primers were modified onto substrates, the chemical bonds that connect the oligonucleotides and the substrates cannot be damaged by thermocycling conditions, especially the high temperatures of 95 °C.

Hoffmann et al. [224] summarized a universal protocol for grafting PCR primers onto solid body for solid-phase PCR. The primers are labeled by using PCR-compatible method. The DNA microarrays can be integrated into microfluidic lab-on-a-chip cartridges of various materials by immobilization and SP-PCR protocols that have been reported. What is more, covering the inner space with PCR primers makes the generated PCR products recovered in digital PCR.

DNA microarrays make it possible to analyze the types of microorganisms by using specific signatures. There are researches utilizing DNA microarrays modified with markers in bacterial genomes. Using this method, multiple targets can be detected at the same time and screening of a variety of specimens [225].
  1. (b)

    Detection Techniques Based on Liquid Arrays

     

Kopp et al. designed continuous-flow PCR chip for the first time in 1998, which is widely used for microorganism detection [226]. Wang et al. utilized liquid chips to detect Staphylococcus aureus rapidly with the detection limit of 103 CFU/ml and high specification. Another advantage of this method is that it can be used for 200 food samples [133]. What is more, multiple detections of seven microorganisms can be achieved using this technology which is developed by Lü et al. This developed method can detect 140 strains of bacteria and can be utilized for 56 food samples with high specification and a detection limit of 1–100 pg and 105–106 CFU/ml [134].

A miniaturized, disposable microbial culture chip has been fabricated by Colin J. Ingham group with up to one million growth compartments [227]. This chip can be utilized for Escherichia coli detection based on expression of the lacZ reporter gene and high-throughput screening.

2.3.20 Functional Nucleic Acid Based Biosensors for Microorganisms Diversity Analysis

  1. (a)

    16S rDNA and 18S rDNA

     
There are a variety of microorganisms that existing in environments. The different environments can cause genetic diversity even for the same microorganism. As a result, it is important to characterize microorganisms at the genetic level. In genotyping of microorganisms, 16S rDNA (for bacterial) and 18S rDNA (for fungus) are most widely utilized because of both species-specificity and proper length. Microorganisms diversity Analysis helps us to detect food contamination and to understand the principles of how food becomes unsafe.
  1. (b)

    Restriction Fragment Length Polymorphism (RFLP)

     
RFLP is the technology to detect variations in homologous DNA sequences. PCR is utilized for amplification of 16S rDNA in microbial diversity research studies, and amplicons are then digested by restriction enzymes. Electrophoresis and southern blot are used to analyze the length of fragments. Terminal RFLP (T-RFLP) is a technology that combines RFLP and PCR. The PCR primer is modified at the 5′-end with phosphoramidite dyes and electrophoresis is utilized to separate the digested products [228]. It is reported that microorganisms can be detected by T-RFLP [229].
  1. (c)

    Random Amplified Polymorphic DNA (RAPD)

     
Random amplified polymorphic DNA is an extensively applied method for the characterization of microorganisms. The genomic DNA of microorganism is amplified using 10 bp primers and PCR products are analyzed by electrophoresis. Compared with RFLD, there is no need to use southern blot in the process of RADP. Even though there is no DNA sequence information, RAPD can also be utilized. But, the repetition of it is not perfect.
  1. (d)

    DGGE and TGGE

     
In general, there are two processes of DGGE and TGGE: PCR amplification of the target sequence and gradient gel electrophoresis. Both the gradient decrease in the electrophoretic mobility of the target sequence in a polyacrylamide gel containing a linear gradient of denaturants in the case of DGGE and a linear temperature gradient in the case of TGGE can lead to separation. The advantage of DGGE and TGGE is that the resolution of it is much higher than other PCR electrophoresis-based methods. In addition to high resolution, it is also inexpensive, rapid, reproducible, and reliable [230], but the shortcoming is that community fingerprints of them cannot be translated into taxonomic information [231].
  1. (e)

    Sanger Sequencing

     
Sanger sequencing is utilized to DNA sequencing. In the process of DNA replication, chain-terminating dideoxynucleotides are incorporated utilizing DNA polymerase for DNA sequencing. To analyze the microbial diversity, the unique template is needed for Sanger sequencing to supply the exhaustive information of species, and 16S rDNA is required. But, this method is gradually substituted by high-throughput sequencing.
  1. (f)

    High-Throughput Sequencing

     

High-throughput sequencing is a rapid method because it can analyze a variety of different DNA sequences at one time. There are so many advantages of it, including high sensitivity, high specificity, low-cost and short detection time, which makes it much better than traditional methods. In microbial diversity research, people often utilize amplicon sequencing and whole-metagenome shotgun sequencing.

2.4 Conclusion and Prospects

This review is focused on the construction, basic principles, amplifications and recent development in functional nucleic acid based biosensor for microorganism detection. In few years, the microorganism detection technologies are developed from traditional approaches such as microorganism cultivation, physiological and biochemical testing, instrument analysis, and immunology, to molecular biological detection methods especially functional nucleic acid detection technology. Because of the development of it, the detecting of microorganisms is becoming quicker, more specific, sensitive and accurate in recent years.

There have been series of functional nucleic acid based biosensors for microorganism detection including functional nucleic acid-aptamer biosensor, functional nucleic acids colorimetric biosensor, new nanomaterial-based biosensor, lateral flow biosensor, high-throughput biosensor, and so on. However, the sensitivity, the specificity and the species of the functional nucleic acid based biosensors still need to be developed. For an instance, we have done a lot of research on the redox activity of the DNAzyme in the detection, while the other activity of the DNAzyme still needs to be developed further. For another example, as we know, the nanomaterials can facilitate the sensitivity and specificity of functional nucleic acid based biosensor in the detection of microorganisms, and a lot of them including the gold-nanoparticles, silver-nanoparticles, QD-nanoparticles, and so on have been applied into the functional nucleic acid based biosensors for microorganism detection. However, there are still some nanomaterials with good property which have not been utilized and developed. In addition, there is another research direction that is combining the good signal recognition technology, signal transduction technology, signal application technology, and signal output technology to develop more sensitive, rapid, easy operation functional nucleic acid based biosensor for microorganism detection.

References

  1. 1.
    M.R. Riley, C.P. Gerba, M. Elimelech, J. Biol. Eng. 5, 2 (2011)PubMedPubMedCentralCrossRefGoogle Scholar
  2. 2.
    M.A. Montgomery, M. Elimelech, Environ Sci Technol. 41,17–24, (2007)Google Scholar
  3. 3.
    M.D. Sobsey, S.D. Pillai, J. Water Health 7, S75–S93 (2009)PubMedCrossRefGoogle Scholar
  4. 4.
    M. Fernández, D.M. Linares, B. del Río, V. Ladero, M.A. Alvarez, J. Dairy Res. 74, 276–282 (2007)PubMedCrossRefGoogle Scholar
  5. 5.
    M. Sasser, Identification of bacteria by gas chromatography of cellular fatty acids (1990)Google Scholar
  6. 6.
    M.A. Claydon, S.N. Davey, V. Edwards-Jones, D.B. Gordon, Nat. Biotechnol. 14, 1584–1586 (1996)PubMedCrossRefGoogle Scholar
  7. 7.
    B. Buszewski, M. Szumski, E. Kłodzińska, H. Dahm, J. Sep. Sci. 26, 1045–1049 (2003)CrossRefGoogle Scholar
  8. 8.
    L. Marilley, S. Ampuero, T. Zesiger, M.G. Casey, Int. Dairy J. 14, 849–856 (2004)CrossRefGoogle Scholar
  9. 9.
    Y. Zhao, M. Ye, Q. Chao, N. Jia, Y. Ge, H. Shen, J. Agric. Food Chem. 57, 517–524 (2008)CrossRefGoogle Scholar
  10. 10.
    W. Dudman, J. Bacteriol. 88, 782–794 (1964)PubMedPubMedCentralGoogle Scholar
  11. 11.
    Y.-s. Peng, K.-y. Peng, J. Invertebr. Pathol. 33, 284–289 (1979)CrossRefGoogle Scholar
  12. 12.
    J. Li, K. Xia, C. Yu, Food Control 30, 251–254 (2013)CrossRefGoogle Scholar
  13. 13.
    J.-Y. Hou, T.-C. Liu, G.-F. Lin, Z.-X. Li, L.-P. Zou, M. Li, Y.-S. Wu, Anal. Chim. Acta 734, 93–98 (2012)PubMedCrossRefGoogle Scholar
  14. 14.
    K. Wong, K. Chua, S. Lam, Ann. Neurol. 45, 271–272 (1999)PubMedCrossRefGoogle Scholar
  15. 15.
    M. Famulok, J.S. Hartig, G. Mayer, Chem. Rev. 107, 3715–3743 (2007)PubMedCrossRefGoogle Scholar
  16. 16.
    D. Dell’Atti, S. Tombelli, M. Minunni, M. Mascini, Biosens. Bioelectron. 21, 1876–1879 (2006)PubMedCrossRefGoogle Scholar
  17. 17.
    S.R. Mikkelsen, Electroanalysis 8, 15–19 (1996)CrossRefGoogle Scholar
  18. 18.
    U. Bora, A. Sett, D. Singh, Biosens. J. 1, 104 (2013)Google Scholar
  19. 19.
    J. Zhai, H. Cui, R. Yang, Biotechnol. Adv. 15, 43–58 (1997)PubMedCrossRefPubMedCentralGoogle Scholar
  20. 20.
    J. Wang, Nucleic Acids Res. 28, 3011–3016 (2000)PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    T.M. Herne, M.J. Tarlov, J. Am. Chem. Soc. 119, 8916–8920 (1997)CrossRefGoogle Scholar
  22. 22.
    C. Teller, I. Willner, Curr. Opin. Biotechnol. 21, 376–391 (2010)PubMedCrossRefGoogle Scholar
  23. 23.
    S. Nagatoishi, N. Isono, K. Tsumoto, N. Sugimoto, Biochimie 93, 1231–1238 (2011)PubMedCrossRefGoogle Scholar
  24. 24.
    J. Liu, Z. Cao, Y. Lu, Chem. Rev. 109, 1948–1998 (2009)PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Y. Li, D. Sen, Nat. Struct. Mol. Biol. 3, 743–747 (1996)CrossRefGoogle Scholar
  26. 26.
    P. Travascio, Y. Li, D. Sen, Chem. Biol. 5, 505–517 (1998)PubMedCrossRefGoogle Scholar
  27. 27.
    M. Wang, Y. Han, Z. Nie, C. Lei, Y. Huang, M. Guo, S. Yao, Biosens. Bioelectron. 26, 523–529 (2010)PubMedCrossRefGoogle Scholar
  28. 28.
    D.S. Wilson, J.W. Szostak, Annu. Rev. Biochem. 68, 611–647 (1999)PubMedCrossRefGoogle Scholar
  29. 29.
    R.R. Breaker, Chem. Rev. 97, 371–390 (1997)PubMedCrossRefGoogle Scholar
  30. 30.
    R.R. Breaker, Curr. Opin. Chem. Biol. 1, 26–31 (1997)PubMedCrossRefPubMedCentralGoogle Scholar
  31. 31.
    M.M. Ali, S.D. Aguirre, H. Lazim, Y. Li, Angew. Chem. Int. Ed. 50, 3751–3754 (2011)CrossRefGoogle Scholar
  32. 32.
    M.D. Frank-Kamenetskii, S.M. Mirkin, Annu. Rev. Biochem. 64, 65–95 (1995)PubMedCrossRefPubMedCentralGoogle Scholar
  33. 33.
    J. Ikbal, G.S. Lim, Z. Gao, TrAC Trends Anal. Chem. 64, 86–99 (2015)CrossRefGoogle Scholar
  34. 34.
    R.M. Clegg, Curr. Opin. Biotechnol. 6, 103–110 (1995)PubMedCrossRefGoogle Scholar
  35. 35.
    R. Jin, G. Wu, Z. Li, C.A. Mirkin, G.C. Schatz, J. Am. Chem. Soc. 125, 1643–1654 (2003)PubMedCrossRefGoogle Scholar
  36. 36.
    Y. Li, H.J. Schluesener, S. Xu, Gold Bull. 43, 29–41 (2010)CrossRefGoogle Scholar
  37. 37.
    Y. Zhou, S. Wang, K. Zhang, X. Jiang, Angew. Chem. 120, 7564–7566 (2008)CrossRefGoogle Scholar
  38. 38.
    P.M. Lizardi, X. Huang, Z. Zhu, P. Bray-Ward, D.C. Thomas, D.C. Ward, Nat. Genet. 19, (1998)Google Scholar
  39. 39.
    T. Notomi, H. Okayama, H. Masubuchi, T. Yonekawa, K. Watanabe, N. Amino, T. Hase, Nucleic Acids Res. 28, e63–e63 (2000)PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    B. Li, X. Chen, A.D. Ellington, Anal. Chem. 84, 8371–8377 (2012)PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    M. Parida, S. Sannarangaiah, P.K. Dash, P. Rao, K. Morita, Rev. Med. Virol. 18, 407–421 (2008)PubMedCrossRefPubMedCentralGoogle Scholar
  42. 42.
    P. Gill, A. Ghaemi, Nucleosides Nucleotides Nucleic Acids 27, 224–243 (2008)PubMedCrossRefPubMedCentralGoogle Scholar
  43. 43.
    L. Yan, J. Zhou, Y. Zheng, A.S. Gamson, B.T. Roembke, S. Nakayama, H.O. Sintim, Mol. BioSyst. 10, 970–1003 (2014)PubMedCrossRefPubMedCentralGoogle Scholar
  44. 44.
    E.A. Oblath, W.H. Henley, J.P. Alarie, J.M. Ramsey, Lab Chip 13, 1325–1332 (2013)PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    N. Hierro, B. Esteve-Zarzoso, Á. González, A. Mas, J.M. Guillamón, Appl. Environ. Microbiol. 72, 7148–7155 (2006)PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    R.A. White, P.C. Blainey, H.C. Fan, S.R. Quake, BMC Genomics 10, 116 (2009)PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    T.G. Kim, S.-Y. Jeong, K.-S. Cho, Appl. Microbiol. Biotechnol. 98, 6105–6113 (2014)PubMedCrossRefPubMedCentralGoogle Scholar
  48. 48.
    E.A. Ottesen, J.W. Hong, S.R. Quake, J.R. Leadbetter, Science 314, 1464–1467 (2006)PubMedCrossRefPubMedCentralGoogle Scholar
  49. 49.
    S. Song, L. Wang, J. Li, C. Fan, J. Zhao, TrAC Trends Anal. Chem. 27, 108–117 (2008)CrossRefGoogle Scholar
  50. 50.
    H. Wei, B. Li, J. Li, E. Wang, S. Dong, Chem. Commun. 36, 3735–3737 (2007)CrossRefGoogle Scholar
  51. 51.
    J. Liu, Y. Lu, Nat. Protoc. 1, 246 (2006)PubMedCrossRefGoogle Scholar
  52. 52.
    C. Yang, V. Lates, B. Prieto-Simón, J.-L. Marty, X. Yang, Biosens. Bioelectron. 32, 208–212 (2012)PubMedCrossRefGoogle Scholar
  53. 53.
    V. Pavlov, Y. Xiao, R. Gill, A. Dishon, M. Kotler, I. Willner, Anal. Chem. 76, 2152–2156 (2004)PubMedCrossRefGoogle Scholar
  54. 54.
    Y. Xiao, V. Pavlov, T. Niazov, A. Dishon, M. Kotler, I. Willner, J. Am. Chem. Soc. 126, 7430–7431 (2004)PubMedCrossRefGoogle Scholar
  55. 55.
    A.D. Ellington, J.W. Szostak, Nature 346, 818 (1990)PubMedCrossRefGoogle Scholar
  56. 56.
    R. Stoltenburg, C. Reinemann, B. Strehlitz, Biomol. Eng. 24, 381–403 (2007)PubMedCrossRefPubMedCentralGoogle Scholar
  57. 57.
    M. Berezovski, M. Musheev, A. Drabovich, S.N. Krylov, J. Am. Chem. Soc. 128, 1410–1411 (2006)PubMedCrossRefPubMedCentralGoogle Scholar
  58. 58.
    R. White, C. Rusconi, E. Scardino, A. Wolberg, J. Lawson, M. Hoffman, B. Sullenger, Mol. Ther. 4, 567–573 (2001)PubMedCrossRefPubMedCentralGoogle Scholar
  59. 59.
    Z. Zhu, Y. Song, C. Li, Y. Zou, L. Zhu, Y. An, C.J. Yang, Anal. Chem. 86, 5881–5888 (2014)PubMedCrossRefPubMedCentralGoogle Scholar
  60. 60.
    D.A. Daniels, H. Chen, B.J. Hicke, K.M. Swiderek, L. Gold, Proc. Natl. Acad. Sci. 100, 15416–15421 (2003)PubMedCrossRefPubMedCentralGoogle Scholar
  61. 61.
    C.L. Hamula, J.W. Guthrie, H. Zhang, X.-F. Li, X.C. Le, TrAC Trends Anal. Chem. 25, 681–691 (2006)CrossRefGoogle Scholar
  62. 62.
    G. Mayer, L.A. Marie-sophie, A. Dolf, E. Endl, P.A. Knolle, M. Famulok, Nat. Protoc. 5, 1993 (2010)PubMedCrossRefPubMedCentralGoogle Scholar
  63. 63.
    S.D. Mendonsa, M.T. Bowser, J. Am. Chem. Soc. 126, 20–21 (2004)PubMedCrossRefPubMedCentralGoogle Scholar
  64. 64.
    M. Berezovski, A. Drabovich, S.M. Krylova, M. Musheev, V. Okhonin, A. Petrov, S.N. Krylov, J. Am. Chem. Soc. 127, 3165–3171 (2005)PubMedCrossRefPubMedCentralGoogle Scholar
  65. 65.
    L. Johnson, P.D. Gershon, Nucleic Acids Res. 27, 2708–2721 (1999)PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    S.M. Shamah, J.M. Healy, S.T. Cload, Acc. Chem. Res. 41, 130–138 (2008)CrossRefPubMedGoogle Scholar
  67. 67.
    Y.-C. Chang, C.-Y. Yang, R.-L. Sun, Y.-F. Cheng, W.-C. Kao, P.-C. Yang, Sci. Rep. 3 (2013)Google Scholar
  68. 68.
    M. Yang, Z. Peng, Y. Ning, Y. Chen, Q. Zhou, L. Deng, Sensors 13, 6865–6881 (2013)PubMedCrossRefGoogle Scholar
  69. 69.
    P. Xie, L. Zhu, X. Shao, K. Huang, J. Tian, W. Xu, Sci. Rep. 6, 29524 (2016)PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    L. Soleymani, Z. Fang, B. Lam, X. Bin, E. Vasilyeva, A.J. Ross, E.H. Sargent, S.O. Kelley, ACS Nano 5, 3360–3366 (2011)PubMedCrossRefPubMedCentralGoogle Scholar
  71. 71.
    J.J. Li, W. Tan, Nano Lett. 2, 315–318 (2002)CrossRefGoogle Scholar
  72. 72.
    J. Liu, Y. Lu, J. Am. Chem. Soc. 126, 12298–12305 (2004)PubMedCrossRefPubMedCentralGoogle Scholar
  73. 73.
    J. Liu, Y. Lu, J. Am. Chem. Soc. 127, 12677–12683 (2005)PubMedCrossRefPubMedCentralGoogle Scholar
  74. 74.
    H.M. So, D.W. Park, E.K. Jeon, Y.H. Kim, B.S. Kim, C.K. Lee, S.Y. Choi, S.C. Kim, H. Chang, J.O. Lee, Small 4, 197–201 (2008)PubMedCrossRefPubMedCentralGoogle Scholar
  75. 75.
    C.L. Hamula, H. Zhang, L.L. Guan, X.-F. Li, X.C. Le, Anal. Chem. 80, 7812–7819 (2008)PubMedCrossRefPubMedCentralGoogle Scholar
  76. 76.
    J.J. DeStefano, J.V. Cristofaro, Nucleic Acids Res. 34, 130–139 (2006)PubMedPubMedCentralCrossRefGoogle Scholar
  77. 77.
    L. Zhu, X. Shao, Y. Luo, K. Huang, W. Xu, ACS Chem. Biol. 12, 1373–1380 (2017)PubMedCrossRefPubMedCentralGoogle Scholar
  78. 78.
    J. Kosman, B. Juskowiak, Anal. Chim. Acta 707, 7–17 (2011)PubMedCrossRefPubMedCentralGoogle Scholar
  79. 79.
    R. Kelley, C. Patterson, J. Thromb. Haemost. 4, 1476–1478 (2006)PubMedCrossRefPubMedCentralGoogle Scholar
  80. 80.
    L. Zhu, Y. Xu, N. Cheng, P. Xie, X. Shao, K. Huang, Y. Luo, W. Xu, Sensors Actuators B Chem. 242, 880–888 (2017)CrossRefGoogle Scholar
  81. 81.
    W. Xu, J. Tian, X. Shao, L. Zhu, K. Huang, Y. Luo, Biosens. Bioelectron. 89, 795–801 (2017)PubMedCrossRefGoogle Scholar
  82. 82.
    A.H. Free, E.C. Adams, M.L. Kercher, H.M. Free, M.H. Cook, Clin. Chem. 3, 163–168 (1957)PubMedGoogle Scholar
  83. 83.
    B. Ngom, Y. Guo, X. Wang, D. Bi, Anal. Bioanal. Chem. 397, 1113–1135 (2010)PubMedCrossRefGoogle Scholar
  84. 84.
    W. Xu, Functional Nucleic Acids Detection Food Safety. Springer (2016)Google Scholar
  85. 85.
    C. Parolo, A. Merkoçi, Chem. Soc. Rev. 42, 450–457 (2013)PubMedCrossRefGoogle Scholar
  86. 86.
    J. Sun, Y. Xianyu, X. Jiang, Chem. Soc. Rev. 43, 6239–6253 (2014)PubMedCrossRefGoogle Scholar
  87. 87.
    P. Yager, G.J. Domingo, J. Gerdes, Annu. Rev. Biomed. Eng. 10, 107–144 (2008)PubMedCrossRefGoogle Scholar
  88. 88.
    J. Hu, S. Wang, L. Wang, F. Li, B. Pingguan-Murphy, T.J. Lu, F. Xu, Biosens. Bioelectron. 54, 585–597 (2014)PubMedCrossRefGoogle Scholar
  89. 89.
    M. Sajid, A.-N. Kawde, M. Daud, J. Saudi Chem. Soc. 19, 689–705 (2015)CrossRefGoogle Scholar
  90. 90.
    D. Quesada-González, A. Merkoçi, Biosens. Bioelectron. 73, 47–63 (2015)PubMedCrossRefPubMedCentralGoogle Scholar
  91. 91.
    R. Higuchi, C. Fockler, G. Dollinger, R. Watson, Nat. Biotechnol. 11, 1026–1030 (1993)CrossRefGoogle Scholar
  92. 92.
    H. Zipper, H. Brunner, J. Bernhagen, F. Vitzthum, Nucleic Acids Res. 32, e103–e103 (2004)PubMedPubMedCentralCrossRefGoogle Scholar
  93. 93.
    C.A. Heid, J. Stevens, K.J. Livak, P.M. Williams, Genome Res. 6, 986–994 (1996)PubMedCrossRefPubMedCentralGoogle Scholar
  94. 94.
    I. Taverniers, E. Van Bockstaele, M. De Loose, Anal. Bioanal. Chem. 378, 1198–1207 (2004)PubMedCrossRefGoogle Scholar
  95. 95.
    A. Holck, M. Vaïtilingom, L. Didierjean, K. Rudi, Eur. Food Res. Technol. 214, 449–454 (2002)CrossRefGoogle Scholar
  96. 96.
    W.-T. Xu, K.-L. Huang, A.-K. Deng, Z.-h. Liang, Y.-B. Luo, Food Control 18, 1300–1306 (2007)CrossRefGoogle Scholar
  97. 97.
    M. Sakamoto, Y. Takeuchi, M. Umeda, I. Ishikawa, Y. Benno, Microbiol. Immunol. 45, 39–44 (2001)PubMedCrossRefPubMedCentralGoogle Scholar
  98. 98.
    C.E. Shelburne, A. Prabhu, R.M. Gleason, B.H. Mullally, W.A. Coulter, J. Microbiol. Methods 39, 97–107 (2000)PubMedCrossRefPubMedCentralGoogle Scholar
  99. 99.
    S.R. Lyons, A.L. Griffen, E.J. Leys, J. Clin. Microbiol. 38, 2362–2365 (2000)PubMedPubMedCentralGoogle Scholar
  100. 100.
    J. Kim, D.J. Cha, M. Kwon, in: 2016 한국응용곤충학회 임시총회 및 추계학술발표회, (2016), pp. 100–100Google Scholar
  101. 101.
    B. Babu, A. Jeyaprakash, D. Jones, T.S. Schubert, C. Baker, B.K. Washburn, S.H. Miller, K. Poduch, G.W. Knox, F.M. Ochoa-Corona, J. Virol. Methods 235, 41–50 (2016)PubMedCrossRefGoogle Scholar
  102. 102.
    B. Vogelstein, K.W. Kinzler, Proc. Natl. Acad. Sci. 96, 9236–9241 (1999)PubMedCrossRefGoogle Scholar
  103. 103.
    D.-K. Kang, M.M. Ali, K. Zhang, S.S. Huang, E. Peterson, M.A. Digman, E. Gratton, W. Zhao, Nat. Commun. 5, 5427 (2014)Google Scholar
  104. 104.
    D. Porcellato, J. Narvhus, S.B. Skeie, J. Microbiol. Methods 127, 1–6 (2016)PubMedCrossRefGoogle Scholar
  105. 105.
    A. Hennebique, M. Bidart, S. Jarraud, L. Beraud, C. Schwebel, M. Maurin, S. Boisset, Antimicrob. Agents Chemother 61, e00628 (2017)PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    Y. Mori, H. Kanda, T. Notomi, J. Infect. Chemother. 19, 404 (2013)PubMedCrossRefGoogle Scholar
  107. 107.
    T. Song, C. Toma, N. Nakasone, M. Iwanaga, FEMS Microbiol. Lett. 243, 259–263 (2005)PubMedCrossRefGoogle Scholar
  108. 108.
    Y. Yuan, W. Xu, Z. Zhai, H. Shi, Y. Luo, Z. Chen, K. Huang, J. Food Sci. 74, M446–M452 (2009)PubMedCrossRefGoogle Scholar
  109. 109.
    F. Wang, L. Jiang, B. Ge, J. Clin. Microbiol. 50, 91 (2012)PubMedPubMedCentralCrossRefGoogle Scholar
  110. 110.
    N. Cheng, Y. Xu, X. Yan, Y. Shang, P. Zhu, W. Tian, Z. Liang, W. Xu, J. Food Saf. 36, 237–246 (2016)CrossRefGoogle Scholar
  111. 111.
    G.T. Walker, J.G. Nadeau, P.A. Spears, J.L. Schram, C.M. Nycz, D.D. Shank, Nucleic Acids Res. 22, 2670 (1994)PubMedPubMedCentralCrossRefGoogle Scholar
  112. 112.
    P. Gill, A. Ghaemi, Nucleosides Nucleotides Nucleic Acids 27, 224 (2008)PubMedCrossRefPubMedCentralGoogle Scholar
  113. 113.
    J.C. Detter, J.M. Jett, S.M. Lucas, E. Dalin, A.R. Arellano, M. Wang, J.R. Nelson, J. Chapman, Y. Lou, D. Rokhsar, Genomics 80, 691–698 (2002)PubMedCrossRefGoogle Scholar
  114. 114.
    S.J. Tehrani, M. Aliabadian, A. Fata, M.J. Najafzadeh, Rev. Invest. Clín 53, 21–27 (2014)Google Scholar
  115. 115.
    D. Zhu, Y. Yan, P. Lei, B. Shen, W. Cheng, H. Ju, S. Ding, Anal. Chim. Acta 846, 44 (2014)PubMedCrossRefGoogle Scholar
  116. 116.
    S.A. Simpkins, A.B. Chan, J. Hays, B. Pöpping, N. Cook, Lett. Appl. Microbiol. 30, 75 (2000)PubMedCrossRefGoogle Scholar
  117. 117.
    J. O’Grady, K. Lacey, B. Glynn, T.J. Smith, T. Barry, M. Maher, FEMS Microbiol. Lett. 301, 218–223 (2009)PubMedCrossRefPubMedCentralGoogle Scholar
  118. 118.
    E.M. Fykse, T. Nilsen, A.D. Nielsen, I. Tryland, S. Delacroix, J.M. Blatny, Mar. Pollut. Bull. 64, 200 (2012)PubMedCrossRefPubMedCentralGoogle Scholar
  119. 119.
    M. Vincent, Y. Xu, H. Kong, EMBO Rep. 5, 795–800 (2004)PubMedPubMedCentralCrossRefGoogle Scholar
  120. 120.
    P. Gill, A.H. Alvandi, H. Abdultehrani, M. Sadeghizadeh, Diagn. Microbiol. Infect. Dis. 62, 119 (2008)PubMedCrossRefPubMedCentralGoogle Scholar
  121. 121.
    H. Kong, L.S. Higgins, M.A. Dalton, R.B. Kucera, I. Schildkraut, G.G. Wilson. N. BstNBI. nicking endonuclease and methods for using endonucleasi in single-stranded displacement amplification in WO, 094544 (2003)Google Scholar
  122. 122.
    X. Wen-Tao, B. Wei-Bin, L. Yun-Bo, Y. Yan-Fang, H. Kun-Lun, Chin. J. Agric. Biotechnol. 6, 1–9 (2009)CrossRefGoogle Scholar
  123. 123.
    K.D. Chavda, M.J. Satlin, L. Chen, C. Manca, S.G. Jenkins, T.J. Walsh, B.N. Kreiswirth, Antimicrob. Agents Chemother. 60, 6957–6961 (2016)PubMedPubMedCentralCrossRefGoogle Scholar
  124. 124.
    W. Xu, Z. Zhai, K. Huang, N. Zhang, Y. Yuan, Y. Shang, Y. Luo, PLoS One 7, e22900 (2012)PubMedPubMedCentralCrossRefGoogle Scholar
  125. 125.
    W. Xu, Y. Yuan, Y. Luo, W. Bai, C. Zhang, K. Huang, J. Agric. Food Chem. 57, 395–402 (2008)CrossRefGoogle Scholar
  126. 126.
    H. Shi, Q. Trinh, W. Xu, B. Zhai, Y. Luo, K. Huang, Appl. Microbiol. Biotechnol. 95, 1579–1587 (2012)PubMedCrossRefPubMedCentralGoogle Scholar
  127. 127.
    T. Yonekita, R. Ohtsuki, E. Hojo, N. Morishita, T. Matsumoto, T. Aizawa, F. Morimatsu, J. Microbiol. Methods 93, 251–256 (2013)PubMedCrossRefPubMedCentralGoogle Scholar
  128. 128.
    Y. Chen, N. Cheng, Y. Xu, K. Huang, Y. Luo, W. Xu, Biosens. Bioelectron. 81, 317–323 (2016)PubMedCrossRefPubMedCentralGoogle Scholar
  129. 129.
    N. Duan, S. Wu, C. Zhu, X. Ma, Z. Wang, Y. Yu, Y. Jiang, Anal. Chim. Acta 723, 1–6 (2012)PubMedCrossRefPubMedCentralGoogle Scholar
  130. 130.
    K.O. Duedu, C.E. French, J. Microbiol. Methods 135, 85–92 (2017)PubMedCrossRefPubMedCentralGoogle Scholar
  131. 131.
    M. Schena, D. Shalon, R.W. Davis, P.O. Brown, Sci. NY Wash. 270, 467–467 (1995)Google Scholar
  132. 132.
    S.P. Fodor, J.L. Read, M.C. Pirrung, L. Stryer, A.T. Lu, D. Solas, Science. 251, 767–773 (1991)PubMedCrossRefPubMedCentralGoogle Scholar
  133. 133.
    Y. Wang, Y. Cai, T. Liu, Z. Ong, N. Wang, Z. Wang, Chin. J. Biol. 25, 1383–1386 (2012)Google Scholar
  134. 134.
    D. Lü, X. Shi, M. Chen, P. Wu, L. He, Y. Li, Y. Lin, Y. Qiu, Q. Hu, Wei sheng yan jiu= J. Hyg. Res. 41, 96–101 (2012)Google Scholar
  135. 135.
    J.P. Schouten, C.J. McElgunn, R. Waaijer, D. Zwijnenburg, F. Diepvens, G. Pals. Nucleic Acids Res. 30, e57–e57 (2002)PubMedPubMedCentralCrossRefGoogle Scholar
  136. 136.
    L. Véronèse, O. Tournilhac, P. Combes, N. Prie, E. Pierre-Eymard, R. Guieze, R. Veyrat-Masson, J.-O. Bay, P. Vago, A. Tchirkov, Cancer Genet. 206, 19–25 (2013)PubMedCrossRefGoogle Scholar
  137. 137.
    J. Cui, M. Azimi, A.D. Adekile, H. Al Awadhi, C.C. Hoppe, Hemoglobin 36, 276–282 (2012)PubMedPubMedCentralCrossRefGoogle Scholar
  138. 138.
    J.M. Zhang, J.F. Sun, P.Y. Feng, X.Q. Li, C.M. Lu, S. Lu, W.Y. Cai, L.Y. Xi, G.S. de Hoog, J. Microbiol. Methods 85, 33–39 (2011)PubMedCrossRefGoogle Scholar
  139. 139.
    Q. Zhong, S. Bhattacharya, S. Kotsopoulos, J. Olson, V. Taly, A.D. Griffiths, D.R. Link, J.W. Larson, Lab Chip 11, 2167–2174 (2011)PubMedCrossRefGoogle Scholar
  140. 140.
    M. Burns, A. Burrell, C. Foy, Eur. Food Res. Technol. 231, 353–362 (2010)CrossRefGoogle Scholar
  141. 141.
    M. Machida, K. Asai, M. Sano, T. Tanaka, T. Kumagai, G. Terai, K.-I. Kusumoto, T. Arima, O. Akita, Y. Kashiwagi, Nature 438, 1157 (2005)PubMedCrossRefGoogle Scholar
  142. 142.
    A. Calistri, G. Palù, Clinical Infections Diseases. 60, 889–891 (2015)Google Scholar
  143. 143.
    S. Chen, A. Yee, M. Griffiths, C. Larkin, C.T. Yamashiro, R. Behari, C. Paszko-Kolva, K. Rahn, A. Stephanie, Int. J. Food Microbiol. 35, 239–250 (1997)PubMedCrossRefGoogle Scholar
  144. 144.
    R. Oberst, M. Hays, L. Bohra, R. Phebus, C. Yamashiro, C. Paszko-Kolva, S. Flood, J. Sargeant, J. Gillespie, Appl. Environ. Microbiol. 64, 3389–3396 (1998)PubMedPubMedCentralGoogle Scholar
  145. 145.
    H.A. Bassler, S.J. Flood, K.J. Livak, J. Marmaro, R. Knorr, C.A. Batt, Appl. Environ. Microbiol. 61, 3724–3728 (1995)PubMedPubMedCentralGoogle Scholar
  146. 146.
    C. Schoen, D. Knorr, G. Leone, Phytopathology 86, 993–999 (1996)CrossRefGoogle Scholar
  147. 147.
    N. Schaad, Y. Berthier-Schaad, A. Sechler, D. Knorr, Plant Dis. 83, 1095–1100 (1999)CrossRefGoogle Scholar
  148. 148.
    P.M. Holland, R.D. Abramson, R. Watson, D.H. Gelfand, Proc. Natl. Acad. Sci. 88, 7276–7280 (1991)PubMedCrossRefGoogle Scholar
  149. 149.
    L.G. Lee, C.R. Connell, W. Bloch, Nucleic Acids Res. 21, 3761–3766 (1993)PubMedPubMedCentralCrossRefGoogle Scholar
  150. 150.
    S. Weller, J. Elphinstone, N. Smith, N. Boonham, D. Stead, Appl. Environ. Microbiol. 66, 2853–2858 (2000)PubMedPubMedCentralCrossRefGoogle Scholar
  151. 151.
    J. Day, U. Basavanna, S. Sharma, Appl. Environ. Microbiol. 75, 5321–5327 (2009)PubMedPubMedCentralCrossRefGoogle Scholar
  152. 152.
    R.C. Charles, T. Sultana, M.M. Alam, Y. Yu, Y. Wu-Freeman, M.K. Bufano, S.M. Rollins, L. Tsai, J.B. Harris, R.C. LaRocque, PLoS Negl. Trop. Dis. 7, e2335 (2013)PubMedPubMedCentralCrossRefGoogle Scholar
  153. 153.
    M.A. Nadkarni, F.E. Martin, N.A. Jacques, N. Hunter, Microbiology 148, 257–266 (2002)PubMedCrossRefGoogle Scholar
  154. 154.
    H. Shi, W. Xu, Q. Trinh, Y. Luo, Z. Liang, Y. Li, K. Huang, Food Control 27, 81–86 (2012)CrossRefGoogle Scholar
  155. 155.
    G.-J. Zhang, G. Zhang, J.H. Chua, R.-E. Chee, E.H. Wong, A. Agarwal, K.D. Buddharaju, N. Singh, Z. Gao, N. Balasubramanian, Nano Lett. 8, 1066–1070 (2008)PubMedCrossRefGoogle Scholar
  156. 156.
    B. Cai, S. Wang, L. Huang, Y. Ning, Z. Zhang, G.J. Zhang, ACS Nano 8, 2632 (2014)PubMedCrossRefGoogle Scholar
  157. 157.
    L. Mendes, R. Rocha, A.S. Azevedo, C. Ferreira, M. Henriques, M.G. Pinto, N.F. Azevedo, Microbiol. Res. 192, 185–191 (2016)PubMedCrossRefGoogle Scholar
  158. 158.
    S.P. Lopes, D.T. Carvalho, M.O. Pereira, N.F. Azevedo, Biotechnol. Bioeng. 114, 355–367 (2017)PubMedCrossRefGoogle Scholar
  159. 159.
    I. Willner, B. Shlyahovsky, M. Zayats, B. Willner, Chem. Soc. Rev. 37, 1153–1165 (2008)PubMedCrossRefGoogle Scholar
  160. 160.
    D.M. Kolpashchikov, J. Am. Chem. Soc. 130, 2934–2935 (2008)PubMedPubMedCentralCrossRefGoogle Scholar
  161. 161.
    Z. Cheglakov, Y. Weizmann, M.K. Beissenhirtz, I. Willner, Chem. Commun., 3205–3207 (2006)Google Scholar
  162. 162.
    F. Du, Z. Tang, Chembiochem 12, 43–46 (2011)PubMedCrossRefGoogle Scholar
  163. 163.
    K. Schlosser, Y. Li, Chembiochem 11, 866–879 (2010)PubMedCrossRefGoogle Scholar
  164. 164.
    S.W. Santoro, G.F. Joyce, Proc. Natl. Acad. Sci. 94, 4262–4266 (1997)PubMedCrossRefGoogle Scholar
  165. 165.
    R.R. Breaker, G.F. Joyce, Chem. Biol. 1, 223–229 (1994)PubMedCrossRefGoogle Scholar
  166. 166.
    E. Mokany, S.M. Bone, P.E. Young, T.B. Doan, A.V. Todd, J. Am. Chem. Soc. 132, 1051–1059 (2009)PubMedCentralCrossRefPubMedGoogle Scholar
  167. 167.
    K. Zagorovsky, W.C. Chan, Angew. Chem. Int. Ed. 52, 3168–3171 (2013)CrossRefGoogle Scholar
  168. 168.
    P.Y. Hsieh, M. Monsur Ali, K. Tram, S. Jahanshahi-Anbuhi, C.L. Brown, J.D. Brennan, C.D. Filipe, Y. Li, Chembiochem 18, 502–505 (2017)PubMedCrossRefGoogle Scholar
  169. 169.
    F. Yu, Y. Li, M. Li, L. Tang, J.-J. He, Biosens. Bioelectron. 89, 880–885 (2017)PubMedCrossRefGoogle Scholar
  170. 170.
    J. Cao, C. Feng, Y. Liu, S. Wang, F. Liu, Biosens. Bioelectron. 57, 133–138 (2014)PubMedCrossRefGoogle Scholar
  171. 171.
    M. Donmez, M.D. Yilmaz, B. Kilbas, J. Hazard. Mater. 324, 593–598 (2016)PubMedCrossRefGoogle Scholar
  172. 172.
    B. Jin, S. Wang, M. Lin, Y. Jin, S. Zhang, X. Cui, Y. Gong, A. Li, F. Xu, T.J. Lu, Biosens. Bioelectron. 90, 525 (2017)PubMedCrossRefGoogle Scholar
  173. 173.
    G. Naja, P. Bouvrette, S. Hrapovic, J.H. Luong, Analyst 132, 679–686 (2007)PubMedCrossRefGoogle Scholar
  174. 174.
    H. Zhou, D. Yang, N.P. Ivleva, N.E. Mircescu, R. Niessner, C. Haisch, Anal. Chem. 86, 1525–1533 (2014)PubMedCrossRefGoogle Scholar
  175. 175.
    L. Zhang, Y. Chen, N. Cheng, Y. Xu, K. Huang, Y. Luo, P. Wang, D. Duan, W. Xu, Ultrasensitive detection of viable Enterobacter sakazakii by a continual cascade nanozyme biosensor. Anal. Chem. 89, 10194–10200 (2017)PubMedCrossRefPubMedCentralGoogle Scholar
  176. 176.
    D. Duan, K. Fan, D. Zhang, S. Tan, M. Liang, Y. Liu, J. Zhang, P. Zhang, W. Liu, X. Qiu, Biosens. Bioelectron. 74, 134–141 (2015)PubMedCrossRefPubMedCentralGoogle Scholar
  177. 177.
    S. Fu, S. Wang, X. Zhang, A. Qi, Z. Liu, X. Yu, C. Chen, L. Li, Colloids Surf. B: Biointerfaces 154, 239–245 (2017)PubMedCrossRefGoogle Scholar
  178. 178.
    S. Singh, P. Tripathi, N. Kumar, S. Nara, Biosens. Bioelectron. 92, 280–286 (2017)PubMedCrossRefGoogle Scholar
  179. 179.
    A.P. Nagvenkar, A. Gedanken, ACS Appl. Mater. Interfaces 8, 22301–22308 (2016)PubMedCrossRefPubMedCentralGoogle Scholar
  180. 180.
    W. Li, B. Chen, H. Zhang, Y. Sun, J. Wang, J. Zhang, Y. Fu, Biosens. Bioelectron. 66, 251–258 (2015)PubMedCrossRefPubMedCentralGoogle Scholar
  181. 181.
    X. Lin, Y. Liu, Z. Tao, J. Gao, J. Deng, J. Yin, S. Wang, Biosens. Bioelectron. 94, 471–477 (2017)PubMedCrossRefPubMedCentralGoogle Scholar
  182. 182.
    J. Gao, L. Li, P.L. Ho, G.C. Mak, H. Gu, B. Xu, Adv. Mater. 18, 3145–3148 (2006)CrossRefGoogle Scholar
  183. 183.
    H.J. Chung, T. Reiner, G. Budin, C. Min, M. Liong, D. Issadore, H. Lee, R. Weissleder, ACS Nano 5, 8834–8841 (2011)PubMedPubMedCentralCrossRefGoogle Scholar
  184. 184.
    S. Wu, N. Duan, Z. Shi, C. Fang, Z. Wang, Anal. Chem. 86, 3100–3107 (2014)PubMedCrossRefGoogle Scholar
  185. 185.
    S.A. Marras, Mol. Biotechnol. 38, 247–255 (2008)PubMedCrossRefGoogle Scholar
  186. 186.
    M. Sunbul, A. Jäschke, Angew. Chem. Int. Ed. 52, 13401–13404 (2013)CrossRefGoogle Scholar
  187. 187.
    W.C. Chan, S. Nie, Science 281, 2016–2018 (1998)CrossRefPubMedGoogle Scholar
  188. 188.
    Z. Zhelev, H. Ohba, R. Bakalova, R. Jose, S. Fukuoka, T. Nagase, M. Ishikawa, Y. Baba, Chem. Commun. 21, 1980–1982 (2005)Google Scholar
  189. 189.
    E. Zahavy, V. Heleg-Shabtai, Y. Zafrani, D. Marciano, S. Yitzhaki, J. Fluoresc. 20, 389–399 (2010)PubMedCrossRefPubMedCentralGoogle Scholar
  190. 190.
    B. Dubertret, P. Skourides, D.J. Norris, V. Noireaux, A.H. Brivanlou, A. Libchaber, Science 298, 1759–1762 (2002)PubMedPubMedCentralCrossRefGoogle Scholar
  191. 191.
    N. Duan, S. Wu, Y. Yu, X. Ma, Y. Xia, X. Chen, Y. Huang, Z. Wang, Anal. Chim. Acta 804, 151–158 (2013)PubMedCrossRefPubMedCentralGoogle Scholar
  192. 192.
    J. Huang, Y. Wu, Y. Chen, Z. Zhu, X. Yang, C.J. Yang, K. Wang, W. Tan, Angew. Chem. Int. Ed. 50, 401–404 (2011)CrossRefGoogle Scholar
  193. 193.
    I. Palchetti, M. Mascini, Anal. Bioanal. Chem. 391, 455–471 (2008)PubMedCrossRefPubMedCentralGoogle Scholar
  194. 194.
    O. Lazcka, F.J. Del Campo, F.X. Munoz, Biosens. Bioelectron. 22, 1205–1217 (2007)PubMedCrossRefPubMedCentralGoogle Scholar
  195. 195.
    P. Leonard, S. Hearty, J. Brennan, L. Dunne, J. Quinn, T. Chakraborty, R. O’Kennedy, Enzym. Microb. Technol. 32, 3–13 (2003)CrossRefGoogle Scholar
  196. 196.
    R. Chen, E.C. Holmes, Mol. Biol. Evol. 23, 2336–2341 (2006)PubMedCrossRefPubMedCentralGoogle Scholar
  197. 197.
    I. Grabowska, K. Malecka, U. Jarocka, J. Radecki, H. Radecka, Acta Biochim. Pol. 61, 471–478 (2014)PubMedPubMedCentralGoogle Scholar
  198. 198.
    K. Malecka, A. Stachyra, A. Góra-Sochacka, A. Sirko, W. Zagórski-Ostoja, H. Radecka, J. Radecki, Sensors Actuators B Chem. 224, 290–297 (2016)CrossRefGoogle Scholar
  199. 199.
    J.W.-F. Law, N.-S. Ab Mutalib, K.-G. Chan, L.-H. Lee, Front. Microbiol. 5, 770 (2014)PubMedPubMedCentralGoogle Scholar
  200. 120.
    V. Velusamy, K. Arshak, O. Korostynska, K. Oliwa, C. Adley, Biotechnol. Adv. 28, 232–254 (2010)PubMedCrossRefPubMedCentralGoogle Scholar
  201. 201.
    H. Sharma, R. Mutharasan, Sensors Actuators B Chem. 183, 535–549 (2013)CrossRefGoogle Scholar
  202. 202.
    Y. Wang, J.K. Salazar, Compr. Rev. Food Sci. Food Saf. 15, 183–205 (2016)CrossRefGoogle Scholar
  203. 203.
    M. Xu, R. Wang, Y. Li, Talanta 162, 511–522 (2017)PubMedCrossRefPubMedCentralGoogle Scholar
  204. 204.
    E. Sheikhzadeh, M. CHamsaz, A. Turner, E. Jager, V. Beni, Biosens. Bioelectron. 80, 194–200 (2016)PubMedCrossRefPubMedCentralGoogle Scholar
  205. 205.
    Z. Izadi, M. Sheikh-Zeinoddin, A.A. Ensafi, S. Soleimanian-Zad, Biosens. Bioelectron. 80, 582–589 (2016)PubMedCrossRefPubMedCentralGoogle Scholar
  206. 206.
    L. Reverté, B. Prieto-Simón, M. Campàs, Anal. Chim. Acta 908, 8–21 (2016)PubMedCrossRefPubMedCentralGoogle Scholar
  207. 207.
    E. Le Ru, P. Etchegoin, Principles of Surface-Enhanced Raman Spectroscopy: And Related Plasmonic Effects. 655–665 (2009).Google Scholar
  208. 208.
    D. Cialla, A. März, R. Böhme, F. Theil, K. Weber, M. Schmitt, J. Popp, Anal. Bioanal. Chem. 403, 27–54 (2012)PubMedCrossRefPubMedCentralGoogle Scholar
  209. 209.
    W. Cai, X. Wang, Y. Yan, Mater. Res. Bull. 52, 1–5 (2014)CrossRefGoogle Scholar
  210. 210.
    T.N. Huan, S. Kim, P. Van Tuong, H. Chung, RSC Adv. 4, 3929–3933 (2014)CrossRefGoogle Scholar
  211. 211.
    T.T.B. Quyen, C.-C. Chang, W.-N. Su, Y.-H. Uen, C.-J. Pan, J.-Y. Liu, J. Rick, K.-Y. Lin, B.-J. Hwang, J. Mater. Chem. B 2, 629–636 (2014)CrossRefGoogle Scholar
  212. 212.
    L. Zeiri, B. Bronk, Y. Shabtai, J. Eichler, S. Efrima, Appl. Spectrosc. 58, 33–40 (2004)PubMedCrossRefGoogle Scholar
  213. 213.
    S.P. Ravindranath, Y. Wang, J. Irudayaraj, Sensors Actuators B Chem. 152, 183–190 (2011)CrossRefGoogle Scholar
  214. 214.
    H. Zhang, X. Ma, Y. Liu, N. Duan, S. Wu, Z. Wang, B. Xu, Biosens. Bioelectron. 74, 872–877 (2015)PubMedCrossRefGoogle Scholar
  215. 215.
    S. Zeng, D. Baillargeat, H.-P. Ho, K.-T. Yong, Chem. Soc. Rev. 43, 3426–3452 (2014)PubMedCrossRefGoogle Scholar
  216. 216.
    H.J. Lee, Y. Li, A.W. Wark, R.M. Corn, Anal. Chem. 77, 5096–5100 (2005)PubMedCrossRefGoogle Scholar
  217. 217.
    T.T. Nguyen, K.T.L. Trinh, W.J. Yoon, N.Y. Lee, H. Ju, Sensors Actuators B Chem. 242, 1–8 (2017)CrossRefGoogle Scholar
  218. 218.
    A. Adan, G. Alizada, Y. Kiraz, Y. Baran, A. Nalbant, Crit. Rev. Biotechnol. 37, 163–176 (2017)PubMedCrossRefGoogle Scholar
  219. 219.
    H.M. Shapiro, W.G. Telford, Curr. Protocol. Cytom 49, 1.9.1–1.9.17 (2009)CrossRefGoogle Scholar
  220. 220.
    M.J. Wilkerson, Vet. Clin.: Small Anim. Pract. 42, 53–71 (2012)Google Scholar
  221. 221.
    D. Wlodkowic, J. Skommer, Z. Darzynkiewicz, Mammalian Cell Viability: Methods and Protocols, Met. Mol. Biol. 740 (2011) pp. 81–89Google Scholar
  222. 222.
    M. Berney, F. Hammes, F. Bosshard, H.-U. Weilenmann, T. Egli, Appl. Environ. Microbiol. 73, 3283–3290 (2007)PubMedPubMedCentralCrossRefGoogle Scholar
  223. 223.
    S. Ozawa, in, MS thesis. Sapporo, Hokkaido, Japan: Hokkaido University, (2013). Abstract Available at: www.eng.hokudai.ac.jp/e3/alumni/files/abstract/m237.pdf. Accessed 27 Jan 2016
  224. 224.
    J. Hoffmann, S. Hin, F. von Stetten, R. Zengerle, G. Roth, RSC Adv. 2, 3885–3889 (2012)CrossRefGoogle Scholar
  225. 225.
    Y. Liu-Stratton, S. Roy, C.K. Sen, Toxicol. Lett. 150, 29–42 (2004)PubMedCrossRefGoogle Scholar
  226. 226.
    M.U. Kopp, A.J. De Mello, A. Manz, Science 280, 1046–1048 (1998)PubMedCrossRefGoogle Scholar
  227. 227.
    C.J. Ingham, A. Sprenkels, J. Bomer, D. Molenaar, A. van den Berg, J.E. van Hylckama Vlieg, W.M. de Vos, Proc. Natl. Acad. Sci. 104, 18217–18222 (2007)PubMedCrossRefGoogle Scholar
  228. 228.
    G. Giraffa, E. Neviani, Int. J. Food Microbiol. 67, 19–34 (2001)PubMedCrossRefGoogle Scholar
  229. 229.
    U.M. Schütte, Z. Abdo, S.J. Bent, C. Shyu, C.J. Williams, J.D. Pierson, L.J. Forney, Appl. Microbiol. Biotechnol. 80, 365–380 (2008)PubMedCrossRefGoogle Scholar
  230. 230.
    G. Muyzer, Curr. Opin. Microbiol. 2, 317–322 (1999)PubMedCrossRefPubMedCentralGoogle Scholar
  231. 231.
    F. Sanger, A.R. Coulson, J. Mol. Biol. 94 (1975) 441IN19447-19446IN20448Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Yunbo Luo
    • 1
  1. 1.Food Science & Nutritional EngineeringChina Agricultural UniversityBeijingChina

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