# Whole-cell aptamer-based techniques for rapid bacterial detection: Alternatives to traditional methods

Juliette Nourry, Pauline Chevalier, Emmanuelle Laurenceau, Xavier Cattoën, Xavier Bertrand, Basile Pérès, Farid Oukacine, Eric Peyrin, Luc Choisnard

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Juliette Nourry, Pauline Chevalier, Emmanuelle Laurenceau, Xavier Cattoën, Xavier Bertrand, et al.. Whole-cell aptamer-based techniques for rapid bacterial detection: Alternatives to traditional methods. Journal of Pharmaceutical and Biomedical Analysis, 2025, 255, pp.116661. 10.1016/j.jpba.2025.116661. hal-05008796

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# Whole-Cell Aptamer-Based Techniques for Rapid Bacterial Detection: Alternatives to Traditional Methods

Juliette Nourry<sup>a,‡</sup>, Pauline Chevalier<sup>a,‡</sup>, Emmanuelle Laurenceau<sup>b</sup>, Xavier Cattoën<sup>c</sup>, Xavier Bertrand<sup>d</sup>, Basile Peres<sup>a</sup>, Farid Oukacine<sup>a</sup>, Eric Peyrin<sup>a*</sup>, Luc Choisnard<sup>a*</sup>

<sup>a</sup> University Grenoble Alpes, DPM UMR 5063, F-38041 Grenoble, CNRS, France

<sup>b</sup> University Lyon, University Claude Bernard Lyon 1, INL UMR5270, Ecole Centrale Lyon, CNRS, INSA Lyon, CPE Lyon, F-69130 Ecully, France

<sup>c</sup> University Grenoble Alpes, Grenoble INP, Institut Néel, F-38000 Grenoble, CNRS, France

<sup>d</sup> University Bourgogne Franche-Comté, Chrono-environnement, UMR 6249, CNRS, France

<sup>‡</sup> Equal contribution

* Corresponding authors: eric.peyrin@univ-grenoble-alpes.fr; luc.choisnard@univ-grenoble-alpes.fr
ABSTRACT

Controlling the spread of bacterial infectious diseases is a major public health issue, particularly in view of the pandemic of bacterial resistance to antibiotics. In this context, the detection and identification of pathogenic bacteria is a prerequisite for the implementation of control measures. Current reference methods are mainly based on culture methods, which generate a delay in obtaining a result and requires equipment. Consequently, focusing on the detection of the whole bacterium represents a very attractive alternative, since no culture is required. Several techniques have already been deployed to identify whole-cell bacteria. In recent decades, growing interest in nucleic acid aptamers has emerged as a viable alternative to antibodies as recognition elements, offering preferable stability, cost-efficiency, good specificity and affinity. This review explores current alternative methods for the detection of whole-cell bacteria, with particular emphasis on aptamer-based assays. These assays have shown promising results in various transduction mechanisms, including optical, electrochemical, and mechanical approaches, enhancing their versatility in different diagnostic platforms. The integration of aptamers in these detection methods offers rapid, sensitive, versatile and portable solutions for pathogen identification, positioning them as valuable tools in the fight against bacterial infections.

Keywords: Aptamer, detection, bacteria, whole-cell
# 1 Introduction

The research and development of new methods for identifying and quantifying microorganisms is more intense than ever and has multiple origins. The emergence of antibiotic resistance and the withdrawal of pharmaceutical companies from antibiotic research and development has led bacterial resistance into a critical public health issue. Multi-drug resistant (MDR), extremely-drug resistant (XDR) or even Pan-drug resistant bacteria pose a significant risk: in the United States of America alone, over 2 million annual cases of antibiotic-resistant infections, resulting in about 23,000 deaths according to the Centre for Disease Control (CDC)[1,2]. Moreover, prompt identification of resistance is crucial, especially in hospital healthcare environment,[3] to quickly implement patient isolation measures and control the spread of pathogens, thus reducing the risk of outbreaks.

Several methods for microbiological diagnosis are already implemented to identify bacteria. In recent decades, there has been an increasing interest in the detection of bacteria, including whole bacteria (Figure 1). Their detection can be carried out using three types of recognition elements.

First, antibodies have been recognized as powerful recognition elements in the detection of pathogenic bacteria. While traditional culture methods remain a cornerstone of bacterial identification, immunoassays that use antibodies are pivotal in enhancing detection accuracy and speed. However, the preparation of antibodies involves significant costs and lengthy production cycles, which pose limitations in terms of scalability and efficiency. These challenges highlight the need for innovative alternatives that offer comparable sensitivity while being more practical and cost-effective[4].

Another recognition element, that can be considered for bacterial detection is molecular imprinted polymers (MIPs) which are commonly used due to their specific recognition capabilities. MIPs offer notable benefits compared to enzymes and antibodies, including high stability, cost-effectiveness, reusability, easy storage, and customizable design. Their preparation process allows for selective recognition, making them capable of detecting a wide range of food pollutants like pesticides, antibiotics, viruses, bacteria, and proteins. MIPs are also highly stable in harsh environments and have a low preparation cost. However, traditional MIPs face challenges such as uneven loading of the imprinted cavities and slow mass transfer efficiency[5].

An alternative to antibodies and MIPs is aptamers. Indeed, nucleic acid aptamers have emerged as a promising tool for the detection of bacteria, including whole-cell bacteria. These single-stranded DNA or RNA oligonucleotides (25-90 bases) can specifically bind various small molecule targets (ions, drugs, toxins, pesticides, nucleotides) and bigger molecules (proteins, viruses, bacteria and cells). This specificity is due to their ability to fold into distinct 3D structures, such as stem-loops, hairpins, and G-quadruplexes, which enable precise binding to their targets[6]. These nucleic acid sequences are typically identified thanks to the SELEX process (Systematic Evolution of Ligands by Exponential Enrichment), which involves iterative cycles of *in vitro* selection. [7,8].

In response to these limitations, aptamers have emerged as a promising alternative. They present crucial benefits such as thermal and chemical stability, controlled *in vitro* selection. Their implementation offers several advantages in diagnostic test development[9], including reduced development time, scalability for chemical production, batch reproducibility and cost-effectiveness [10] (300 to 4000$ for aptamer selection versus 8 000$ to 20 000$ for antibody selection[11,12] in 2009/2013). Therefore, their chemical synthesis does not require the use of animals to develop them, which is in favour of animal welfare. Moreover, aptamers have been widely used in sensors for decades, offering high specificity, good biocompatibility, low immunogenicity and ease of engineering for functional customization and modification[13]. These recognition elements possess dissociation constant ($K_d$) values in the range of the micro-nanomolar and are relatively stable. Contrary to antibodies, which bind only immunogenic targets, aptamers can
theoretically recognise anything. They can adopt multiple structural conformations when they bind to their targets. These versatile properties render aptamers highly suitable as identification elements in biosensor development [14]. Table 1 shows the key differences between aptamers and antibodies [15].

Two strategies can be considered for aptamer use: targeting cellular fragments obtained after cell lysis, such as proteins, genetic sequences[16–24], or targeting whole cells. Each strategy has its own advantages and disadvantages. Targeting cellular fragments obtained after cell lysis, such as proteins or genetic sequences, poses several challenges and is not the most effective strategy for aptamer-based assays. Cell lysis can significantly alter the sample composition, as the cell walls of Gram-negative and Gram-positive bacteria may not lyse completely, complicating the extraction of key elements required for bacterial identification. In contrast, targeting whole-cell bacteria offers clear advantages. It enables specific identification of bacterial species, facilitates strain differentiation, and provides accurate infection diagnoses. Furthermore, whole-cell assays often yield faster results, typically within a few hours, as they avoid the time-consuming lysis and purification steps. This speed is crucial for timely treatment decisions, especially in cases of serious infections. Whole-cell assays also allow for antibiotic sensitivity testing, making them versatile diagnostic tools [25,26].

The present review focuses on advances in whole-cell bacterial detection and identification using aptamers. While previous reviews have explored various aspects of bacterial detection, none has merged the two components of aptamer technology and whole-cell bacterial detection. Prior reviews have predominantly focused on various aspects, such as the bacterial genus[27–29], while others scrutinize distinct signalling methodologies such as optical, electrochemical, or gravimetric approaches based on aptamer recognition[30–37]. Others are studying the detection of bacteria by aptamers without focusing on whole cell methods [4,38–42]. One review has discussed biosensors for the detection of bacteria in the form of whole cells, but it does not focus on the use of aptamers [43]. The present review fills a gap in the literature by comprehensively examining the intersection of aptamer technology and whole-cell bacterial detection, offering a perspective that has hitherto been unexplored.

In the first section, we will briefly review commercial and approved methods for detecting and identifying whole-cell bacteria. The subsequent section will delve into ongoing research focused on aptamer-based tests. This section includes a comprehensive table presenting a summary of all the synthesized aptamer sequences reported to date in literature that are specifically targeted towards a wide range of bacteria. Finally, we will consider the prospects and potential limitations of the development of whole-cell aptamer-based assays.

# 2 Routine and commercial bacterial detection methods

## 2.1 Culture-based methods

Routine and research laboratories typically use conventional bacterial detection methods, relying on microbiological culturing as the gold standard [25]. These methods boast high sensitivity and specificity, detecting even a single bacterial cell in a sample [1]. Identification involves bacterial growth on nutrient media typically during 24h to several days followed by serological and biochemical tests like API galleries. Morphological characteristics of colonies, such as size, colour, and shape, gives indicative characteristics on bacterial genus [44]. Numerous official reference methods exist for detecting pathogenic bacteria using conventional culture techniques. Some examples include the FDA method for *Listeria monocytogenes* [45], CFIA method for *Escherichia coli* O157:H7, ANSES guidelines for *Listeria monocytogenes* detection in food processing areas [46], and ISO method for *Campylobacter spp.* These protocols involve selective enrichment, specific media isolation, and confirmation tests. ISO standards, particularly in biology (ISO 07.100 series), cover various subjects like water and food microbiology, providing reliable protocols for bacterial detection and enumeration.
### 2.1.1 API® Galleries

API® galleries are miniaturized biochemical test tubes that contain specific substrates, such as 2-nitrophenyl-βD-galactopyranoside (ONPG), which facilitate diverse microorganism reactions. Positive result is often signalled by a medium colour change due to coloured indicators in the respective tube. The various API kits available on the market are designed to identify different groups of bacteria. Nowadays, the process has been instrumentalized and several manufacturers market this type of test, including VITEK® 2 from Biomérieux, Phoenix® from BD or Walk away® from Siemens [47]. The implementation of miniaturized ranges has reduced this waiting period to between 18 and 72 hours. Notably, these miniaturized ranges have the capability to identify virtually all bacteria thanks to 15 identification systems.

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The main benefit of these systems is the detection of living microorganisms and the simple utilisation and inter-laboratory reproducibility. Nonetheless, API galleries generally require prior time-consuming bacterial culture. In addition, some bacteria may be difficult to culture or require specific culture conditions, such as mycobacteria or *Francisella* [48].

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### 2.1.2 Chromogenic and fluorogenic media

Chromogenic and fluorogenic media (CFM), developed to replace conventional cultures, allows detection, isolation, and differentiation of bacterial pathogens from a sample containing heterogeneous populations of microorganisms according to their specific enzymatic activity. Bacteria detection relies on colour or fluorescence in minute samples[44], specifically staining bacterial colonies according to enzymatic activity[49]. Many different media are available on the market to distinguish a wide variety of bacterial species (forty or so), to list just a few, CHROMagar™ from BD and MRSASelect™ II from Bio-Rad and permit to obtain a selectivity and specificity greater than 95%. Nevertheless, the use of CFM may lead to interactions with diverse bacterial species, potentially non-pathogenic bacteria, yielding false positives or lack of specificity. A study examined commercial chromogenic agar plates effectiveness in swiftly and accurately identifying bacteria causing equine endometritis (*Streptococcus equi* subsp. *zooepidemicus*, *Escherichia coli*, *Pseudomonas aeruginosa*, and *Klebsiella pneumoniae*). Results indicated that relying solely on colour appearance for genus-level identification, without additional tests or concurrent use of other culture media, is unreliable based on current identification guidelines[50].

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### 2.1.3 MALDI-TOF mass spectrometry

MALDI-TOF MS, as a highly sensitive technique, plays a crucial role in characterising bacterial species and subspecies. It analyses entire bacterial populations by studying colonies. A molecular mass spectrum, or a fingerprint is rapidly generated (e.g. MALDI Biotyper® from Bruker, SARAMIS from Anagnos-Tec, VITEK® MS from bioMérieux, Andromas solutions), within minutes[44,47].

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In some cases, the identification of similar species is limited by the content of the database. Indeed, the database must be regularly updated to avoid incorrect species-level identification or even the absence of identification. Misidentification can arise when certain members of a species complex are present in the database while others are not, further complicating accurate identification[51].

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The principal disadvantage is the necessity for a subculture from the initial sample. Nevertheless, certain techniques permit the direct performance of MALDI on urine and blood cultures, provided that there are sufficient bacteria. Additionally, MALDI equipment may require significant financial investment.

## 2.2 Non-culture-based methods

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It is therefore necessary to develop parallel, faster methods. Some alternative methods, such as molecular methods, are already in routine use and are more effective than culture-based methods. Moreover, some micro-organisms can persist in a viable, but non-culturable (VBNC) state, preventing detection through culture-based techniques[52]. Immunological-based methods have been developed as an alternative to
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conventional techniques. These methods offer advantages such as faster detection and reduces complexity, primarily due to their automated nature[25].

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Immunological techniques rely on the interaction between antibodies and antigens to detect pathogens. Usually, species identification of microorganisms involves morphological, physiological, and biochemical studies. The immunological methods employed include slide agglutination, latex-based aggregation, immunofluorescence, and immune-enzymatic techniques like ELISA (Enzyme-Linked Immunosorbent Assay)[53].

### 2.2.1 Latex-based and slide agglutination

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Latex-based agglutination assays are commonly employed in biology and medicine to detect minimal amounts of antibodies or antigens in fluid samples. Polymer-based latex particles aggregation in vitro offers simplicity, versatility, safety, and rapid results (within minutes). This method is straightforward, requiring no specialized equipment or intricate procedures, and can be performed with minimal training. Various tests using this approach are already on the market, such as the Staphaurex™ Latex Agglutination Test, the BactiStaph™ Latex Agglutination Test Kit from Thermo Scientific™, and the Prolex™ Staph Latex Kit from Pro-Lab Diagnostics[54].

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Slide agglutination provides specific detection by enabling direct visualization of agglutinated bacteria under a microscope, rendering it a precise method. Conversely, latex tests offer greater speed and user-friendliness as they eliminate the need for a microscope and can be easily adapted for field use. Nonetheless, they might sacrifice sensitivity compared to slide agglutination. Therefore, the selection between these two techniques hinges on the requirements regarding sensitivity, specificity, and practicality of the laboratory or the context in which they are employed.

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Agglutination techniques may have limited bacterial species identification and subjective interpretation via visual observation, leading to result variability and misinterpretation.

### 2.2.2 Immunofluorescence assay

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This process entails linking the antigen, or occasionally the antibody, with a fluorescent substance to identify the presence of the corresponding antibody or antigen in the sample. This technique can be either direct or indirect[23]. Direct methods entail analysing the sample without any prior treatment, whereas indirect methods necessitate preliminary steps, such as centrifugation or filtration, before the sample can undergo analysis. On the one hand, immunofluorescence enables direct visualization of bacteria in a sample, thanks to the fluorescence emitted by labelled antibodies, making it easier to observe and identify bacteria. On the other hand, it requires expensive equipment and reagents, notably fluorescently labelled antibodies.

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### 2.2.3 ELISA (Enzyme-linked immunosorbent assay)

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One of the most popular immunological tests is the ELISA test, which is undoubtedly sensitive and accurate for the detection of pathogens. Indeed, several kits are commercially available like the Solus Listeria Monocytogenes ELISA Kit and the BIOLINE *Salmonella* ELISA Test for the detection of food pathogens[44]. ELISA's are categorized based on the method used for immobilizing and detecting the antigen. Direct or indirect protocols can then be created. A commonly employed sandwich method involves a two-step antibody labelled with Horseradish Peroxidase (HRP) followed by the addition of a 3,3'-5,5'-tetramethylbenzidine-hydrogen peroxide solution for quantification of bacterial concentration based on the intensity of the resulting blue coloration. It has some limitations, to cite one, the colour change facilitated by enzymes will continue and as the time progresses extensively, the intensity of the colour will no longer accurately indicate the quantity of primary antibody, leading to the production of false-positive outcomes[55].

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#### 2.2.4 LFIAs (Lateral Flow Immuno Assays)

LFIAs, which are dipstick and immunochromatographic strips, have emerged as viable option for the swift, trustworthy, uncomplicated, and on-site identification of pathogens. The lateral flow assay (LFA) serves as a paper-based platform designed for detecting and quantifying analytes with overlapping membranes, creating sample application zones. Samples migrate through in 5 to 30 minutes, interacting with antibodies in the conjugate release pad. Read-out involves visible lines of varying intensities, visible or with a reader.

LFIA employs labels like colloidal gold, monodisperse latex, and fluorescent tags for pathogen detection. New materials like magnetic particles, nano-gold microspheres, or immune-nanoparticles enhance sensitivity, reducing detection limits at the molecular level to at least 0.1 ng/mL[56]. Several immunochromatographic test strips are commercially available, such as RapidChek® for E. coli O157 strain and VIP® Gold for Salmonella strain.

However, the presence of interfering molecules in food samples, such as DNA, proteins, and non-targeted cells, can affect the performance of antibody-based immunodiagnostics[44]. The limit of detection of immunoassays is typically around $10^4 - 10^5$ CFU/mL[26].

Table 2 summarizes the advantages and drawbacks of each commercial method quoted in this part.

# 3 Aptamer-based assays for bacteria detection and identification

## 3.1 Aptamer sequences for the detection of bacteria

Table 3 summarizes the aptamer sequences selected for bacterial detection, together with their characterization technique and affinity to their target.

## 3.2 Sensing approaches

### 3.2.1 Optical detection

Optical aptamer-based sensors/assays can be classified into lots of different detection techniques such as colorimetry, fluorescence, chemiluminescence, Surface Plasmon Resonance (SPR) or Surface-Enhanced Raman Spectroscopy (SERS). They have gained significant popularity in bioanalysis, largely due to their either their simplicity or selectivity and sensitivity[57].

#### 3.2.1.1 Colorimetry

By using gold nanoparticle (AuNPs), Marin *et al.* were able to detect the presence of *Staphylococcus aureus*. In that context, if the aptamers are present, that is to say they have not interacted with bacteria, they prevent the AuNPs from aggregating when salt is added[58]. The AuNPs aggregate upon the addition of salt in presence of specific bacteria. In consequence, the colour change only occurs in positive samples and can be observed visually or measured using a spectrophotometer by calculating the $A_{630}/A_{520}$ ratio. The limit of detection was visually about $10^3$ CFU/mL and 1 CFU/mL. The specificity of this test has been done thanks to a linear DNA oligomer as a control[58].

Similarly to Martin *et al.* work, another study used AuNPs to detect *S. aureus*. Indeed, Lim *et al.* grafted aptamers on AuNPs thanks to an adaptor (thiol modified A20 sequence) to allow the binding of these NPs to *S. aureus* bacteria. When the NPs recognize the target bacteria, pellets can be observed visually until $10^6$ CFU/mL[59].

The use of nanomaterials, as elucidated previously, offers a commendable level of sensitivity in bacterial detection and identification. However, the major limitation of colorimetric testing is the lack of sensitivity of this method, as it is difficult to distinguish any signal at low concentrations (visually $10^3$ to $10^6$ CFU/mL). Nevertheless, amplification methods serve to enhance this sensitivity even further, enabling the detection
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of bacterial signals that may be initially faint, yet potent enough to elicit an analysable response. Most of the time, the amplification is made through an enzyme.

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The work from Mojarad *et. al.* is using an ELASA based detection to target *Vibrio cholerae*. Enzyme Linked Apta Sorbent Assay (ELASA) tests are an adaptation of ELISA tests quoted earlier (2.2.3), the key difference is the use of aptamers instead of antibodies. The meaning behind the term ELASA can also be described by the terms ELONA (Enzyme Linked Oligonucleotide Assay), ELAA (Enzyme Linked Aptamer Assay) and ALISA (Aptamer Linked Immobilized Sorbent Assay)[36]. ELASA is an indirect method that requires more than one recognition site. In that context, this sandwich method is particularly pertinent for bacteria, which possess recurrent molecular complexes (Figure 2). This sandwich ELASA, with a nanobody and a biotinylated aptamer, yielded a detection threshold of 10⁴ CFU/mL[60].

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In this case, as well as in other studies employing the ELASA method, biotinylated-aptamers with HRP-streptavidin were often used to obtain a bacterial detection. They permitted to develop platforms to detect different strains like *Shigella sonnei* [61], *Acinetobacter baumanii* [62], *Salmonella enterica* serovar Typhimurium cells[63], *Mycobacterium tuberculosis* [64] or *Vibrio alginolyticus* [65].

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The advantage of this method over ELISA is linked to the benefits of using aptamers rather than antibodies. One of them is the ability to conjugate a larger variety of surfaces and resins. Moreover, whereas ELISAs are single-use tests, the use of aptamers for ELASAs means that the support can be regenerated, due to the possible aptamer denaturation by heat. Indeed, the heat induces the release of antigen, and the lowering of temperature permits the refolding of aptamers in their recognition structure.

### 3.2.1.2 Fluorescence

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Fluorescence is one of the most frequently used signalling technique[4,39,66–69]. This approach offers various benefits. Attaching the fluorophore is a straightforward process, and there is no need to separate the aptamer/target system from unbound aptamers during analysis to achieve results. Simply passing the entire sample containing the aptamer-target complex is adequate to generate a response. Thus, it permits real-time detection[70].

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These aptamer-based assays primarily rely on the interaction between targets and aptamers labelled with fluorescent probes. It uses either fluorescence polarization or fluorescence intensity change as the basis for detection[39]. Two types of fluorescence-based assay exist: turn on or turn off approaches [38]. To enhance the detection specificity and sensitivity, a sandwich structure can be employed, using two aptamers. The first aptamer serves as a capture agent to bind the target cells, while the second aptamer functions as a fluorescent marker.

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In Ramlal *et. al.* work, a dual aptamer-based detection was used. A first aptamer named RAB 10 (apparent K_d 46 ± 24 nmol) is biotinylated to be able to bind with a streptavidin already coated on the microplate. This aptamer captures *Staphylococcus aureus* bacteria, and unbound bacteria are eliminated through buffer washing. A second aptamer, called RAB 35 (apparent K_d 34 ± 5 nmol), labelled with fluorescein isothiocyanate (FITC), identifies the previously trapped bacteria. After recognition, an additional wash is applied to confirm the signal's specificity. The results are subsequently analysed based on the observed fluorescence intensity. Thanks to this strategy, they can reach a sensitivity of 10² CFU/mL of *S. aureus* even in complex matrix like milk. Regarding the assay's specificity, the detection signal for *S. aureus* was 2.7 times higher than that for the other bacteria (Figure 3)[71].

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Fluorescence measurements are commonly employed due to their ease of use. Employing aptamers tagged with a fluorophore, in conjunction with their interaction with bacteria, facilitates the data processing. However, it should be noted that fluorescence measurements can suffer from photobleaching, reducing signal intensity over time, and limiting long-term bacteria detection. The aforementioned shortcomings serve as the motive behind the introduction of novel nanomaterials, which are designed to
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address these drawbacks. Nanomaterials can address the issue of photobleaching in fluorescence assays for bacterial detection in several ways, by enhancing the stability, brightness, and longevity of the fluorescent signal.

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Nanomaterials have generated significant attention due to their great optical and electric properties, making them highly appealing for enhancing the sensitivity and stability of biosensors[72]. Examples of such materials include nanocrystals (NCs), quantum dots (QDs), upconversion nanoparticles (UCNPs), and more. These tools (NCs, QDs, UCNPs...) are exemplified by techniques that facilitate energy transfer, such as FRET which will be discussed in the subsequent section.

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FRET is a process of energy transfer that occurs at very small distances, typically within nanometer-scale separations of up to 10 nm, between two molecules known as the "FRET pair". FRET can be classified as a direct labelling method as it necessitates no pre-treatment before measurement and involves tagging the aptamers to observe if they experience a quenching effect. The binding process in aptamers involves intermolecular and intramolecular FRET interactions between the donor and acceptor molecules, leading to heightened sensitivity. This enhanced sensitivity is attributed to the relatively small size of aptamers (10-50 kDa) compared to full-size antibodies (around 150 kDa)[14].

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### 3.2.1.2.1 Turn on

Ren *et. al*. worked with CdTe (Cadmium Telluride) QDs to capture *Salmonella Typhimurium*. Quantum dots (QDs) are spherical nanocrystals with fluorescent properties, widely employed as fluorescent probes in recent studies [86–92]. QDs offer several advantages over traditional organic fluorescent probes, including a longer lifetime, less photobleaching and the ability to facilitate multi-signal detection.

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The method for detecting and measuring S. Typhimurium relies on a magnetic separation approach that uses aptamer-coated Fe₃O₄ magnetic particles (Apt-MNPs) in conjunction with QD-labelled ssDNA2, which is the complementary strand of the aptamer, all in less than two hours. Apt-MNPs capture S. Typhimurium specifically, and CdTe QD-labelled ssDNA2 acts as a signalling probe. Initially, CdTe QD-labelled ssDNA2 is combined with Apt-MNPs to create the aptamer-ssDNA2 duplex. When S. Typhimurium is introduced, it selectively binds to the DNA aptamer, causing the aptamer-ssDNA2 duplex to break, resulting in the release of CdTe QD-labelled DNA. As a result, after removing Apt-MNPs magnetically, the fluorescence signal increases. They attained a limit of detection of 1 CFU/mL. Specificity studies were made with other foodborne pathogens such as S. aureus, E. coli, L. monocytogenes and others. Results shown that a significant boost in fluorescence intensity is solely triggered by S. Typhimurium[73].

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Jin *et. al* developed a detection platform based on fluorescence resonance energy transfer (FRET) to target *E. coli* (ATCC 8739) using UCNPs. Upconversion nanoparticles (UCNPs) have interesting photoluminescence properties comparable to quantum dots (QDs), though with a much lower brightness [38,39]. They can be excited upon absorption of two photons in the near infra-red and emit at a lower wavelength [93]. UCNPs have been applied in bacterial detection by using antibody and antigen recognition mechanisms. However, it's important to note that antibodies are sensitive to redox conditions and can be affected by changes in environmental factors such as pH and temperature in solution. This sensitivity can result in a shortened shelf life for the UCNPs and necessitate storage at lower temperatures [94]. This is why it would be advantageous to use aptamers that are more robust than antibodies in this case.

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In the system employed by Jin *et. al*, gold nanoparticles (AuNPs, acceptor) are linked with aptamers, while upconversion nanoparticles (UCNPs, donor) are modified with corresponding complementary DNA (cDNA). The spectral overlap between the fluorescence emission of UCNPs and the absorption of AuNPs allows FRET to occur when the targeted aptamer and cDNA hybridize, leading to the quenching of upconversion fluorescence. When target bacteria are present, the aptamers preferentially bind to them, causing the dissociation of UCNPs-cDNA from AuNPs-aptamers. This results in the restoration of upconversion
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fluorescence. They were able to obtain a sensitivity of 3 CFU/mL. Specificity studies were conducted using other bacteria like *S. aureus*, *Bacillus* and other *E. coli*. They noted that significant fluorescence recovery occurred specifically for *E. coli* ATCC 8739 (the target *E. coli*). Furthermore, they conducted tests on real samples like milk, tap water, and pond water, completing them in under 20 minutes, and obtained results that were in line with the plate-counting method [74].

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The use of nanomaterials, on the one hand QDs by Ren et.al. and UCPNs by Jin et.al. show an excellent detection limit by fluorescence detection.

### 3.2.1.2.2 Turn off

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The fluorescence quenching technique involves the observation of fluorescence quenching responses when an interaction occurs between the bacteria and the aptamers. Prior to the introduction of the detection substance, the fluorescence aptamer sensor emits a strong fluorescence signal. Once the target objects bind to the sensor molecule, the fluorescence intensity decreases significantly or is entirely quenched [38].

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This FRET strategy was used by Yu et. al. to detect specifically *S. aureus*. They used vancomycin-functionalised gold nanoclusters and aptamer-modified gold nanoparticles as energy donors and acceptors, respectively. The emission spectrum of Van-AuNCs overlapped with the absorbance spectrum of aptamer-AuNPs, enabling the use of a FRET method. When both elements bind to the bacteria, their proximity leads to signal attenuation. In contrast, in the absence of the target bacteria or in the presence of different bacterial strains, the distance between Van-AuNCs and aptamer-AuNPs is too high, resulting in a fluorescent signal. Thanks to this strategy they achieved a detection limit of 10 CFU/mL and nontarget bacteria demonstrated negative results illustrating a good specificity [75].

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Another example, illustrated by the work of Tao et. al. in 2021 demonstrates the benefits of using nanomaterials to improve the sensitivity of detection systems. They used quantum dots (QDs) elements combined with FRET to lower values of CFUs per millilitre were observed and improved detection limits (< 10 CFU/mL) in the case of the detection of *Staphylococcus aureus*.

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This study demonstrated a LOD of 2 CFU/mL in buffer and 100 CFU/mL in complex fluids such as milk, orange juice, and human serum. This FRET-based sensor uses aptamer-QDs as the energy donor, whose emission spectrum largely overlaps with the absorption spectrum of Teico-AuNPs, serving as the energy acceptor. When the target is present, both aptamers and teicoplanin bind to *S. aureus* simultaneously, bringing the energy donor (aptamer-QDs) and acceptor (Teico-AuNPs) into proximity, leading to the activation of FRET [76].

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However, it may be noted that FRET requires specialised and expensive microscopy equipment, as well as complex and time-consuming data analysis, stand in need for specific skills and software.

### 3.2.1.3 Surface Plasmon Resonance

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Surface Plasmon techniques do not necessitate labels like fluorescent-based methods since aptamers are immobilized on a conductive metal-coated glass surface, often gold. Incident wave passes through high refractive index glass prism, then reflects at metal interface with medium-dependent angle. Medium modification by target binding to aptamer shifts plasmon resonance angle [34,77].

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LSPR (Localized Surface Plasmon Resonance) spectroscopy, a particularly valuable variant of SPR, leverages surface-confined plasmons generated by nanoparticle[1]. These nanoparticles generate a resonance absorption peak, which shifts if the particles interact with the analyte. LSPR simplifies optics in portable plasmonic instruments by directly illuminating metal colloids, eliminating the need for prisms and polarizers. This enhances stability against vibrations and alignment issues, offering higher potential for miniaturization and portability in LSPR sensors [78].
The main challenge, when using SPR and LSPR for bacterial sensing, is due to the difference in scale between the sensing depth and the size of bacteria, making it difficult to probe whole-cell bacteria effectively[1,79]. The advantages of these techniques are the real-time quantification of molecule interactions without radioactive or fluorescent probes, and the ability to reactivate and reuse the sensor surface with a regeneration buffer after target analyte detection[34].

Some studies use SPR method to detect bacteria (*Brucella melitensis* [80], *Salmonella enteritidis* [81], *Legionella pneumophila* [82], *Vibrio fischeri* [83], *Bacillus cereus* [79]) in varied samples (milk[80], food dispersed in PBS buffer[81]) thanks to the recognition by aptamers[80–87]. Saad et. al. developed a SPRi-based titration assay using *L. pneumophila* aptamers. The assay involves direct capture of target cells by aptamers, and the measurement of unbound aptamers through SPRi. To detect free aptamers, they employed complementary sequences (cApt) on the Au-coated sensor chip. The assay's specificity was assessed using *Pseudomonas fluorescens*. At elevated concentrations of bacteria, increased amounts of free aptamer were detected, indicating minimal binding of the aptamer to the bacterium *P. fluorescens*. This approach enables the detection of varying bacteria concentrations down to a limit of 10⁴ CFU/ml, without requiring sample processing or signal amplification steps.

Yoo et al. developed a sensor system using a multispot gold-capped nanoparticle array (MG-NPA) chip, comprising a dielectric layer with a thin gold (Au) layer on silica (Si) nanoparticles (NPs) absorbed on a glass slide. The sensor successfully detected and identified three bacterial species in a multiplexed mode. This system can detect 30 CFU/mL of *L. acidophilus*, *S. Typhimurium*, or *P. aeruginosa* in an assay. They verified the specificity of their method by employing *E. coli* as a negative control for all their aptamers. Additionally, they demonstrated that the aptamer designed for *P. aeruginosa* specifically targeted that bacterium and did not interact with *L. acidophilus* and *S. Typhimurium* bacteria, for instance[88].

Although the detection limit value is not extremely low in Saad et. al. work, it is still encouraging given that the sample is used without any signal processing or amplification [82]. However, the strategy using LSPR in the case of the study carried out by Yoo et. al. improves the detection limit by reaching a value of 30 CFU/mL.

### 3.2.1.4 Surface Enhanced Raman Spectroscopy

Surface Enhanced Raman Scattering (SERS) is also a separation-free and sometimes label-free technique. It is based on the amplification of the Raman response of molecules by plasmonic nanoparticles. This technique can be used both in solution and on surfaces [89].

Indeed, the Raman response due to an incident light, such a laser, is amplified by the interaction with a metal surface (gold, silver, copper) making it a sensitive method since the enhancement factor can reach 14-15 orders which can enable the access to molecular level information to identify carbohydrates, nucleic acids, lipids and amino acids which are building blocks part of pathogens [35,89–95].

The study of Mi et al., developed FPBA-functionalized magnetic nanoparticles to capture pathogenic bacteria using the boric acid affinity principle. It used aptamer-modified Au@AgNPs (bimetallic core shell nanoparticles made of Au and Ag) as a SERS substrate to create a sandwich structure with the bacteria for detection via portable Raman spectrometer. The optimized conditions enabled SERS detection of *Staphylococcus aureus* at 10² to 10⁶ CFU/mL with a detection limit of 34 CFU/mL. *Escherichia coli* showed a linear relationship at 10² to 10⁶ CFU/mL and a detection limit of 18 CFU/mL. The entire detection process using the portable Raman spectrometer suggests its suitability for point-of-care testing (POCT). There is no specificity test mentioned in the publication for the use of other bacteria. However, tests have been carried out on complex samples such as milk, which may contain other bacterial species (Figure 4)[96].

Variability in reproducibility and the absence of standardized protocols may limit in the clinical applications of SERS. Indeed, this technique relies heavily on various factors such as substrate composition, geometry,
analyte adsorption, and laboratory environment. Due to its high sensitivity, even a minor alteration in any of these factors can result in substantial changes in results, undermining reproducibility [35].

### 3.2.2 Electrochemical signalling

These sensors use current, voltage, and resistance signals, ensuring high sensitivity, reproducibility, and swift analysis. They seamlessly integrate into small electronic devices with low power consumption [37]. They are known for their fast response times and affordable detection equipment, have established themselves as the predominant sensor type on the market. These sensors are available in a variety of forms, from top-of-the-range benchtop models to fully integrated, portable devices [97].

In electrochemical sensors based on aptamer recognition, the aptamer is immobilized on the surface of the electrode as capturing ligands; These sensors are categorized based on their method of producing electrical signals [33], which can determine their classification within the transduction system (amperometric, conductimetric, potentiometric, impedimetric and electrogenerated chemiluminescence (ECL)) thanks to a change in the structure of aptamers on the electrode surface that produces an electrochemical signal [39].

#### 3.2.2.1 Potentiometry

Potentiometric devices detect the buildup of an electrical potential between the working electrode and the reference electrode within an electrochemical cell when there is minimal or zero current passing through them. Essentially, potentiometry offers insights into ion activity during an electrochemical reaction [98]. No electric current is applied in this method, the potential or a charge accumulation at the electrode is measured compared to a reference electrode [98]. The active electrode can take the form of a pH, ammonium, or CO₂ electrode [97]. This method offers benefits due to its simplicity, compactness, and low power requirements [99].

The predominant category within potentiometric sensors comprises ion-selective electrodes (ISEs). Additionally, another set of transducers for potentiometric ion and chemical sensors involves field-effect devices, which can be conveniently miniaturized and manufactured using microelectronic technology. Among these devices, a considerable focus is placed on ion-sensitive field-effect transistors (ISFETs) featuring various ion-selective membranes in numerous research studies [100].

Wu *et al.* worked on graphene-based APG-FETs which uses aptamers as sensing probes for electrical detection of *E. coli*. Conformational changes in the aptamer due to *E. coli* binding increase hole carrier density in graphene, generating an electrical signal. They reached a limit of detection of 100 CFU/mL regarding *E. coli* strain. They also tested other bacteria to investigate the specificity of their biosensor. To do that, they have tested the electrical response for *E. coli* 8739, *E. coli* K12 ER2925, *Streptococcus mutans*, and *Staphylococcus saprophyticus*. The overall response was the largest for *E. coli* 8739, but a small proportion of response was also observed for *E. coli* K12 ER 2925. This can be attributed to the expression of a binding site reliably expressed in *E. coli* K12 [101].

Potentiometric biosensors [101–105] can be convenient for detecting bacteria but it can be sensitive to interferences from other substances in the sample like electroactive species, which can lead to falsified or inaccurate results. The Zelada-Guillén group have set up a filtration system before analysing food samples such as semiskimmed milk or apple juice, to eliminate charged elements such as ions that could interfere with the measurement [102].

#### 3.2.2.2 Voltammetry and amperometry

The principle of voltammetry involves observing the system's response to applied potential changes to initiate electrochemical reactions [97]. Two main methods are used to detect bacteria, CV (Cyclic Voltammetry) and DPV (Differential Pulse Voltammetry).
DPV is also known as square wave voltammetry (SWV) and differential multipulse voltammetry (DMP)[106]. Regarded as one of the cutting-edge voltammetric techniques, this method amalgamates the strengths of pulse techniques (resulting in heightened sensitivity), cyclic voltammetry (providing insights into the electrode mechanism), and impedance techniques (yielding kinetic information on exceedingly rapid electrode processes)[107].

Regarding amperometry systems, they quantify current[98] through oxidation-reduction reactions at fixed potentials[108]. Standard amperometry equipment includes a three-electrode cell with a working, reference, and auxiliary electrode, along with tools for measuring current and voltage. The notable benefits of amperometric biosensors lie in their inherent simplicity and ease of miniaturization. Although, they face a critical constraint in their applicability, as they require the analyte of interest to act as a substrate for an enzymatic reaction. This limitation poses a challenge when attempting to broaden the use of such biosensors[43].

These different approaches were used for the detection of *S. aureus* in several studies. CV was used in an aptasensor combining a redox capacitor amplification system with a specific aptamer to enhance its detection capabilities. Aldehyde-modified aptamers were immobilized on the catechol-chitosan film-modified electrode surface, acting as a specific biological recognition element for *S. aureus*. The electrochemical aptasensor demonstrates a short detection time of 25 minutes, a wide linear range of 10 to 10⁸ CFU/mL, and a LOD of 2 CFU/mL in PBS buffer containing 1,1'-ferrocene dimethanol (Fc) and hexaammineruthenium (III) chloride (Ru³⁺). To investigate the selective response of the electrochemical biosensor, the developed electrochemical aptasensor was employed to detect various bacteria including *S. epidermidis*, *K. pneumoniae*, *A. baumannii*, *P. aeruginosa* and others at concentrations of 10⁸ CFU/mL. The findings demonstrated a notably higher peak current change for *S. aureus* (at 10⁵ CFU/mL) compared to the other bacteria, affirming the biosensor's specificity (Figure 5).

Differential pulsed voltammetry (DPV) analysis was used in the aptasensor of Ghalkhani *et al.* to detect *S. aureus*. A nanocomposite (Ag–Cs-Gr QDs/NTiO₂) containing Ag nanoparticles, chitosan, graphene quantum dots, and nitrogen-doped TiO₂ nanoparticles was synthesized. The modified surface of screen-printed carbon electrodes, using this nanocomposite, served as a compatible platform for attaching aptamers. The aptasensor accurately detected *S. aureus* with a low detection limit of 3.3 CFU/mL. They have tested other bacteria on their aptasensor such as *E. coli*, *S. flexneri* and *Salmonella* bacteria which showed a weak and negligible response compared to *S. aureus* [109].

In the study from Nguyen *et al.*, a sandwich-type electrochemical biosensor was developed to detect *S. aureus* contamination. The biosensor used newly developed aptamers (one as a capturing element and the other as a signalling element) that simultaneously bind to the target *S. aureus* on a screen-printed gold electrode (SPGE). The biosensor employed a chronoamperometry and used horseradish peroxidase (HRP) enzyme and 3,3'-5,5'-tetramethylbenzidine (TMB) to generate a signal-on response with the LOD of 39 CFUs in buffer and tap water. They achieved a specificity control using other bacteria on their biosensor (*E. coli*, *L. monocytogenes*, *S. Typhimurium...*) and the obtained signal is about twice lower than the one for *S. aureus* [110].

Comparing these methods highlights the different approaches and sensitivities in electrochemical aptasensors for detecting *S. aureus*. The CV-based aptasensor by the first team boasts the lowest LOD of 2 CFU/mL and a rapid detection time of 25 minutes, with strong specificity against a range of other bacteria. Ghalkhani *et al.'s* DPV-based aptasensor, using a sophisticated nanocomposite, achieved a slightly higher LOD of 3.3 CFU/mL but demonstrated similar specificity.

Meanwhile, Nguyen *et al.'s* sandwich-type biosensor, although having a higher LOD of 39 CFU/mL, leveraged a dual-aptamer system and a signal-on response mechanism to maintain specificity, showing significantly lower signals for non-target bacteria. Each method demonstrates unique strengths, whether
it be in detection speed, sensitivity, or the innovative use of nanomaterials and signal amplification strategies.

### 3.2.2.3 Impedance techniques

Electrochemical impedance spectroscopy (EIS) is a powerful method that offers exceptional sensitivity and does not require destructive or labelled components for sensing. Impedimetric sensors measure impedance (both resistance and reactance) and field-effect, which uses transistor technology to measure current because of a potentiometric effect at a gate electrode [98].

To enhance impedance measurement, akin to voltammetry, aptamers are often immobilized onto electrode surfaces for direct target capture during detection [41].

This method frequently combines EIS with voltammetric-based approaches like cyclic voltammetry (CV)[109,111,112], differential pulse voltammetry (DPV), and square wave voltammetry (SWV). Employing a technique based on voltammetric peak potentials presents a potential solution to enhance the reproducibility of EIS measurements [4].

A rapid and label-free biosensor to detect *Listeria monocytogenes* was produced by Oliveira *et al*. They functionalized CHI/Pt (platinum and chitosan) brushes with *L. monocytogenes*-specific aptamers. These aptasensors work well at concentrations up to 10⁶ CFU/mL without needing preconcentration or extra reagents. The biosensor achieves improved *L. monocytogenes* detection with a low limit of detection (33 CFU/10 mL in chicken broth). The assessment of selectivity involved testing with additional Gram-positive bacteria (specifically *S. aureus*) and exposure to a food product (chicken broth). The bacterial capture time is around 15 min in addition with a sensing time of 2 min [113].

Impedimetric biosensors are a promising choice for detecting whole-cell bacteria due to their cost-effectiveness, high sensitivity, and resistance to interference from other analytes or coloured compounds. They are easily miniaturized for point-of-care applications. However, they have disadvantages such as variable reproducibility, high limits of detection, and issues with nonspecific binding [43].

### 3.2.2.4 Electrochemiluminescence (ECL)

Electrochemiluminescence (ECL) is a subtype of chemiluminescence. In recent times, it has gained significant interest as a potent analytical technique due to its versatility, minimal background noise, broad dynamic detection range, and precise control. In the ECL process, high-energy electron transfer generates coloured species at the electrode surface, leading to the formation of excited states that subsequently emit light.

A biosensor using *in situ* quenching ECL and aptamer recognition-induced multi-DNA release was designed by Liu *et al*. for pathogenic bacterial detection. The sensor provides an identification of *Staphylococcus aureus* (*S. aureus*) and achieved one-to-multiple amplification by releasing multiple signal transduction DNAs upon detection. Glucose oxidase (GOD) was introduced, leading to H₂O₂ production through catalysis of glucose. This allowed quantification of *S. aureus* with a linear range from 10 to 10⁷ CFU/mL and a limit of detection of 3 CFU/mL. *S. aureus* was compared with the detection capabilities of other pathogenic bacteria (*E. coli*, *B. subtilis*, and *S. mutans*) at an identical concentration of 10⁶ CFU/mL.

Through a comparison of ECL responses, it was evident that only *S. aureus* triggered the release of multiple DNA strands and facilitated the introduction of GOD onto the electrode surface. This process led to a notable reduction in ECL intensity. Conversely, all bacterial controls exhibited high ECL levels akin to those of the blank PBS solution. These findings demonstrate the specificity of the developed ECL method for detecting *S. aureus* (Figure 6)[114].
### 3.2.3 Quartz Crystal Microbalance (QCM) signalling

The QCM (Quartz Crystal Microbalance) method is an extremely sensitive (nanogram scale) technique used for real-time, in situ measurement of mass variation on the surface of a quartz crystal. AT-cut quartz crystals, precisely tuned to various frequencies, exploit their piezoelectric properties to convert deposited material mass into resonant frequencies. These devices facilitate swift and label-free detection of target molecules, promoting specific or non-specific binding on the resonator surface[33]. The advantage of using a gravimetric transducer is that it can offer a higher flexibility concerning sensing layers and coatings which are not going to interfere with the transduction principle unlike electrochemical and optical biosensors. The signal obtained by a change in mass on the sensor makes it universally[115]. The integration of aptamers with QCM as a transducer is highly valuable. The compact size of aptamers provides a distinct advantage for mass-based sensing devices and other transducer applications[33].

QCM-based methods have been developed for the detection of *Salmonella* strains. Ozalp *et al.* used a combination of aptamer-immobilized magnetic beads to first separate *Salmonella* from the sample, in this case milk samples, and then use the QCM to obtain a quantitative answer. They obtained a high sensitivity and a desirable selectivity in a range from 100 to 4 x 10⁴ CFU/mL with a limit of detection of 100 CFU/mL. The aptamer-sensor demonstrated specificity when compared to other bacteria, including gram-negative strains like *E. coli* cells were substituted for *Salmonella* cells in the assay at a concentration of 10⁴ CFU/mL. The findings indicated that the sensor could differentiate the detection of Salmonella at 10⁴ CFU/mL from the presence of other bacteria at an equivalent concentration (Figure 7)[116].

Unlike the approach involving magnetic nanoparticles, Wang *et al.* do not rely on nanoparticles to differentiate bacteria from other species. Instead, they use the modified surface of the QCM support to bind the specifically selected aptamer with *Salmonella Typhimurium*. Indeed, the selection process of the aptamer sequence was elaborated as an efficient QCM-based SELEX. Thanks to their method, they can obtain a limit of detection of 10³ CFU/mL of S. Typhimurium in real sample without a pre-enrichment in 15 to 20 min. However, a specificity analysis revealed that the B5 aptamer exhibited the highest affinity for S. Typhimurium while also demonstrating some affinity for *L. monocytogenes*. This phenomenon might be attributed to the B5 aptamer's interaction with shared targets present in both S. Typhimurium and *L. monocytogenes* [117].

Both methods harness the power of QCM and aptamers for the detection of *Salmonella*, yet they differ in their strategies and sensitivity. Ozalp *et al.*'s use magnetic beads to facilitate the separation of *Salmonella* from complex samples, resulting in higher sensitivity with a LOD as low as 100 CFU/mL. On the other hand, Wang *et al.*'s approach eliminates the need for nanoparticles by modifying the QCM surface, achieving a slightly higher limit of detection at 10³ CFU/mL but offering a faster detection time. While Ozalp *et al.* showed high specificity against other bacteria, Wang *et al.* encountered some cross-reactivity with *L. monocytogenes*, highlighting a trade-off between detection speed and specificity.

Numerous studies utilize QCM aptamer-based biosensors for detecting and quantifying bacteria in samples [33,116-122]. However, the inherent limitation of QCM biosensors lies in their inability to simultaneously detect multiple bacterial species, which hinders their suitability for applications necessitating the identification of a broad range of microorganisms.

## 4 Conclusion and prospects

To summarise, this study has listed 88 aptamer sequences identified by whole-cell bacteria SELEX, targeting 21 bacterial genera. The literature provides a wide array of sequences. The advancement in creating novel analysis systems through aptamers has facilitated the introduction of a variety of products designed to detect toxins released by bacteria, among other applications. This constitutes a promising
575

stride forward in the realm of developing aptamer-based testing systems for the identification of entire bacterial entities[9].

580

Aptamers serve as versatile tools across various fields, including healthcare, agri-food, and environmental protection, facilitating the development of tests for applications such as contaminant detection in water[123]. This review has principally concentrated on the detection and identification of bacteria using aptamers. However, it is essential to highlight their significant potential for broader applications. Aptamers can be employed to detect viruses[124], pesticides[125], contaminants and small molecules in foodstuffs[126,127], showcasing their adaptability across diverse sectors.

585

Indeed, the selection of specific aptamers against bacteria is worthy for biosensor development and targeted therapies. Opting for direct selection on intact bacteria, rather than lysates, offers notable advantages. This method capitalizes on a physiological environment akin to *in vivo* conditions, enhancing aptamer-target recognition. By circumventing pre-treatment steps, it streamlines the selection process, conserving time, and resources. This approach mitigates risks associated with target denaturation or modification, preserving native conformation. In summary, selecting aptamers against whole-cell bacteria presents an efficient strategy, ensuring dependable and expeditious biosensor development.

590

As highlighted in this review, a plethora of studies have been conducted to advance biosensor technologies for detecting and identifying pathogenic bacteria thanks to aptamers. Optical methods for bacterial detection seem to be preferred due to their more widespread representation over electrochemical or mass-based methods for several reasons. They offer greater ease of use for operators, with direct readings and no need for complex sample pre-treatment or device handling. Additionally, optical equipment like plate readers is more widely available, facilitating integration into existing protocols. Moreover, optical methods can be adapted to various applications like fluorescence, surface plasmon resonance (SPR), or spectroscopy, providing flexibility to meet diverse detection needs. It is also crucial to emphasize that across various analytical methods, including those employing optical, electrochemical, or mass-based transduction techniques, substantial efforts are dedicated to refining aptamer-based biosensors.

595

600

In the future, employing aptamers in diagnosis could further advance personalized precision medicine, particularly with Point-Of-Care Testing (POCT). POCT, which involves conducting diagnostic tests near the patient for rapid results, plays a crucial role in detecting pathogenic bacteria, reducing complications, and improving clinical outcomes. Aptamers, utilized in various POCT-based diagnostics, offer shorter analysis timeframes compared to conventional methods.

605

Furthermore, efforts are being made to simplify and integrate sample pretreatment directly into detection systems, such as microfluidic devices. Establishing an automated detection process is expected to be more appealing and promising. The development of compact, portable testing equipment and diagnostic kits would enable rapid on-site detection, offering significant application potential[4].

610

However, even if aptamers are constantly enhanced, they could not yet substitute interactions with antibodies[4]. Indeed, aptamers are selected in the laboratory under specific conditions, including various parameters such as pH, temperature, and cation concentration, with the aim of achieving the best possible affinity with the target, typically a bacterium. Unfortunately, these conditions are often inadequate as they represent simplified environments that are far from reality. Most studies validate their findings using simulated real samples with added target analytes. This means that most aptamers already developed today may be inapplicable or do not necessarily perform well under conditions different from those used during their selection. The complexity of the matrix must be carefully considered when developing aptasensors, otherwise this will not serve as effective tools for microorganism diagnosis.

615
**CRediT authorship contribution statement**

620 Juliette Nourry: Writing – original draft, Writing – review & editing. Pauline Chevalier: Writing – original draft, Writing – review & editing. Emmanuelle Laurenceau: Writing – review. Xavier Cattoen: Writing – review. Xavier Bertrand: Writing – review. Basile Peres: Writing – review. Farid Oukacine: Project administration, Supervision, Funding acquisition, Writing – review & editing. Eric Peyrin: Project administration, Supervision, Funding acquisition, Writing – review & editing. Luc Choisnard: Project administration, Supervision, Funding acquisition, Writing – review & editing.

625

**Declaration of competing interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

630

**Data availability**

No data was used for the research described in the article.

**Acknowledgments**

635

We acknowledge the support of ANR program CE09 - Nanomatériaux et nanotechnologies pour les produits du futur (ANR-20-CE09-0017) and Labex Arcane and CBH-EUR-GS (ANR-17-EURE-0003).
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1230

<table><caption>**Table 1: Comparative table between aptamers and antibodies (based on [15,128])**</caption><thead><tr><th>Properties</th><th>Aptamers</th><th>Antibodies</th></tr></thead><tbody><tr><td>Size</td><td>5-25 kDa</td><td>150-170 kDa</td></tr><tr><td>Target</td><td>Most targets</td><td>Only antigenic targets</td></tr><tr><td>Stability</td><td>Stable under various conditions</td><td>Sensitive to pH, temperature, pressure and denaturation</td></tr><tr><td>Cost of the selection</td><td>$4,000 for individual aptamer sequences</td><td>$8,000 for mouse monoclonal antibody<br>$20,000 for rabbit monoclonal antibody</td></tr><tr><td>Time for selection</td><td>Several weeks</td><td>Several months</td></tr><tr><td>Synthesis and manufacturing</td><td>Chemical synthesis in vitro</td><td>Animal-derived production or recombinant methods</td></tr><tr><td>Inter-batch variability</td><td>Lower</td><td>Higher</td></tr><tr><td>Possibility of modification</td><td>Easily modified</td><td>Limited</td></tr><tr><td>Ethical concerns</td><td>None (chemical synthesis)</td><td>Challenging (highly dependent of living animals)</td></tr></tbody></table>
<table><caption>Table 2: Commercial methods comparison: overview of advantages and drawbacks</caption><thead><tr><th></th><th>Microbial culturing</th><th>API Galleries</th><th>Chromogenic & fluorogenic media</th><th>MALDI-TOF mass spectrometry</th></tr></thead><tbody><tr><th>Culture-based</th><td></td><td></td><td></td><td></td></tr><tr><th>Advantages</th><td>High sensitivity & specifity</td><td>Living organisms' detection, inter-laboratory reproducibility</td><td>Differentiation of bacterial pathogens from a sample containing heterogeneous populations</td><td>Highly sensitive technique</td></tr><tr><th>Drawbacks</th><td>Delayed results (24h to several days)</td><td>Require prior time-consuming bacterial culture</td><td>Interactions with non-pathogenic bacteria yielding false positives or lack of specifity</td><td>Limited by the content of the database</td></tr><tr><th>Non-culture-based</th><td>Latex-based and slide agglutination</td><td>Immunofluorescence assay</td><td>ELISA (Enzyme-linked immunosorbent assay)</td><td>LFIAs (Lateral Flow Immuno Assays)</td></tr><tr><th>Advantages</th><td>Simple, versatile & safe</td><td>Direct and easy visualization of bacteria</td><td>High sensitivity</td><td>Swift and on-site identification of pathogens</td></tr><tr><th>Drawbacks</th><td>Assay specificity varies with design and antibody/antigen quality</td><td>Expensive equipment and reagents (fluorescently labelled antibodies)</td><td>Prolonged enzyme activity causes color overdevelopment, leading to false positives.</td><td>Interfering molecules in food samples impair immunodiagnostic accuracy</td></tr></tbody></table>
<table><caption>**Table 2: List of studies reporting the selection of aptamers targeting a pathogenic bacterium using a Cell-SELEX method. (-/+):** (Gram negative/Gram positive). Surface Plasmon Resonance (SPR); Aptamer-modified Graphene Field-Effect Transistors (APG-FETs); quantitative Polymerase Chain Reaction (qPCR); Surface Enhanced Raman Spectroscopy (SERS); Single-Walled carbon Nanotubes Field-Effect Transistors (SWNT-FETs); Differential Pulse Voltammetry (DPV); Enzyme-Linked Immunosorbent Assay (ELISA); Quartz Crystal Microbalance (QCM); quantitative Real Time Polymerase Chain Reaction (qRT-PCR); Aptamer-Linked Immobilized Sorbent Assay (ALISA); Enzyme Linked Aptasorbent Assay (ELASA); Strand Displacement Amplification (SDA).</caption><thead><tr><th>Ref</th><th>Targeted bacteria (-/+)</th><th>Binding/Incubation/Selection buffer</th><th>Characterisation method</th><th>K<sub>d</sub></th></tr></thead><tbody><tr><td>[11]</td><td>Acinetobacter baumanii (-)</td><td>PBS 1X, 1 mM MgCl<sub>2</sub>, 0.2% BSA, 0.05% Tween 20</td><td>Flow cytometry</td><td>107.6 ± 67.8 pM</td></tr><tr><td>[129]</td><td>Acinetobacter baumanii (-)</td><td>1X PBS, 0.9% NaCl</td><td>Flow cytometry</td><td>10.30 nM</td></tr><tr><td>[62]</td><td>Acinetobacter baumanii (-)</td><td>PBS 1X, 1% BSA, 0.02% Tween 20</td><td>Flow cytometry</td><td>110 nM</td></tr><tr><td>[122]</td><td>Brucella melitensis (-)</td><td>0.1 mM of NaCl in phosphate buffer (10 mM, pH 7.4)</td><td>/</td><td>Not mentioned</td></tr><tr><td>[80]</td><td>Brucella melitensis (-)</td><td>PBS</td><td>SPR</td><td>Not mentioned</td></tr><tr><td>[130]</td><td>Campylobacter jejuni</td><td>1X Dulbecco's PBS (pH 7.1) with MgCl<sub>2</sub> and CaCl<sub>2</sub></td><td>Flow cytometry</td><td>9.04 ± 2.08 nM</td></tr><tr><td>[131]</td><td>Cronobacter sakazakii (-)</td><td>50 mM Tris-HCl, 5 mM MgCl<sub>2</sub>, 150 mM NaCl</td><td>UV spectrophotometry</td><td>110 nM</td></tr><tr><td>[57]</td><td>Escherichia coli (ATCC 11775) (-)</td><td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 100 mM NaCl, 5 mM KCl</td><td>Flow cytometry</td><td>15 to 106 nM</td></tr><tr><td>[132]</td><td>Escherichia coli (ATCC 8739) Crooks strain (-)</td><td>PBS</td><td>APG-FETs</td><td>Not mentioned</td></tr><tr><td>[133]</td><td>Escherichia coli (ATCC 25922) (-)</td><td>Selection buffer: PBS, 1.4 mM MgCl<sub>2</sub>, 0.05% BSA<br/>Binding buffer: selection buffer + 0.05% BSA</td><td>qPCR</td><td>11.97 to 161 nM</td></tr><tr><td>[74]</td><td>Escherichia coli (ATCC 8739) Crooks strain (-)</td><td>PBS</td><td>Fluorescence spectroscopy</td><td>Not mentioned</td></tr><tr><td>[134]</td><td>Escherichia coli (KCTC 2571) (-)</td><td>1X PBS, 1% BSA, 0.05% Tween 20 [v/v], 0.1 mg/ml salmon sperm DNA<br/>Washing buffer: 1X PBS, 0.05% Tween 20</td><td>Fluorescence spectroscopy</td><td>12.4 to 25.2 nM</td></tr><tr><td>[96]</td><td>Escherichia coli (336902 BNCC) (-)</td><td>PBS</td><td>SERS</td><td>Not mentioned</td></tr><tr><td>[135]</td><td>Escherichia coli (CMCC 44102) (-)</td><td>PBS</td><td>Fluorescence spectroscopy</td><td>133.87 to 199.44 nM</td></tr><tr><td>[136]</td><td>Escherichia coli DH5α (-)</td><td>SELEX buffer</td><td>SWNT-FETs</td><td>Not mentioned</td></tr><tr><td>[23]</td><td>Escherichia coli O111 (-)</td><td>20 mM Tris-HCl, 5 mM MgCl<sub>2</sub>, 0.10 M NaCl, 0.005% Tween 20 pH 7.4</td><td>DPV</td><td>Not mentioned</td></tr><tr><td>[70]</td><td>Escherichia coli K88 (-)</td><td>50 mM Tris-HCl (pH = 7.4), 1 mM MgCl<sub>2</sub>, 100 mM NaCl, 5 mM KCl, 0.1% Na<sub>3</sub></td><td>ELISA</td><td>15 ± 4 nM</td></tr><tr><td>[137]</td><td>Escherichia coli NSM59 (-)</td><td>10 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 150 mM NaCl, 5 mM KCl, 1 mM BSA, 1 mM tRNA derived from E. coli MRE600<br/>50 mM Tris-HCl, 1 mM MgCl<sub>2</sub>, 100 mM NaCl, 5 mM KCl</td><td>Blotting assay</td><td>110 nM</td></tr><tr><td>[138]</td><td>Escherichia coli O157:H7 (-)</td><td>Washing buffer: 1L PBS 1X, 4.5gr Glucose, 5 ml MgCl<sub>2</sub> 1M Elution in distilled water</td><td>Flow cytometry</td><td>107.6 ± 67.8 pM</td></tr></tbody></table>
<table>
  <tr>
    <td>[121]</td>
    <td>Escherichia coli O157:H7 (-)</td>
    <td>50 mM Tris-HCl (pH 7.5), 5 mM MgCl<sub>2</sub>, 25 mM NaCl PBS 1X, 1.5 mM MgCl<sub>2</sub>, 0.05% Tween 20</td>
    <td>QCM</td>
    <td>10.3 nM</td>
  </tr>
  <tr>
    <td>[139]</td>
    <td>Escherichia coli O157:H7 (-)</td>
    <td>Blocking buffer: Binding buffer, 5% BSA/10 mg yeast tRNA</td>
    <td>qRT-PCR</td>
    <td>110 nM</td>
  </tr>
  <tr>
    <td>[140]</td>
    <td>Escherichia coli O157:H7 (-)</td>
    <td>PBS</td>
    <td>/</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[141]</td>
    <td>Escherichia coli O157:H7 (-)</td>
    <td>In silico studies</td>
    <td>In silico studies</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[142]</td>
    <td>Escherichia coli O157:H7 (-)</td>
    <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 5 mM KCl, 100 mM NaCl Selection buffer: 1X binding buffer + excessive yeast tRNA and 1 mg/mL BSA</td>
    <td>Flow cytometry</td>
    <td>9.04 ± 2.80 nM</td>
  </tr>
  <tr>
    <td>[143]</td>
    <td>Fluorobacter bozemanae (-)</td>
    <td>50 mM Tris (pH = 7.5), 2 mM MgCl<sub>2</sub>, 150 mM NaCl</td>
    <td>Fluorescence spectroscopy</td>
    <td>9.516 to 234.4 nM</td>
  </tr>
  <tr>
    <td>[144]</td>
    <td>Haemophilus influenzae type b (-)</td>
    <td>1X PBS, 1% BSA, 0.05% Tween 20</td>
    <td>Flow cytometry</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[145]</td>
    <td>Helicobacter pilori (-)</td>
    <td>50 mM Tris-HCl (pH 7.5), 1 mM MgCl<sub>2</sub>, 100 mM NaCl, 5 mM KCl</td>
    <td>Fluorescence spectroscopy</td>
    <td>19.3 ± 3.2 nM</td>
  </tr>
  <tr>
    <td>[146]</td>
    <td>Klebsiella pneumoniae (-)</td>
    <td>PBS, 1.4 mM MgCl<sub>2</sub>, 0.05% BSA</td>
    <td>qPCR</td>
    <td>195.90 to 256.10 nM</td>
  </tr>
  <tr>
    <td>[147]</td>
    <td>Legionella pneumophila (-)</td>
    <td>PBS, 1% BSA, 0.05% Tween 20, 0.1 mg/ml salmon sperm DNA</td>
    <td>Flow cytometry</td>
    <td>116 to 135 nM</td>
  </tr>
  <tr>
    <td>[148]</td>
    <td>Pseudomonas aeruginosa (-)</td>
    <td>PBS, 0.1% BSA, 0.05% Tween 20</td>
    <td>Flow cytometry</td>
    <td>17.27 ± 5 nM</td>
  </tr>
  <tr>
    <td>[149]</td>
    <td>Salmonella enteric serovar typhimurium (-)</td>
    <td>10 mM PBS (pH 7.4), 0.01% sodium dodecyl sulfate</td>
    <td>UV spectrophotometry</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[150]</td>
    <td>Salmonella enteritidis (-)</td>
    <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 5 mM KCl, 100 mM NaCl</td>
    <td>Fluorescence spectroscopy</td>
    <td>43.81 nM</td>
  </tr>
  <tr>
    <td>[151]</td>
    <td>Salmonella enteritidis (-)</td>
    <td>PBS</td>
    <td>Fluorescence spectroscopy</td>
    <td>6.3 nM</td>
  </tr>
  <tr>
    <td>[152]</td>
    <td>Salmonella O8 (-)</td>
    <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 5 mM KCl, 100 mM NaCl Tris HCl (pH 7.4), 5 mM MgCl<sub>2</sub>, 100 mM NaCl, 2 mM KCl, 2 mM CaCl<sub>2</sub>, 0.1% BSA</td>
    <td>ALISA</td>
    <td>32.04 nM</td>
  </tr>
  <tr>
    <td>[153]</td>
    <td>Salmonella paratyphi A (-)</td>
    <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 100 mM NaCl, 5 mM KCl, and 0.1% NaN<sub>3</sub></td>
    <td>ELISA</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[154]</td>
    <td>Salmonella paratyphi A (-)</td>
    <td>Dulbecco's phosphate buffered saline / 20 mM Tris-CLO<sub>4</sub> buffer (pH 8.6), 2.5 mM K<sub>4</sub>Fe (CN)<sub>6</sub> and 2.5 mM K<sub>3</sub>Fe(CN)<sub>6</sub></td>
    <td>Fluorescence spectroscopy</td>
    <td>47 ± 3 nM</td>
  </tr>
  <tr>
    <td>[112]</td>
    <td>Salmonella typhimurium (-)</td>
    <td>Binding buffer with 0.05% BSA and 5-fold molar excess of yeast tRNA</td>
    <td>Flow cytometry</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[63]</td>
    <td>Salmonella typhimurium (-)</td>
    <td>Binding buffer: 50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 5 mM KCl, 100 mM NaCl</td>
    <td>Fluorescence spectroscopy</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[155]</td>
    <td>Salmonella typhimurium (-)</td>
    <td>Binding buffer: Binding buffer + 0.05% BSA</td>
    <td>Flow cytometry</td>
    <td>6.33 ± 0.58 nM</td>
  </tr>
  <tr>
    <td>[117]</td>
    <td>Salmonella typhimurium (-)</td>
    <td>50 mM Tris-HCl (pH 7.5), 5 mM MgCl<sub>2</sub>, 25 mM NaCl</td>
    <td>QCM</td>
    <td>58.5 nM</td>
  </tr>
  <tr>
    <td>[156]</td>
    <td>Salmonella typhimurium (-)</td>
    <td>PBS</td>
    <td>Fluorescence spectroscopy</td>
    <td>Not mentioned</td>
  </tr>
</table>
<table>
  <tbody>
    <tr>
      <td>[157]</td>
      <td>Salmonella typhimurium (-)</td>
      <td>PBS + 0.025% Tween 20</td>
      <td>Flow cytometry</td>
      <td>1.73 ± 0.54 μM</td>
    </tr>
    <tr>
      <td>[158]</td>
      <td>Salmonella typhimurium &amp; Salmonella enterica (-)</td>
      <td>Dulbecco phosphate buffer saline (DPBS)</td>
      <td>Flow cytometry</td>
      <td>7 to 25 nM</td>
    </tr>
    <tr>
      <td>[159]</td>
      <td>Salmonella typhimurium &amp; Salmonella enteridis (-)</td>
      <td>Tris 30 mM, NaCl 200 mM</td>
      <td>Microplate fluorescence</td>
      <td>0,53 to 4,66 μM</td>
    </tr>
    <tr>
      <td>[160]</td>
      <td>Salmonella typhimurium (CMCC50115) (-)</td>
      <td>Phosphate buffer solution (20 mM, pH 7.4)</td>
      <td>SELEX</td>
      <td>30 ± 4 nM</td>
    </tr>
    <tr>
      <td>[161]</td>
      <td>Shigella dysenteriae (-)</td>
      <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 5 mM KCl, 100 mM NaCl</td>
      <td>Fluorescence spectroscopy</td>
      <td>23.47 ± 2.48 nM</td>
    </tr>
    <tr>
      <td>[162]</td>
      <td>Shigella flexneri (-)</td>
      <td>/</td>
      <td>Flow cytometry</td>
      <td>144 to 329 nM</td>
    </tr>
    <tr>
      <td>[163]</td>
      <td>Shigella flexneri (-)</td>
      <td>10 mM Tris-HCl, 10 mM MgCl<sub>2</sub>, 0.1 mg/mL BSA</td>
      <td>Fluorescence spectroscopy</td>
      <td>29 ± 4 nM</td>
    </tr>
    <tr>
      <td>[61]</td>
      <td>Shigella sonnei (-)</td>
      <td>25 mM Tris-HCl, 50 mM KCl, 200 mM NaCl, 0.2 mM EDTA, 5% (v/v) glycerol, and 0.5 mM DTT</td>
      <td>ELASA</td>
      <td>Not mentioned</td>
    </tr>
    <tr>
      <td>[87]</td>
      <td>Shigella sonnei (-)</td>
      <td>10 mM Tris-HCl (pH 8.0), 0.85% NaCl</td>
      <td>RT-PCR</td>
      <td>15.89 to 39.32 nM</td>
    </tr>
    <tr>
      <td>[164]</td>
      <td>Vibrio alginolyticus (-)</td>
      <td>50 mM Tris HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 0.1 M NaCl, 5 mM KCl</td>
      <td>asymmetric PCR</td>
      <td>12.82 to 46,89 nM</td>
    </tr>
    <tr>
      <td>[165]</td>
      <td>Vibrio alginolyticus (-)</td>
      <td>10 mM Na<sub>2</sub>HPO<sub>4</sub>·12H<sub>2</sub>O, 2 mM KH<sub>2</sub>PO<sub>4</sub>, 137 mM NaCl, 0.1 mg/ml yeast tRNA, 1.0 g/L BSA</td>
      <td>Flow cytometry</td>
      <td>14.31 to 90,00 nM</td>
    </tr>
    <tr>
      <td>[166]</td>
      <td>Vibrio alginolyticus (-)</td>
      <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 0.1 M NaCl, 5 mM KCl</td>
      <td>UV spectrophotometry</td>
      <td>Not mentioned</td>
    </tr>
    <tr>
      <td>[60]</td>
      <td>Vibrio cholerae O1 (-)</td>
      <td>PBS 1X, 0.02% Tween 20, 1% BSA</td>
      <td>Flow cytometry</td>
      <td>15,404 to 20,186 pM</td>
    </tr>
    <tr>
      <td>[83]</td>
      <td>Vibrio fischeri (-)</td>
      <td>1X nutrient broth, 3% NaCl</td>
      <td>SPR</td>
      <td>0,128 to 1,25 nM</td>
    </tr>
    <tr>
      <td>[167]</td>
      <td>Vibrio harveyi (Vh) &amp; Vibrio alginolyticus (Va) (-)</td>
      <td>6.06 g of Tris-HCl, and 2.033 g of MgCl<sub>2</sub>·6H<sub>2</sub>O, 5.844 g of NaCl, 3.725 g of KCl</td>
      <td>UV spectrophotometry</td>
      <td>33.97 to 55,76 nM (Vh), 43.95 to 82.88 nM (Va)</td>
    </tr>
    <tr>
      <td>[168]</td>
      <td>Vibrio parahemolyticus (-)</td>
      <td>10 mM Na<sub>2</sub>HPO<sub>4</sub>, 2 mM KH<sub>2</sub>PO<sub>4</sub>, 137 mM NaCl, 2.7 mM KCl, 4 mg/L BSA, 0.05 Tween 20% [v/v]</td>
      <td>Flow cytometry</td>
      <td>10,3 ± 2,5 nM</td>
    </tr>
    <tr>
      <td>[169]</td>
      <td>Vibrio parahemolyticus (-)</td>
      <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 5 mM KCl, 100 mM NaCl</td>
      <td>Flow cytometry</td>
      <td>16,88 ± 1,92 nM</td>
    </tr>
    <tr>
      <td>[170]</td>
      <td>Vibrio parahemolyticus (-)</td>
      <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 100 mM NaCl, 5 mM KCl</td>
      <td>SDA assay</td>
      <td>Not mentioned</td>
    </tr>
    <tr>
      <td>[85]</td>
      <td>Vibrio parahemolyticus (-)</td>
      <td>10 mM Tris-HCl (pH 8.0), 0.85% NaCl</td>
      <td>SPR</td>
      <td>2,04 nM to 8,22 μM</td>
    </tr>
    <tr>
      <td>[171]</td>
      <td>Vibrio parahemolyticus (-)</td>
      <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 5 mM KCl, 100 mM NaCl</td>
      <td>FRET</td>
      <td>Not mentioned</td>
    </tr>
    <tr>
      <td>[172]</td>
      <td>Vibrio vulnificus (-)</td>
      <td>50 mM Tris-HCl (pH 7.5), 1 mM MgCl<sub>2</sub>, 100 mM NaCl, 5 mM KCl, 50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 5 mM KCl, 100 mM NaCl</td>
      <td>Fluorescence microscopy</td>
      <td>26,8 ± 5,3 nM</td>
    </tr>
    <tr>
      <td>[173]</td>
      <td>Yersinia enterocolitica (-)</td>
      <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 5 mM KCl, 100 mM NaCl</td>
      <td>Flow cytometry</td>
      <td>37.93 to 74.96 nM</td>
    </tr>
    <tr>
      <td>[174]</td>
      <td>Bacillus carboniphilus (+)</td>
      <td>1X PBS, 50 mM MgCl<sub>2</sub>, 0.1% BSA, 0.01% tRNA, 0.45% glucose</td>
      <td>Fluorescence spectroscopy</td>
      <td>13.2 to 26.3 nM</td>
    </tr>
    <tr>
      <td>[175]</td>
      <td>Bacillus cereus (+)</td>
      <td>50 mM Tris-HCl, 1 mM MgCl<sub>2</sub>, 5 mM KCl, 100 mM NaCl</td>
      <td>Flow cytometry</td>
      <td>16,13 to 20,67 nM</td>
    </tr>
    <tr>
      <td>[176]</td>
      <td>Lactobacillus acidophilus (+)</td>
      <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl<sub>2</sub>, 5 mM KCl, 100 mM NaCl, 0.05% BSA</td>
      <td>Flow cytometry</td>
      <td>13 ± 3 nM</td>
    </tr>
  </tbody>
</table>
<table>
  <tr>
    <td>[177]</td>
    <td>Listeria monocytogenes (+)</td>
    <td>Binding buffer: 50 mM Tris-HCl (pH 7.4), 1 mM MgCl2, 5 mM KCl, 100 mM NaCl<br/>Incubation buffer: Binding buffer + tRNA and BSA</td>
    <td>Fluorescence spectroscopy</td>
    <td>48,74 ± 3.11 nM</td>
  </tr>
  <tr>
    <td>[178]</td>
    <td>Listeria monocytogenes (+)</td>
    <td>Tris-buffered saline (TBS; Tris base 10 mmol/L, NaCl 0,85%, pH 8.0)</td>
    <td>Fluorescence microscopy</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[179]</td>
    <td>Listeria monocytogenes (+)</td>
    <td>PBS</td>
    <td>Flow cytometry</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[86]</td>
    <td>Listeria monocytogenes (+)</td>
    <td>Nutrient broth</td>
    <td>SPR</td>
    <td>2,01 to 156 pM</td>
  </tr>
  <tr>
    <td>[180]</td>
    <td>Listeria monocytogenes (+)</td>
    <td>40 mM Tris-HCl (pH 7.5), 2 mM MgCl2, 5 mM KCl, 1 M NaCl</td>
    <td>ALISA</td>
    <td>60,01 nM</td>
  </tr>
  <tr>
    <td>[181]</td>
    <td>Listeria spp. (+)</td>
    <td>PBS 1X, 0.05% Tween 20</td>
    <td>Flow cytometry</td>
    <td>35,7 ± 8,02 µM</td>
  </tr>
  <tr>
    <td>[182]</td>
    <td>Mycobacterium tuberculosis (+)</td>
    <td>PBS</td>
    <td>qPCR</td>
    <td>5,09 ± 1,43 nM</td>
  </tr>
  <tr>
    <td>[64]</td>
    <td>Mycobacterium tuberculosis (+)</td>
    <td>20 mM HEPES (pH 7.35), 1 mM MgCl2, 120 mM NaCl, 5 mM KCl, 1 mM CaCl2</td>
    <td>ELISA</td>
    <td>12,02 nM</td>
  </tr>
  <tr>
    <td>[183]</td>
    <td>Staphylococcus aureus (ATCC 26003) (+)</td>
    <td>PBS (pH 8.0)</td>
    <td>Fluorescence spectroscopy</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[184]</td>
    <td>Staphylococcus aureus (ATCC 6538DR) (+)</td>
    <td>40 mM HEPES (pH 8.0), 2 mM MgCl2, 5 mM KCl, 1 mM CaCl2</td>
    <td>qPCR</td>
    <td>3,03 to 9,9 nM</td>
  </tr>
  <tr>
    <td>[185]</td>
    <td>Staphylococcus aureus (ATCC 8325-4) (+)</td>
    <td>PBS 1X, 1% BSA, 0.05% Tween 20 (v/v), 0.1 mg/ml salmon sperm DNA, 0.1 mg/ml tRNA</td>
    <td>Flow cytometry</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[186]</td>
    <td>Staphylococcus aureus (+)</td>
    <td>PBS (pH 7.4)</td>
    <td>Flow cytometry</td>
    <td>3,49 ± 1,43 nM</td>
  </tr>
  <tr>
    <td>[110]</td>
    <td>Staphylococcus aureus (+)</td>
    <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl2, 5 mM KCl, 100 mM NaCl, 0.05% BSA</td>
    <td>Flow cytometry</td>
    <td>14,47 to 16,5 nM</td>
  </tr>
  <tr>
    <td>[71]</td>
    <td>Staphylococcus aureus (+)</td>
    <td>Binding buffer, BSA, 5X excess of yeast tRNA</td>
    <td>Flow cytometry</td>
    <td>34 to 128 nM</td>
  </tr>
  <tr>
    <td>[187]</td>
    <td>Staphylococcus aureus (ATCC 25923) (+)</td>
    <td>50 mM Tris-HCl (pH=7.4), 5 mM MgCl2, 5 mM KCl, 100 mM NaCl</td>
    <td>Piezoelectric quartz crystal</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[188]</td>
    <td>Staphylococcus epidermidis (+)</td>
    <td>PBS, 1% BSA, 0.05% Tween 20</td>
    <td>UV spectrophotometry</td>
    <td>Not mentioned</td>
  </tr>
  <tr>
    <td>[189]</td>
    <td>Streptococcus mutans (+)</td>
    <td>PBS 1X, 1% BSA, 0.05% Tween-20 (v/v), 0.1 mg/ml salmon sperm DNA, 0.1 mg/ml tRNA</td>
    <td>Flow cytometry</td>
    <td>69,45 ± 38,53 nM</td>
  </tr>
  <tr>
    <td>[190]</td>
    <td>Streptococcus pneumoniae (+)</td>
    <td>Dulbecco's PBS (pH 7.2), 5 mM MgCl2, 1 mg/mL BSA in with CaCl2, 4.5 g/L glucose</td>
    <td>Fluorescence spectroscopy</td>
    <td>661,8 to 1984,8 nM</td>
  </tr>
  <tr>
    <td>[191]</td>
    <td>Streptococcus pyogenes (+)</td>
    <td>Binding buffer, BSA, tRNA</td>
    <td>Flow cytometry</td>
    <td>7 to 71 nM</td>
  </tr>
  <tr>
    <td>[192]</td>
    <td>Streptococcus pyogenes (+)</td>
    <td>50 mM Tris-HCl (pH 7.4), 1 mM MgCl2, 5 mM KCl, 100 mM NaCl, 0,05% BSA, 50nM tRNA</td>
    <td>Flow cytometry</td>
    <td>44 ± 5 nM</td>
  </tr>
  <tr>
    <td>[193]</td>
    <td>Streptococcus pyogenes (+)</td>
    <td>50 mM Tris HCl (pH 7.4), 1 mM MgCl2, 5 mM KCl, 100 mM NaCl</td>
    <td>Flow cytometry</td>
    <td>4 to 86 nM</td>
  </tr>
  <tr>
    <td>[194]</td>
    <td>Streptococcus pyogenes M3 (+)</td>
    <td>PBS, 1% BSA, 0.05% Tween 20</td>
    <td>Flow cytometry</td>
    <td>7,47 to 8,25 pM</td>
  </tr>
</table>
**Figure 1:** Trends in aptamer-based detection of whole-cell bacteria – The results were obtained by searching the Web of Science database using the specified keywords (whole-cell bacteria, detection, aptamers – bacteria, detection, aptamers – whole-cell bacteria detection – bacteria detection) between 2010 and 2024.

<figure><img src="image_2.png" /></figure>

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**Figure 2: Configuration of (a) Direct ELASA, (b) Indirect ELASA, (c) Aptamer-target-antibody, (d) Antibody-target-aptamer.** This figure was reprinted with permission from ref [36]. Copyright 2015 Elsevier.

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>
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**Figure 3: Detection of *Staphylococcus aureus* by a dual labelled aptamer-based assay.** A strong interaction is established between the streptavidin-coated surface and the biotinylated aptamer (A). Upon the introduction of the target, the aptamer undergoes a conformational change, enabling it to bind specifically to the target bacteria (B). Finally, a second aptamer, labelled with a fluorophore, binds to the target, producing a detectable signal as a result of the entire process (D). This figure was reprinted with permission from ref [71] Copyright 2018 Elevier.

<figure><img src="image_7.png" /></figure>
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**Figure 4:** Schematic of a label-free SERS biosensor for detection of pathogenic bacteria (PEG: polyethylene glycol; FPBA: 4-formylphenylboric acid; MNPs: magnetic nanoparticles; AuNPs: gold nanoparticles). Synthesis pathway for producing magnetic nanoparticles (MNPs) (A). Preparation of gold nanoparticles, treated with AgNO₃ prior to aptamer incubation, to form the SERS substrate (B). These nanoparticles are designed to selectively bind to various target bacteria. In the presence of an external magnetic field, the MNPs facilitate the formation of a MNP-bacteria complex, enabling efficient target capture and separation (C). This figure was reprinted with permission from ref [96] Copyright 2023 Elsevier.

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<figure><img src="image_8.png" /></figure>
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**Figure 5 :** Scheme depicting the electrochemical aptasensor employing cyclic voltammetry (GCE: glassy carbon electrode). Initially, the surface was treated with chitosan, which, due to its positive charge in solution, facilitates electrostatic interactions with the aptamers, promoting more effective and stable adsorption onto the modified surface. Subsequently, catechol was applied to enhance surface homogeneity and reactivity, further increasing the density of aptamers on the surface. The functionalization of the surface with aptamers enables the selective recognition of *S. aureus*, leading to a detectable change in the voltammetry signal. This figure was reprinted with permission from [195]Copyright 2022 Elsevier.

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<figure><img src="image_9.png" /></figure>
Figure 6 : Scheme depicting an in-situ quenching Electrochemiluminescence (ECL) sensor sensitized through aptamer recognition, leading to the release of multiple DNA strands for the detection of S. aureus (MBS: maleimideobenzoic acid N-hydroxy-suuccinimide ester; GOD: glucose oxidase; TPrA: tripropylamine). Initially, magnetic beads functionalized with aptamers were hybridized with multiple DNA strands. These beads were then employed to capture S. aureus, resulting in the release of four distinct signal transduction DNAs (M1, M2, M3, and M4) (A). Therefore, the detection of S. aureus was translated into the quantification of multiple DNA strands, which were then hybridized with capture DNA immobilized on an electrode modified with RuSi NPs. The conjugation of glucose oxidase (GOD) to the DNA strands introduced GOD to the sensing interface, where it catalyzed the conversion of glucose into hydrogen peroxide (H₂O₂). The presence of H₂O₂ generated an electrochemiluminescence (ECL) signal, enabling the detection of the target (B). This figure was reprinted with permission from ref [114]. Copyright 2022 Elsevier.

<figure><img src="image_10.png" /></figure>
<caption>**Figure 7:** Schematic diagram of the QCM aptasensor for the rapid detection of *Salmonella* (CDI: carbonyldiimidazole). Initially, the surface is treated with 6-mercaptohexanoic acid, which introduces free carboxyl (—COOH) groups. These carboxyl groups are then activated by the CDI coupling agent, forming highly reactive intermediates that facilitate covalent bonding with the aptamer. Consequently, the surface becomes fully prepared to capture the bacterial target. This figure was reprinted with permission from ref [116]. Copyright 2015 Elsevier.</caption>

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<figure><img src="image_11.png" /></figure>

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