The Recent Original Perspectives on Nonculture-Based Bacteria Detection Methods: A Comprehensive Review

Abstract

Foodborne diseases that are primarily caused by pathogenic bacteria are of major public health concern globally. One of the key strategies in minimizing and controlling the risk of contamination of food with such pathogens requires establishing effective detection and tracking methods of zoonotic bacteria. Although culture-based pathogen detection methods are developed and widely used by the industry traditionally, nonculture-based zoonotic bacteria detection methods are now more widely investigated and used owing to the recent developments in nucleic acid and immunological-based detection methods. These rapid detection methods provide the opportunity of acquiring real-time test results and high-throughput screening of a large number of samples at a time. One of the key aspects of rapid detection methods is the development of effective sample processing methods as food samples are heterogeneous and highly complex in composition. In this review, variety of sample processing methods, in terms of nonspecific and target-specific sample processing as well as thorough overview of recent developments in nonculture-based bacteria detection methods are presented.

Introduction

Foodborne diseases pose a significant threat to public health and are major cause of economic losses in food industry globally. One in 10 people worldwide get sick every year because of food contamination, causing more than 420,000 deaths (WHO, 2020). These diseases are primarily caused by viruses (such as Norovirus, hepatitis E virus), or bacteria (such as Campylobacter spp., Salmonella spp., Listeria monocytogenes, Escherichia coli [O157:H7 and other Shiga toxin–producing] and Staphylococcus aureus) (Wang and Salazar, 2016; Abebe et al., 2020). Majority of these pathogens have a zoonotic origin, and the primary source of transmission are foods that are animal-based such as meat, dairy products, and eggs; or plant-based that are contaminated in the field or during processing.

Contamination could occur at any stage of the food production from farm-to-fork. At the food production facility (i.e., farm), contamination could occur through wild animals or contact with soil or water. At the processing plant, contaminated food contact surfaces or human contact with the food could lead to contamination. This highly complex nature of food production; the difficulty of tracking and controlling contamination of food products; and high prevalence of foodborne disease outbreaks highlight the importance of development and application of rapid, easy-to-use, and accurate detection methods to improve food safety and public health worldwide and minimize the economic losses owing to recalls.

Culture-based methods are usually referred as gold standard for bacterial detection owing to their sensitivity, ease of use, and cost-effectiveness (Dwivedi and Jaykus, 2011). They are fundamentally based on replication ability of pathogenic bacteria. In general, these methods involve the following steps: enrichment, selective plating, and phenotypic or genotypic characterization of the isolated microorganism for identification of the specific contaminant. Despite the fact that culture-based methods, where pathogens are isolated and selected for identification, are commonly used for detection of zoonotic pathogens, they pose certain disadvantages.

One key disadvantage of culture-based detection methods is the extent of the time it takes to get actionable results. This often delays the time that control measures could be taken, leading to economic losses owing to recalls or causing foodborne disease outbreaks. Another disadvantage of culture-based methods is that they are highly dependent on the metabolic state of pathogens as they rely on the replication of bacteria (Foddai and Grant, 2020). For instance, as a survival strategy pathogenic bacteria could switch to viable-but-nonculturable (VBNC) state, because of a sublethal stress they face. For these bacteria, culture-based methods could give false-negative results (Kell et al., 1998; Năşcuţiu, 2010; Zhao et al., 2017).

Most of the zoonotic pathogenic bacteria were reported to exist in VBNC state such as, E. coli, Campylobacter spp. Salmonella spp., and Yersinia enterocolitica (Năşcuţiu, 2010; Zhao et al., 2017). For these bacteria it is crucial to develop sensitive and specific nonculture-based detection methods.

With the advances in immunological and molecular methods, alternative bacteria detection methods are being developed. These could be used in combination with culture-based methods or could be used by themselves. Regardless of the method being used, the steps of bacterial detection could be categorized as sampling, antigen separation (or capture), and detection of the bacteria. Each of these steps is crucial for the development of a highly sensitive and specific detection method. In this review, our aim was to provide a comprehensive review of literature and highlight key advancements in each of these steps for nonculture-based bacteria detection.

Sample Processing and Preparation

There are a number of hurdles in developing an effective detection methodology, one of which is the development of a test that could be able to detect zoonotic pathogenic bacteria from various food samples. Food samples by their nature have a vast variety of chemical compositions that significantly impact the performance of detection tests. Food matrices could have various organic and/or inorganic particles, fats and lipids, proteins, enzymes, and so on, all of which could interfere with traditional or novel pathogen detection methods (Swaminathan and Feng, 1994; Brehm-Stecher et al., 2009). Therefore, optimization of the sample processing and preparation method is crucial in the development of a sensitive and specific detection test.

Preanalytic or upstream sample processing enables more effective isolation of viable and intact pathogens by reducing heterogeneity of the sample, eliminating detection inhibiting molecules, and concentrating the target microorganism. In sample processing, nonspecific or target-specific methods are generally applied in combination. Whereas non-specific methods mainly aim to concentrate the target cells by relying on chemical principles, target-specific methods make use of ligands that selectively bind to the target microorganism improving the sensitivity and specificity of the detection test (Dwivedi and Jaykus, 2011).

Nonspecific sample preparation methods

For achieving high sensitivity in a detection method, target pathogenic cells should be separated from the food matrix without inhibitors bound to them. Applications with simplicity, minimum equipment requirement, and that are rapid and low cost are preferable. For example, devices called stomachers were introduced to homogenize the sample and release the bacteria from food matrix into a diluent with the use of paddles. Their utility eliminated the laborious work needed for sterilizing the conventional homogenizers besides being simple, inexpensive, portable, and efficient to keep bacteria in the sample viable (Sharpe and Jackson, 1972; Logue and Nde, 2017).

Centrifugation

This is a widely used nonspecific sample preparation method to concentrate target pathogens in various sample types. Some of the key parameters in centrifugation are density of bacterial cells and solution, sample volume, and speed of centrifugation. Differential and density gradient centrifugations help differential sedimentation of bacterial cells and other components in the food sample, allowing more effective isolation of target microorganisms (Dwivedi and Jaykus, 2011).

Filtration

Another nonspecific sample preparation technique allowing isolation of target microorganisms from larger size impurities in food sample is filtration. It helps separate target bacteria from a food sample based on their size. Majority of the bacteria are ∼1–2 μm in size and using filters with larger pore sizes than this allows separation of the bacteria from impurities in food samples. One major point that needs to be taken into account when applying filtration is the interaction of bacteria with other components of the food sample. Bacteria usually have the tendency to attach to protein or fat content of food and this might lead to capture of target bacteria with other impurities, negatively impacting the performance of detection tests.

To overcome this issue, food sample to be tested could be subjected to an elution buffer in which physicochemical interactions between the components of food and bacteria, such as Van der Waals interactions or hydrophobic forces, are broken by chemical manipulations such as changing pH or salt concentrations. Genus and species of target bacterial pathogens have significant impact on optimization of elution buffer owing to different compositions of their cell walls (Payne and Kroll, 1991).

Another application of filtration as a part of sample preparation is capture of target bacteria using a membrane with smaller pore sizes (filter pore size could be <1 μm depending on the size of the target bacteria). This method is usually applied in liquid samples with no large impurities that could clog the filter membrane (i.e., water samples). This way, pathogens in liquid food samples could be concentrated, improving the limit of detection (LOD) of the applied test (Morales-Morales et al., 2003; Wolffs et al., 2006).

Other nonspecific sample preparation methods

Depending on the physical and chemical properties of the tested food sample and target bacteria, other nonspecific sample preparation methods could be applied individually or in combination with others. One such method is immobilization of the bacteria using metal hydroxides (such as titanous and zirconium) or ion exchange resins (with positively charged beads). The hydroxyl groups of metal hydroxides covalently bind to the surface amino acids of pathogenic cells and in combination with centrifugation, affinity concentration of the bacteria is achieved without impacting cell viability (Reiser et al., 1974; Kennedy et al., 1976; Payne and Kroll, 1991; Dwivedi and Jaykus, 2011).

Another method that allows separation of target bacteria from a food sample is ultrasound treatment. This method applies ultrasonic vibration and separates bacterial cells by translation of mechanical force into shock waves in the liquid (Logue and Nde, 2017).

Target-specific sample preparation methods

Target-specific sample preparation methods utilize molecules that specifically bind to the pathogens, such as immunomagnetic particles, bacteriophages, and aptamers. The selection of the sample preparation method depends on chemical and physical properties of food matrix and target pathogen, and these methods could be applied in combination (Dwivedi and Jaykus, 2011).

Immunomagnetic separation

In this method, paramagnetic particles like magnetic beads or nanoparticles, which are magnetized only under a magnetic field, are used to capture bacterial cells in suspension of food sample. Surface of these particles could be coated with antibodies that are specific to the target bacteria. Majority of these applications for detection of pathogens use monoclonal antibodies, which are able to recognize and bind to a unique and specific epitope or surface antigen on target bacteria. Efficiency of separation depends on the size of paramagnetic particles, type of pathogen, expression of antigen on pathogen surface, and antibodies' affinity for that antigen (Gee, 1997; Rochelle et al., 1999; Dwivedi and Jaykus, 2011).

This method has been applied in conjunction with polymerase chain reaction (PCR) to detect L. monocytogenes and Salmonella enterica and significantly improve the detection sensitivity and accuracy in different food sample types (Hsih and Tsen, 2001). Despite being an advantageous sample preparation method owing to its highly specific and efficient separation and concentration of target pathogen, immunomagnetic separation (IMS) poses some disadvantages, such as its high cost and possible interaction of the paramagnetic particles with protein and fat content of the food sample (Stevens and Jaykus, 2004).

Nucleic acid aptamers

Short single-stranded DNA (ssDNA) or single-stranded RNA (ssRNA) molecules generated from oligonucleotide libraries, called nucleic acid aptamers, have the ability to bind specific pathogens with hydrogen bonding. After aptamers with high affinity for target bacteria are selected from a random library, they are amplified with PCR.

Suh and Jaykus (2013) coated magnetic beads with specifically designed DNA aptamers to capture L. monocytogenes. By using this method, ∼60 colony-forming units (CFU) of L. monocytogenes per 500 μL of heterogeneous solution of different bacterial cells were detected with quantitative PCR (qPCR). This method provided capturing efficiency that is much higher than capturing the same bacteria by IMS using biotinylated rabbit polyclonal anti-L. monocytogenes antibody. Aptamers could be much more efficient than antibodies in detection assays owing to their smaller size, stability, and easier synthesis (Khati, 2010; Dwivedi and Jaykus, 2011; Bauer et al., 2019; Arshavsky-Graham et al., 2020).

Lectins

These proteins bind to carbohydrates on bacterial cell surface by recognizing N-acetyl glucosamine residue. They could be used for rapid adsorption of pathogenic bacteria by fixation with magnetic or agarose beads. The capturing efficiency is dependent on properties of food matrix, source of lectin, and supporting material. The challenge is lectin isolation from biological sources and finding agent for elution because some of them can interfere with subsequent detection procedures, especially PCR (Lantz et al., 1994; Dwivedi and Jaykus, 2011).

Nonculture-Based Zoonotic Bacteria Detection Methods

Immunological (enzyme-linked immunosorbent assay [ELISA]) and nucleic acid-based (PCR) assays are the two common areas, where nonculture-based detection methods are heavily being developed. Traditionally, these methods were used in combination with culture-based methods, particularly following enrichment of the pathogen in enrichment or selective media for specific identification of the bacteria (Dwivedi and Jaykus, 2011).

Nucleic acid-based zoonotic bacteria detection methods

Nucleic acid-based pathogen detection methods mainly rely on one of two distinctive principles: amplification of the nucleic acid or hybridization of nucleic acids to specifically designed probes (Zhao et al., 2014; Wang and Salazar, 2016). In this section, detection methods based on PCR, loop-mediated isothermal amplification (LAMP) and clustered regularly interspaced short palindromic repeats-associated (CRISPR-Cas) system are discussed.

Polymerase chain reaction

PCR-based methods are widely used for detection of pathogenic bacteria and their toxin-encoding genes. These methods are by far the most commonly used nonculture-based detection methods owing to their high sensitivity in detection of the target DNA of the pathogen. Since the introduction of PCR methodology, different PCR-based methods are developed and validated to detect pathogens from different food samples. By developing specific oligonucleotides, species, serotype, strain, and so on, or presence of a zoonotic pathogen could be determined. In development and implementation of PCR-based methods, preprocessing and preparation of food sample are key steps as the components of food matrix could interfere with enzymatic reactions of PCR (Lantz et al., 1998; Yang et al., 2007; Wang and Salazar, 2016).

With the development of real-time PCR methods (qPCR), detection of zoonotic bacteria using this nucleic acid-based technology became more appealing, because the method could provide quantitative results and could be incorporated in a high-throughput detection format.

In qPCR, specific DNA fragment amplification is screened by measurement of fluorescence by a specialized PCR thermocycler. The intensity of fluorescent signal indicates the amount of DNA amplicon in the sample at a specific time (Kralik and Ricchi, 2017). For generation of fluorescence in qPCR, nonspecific fluorescent dyes, that is, SYBR^® Green or fluorescently labeled oligonucleotide probes could be used. In earlier qPCR assays SYBR Green was more commonly used, but as they provide more specific detection and quantification capability, fluorescent probes are now more often used in application of this method for detection (Kralik and Ricchi, 2017).

Development of qPCR enabled application of PCR-based detection methods more frequently as it minimizes the steps of traditional PCR methods and provides quantitative or semi-quantitative results for sensitive and specific detection of zoonotic pathogens in a sample. Examples of studies evaluating PCR methods with the summary of their results are given in Table 1.

Table 1.

Summary of Studies Using Real-Time Polymerase Chain Reaction (Quantitative Polymerase Chain Reaction) for Detection of Zoonotic Bacteria

Study	Sample type	Targeted bacteria	Preprocessing method	Sensitivity	Specificity (%)	Limit of detection	Accuracy (%)	Time of analysis
Fusco et al. (2011)	Milk	Staphylococcus aureus strains producing enterotoxins	Centrifugation	NA	100	10³ CFU/mL (SYBR Green) and 10⁴ CFU/mL	96–107 relative to culture-based polymerase chain reaction	6 h
Garrido-Maestu et al. (2018)	Chicken, beef, mussel, sardines, and tuna	Listeria monocytogenes	Filtration (after 24 h enrichment)	96.4%	100	9.5 CFU/25 g	97.4	1 h
Vinayaka et al. (2018)	Vegetable salad, egg, and meat	Salmonella typhimurium	AG magnetic beads (IMS)	91.6%	100	2 CFU/mL	98.3	3 h
Maurischat et al. (2015)	Neck skin of poultry and fresh meat	Salmonella Enteritidis and Salmonella Typhimurium	Stomacher and DNA extraction	100%	100	130 CFU/mL	100	24 h including pre-enrichment
Yang et al. (2013)	Lettuce, tomato, and ground beef	Salmonella Typhimurium, Escherichia coli O157:H7 and L. monocytogenes	IMS with magnetic nanobeads	NA	100	10³ CFU/mL	Not stated	4 h
Vencia et al. (2014)	Vegetable based and dairy products	Campylobacter jejuni, Campylobacter coli, and Campylobacter lari	DNA extraction	96.77% (vegetables) 88.24% (dairy)	100	4 CFU/25 g or mL	98.33 (vegetables), 93.3 (dairy)	48 h including pre-enrichment

CFU, colony-forming unit; IMS, immunomagnetic separation; NA, not available.

Loop-mediated isothermal amplification

LAMP is a rapid, target-specific, and easy to amplify nucleic acid-based detection method (Notomi et al., 2000). LAMP has similar principles of the PCR method, having a reaction of autocycling strand displacement DNA synthesis (Logue and Nde, 2017). Key advantage of LAMP is the entire amplification reaction takes place at a constant temperature. In this method, Bst DNA polymerase and four primers corresponding to six to eight specific regions on the target DNA enable highly efficient and specific DNA fragment amplification to allow specific detection at constant temperature (between 59°C and 60°C) (Notomi et al., 2015; Wang and Salazar, 2016). The amplified fragment can be viewed by gel electrophoresis, turbidity formation in the tube, or using fluorescent dyes like SYBR Green (Zende et al., 2017).

Wan et al. (2012) reported detection of hlyA gene in viable L. monocytogenes cells with application of propidium monoazide followed by LAMP reaction in ground beef, chicken, milk, and pork samples. L. monocytogenes was detected with SYBR Green I under UV light, in ∼2 h with LOD of 10³ CFU/g and 100% specificity. In another study, LAMP was integrated with antibody-coated magnetic beads to detect Shigella from milk samples. The ipaH gene of the bacteria was amplified and detected by LAMP reaction following the capture of the bacteria with magnetic beads in an hour and the LOD was 8.7 CFU/mL (Zhang et al., 2018).

A digital LAMP with automated DNA extraction method was recently developed for detection of Mycobacterium bovis that causes zoonotic tuberculosis. After centrifugation and DNA extraction, milk samples were placed in 96-well plates. Then, mpb70 gene of M. bovis was isothermally amplified and samples were imaged with inverted fluorescence microscope. The method enabled detection of M. bovis in milk samples with an LOD of 14 CFU/mL and high specificity in 2 h (Tao et al., 2020).

CRISPR-Cas-based detection methods

CRISPR-Cas is an adaptive immunity system developed in bacteria and archaea against viruses, plasmids, or other foreign genetic elements. Owing to their novel features, CRISPR-Cas-based detection methods are being more broadly investigated. CRISPR-Cas system could be applied for cost effective and rapid detection of pathogens with less equipment and trained personnel requirement than PCR (Wang et al., 2020a).

The CRISPR-Cas system involves dual action of Cas proteins and CRISPR RNAs (crRNA) that are encoded by arrays of CRISPR. The Cas proteins form complex with crRNAs and generally recognize the target sequences through protospacer adjacent motif (PAM) on the foreign genetic element and cleave it. Some of the Cas proteins may require trans-activating CRISPR RNA (tracrRNA) for their activities such as cleavage of target nucleic acids and crRNA maturation (Bhaya et al., 2011; Kaminski et al., 2021). Based on the construction of Cas effector proteins, CRISPR-Cas systems could be divided into two major groups: Class 1 and Class 2.

Majority of Cas proteins used for detection, such as Cas9, Cas12, Cas13, and Cas14 belong to Class 2. Class 2 CRISPR-Cas systems use only one type of effector protein and are easy to utilize, whereas Class 1 systems utilize multiple effector protein complexes (Koonin et al., 2017; Wang et al., 2020b; Zhu et al., 2020). Some Cas proteins have collateral (trans cleavage) activity for cleaving random nucleic acids in the medium. Based on the type of Cas protein selected, they could cleave DNAs or RNAs that can be double or single stranded (Kaminski et al., 2021). Some of the features of these Cas proteins are given in Table 2.

Table 2.

Features of Commonly Used Cas Proteins for Detection Assay Development

	Class/type	Target	Nuclease domains	Collateral (trans) activity	Recognition sequence	tracrRNA need for crRNA maturation and target cleavage
Cas9	2-II	dsDNA	HNH and RuvC	Absent	5′-NGG-3′ PAM	Yes
Cas12a	2-V	dsDNA/ssDNA	RuvC	Present	5′-TTTN-3′ PAM	No
Cas13a	2-VI	ssRNA	HEPN	Present	3′ non-G PFS (varying)	No
Cas14a	2-V	ssDNA	RuvC	Present	NA	Yes

Bonini et al. (2021), Koonin et al. (2017), Harrington et al. (2018), and Zhu et al. (2020).

dsDNA, double-stranded DNA; NA, not available; PAM, protospacer motif; PFS, protospacer-flanking sequence; ssDNA, single-stranded DNA; ssRNAs, single-stranded RNAs; tracrRNA, transactivating CRISPR RNA.

The CRISPR-Cas-based detection methods generally include amplification of specific DNA or RNA fragment of the pathogen in a sample and cleavage of the target nucleic acid by CRISPR-Cas system. This event is monitored by colorimetric sensors or probes that are cleaved by Cas proteins. A single guide RNA (gRNA) is generally designed by combining crRNA and tracrRNA of interest (Wang et al., 2020a).

Huang et al. (2017) applied CRISPR/Cas9-triggered isothermal exponential amplification reaction (EXPAR) following isolation of L. monocytogenes DNA. A PAM-presenting oligonucleotide that is complementary to “-CC” sequence of target hemolysin (hly) gene of L. monocytogenes and specifically engineered gRNA were included in the reaction to activate Cas9 for target cleavage. Then, cleaved fragment of the target was hybridized with EXPAR template DNA and a nicking endonuclease nicked the formed duplex so that the target fragment can rejoin the amplification cycle. These formed duplexes could be detected by real-time fluorescent probes like SYBR Green within an hour. This method had LOD of 0.82 amol DNA and was highly specific.

Another study focused on application of CRISPR/Cas9-mediated lateral flow nucleic acid assay for detection of double-stranded DNA (dsDNA) of L. monocytogenes (Wang et al., 2020b). After amplification of hlyA genes of L. monocytogenes by isothermal amplification with biotinylated primers, the amplicons were hybridized with the complex of Cas9 and designed gRNA. This mixture was placed onto the sample pad, flowing through the strip by capillary force. First, biotinylated amplicons were captured by designed gold nanoparticle (AuNP)-DNA probes in the conjugate pad. Second, the biotinylated complex bound to streptavidin in the test line. Finally, excess AuNP-DNA probes were captured by complementary DNA probes in the control line. In this way, nucleic acids of the pathogen could be detected with a colored band in the test line in an hour with an LOD of 100 copies and 100% accuracy (Wang et al., 2020b).

Cas12a, Cas13a, and Cas14 proteins were applied in several studies for virus and bacteria detection with their collateral activity, splitting nontarget nucleic acids upon recognition of the target (Wang et al., 2020a; Zhu et al., 2020). Recently, a system called allosteric probe-initiated catalysis and CRISPR-Cas13a was developed, which combines elements of CRISPR-Cas13a and nucleic acid-based allosteric probes, for detection of Salmonella Enteritidis in milk samples with 100% specificity in 140 min (Shen et al., 2020).

This method can be applied without the need for DNA isolation or extraction. The mechanism involves binding of an allosteric probe to the target pathogen so that probe is activated because of conformational change. The activated probe and a primer then form a dsDNA with the help of DNA polymerase activity as a primary amplification. Although several ssRNAs are transcribed from this dsDNA by RNA polymerase, the pathogen is displaced from the probe to join another cycle for secondary amplification. These ssRNAs then activate Cas13a-crRNA complex that cleaves all the RNAs in the medium including the nontarget RNA reporter probes to produce detectable fluorescent signal (Shen et al., 2020).

Peng et al. (2020) utilized Cas12a to detect species-specific methicillin resistance gene (femA) of S. aureus in milk samples. After PCR amplification of the target gene, the crRNA-Cas12a complex is added to the medium. When the complex recognizes the target with its complementary gRNA sequence, Cas12a cleaves all the ssDNAs in the medium including ssDNA reporter, generating strong fluorescence signal. The strategy led to detection of as low as 10³ CFU/mL methicillin-resistant S. aureus with high specificity in 2 h.

Immunological-based methods

Immuno-based detection methods are commonly based on antigen-antibody interactions. Most widely used immuno-based detection tests are ELISA and lateral flow immunoassays. These methods can be developed in combination with culture-based methods or could be used individually for quantitative or semiquantitative detection of zoonotic pathogens.

Enzyme-linked immunosorbent assay

ELISA-based detection methods are widely used both in diagnosis of diseases or detection of specific antigens, including bacteria and viruses, in a wide variety of sample types. As a detection method, ELISA uses the antigen-specific binding ability of antibodies for sensitive and specific detection. There are multiple different approaches that ELISA could be developed depending on the intended application of the technology. Some of the key aspects of development of a highly sensitive and specific ELISA test are as follows: development and selection of sensitive and specific antibodies, development of robust sample processing step, and adjustment of optimum buffer conditions to use in the assay.

One of the most commonly applied formats of ELISA in bacterial detection is sandwich ELISA format in which the target antigen is captured by a capture antibody. It is then detected by another detector antibody bound with a reporter enzyme that produces a detectable signal upon substrate addition. In recent years, with the advancements in nanotechnology, different approaches are applied in ELISA tests, such as the application of immuno-magnetic separation of pathogens from the test sample. Methods like these are intended to increase the sensitivity and specificity of the assay. Summary of studies evaluating the performance of ELISA in detecting zoonotic bacteria is given in Table 3.

Table 3.

Examples of Studies That Utilize Enzyme-Linked Immunosorbent Assay Method for Detection of Bacteria

Study	Assay method	Sample type	Targeted bacteria	Preprocessing method	Specificity	Limit of detection	Time of analysis (h)
Cho and Irudayaraj (2013)	Network of AuNPs coated with three different Abs (binding to the target) and two HRP-linked Abs for signal enhancement	Milk, ground beef, and pineapple juice	Salmonella Typhimurium and Escherichia coli O157:H7	IMC/S with monoclonal Abs	No cross-reactivity with other contaminants	15 CFU/mL for Salmonella Typhimurium (juice) and 3 CFU/mL for E. coli (milk and beef)	2
Hu et al. (2018)	Combination of ELISA and polymerase chain reaction where HRP-linked Ab binds to hybrid of the labeled gene with a biotin probe	Cabbage, shrimp, chicken, pork, beef, juice, and milk	Salmonella spp. and E. coli O157: H7	Stomacher and centrifugation	No cross-reactivity	1 CFU/mL	∼5
Febo et al. (2019)	Capture of flagella antigens by specific monoclonal Ab (4E6F11) on microplates	Meat, dairy products, egg, animal feed, pasta, and flour	Salmonella enterica subsp. Enterica	Stomacher	81%	7.8 × 10² CFU/mL	∼3
Wu et al. (2014)	Sandwich between aptamer-linked magnetic microparticles and nanoprobes consisting of enzyme and Ab-linked AuNPs	Milk	Salmonella enterica serovar Typhimurium	Concentration with species-specific aptamer	Low cross-reactivity	10³ CFU/mL	3
Zhang et al. (2016)	Sandwich ELISA with a polyclonal Ab and a monoclonal Ab for membrane protein of E. coli	Vegetables, beef, and fish	E. coli O157:H7	Filtration	94%	10⁴ CFU/g	∼6
Cho et al. (2014)	Sandwich between Ab-coated magnetic beads and Ab bound to fluorophore-labeled BSA	Milk, spinach, and chicken	Listeria monocytogenes, E. coli O157:H7, and Salmonella Typhimurium	IMC	No cross-reactivity	<5 CFU/mL	2

Ab, antibody; AuNP, gold nanoparticle; BSA, bovine serum albumin; ELISA, enzyme-linked immunosorbent assay; HRP, horseradish peroxidase; IMC/S, immunomagnetic concentration and separation.

Lateral flow assay

Lateral flow assay (LFA) allows rapid point-of-use testing of different sample types using specialized membrane strips. The fundamental idea of LFA is similar to ELISAs, using immuno-based capture for detection of the antigens. The design of the LFA enables them to be easily used by untrained individuals. Traditional LFA uses AuNPs as a signaling component and the results are interpreted by color change at the test region of the LFA membrane (Tsui et al., 2013; Wang et al., 2018; Kim et al., 2019). In recent years, different signaling technologies are being applied in LFAs to improve their sensitivity.

A typical LFA includes a membrane on which the sample flows through from a sample pad. Conjugate pad lies just after the sample pad, where there are signaling compounds like AuNPs, linked with target-specific antibodies. On the test line, there are other target-specific antibodies binding to the target–AuNP complex, different from the antibodies on the control line that bind to the AuNP-conjugated antibodies regardless of the target presence (Zhang et al., 2019a). Summary of studies evaluating the performance of LFA methods in detecting zoonotic bacteria is given in Table 4.

Table 4.

Summary of Studies Evaluating Lateral Flow Assay Based Bacteria Detection Methods in Food Samples

Study	Assay method	Sample type	Targeted bacteria	Pre-processing method	Limit of detection	Time of analysis
Tsui et al. (2013)	SEB capture and detection with two SEB-specific PAbs, one conjugated with AuNP	Juice, milk, coffee, cola, honey, yogurt, mustard, beef, macaroni, tempura, etc.	SEB	Centrifugation	10 ng/mL	10–30 min
Ching et al. (2015)	Capture and detection of Shiga toxins with two MAbs, one conjugated with AuNP	Lettuce, milk and ground beef	Shiga toxin–producing Escherichia coli	Centrifugation	∼1 ng/mL	10 min
Kim et al. (2019)	Amplification of genes of pathogens with cPCR using biotin as a primer tag and detection by AuNP-conjugated antibodies that bind to biotin	Milk	Salmonella Enteritidis, Salmonella Typhimurium, and E. coli O157:H7	DNA extraction	4.5 × 10³ CFU/mL for Salmonella spp. and 2.3 × 10³ CFU/mL for E. coli	20 min
Zhuang et al. (2020)	Strand exchange isothermal amplification-based LFA that detects digoxigenin tagged bcfC gene of the pathogen by anti-digoxigenin antibody	Raw milk, chicken, duck, pork, beef, and sea food	Salmonella spp.	DNA extraction	6 × 10⁰ CFU/mL	30 min
Wang et al. (2018)	Quantitative detection with MAbs and Raman reporter-linked AuNPs that give characteristic Raman peak (SERS)	Bacterial suspension	Yersinia pestis, Bacillus anthracis, Francisella tularensis	NA	43 CFU/mL for Y. pestis, 357 CFU/mL for B. anthracis, 45.8 CFU/mL for F. tularensis	15 min

AuNP, gold nanoparticle; cPCR, convection polymerase chain reaction; LFA, lateral flow assay; MAbs, monoclonal antibodies; NA, not available; PAbs, polyclonal antibodies; SEB, Staphylococcal enterotoxin B; SERS, surface-enhanced Raman scattering.

Bacteriophage-based methods

Bacteriophages (phages) have inherent affinity and high specificity against specific bacterial pathogens and they could be used for capture or detection of pathogenic bacteria in different detection assays. Besides being robust, cheap, and simple to design, phages' need for viable bacterial cells to replicate make these virulent agents good indicator of viable cells in the sample being tested (Richter et al., 2018; Foddai and Grant, 2020).

Lytic phages like T3, T4, T7, and MS2 lead to lysis of bacterial cells after injecting their DNA or RNA into the host resulting in production of virions. Lysogenic phages like λ or filamentous phages remain dormant after inserting their genetic material into the host genome until a stimulant appears (Singh et al., 2012). Along with the use of phages found in nature, they can be engineered by selection from libraries of clones having numerous recognition sites for the target pathogen (Dwivedi and Jaykus, 2011). Examples of phage-based bacteria detection methods are summarized in Table 5.

Table 5.

Studies Using Bacteriophage Based Methods for Bacterial Detection in Food Samples

Study	Assay method	Sample type	Targeted bacteria	Preprocessing method	Limit of detection	Detection technique	Time of analysis
Stambach et al. (2015)	Amplification of the target with A511 bacteriophage and detection with Ab bound to surface enhanced Raman spectroscopy nanoparticles on LFA strip	Bacterial suspension	Listeria monocytogenes	Filtration	6 × 10⁶ PFU/mL	Raman spectrometer	45 min
Denyes et al. (2017)	Sandwich of the target between magnetic beads coated with biotinylated LTF of bacteriophage S16 and HRP-conjugated LTF	Chicken, infant formula and milk	Salmonella	NA	10² CFU/mL	Colorimetric detection with HRP and spectrophotometric quantification	2 h
Laube et al. (2014)	Phagomagnetic separation with magnetic beads bound to P22 phages and immunoassay with HRP-linked antibodies	Milk	Salmonella	NA	19 CFU/mL	Optical detection of product of HRP with substrate addition	2.5 h
Anany et al. (2018)	Amplification with AG11, rV5, AG2A, and CGG4-1 phages immobilized onto bioactive paper	Chicken, spinach, and ground beef homogenates	Salmonella, Escherichia coli O157:H7 and E. coli O45:H2	NA	10–50 CFU/mL	qPCR for phages' nucleic acid detection after removal of the paper	8 h
Wisuthiphaet et al. (2019)	Reporter phage T7-ALP that leads to overexpression of AP in pathogen	Apple juice and coconut water	E. coli	Filtration	100 CFU/g	Fluorescence imaging with fluorescent product of reaction of AP and its substrate ELF-97	6 h
Franche et al. (2017)	Reporter HK620 and HK97 phages for insertion of luxCDABE operon expressing both luciferase and the substrate to DNA of the target	Water	E. coli	Centrifugation	10⁴ cells/mL	Luminescence emission assay (substrate independent)	1.5 h
Martelet et al. (2015)	Amplification with T4 phage followed by immunomagnetic separation with anti-T4 Abs for concentrating phage marker proteins	Milk	E. coli	Trypsin digestion	1 CFU/mL	MS coupled to liquid chromatography for detection of phage marker proteins	12 h
Wang et al. (2017)	Reporter T7 lacZ phages that overexpress β-gal enzyme in the target	Drinking water, apple juice, and skim milk	E. coli	NA	10² CFU/mL	Electrochemical detection of PAP after conversion of PAPG substrate by β-gal	7 h
Botsaris et al. (2013)	Phage plaque assay combined with PCR for amplification of IS900 gene of the pathogen	Bulk tank milk	Mycobacterium avium subsp. paratuberculosis	Centrifugation	∼50 PFU/mL	Visualization of PCR product under UV light	24 h
Singh et al. (2011)	Capture with Gp48 RBP of NCTC 12673 phage immobilized on SPR surface after RBP-functionalized microbeads-based preconcentration	Bacterial suspension	Campylobacter jejuni	Centrifugation	10² CFU/mL	SPR	∼45 min

Ab, antibody; AP, alkaline phosphatase; HRP, horseradish peroxidase; LFA, lateral flow assay; LTF, long tail fiber; NA, not available; PAP, p-aminophenol; PAPG, 4-aminophenyl-β-galactopyranoside; PCR, polymerase chain reaction; PFU, plague-forming unit; qPCR, quantitative PCR; RBP, receptor binding protein; SPR, surface plasmon resonance; β-gal, beta galactosidase.

Phage-based methods can be divided into three. First method is detecting target bacteria in a sample by plaque formation. After infection of bacteria with lytic phages and then removal of exogenous phages with chemical or physical treatment, the sample and indicator bacteria are added onto an agar plate. As the phages could only replicate in specific bacteria, formed plaque zones after incubation indicate original number of bacteria in the sample. This method could require further validation with other tests because of the potential false-positive results (Stewart et al., 1998; Stanley et al., 2007; Foddai and Grant, 2020). Second method is the combination of phage amplification through lysis phase of the plaque assay and detection of released progeny phages with immuno- or molecular assays like ELISA, LFA, or qPCR (Richter et al., 2018; Foddai and Grant, 2020).

Third application of phages is using them as recognition elements for capture and detection of the target bacteria, which are combined with downstream colorimetric, enzymatic, or electrochemical transduction techniques. Phages could be engineered to produce reporter phages, phage display peptides, and phage receptor-binding proteins (RBPs) (Singh et al., 2013; Peltomaa et al., 2016; Richter et al., 2018). Engineered reporter phages carry reporter genes that express the reporter protein upon injection to the host genome. These reporter proteins have the ability to give signal in the form of bioluminescence, fluorescence, or color change to indicate presence of target pathogen (Smartt and Ripp, 2011).

Target-specific peptides or cellular proteins can be produced; genes encoding them can be inserted into the phage surface protein encoding gene so that these peptides can be displayed on the surface of the phage. When they are immobilized onto a solid surface like AuNPs, the specificity of bacteria detection by these biosensors can be facilitated (Gervais et al., 2007; Singh et al., 2013).

RBPs at tail fibers of phages bind to the carbohydrates or proteins on the cell surface receptors of bacteria, assisting the insertion of genetic material into the host. Engineered RBPs can acquire new tropisms that are important for binding to the host. Their binding affinity can be altered and tags can be added without an effect on affinity. In addition, they are resistant to environmental factors, which makes them appealing biosensing agents compared with antibodies (Singh et al., 2013; Peltomaa et al., 2016).

Biosensor-based detection methods

With the use of biosensors, biological reactions of the target and bioreceptors in the assay can be coupled to optical or electrochemical signals for rapid detection. Antibodies, bacteriophages, and aptamers can be used as bioreceptors for capturing and/or detection, and they can be attached to flat surfaces like membranes or to nanomaterials like magnetic beads or quantum dots for signal transduction (Taitt et al., 2005; Wang and Salazar, 2016). These signals can be obtained indirectly using two ligands, one of them binding to a surface for capture and other could be used for detection (Dwivedi and Jaykus, 2011). Biosensors can be classified into three categories according to type of signal: optical, electrochemical, and micromechanical biosensors. Different types of biosensors are presented with their performance results in Table 6.

Table 6.

Summary of Studies That Applied Biosensors for Detection of Bacterial Pathogens in Food Samples

Study	Assay method	Bioreceptor	Sample type	Targeted bacteria	Preprocessing method	Limit of detection	Detection technique	Time of analysis
Masdor et al. (2017)	Sandwich between detecting PAbs and capturing PAbs coated on sensor monolayer	Antibody	Bacterial suspension	Campylobacter jejuni	None	4 × 10⁴ CFU/mL	SPR sensor chip with gold surface	∼20 min
Kim et al. (2018)	Aptasensors composed of target-specific aptamers and AuNPs that are aggregated in the presence of the pathogen due to hindrance of aptamer	Aptamer	Chicken carcass	C. jejuni and Campylobacter coli	Filtration after 48-h enrichment	7.2 × 10⁵ and 5.6 × 10⁵ CFU/mL	Colorimetric detection of aggregated AuNP, giving purple color	30 min
Ohk and Bhunia (2013)	Sandwich between biotinylated rabbit PAbs immobilized on streptavidin-coated waveguides and fluorophore-conjugated mouse MAbs	Antibody	Meat	Escherichia coli O157:H7, Listeria monocytogenes and Salmonella Enteritidis	Stomacher and 18-h enrichment	10³ CFU/mL	Fiber optic sensor with spectrofluorometer	∼4 h
Cho et al. (2015)	Sandwich between MAb-conjugated magnetic bead and a probe containing AuNP coated with PAb and Raman reporter	Antibody	Ground beef	E. coli O157:H7	IMC/S	10 CFU/mL	Surface-enhanced Raman spectroscopy	∼4 h
Giovanni et al. (2015)	Capture between PAb-linked DNA nanopyramid immobilized on gold surface of electrode and PAb-conjugated FeC redox tag for detection	Antibody	Skim milk mixture	E. coli	NA	1.20 CFU/mL	Amperometric measure with square wave voltammetry	∼2 h
Park et al. (2013)	Capture with engineered E2 phage immobilized on the sensor	Bacteriophage	Spinach leaves	Salmonella Typhimurium	None	1.94 log CGU/spinach	Magnetoelastic sensor measuring resonance frequency	30 min
Fulgione et al. (2018)	PAbs immobilized on gold electrode of quartz crystal for the best orientation for binding to the target	Antibody	Chicken	Salmonella Typhimurium	Stomacher and centrifugation	<10 CFU/mL	Quartz-crystal microbalance for resonance frequency	<4 h including 2 h pre-enrichment
Liu et al. (2019)	Sandwich between aptamer-linked magnetic NPs and IgY-coated BSA-MnO₂ NPs that catalyze conversion of TMB into TMB²⁺ following magnetic separation	Aptamer and IgY	Pork	L. monocytogenes	Filtration and centrifugation (30 min)	10 CFU/mL	Multicolorimetric detection of different concentration of the pathogen with the reaction of TMB²⁺ and AuNRs	NA
Tawil et al. (2012)	Specific BP14 phages-coated gold chips for capture and detection of the target	Bacteriophage	Bacterial suspension	Methicillin-resistant Staphylococcus aureus	NA	10³ CFU/mL	SPR	20 min
Song et al. (2017)	Capture by IgG-conjugated magnetic beads and detection with IgY-linked QD probes	Antibody	Pasteurized milk and lamb steep liquor	Brucella melitensis 16M	None	10² CFU/mL	Fluorescence spectrometer	105 min
Izadi et al. (2016)	Detection of the bacterial DNA by hybridizing it with ssDNA of nheA gene that is immobilized on AuNP-coated pencil graphite electrode	ssDNA	Raw and pasteurized milk and infant formula	Bacillus cereus	Centrifugation and DNA extraction	10 CFU/mL	Electrochemical impedance spectroscopy	∼2 h

AuNP, gold nanoparticle; AuNR, gold nanorod; BSA, bovine serum albumin; FeC, ferrocene; IMC/S, immunomagnetic concentration and separation; MAb, monoclonal antibody; NA, not available; NP, nanoparticle; PAb, polyclonal antibody; QD, quantum dot; SPR, surface plasmon resonance; ssDNA, single-stranded DNA; TMB, 3,3′,5,5′-tetramethylbenzidine.

Labeled or label-free optical biosensors work based on different principles such as scattering, absorbance, and polarization of light, fluorescence, bio/chemiluminescence, or plasmonic techniques like surface plasmon resonance (SPR) and surface-enhanced Raman scattering. SPR allows real-time and label-free detection with high sensitivity (Singh et al., 2013; Yoo and Lee, 2015).

Electrochemical biosensors include amperometric, potentiometric, and conductometric assays, and use biorecognition elements combined with electrodes for signal transduction (Zhang et al., 2019b). Whereas amperometric biosensors measure the current generated mainly from enzymatic reactions by electrodes with voltage application, conductometric assays measure the resistance alteration of the biological ligand owing to interaction with target (Singh et al., 2013; Wang and Salazar, 2016).

Mechanical biosensors using quartz crystal microbalance sensors measure mass of the target based on the mechanical oscillation following application of an electrical field with probes bound to a piezoelectric surface (Singh et al., 2013; Peltomaa et al., 2016). Wireless magnetoelastic (ME) biosensors are composed of probes having alloy surface that can detect the pathogen with respect to change in ME resonance frequency after application of a magnetic field (Wang and Salazar, 2016).

Conclusion

Culture-based bacteria detection methods are generally referred as gold standard in the industry. With the evolving developments in the field, these methods are being replaced by rapid, nonculture-based detection methods, primarily by PCR and ELISA. As discussed in this review article, developing a highly sensitive and specific detection test in food samples is a challenging process because of complexity of food samples and interaction of the pathogenic bacteria with the components of food being tested. It is crucial to take all aspects of testing into account from sample processing to selection of detection method, when establishing an effective detection and tracking method for zoonotic bacteria. Some of the technologies discussed, such as application of CRISPR-Cas-based detection methods, are recently being developed and their broad applicability for high-throughput testing of bacterial contaminants still needs to be investigated.

Footnotes

Acknowledgments

This article has been produced benefiting from the 2232 International Fellowship for Outstanding Researchers Program of TÜBİTAK (Project No: 118C377). However, the entire responsibility of the publication belongs to the owner of the publication.

Disclosure Statement

No competing financial interests exist.

Funding Information

The financial support received from TÜBİTAK does not mean that the content of the publication is approved in a scientific sense by TÜBİTAK.

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