“We have here the first description of the Petri dish, a simple yet effective device for culturing microorganisms on solid media” – R.J. Petri, 1887 (Petri, 1887). Julius Richard Petri, a military physician working in Robert Koch’s lab in Germany during the 1880s invented the Petri plate. In 1887, Petri was trying to find a solution to the problem of culture contamination. Before the development of the Petri plate, scientists used various growth media that would allow their cultures to grow, such as sliced potato, cooked egg whites, and gelatin. These were all attempts at creating a solid growth medium that would facilitate the growth of microorganisms. To make the sample contaminant-free, a bell jar was placed over the culture. Petri’s innovation created a simple tool that effectively provides a small, isolated environment for a culture to grow free of contamination.

The modern Petri plate was the culmination of several innovations that created a sterile environment for bacterial growth. In 1882, Frannie Hesse, the wife and lab technician of Walter Hesse, began substituting agar for the gelatin that she was putting into his solid media in tubes. By replacing gelatin with agar, scientists were able to have a stable solid media that could be incubated, could not easily be degraded by microbes, and was transparent. The invention of the autoclave by Charles Chamerbland in 1884 allowed for the complete sterilization of equipment and media. In 1887, when Julius Petri developed a glass plate that had another, larger plate as its lid, he created an intuitive tool that was compact, reusable, and able to house any solid media. With the combination of these three technologies, the modern Petri plate was born. Scientists could now be certain that their cultures were stable due to their agar base, sterile because of the use of the autoclave, and safe from contamination. This simple solution was fundamental to the primary use of solid media for the isolation and separation of culturable bacteria. His plates have since allowed researchers to easily isolate, observe, study, and manipulate the microorganisms. The primary function of routinely separating and isolating microbes on solid media can not be trivialized for its impact as a routine and integral part of traditional or modern microbiology laboratories. Its use has affected and still impacts all aspects of our lives. Some primary examples can be seen in food processing for evaluating the efficacy of microbial inactivation methods to eliminate pathogens and spoilage organisms and biotechnology for isolating recombinant organisms expressing foreign proteins for medical and commercial applications. An example of the Petri plate’s importance in research can be illustrated in its use in the transformative publication “Studies on the chemical nature of the substance inducing transformation of pneumococcal types” (Avery et al., 1944). The Petri plate was the tool used to determine that DNA was the genetic material by examining the rough and smooth phenotypes of Streptococcus pneumoniae after growth on plates.

The differences in phenotypes in Avery et al.’s work (Avery et al., 1944) were obvious and visualized with the naked eye. However, if a more detailed nondestructive analysis of bacterial cultures on plates could be obtained to detect differences in genus, species, and even strains of bacteria it would be of great value to the microbiology community. In 2007, our lab at Purdue University reported a prototype laser optical sensor, called BARDOT (bacterial rapid detection using optical scattering technology) that is capable of bacterial colony detection and characterization directly on the agar surface of the Petri plate using a red-diode laser beam (Bae et al., 2007; Banada et al., 2007) culminated from the earlier groundbreaking work using a laser to physically map bacterial cell morphology on a Petri plate (Nebeker et al., 2001).

Laser-based interrogation of individual bacterial cells in liquid suspension has been previously attempted by P.J. Wyatt and his team in the late ‘60s (Wyatt, 1968; Wyatt, 1969). Measuring the intensities of the full 4-π radian of scattered light from single cells was the detection principle where the light scattering phenomenon occurs in a single scattering regime. The system requires the suspension of one type of organism (purified target culture) to avoid the generation of multiple overlapping scatter signatures from mixed cultures since multiple scattering events may interfere with the specific detection of the target (Haavig et al., 2017). Therefore, pure cultures are essential, which could be obtained from isolated colonies from a Petri plate. Furthermore, such a detection approach also requires a very low cell density (about 100 cells/ml) to ensure capturing of the single scattering event and avoiding interferences from other scatterers.

Our approach in using elastic light scattering for bacterial interrogation was focused on bacterial microcolonies (0.7 mm to 1.2 mm diameter). Single bacterial cells, through binary fission, give rise to a colony on an agar surface, providing masses of cells that can provide a volume of scattered light for the interrogation of those bacteria with relative ease. In BARDOT, a laser beam with 635 nm (1 mm diameter) wavelength and 1 mW power is passed through the center of spatially located well-separated bacterial colonies (about 1 mm diameter) on a Petri dish and generates a unique scatter signature with circular boundaries with concentric rings, radial spokes, wavy lines, or speckles as a fingerprint. Each scatter pattern is unique for a bacterial culture at the species and serovar level on an agar plate containing specific growth media at a specified time of growth (Banada et al., 2009; Bae et al., 2011a). Colony scatter patterns changes with time; therefore, it is also critical to find a time window when a scatter signature with multiple features can be reliably used for bacterial identification (Bae et al., 2008; Banada et al., 2009). The physics behind the forward scattering is well understood ( Figure 1 ): An incoming wavefront is capable of interacting with the micro/macro structures of a colony which ‘imprints’ its signature on the outgoing wavefront. This is further propagated to the detector and decodes the characteristics of scattered light intensity (Bae et al., 2007; Banada et al., 2007; Bae et al., 2010).

The BARDOT unit is designed with two subcomponents: a microbial colony locator and a forward scatterometer. The first component is responsible for counting and locating the center coordinates of the individual colony and excluding those that don’t match the detection criteria (doublets or diameter outside of the detection range). The colony locator consists of a ring-type light-emitting diode (LED) array for illumination purposes along with the plate diffuser to provide equal illumination across the plate. A monochromatic CMOS (complementary metal-oxide-semiconductor) camera with 1024 x1280 pixels is located on the top of the plate along with an imaging lens with a viewing angle of 34° x 25.6°. Once the candidate colonies for detection are determined, the list of center locations for those colonies is sent to the forward scatterometer. This subcomponent is responsible for capturing the forward scatter patterns from each colony and the scatter features are captured with a second CCD (charge-coupled device) camera placed on the bottom of the Petri dish. To ensure the capture of a quality scatter pattern, a centering algorithm that minimizes the distance between the center of the laser and the colony has been implemented by calculating the difference in geometric moments ( Figure 1 ).

A four-quadrant balancing algorithm was used for quantitatively aligning the laser with the colony while a “traveling salesman algorithm” was implemented to minimize the traveling time between two colonies (Bae et al., 2009). At the same time, automated image processing and classification software were also integrated for seamless analysis and identification of microbes for high throughput screening (Rajwa et al., 2010).

Using the same optical scattering principle, Buzalewicz et al. (Buzalewicz et al., 2019) reported the development of BISLD (Bacteria Identification System by Light Diffraction) for the detection of variable-size colonies at a fixed incubation period by adjusting laser beam diameter. A similar fixed incubation approach using the laser scattering method was also employed by others to interrogate variable-size bacterial colonies on a Petri dish (Marcoux et al., 2014; Minoni et al., 2015). Such an approach can overcome the major limitation of BARDOT which uses a fixed-diameter laser beam targeting colonies of a specific size range (0.7 mm – 1.2 mm) while ignoring the colonies outside this range.

Once the scattering patterns are captured, they are stored in the database as a fingerprint library for future detection and presumptive identification of the bacteria using advanced classification algorithms. The captured scatter patterns of the bacterial colony are automatically analyzed by the quantitative image processing software. Two major features are used for image analysis: the rotation-invariant feature (circularly symmetric patterns) and texture features (random and speckle patterns). The performance of the classifier is estimated using cross-validation (Baldi et al., 2000). The importance of quantitative classification software is needed to reduce human errors, which in turn can provide higher sensitivity and specificity than visual observation. Each 2-D scatter pattern is analyzed via Zernike moments and Haralick textures (Bayraktar et al., 2006). The former extracts features of circularly symmetric features from the scatter patterns, while the Haralick describes the texture of the scatter patterns. The combination of these two features results in hundreds of signature attributes from a single 2-D image which can be used as an orthogonal basis for the fingerprint library in the classification of scattering patterns of bacterial test samples.

Once the training library is built, the sample under investigation can be compared against the fingerprint library that is already built and trained. The results are then reported in a matrix format. The diagonal numbers represent the expected correct classification rate (true positive and true negative) while the off-diagonal numbers show the missed classification (false positive and false negative). More details about the CV matrix are discussed in our relevant publications (Banada et al., 2009; Rajwa et al., 2010).

Raman spectroscopy uses a laser to record the vibrational and rotational properties of molecules yielding a scattering signature referred to inelastic scattering (Petry et al., 2003; Stöckel et al., 2016). Earlier attempts to detect microcolonies of clinically relevant bacterial pathogens of Staphylococcus aureus, Staphylococcus epidermidis, Escherichia coli and Enterococcus faecium directly from agar plate using a Raman microspectrometer equipped with an 830-nm titanium−sapphire laser at wavenumber 250 to 2150 cm^-1 was moderately successful (Maquelin et al., 2000). Later, Rosch et al. (Rösch et al., 2003) used lasers with three different wavelengths (785 nm; 633, and 514 nm) to differentiate colonies of test organisms (Micrococcus luteus, Bacillus subtilis, and Pseudomonas fluorescens) where chromophores produced by these organisms aided in spectral classification. However, bacterial viability was lost due to the destruction of bacterial cells during laser exposure, a major impediment to the isolation of viable cells after Raman spectroscopy (Yuan et al., 2018). Raman spectroscopy was also successfully used for the detection of colonies of clinically relevant Staphylococcus epidermidis, S. aureus and Escherichia coli strains on blood agar or Mueller-Hinton agar plates (Almarashi et al., 2012; Rebrošová et al., 2017b). Most recently, Shen et al. (Shen et al., 2022) reported a rapid fiber probe-based Raman (785 nm diode laser) technique for the classification and identification of 33 strains of 8 different species including Candida albicans, Staphylococcus epidermidis, S. aureus, Klebsiella pneumoniae, K. oxytoca, Escherichia coli, Enterococcus faecalis, E. faecium, and Acinetobacter baumannii on Luria–Bertani (LB) agar plates. Nevertheless, Raman spectroscopy continued to be an attractive on-plate microbial pathogen detection tool for foodborne (Huayhongthong et al., 2019), and clinical relevant pathogens (Rebrošová et al., 2017a; Nakar et al., 2022). However, bacterial physiological state, growth phase and growth media can affect the spectral fingerprints thus these parameters should be controlled with care (Assaf et al., 2014; Mlynáriková et al., 2015).

Hyperspectral imaging (HSI) technology combines spectroscopy and imaging as a reliable nondestructive technique for bacterial colony counting, and detection and identification on various food, inert surfaces, or clinical specimens (Gowen et al., 2015; Park et al., 2015; Bonah et al., 2019; Lu et al., 2020; Soni et al., 2022). Lights with small wavelength bandwidth from visible to near-infrared (Vis-NIR) are often used to generate a complete spatiospectral map of a colony for pathogen detection and identification. Scientists at the US Department of Agriculture developed a hyperspectral imaging system with a spectral range from 400 to 1000 nm to detect and differentiate serovars of Shiga-toxin-producing Escherichia coli (STEC) pathogens on Rainbow agar with very high accuracy (Windham et al., 2012; Yoon et al., 2015). They also successfully used this platform to detect Campylobacter species (Yoon et al., 2009).

Direct identification of colonies on culture plates is also highly important for clinical diagnostic applications. Arrigoni et al. (Arrigoni et al., 2017) applied HSI coupled with a classification algorithm to identify pathogens that are responsible for urinary tract infection, including Escherichia coli, Enterococcus faecalis, Staphylococcus aureus, Proteus mirabilis, Proteus vulgaris, Klebsiella pneumoniae, and Pseudomonas aeruginosa on blood agar plates. HSI was also used for the discrimination of colonies of three different bacterial cultures including E. coli, Listeria monocytogenes and Staphylococcus aureus for application in food safety (Feng et al., 2018). This method employed a non-selective agar plate (tryptic soy agar, TSA) for colony identification. Likewise, using HSI and chemometric classification algorithms, Gu et al. (Gu et al., 2020) differentially distinguished colonies of Escherichia coli, Staphylococcus aureus, and Salmonella enterica on three different nonselective agar plates. However, the drawback of using non-selective agar plates for HSI application is that the growth of commensal bacteria can interfere with target organism identification when testing with food samples, thus conventional broth culturing techniques using selective antimicrobial agents must be employed before testing food samples on agar plates. Near-infrared (NIR) HSI with multivariate data analysis was shown to be useful for the discrimination of colonies of Bacillus cereus, Escherichia coli, Salmonella enterica serovar Enteritidis, Staphylococcus aureus and S. epidermidis (Kammies et al., 2016). The application of HSI in a reflectance mode is highly useful for differentially distinguishing bacterial colonies from particulate foods on agar plate surfaces for food safety analysis (Shi et al., 2019).

As a routine microbiological laboratory practice, bacterial cells collected from a well-separated colony (to assure pure culture) from a Petri dish, are tested for their unique sugar or amino acid utilization patterns as an identifying tool (Bhunia, 2014; Vasavada et al., 2020). Likewise, bacterial cells from colonies are also tested by using the matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry. A laser beam ionizes the sample matrix creating single protonated ions from analytes in the sample. Using acceleration at a stable potential, protonated ions are separated based on their mass-to-charge ratio. The time of flight measures the mass-to-charge ratio by the time it takes the ion to travel the length of the flight tube (Singhal et al., 2015). The spectral signatures are matched with the database for identification. This method is reliably used for pathogen detection from food and clinical samples (Feucherolles et al., 2019; Nomura et al., 2020).

Inelastic scattering technologies such as near-infrared (NIR) (Dubois et al., 2005), Fourier transform Infrared (FT-IR) (Naumann et al., 1991), Raman (Stöckel et al., 2016), and hyperspectral imaging (Soni et al., 2022) have been used to identify bacterial pathogens that are obtained from isolated colonies from a Petri-dish. Often colony isolated bacterial cell suspensions are dispersed on appropriate substrates (for example, silicon wafer, CaF₂) and applied to above mentioned inelastic/vibrational spectroscopy for pathogen identification. Michael et al. (Michael et al., 2019) applied HSI to identify several bacterial pathogens including Cronobacter sakazakii, Salmonella spp., Escherichia coli, Listeria monocytogenes and Staphylococcus aureus smeared on glass slides obtained from an isolated colony from agar plates. Likewise, Raman (Craig et al., 2013; Dina et al., 2017; Liu et al., 2021; Rousseau et al., 2021; Wang et al., 2021; Yan et al., 2021) and FT-IR (Davis and Mauer, 2010; Zarnowiec et al., 2015; Lasch et al., 2018; Vogt et al., 2019) spectrocopies have been shown to be very promising diagnostic tools for detection, identification or antibiotic susceptibility testing of various pathogens obtained from isolated colonies.

In addition, laser-induced breakdown spectroscopy (LIBS) was developed in response to the rapid identification of biothreat agents including pathogens or toxic gas. In this technique, the breakdown of the target analyte by a laser shot (1 ms) reaching a temperature of >10,000 K can generate plasma composed of ionic and atomic species (Morel et al., 2003). Quantitative spectrochemical analyses of plasma allow rapid identification of a target analyte. LIBS have been used for the differentiation and classification of foodborne and clinically relevant microbial pathogens obtained from isolated colonies (Multari et al., 2013; Singh et al., 2018; Rehse, 2019).

Molecular methods are also increasingly becoming integral to the pathogen detection regimen in agriculture, food, and medicine. Historically, the colony hybridization technique has been a quintessential tool for verification of the acquisition of a target gene(s) by host microbes, where colonies are transferred from a Petri dish to a membrane for hybridization with a pre-labeled nucleic acid probe (Grunstein and Hogness, 1975). Membrane-transferred colonies are also probed with antibodies (colony immunoblot) for the detection and identification of many pathogens including E. coli O157:H7 (Meng et al., 1996), L. monocytogenes (Bhunia et al., 1992; Carroll et al., 2000), Helicobacter pylori (Rojas-Rengifo et al., 2015) and Campylobacter species (Huang et al., 2020a). Polymerase chain reaction (PCR) assay has been also applied to colonies (Colony PCR) for the detection of various bacterial (Zimmermann et al., 2014; Daramroodi et al., 2018) and fungal (Lau et al., 2008; Walch et al., 2016) pathogens. Recently, whole genome sequencing (WGS) of isolated colonies is also finding widespread application in food safety and clinical medicine (Hasman et al., 2014; Joensen et al., 2014; Köser et al., 2014; Purushothaman et al., 2022).

Listeria monocytogenes is an opportunistic invasive pathogen and is the primary pathogenic member of the genus which has over 27 species. Food is the primary vehicle for transmission. L. monocytogenes infects immunocompromised individuals such as the elderly, neonates, and pregnant women resulting in premature birth or stillbirth and the case fatality rate is about 19%. Depending on the official scheme as outlined by USDA-FSIS, FDA, or ISO methods (Barre et al., 2016) for Listeria from food or environmental samples, detection involves culturing in primary (ex. UVM, University of Vermont Medium) and/or secondary enrichment broths (ex. Fraser broth) followed by plating on various selective agar plates (ex. Modified Oxford agar, MOX) for isolation of individual colonies exhibiting typical colony morphology that aid in presumptive identification. PCR and lateral flow immunoassays are used for the detection of Listeria from enrichment broths for faster results and to bypass the lengthy plating and colony isolation steps (Bhunia, 2014; Fogaça et al., 2021; Lopes-Luz et al., 2021; Xu et al., 2021).

For the application of BARDOT in Listeria detection, the laser beam was directly applied to the microcolonies (about 1 mm diameter) on agar plates (MOX prepared without ferric ammonium citrate to prevent the black precipitate formation at the center of the colony or BHI plates) to demonstrate the feasibility of differentiating species of Listeria within the genus based on their scatter patterns (Banada et al., 2007; Banada et al., 2009). Image analysis software using Zernike moment invariants and principal component analysis subjected 91–100% accuracy in detecting different species of Listeria. Diffraction theory was used to model the scattering patterns to explain the appearance of radial spokes and the rings seen in the scattering images of L. monocytogenes ( Figure 1 and Figure 2 ).

In another study, Kim et al. (Kim et al., 2015) employed BARDOT as a complementary tool to differentiate between certain species of Listeria sensu stricto and Listeria sensu lato (Orsi and Wiedmann, 2016) using Listeria species-specific PCR assays. PCR assay amplified a housekeeping gene (lmo1634) encoding acetaldehyde alcohol dehydrogenase (AdhE), also known as Listeria adhesion protein (LAP) (Drolia et al., 2018). Both PCR and BARDOT were complementary in their abilities to detect Listeria from inoculated food samples that contained mixed Listeria cultures with a detection limit of about 10⁴ CFU/mL.

In two separate studies, Koo et al. (Koo et al., 2011) and Mendonca et al. (Mendonca et al., 2012) used BARDOT to confirm the presence of L. monocytogenes in food samples. They used antibody- or receptor-coated magnetic beads to capture L. monocytogenes from enriched food samples before plating them onto selective agar plates. Colonies from Petri plates were analyzed by BARDOT for confirmation. More recently, Zhu et al. (Zhu et al., 2020) used BARDOT to detect L. monocytogenes from inoculated milk samples. Application of BARDOT to detect L. monocytogenes from experimentally infected mouse tissues was also demonstrated (Banada et al., 2009) where, L. monocytogenes was successfully detected in liver, spleen, and intestinal chymus demonstrating the feasibility of BARDOT in potential clinical diagnostics.

Salmonella enterica causes typhoid fever and gastroenteritis. It is estimated that each year in the United States, among 9.4 million foodborne illnesses, gastroenteritis causing non-typhoidal Salmonella (NTS) alone is responsible for 1 million illnesses, 19,581 hospitalizations, and 378 deaths (Scallan et al., 2011). Salmonella is a robust organism and can survive at low pH, high salt, desiccation, and thermal processing, making it imperative for food companies to develop comprehensive food safety programs to reduce contamination and prevent contaminated products from reaching consumers.

BARDOT was applied to investigate its ability to selectively detect and identify NTS from the top twenty frequently reported serovars of Salmonella enterica (Singh et al., 2014). The initial study involved the capacity of BARDOT to classify colonies of six Salmonella serovars grown on brain heart infusion (BHI), brilliant green (BG), xylose lysine deoxycholate (XLD), and xylose lysine tergitol 4 (XLT4) agar plates. Cultures on XLT generated highly accurate discriminatory (95.9%) scatter signatures among the S. enterica serovars ( Figure 3 ). Later, BARDOT yielded classification precision of 88-100% when tested with 36 serovars (top 20 plus 16 miscellaneous serovars), which showed a strong correlation with pulsed-field gel electrophoresis (PFGE)-based genetic fingerprints.

For testing of food samples for Salmonella using BARDOT, a sequential enrichment in nonselective (buffered peptone water) and selective enrichment (modified Rappaport Vassiliadis) broths for 4 h each followed by growth on XLT4 (~16 h) was used. BARDOT delivered results within 24 h with a detection sensitivity of 1.2×10² CFU/30 g, much faster than the USDA-FSIS method, which requires about 72 h. Genetic analysis (16S rRNA gene sequencing and PFGE) also confirmed BARDOT results.

In another study, a combination of a fiber optic immunosensor (Valadez et al., 2009) and BARDOT was used to detect Salmonella from naturally contaminated poultry samples (Abdelhaseib et al., 2016). Poultry samples were sequentially enriched in primary and secondary enrichment broths for 4 h each before plating on selective agar plates (XLT4). The scatter signatures of colonies on XLT4 generated by BARDOT were matched with the image library for identification of Salmonella in less than 24 h. While the fiber optic sensor was applied directly to the broth sample from secondary enrichment thus results were obtained in less than 12 h. Though the fiberoptic sensor provided faster results, the BARDOT-based detection approach provides an opportunity to obtain pure isolated colonies that can be further used for antibiotic sensitivity testing, whole-genome sequencing, source tracking and other studies including pathogenicity assays.

The genus Bacillus comprises pathogens, nonpathogens, and industrially relevant beneficial bacteria. Bacilli are spore-forming Gram-positive bacteria. Singh et al. (Singh et al., 2015b), used BARDOT to screen and differentiate colonies of Bacillus species on Petri plates containing phenol red mannitol (PRM) agar ( Figure 4 ). Colony morphology is highly diverse among the species of bacillus producing a flat surface with rough topography therefore the colony scatter patterns consisting of speckles are unique and do not overlap with scatter patterns from other bacterial species (Kim et al., 2014). Initially, a colony scatter image library was created using a total of 265 Bacillus and non-Bacillus isolates from our collection. Cross-validation experiments demonstrated that all Bacillus species (n = 118) gave a positive predictive value (PPV) above 90% while non-Bacillus spp. showed a PPV of <0.5%.

Spiked baby formula and cheese samples, or naturally contaminated bovine unpasteurized milk samples were surface-plated on PRM and the microcolonies were scanned by BARDOT in 7-16 h to capture colony scatter signatures. BARDOT-identified Bacillus cultures were further verified by using PCR and 16S rRNA gene sequencing to provide high accuracy (Singh et al., 2015b).

Escherichia coli is one of the ubiquitous Gram-negative bacteria that resides in the intestine of animals and humans. A majority of E. coli are nonpathogenic while a small subset is pathogenic and causes diseases including gastroenteritis, urinary tract infection, kidney disease, and central nervous system infection. Based on the nature of the gastrointestinal infection, E. coli is grouped into 5 major pathotypes; enterotoxigenic E. coli (ETEC), enteroaggregative E. coli (EAgEC), enteroinvasive E. coli (EIEC), enteropathogenic E. coli (EPEC), and enterohaemorrhagic E. coli (EHEC), which is a subset of broadly defined Shiga toxin-producing E. coli (STEC) (Croxen and Finlay, 2010; Castro et al., 2017). STEC has emerged as an important foodborne pathogen, among which seven serogroups (O26, O45, O103, O111, O121, O145, O157) are most frequently implicated in human infection. Tang et al. (Tang et al., 2014) used BARDOT to differentiate STEC serovars. The goal was to determine if BARDOT can be used to rapidly identify the colonies of STEC serogroups on selective agar plates. Multiple selective/differential agar media on the Petri plate was evaluated that including sorbitol MacConkey (SMAC), Rainbow^® Agar O157, BBL™ CHROMagarO157, and R&F^® E. coli O157:H7, and BHI ( Figure 2 ). Colony scatter signatures obtained after 10-12 h growth on both SMAC and Rainbow produced results that successfully differentiated all seven serovars (O26, O45, O103, O111, O121, O145, O157) of STEC with greater than 90% accuracy. Colonies of E. coli O157 and O26 serovars in a mixed culture and inoculated food samples (lettuce and ground beef) were accurately identified by BARDOT requiring a sample-to-result in less than 24 h (Tang et al., 2014).

The genus Vibrio consists of three major human pathogens including Vibrio cholerae, V. parahaemolyticus, V. vulnificus that are associated with water- and seafood-related outbreaks worldwide (Baker-Austin et al., 2018). V. cholerae is responsible for cholera, a severe diarrheal disease. V. parahaemolyticus and V. vulnificus cause gastroenteritis but may also cause fatal septicemic disease and are primarily transmitted via seafood. Their primary habitat is an aquatic and marine environment with a high preference for brackish (salt and fresh water mix) water. These halophiles (salt-loving) organisms also undergo a viable but nonculturable state under stressful conditions thus their culturing becomes very difficult. Culture enrichment in alkaline peptone water can help resuscitate a culturable state. For isolation of individual colonies, the enriched samples can be plated on BHI containing 1% NaCl or on selective Thiosulphate Citrate Bile Salts Sucrose (TCBS) agar. BARDOT was applied for Vibrio detection on Petri plates after 12 h growth at 30°C and it successfully detected V. cholerae, V. parahaemolyticus, and V. vulnificus present in oyster or water samples in 18 h even in the presence of other vibrios or other bacteria, indicating the suitability of the sensor as a powerful screening tool for pathogens on agar plates (Huff et al., 2012).

Staphylococcus aureus is a major pathogen responsible for nosocomial infections and foodborne illnesses (Cheung et al., 2021). Common habitat for S. aureus is nares and skin. BARDOT was used for rapid colony screening and detection of Staphylococcus on an agar plate and to differentiate these colonies from non-Staphylococcus spp. (Alsulami et al., 2018). Phenol red mannitol agar (PRMA) was used for building the Staphylococcus species scatter image libraries since this medium generated distinguishing scatter signatures when compared with other species. The scatter image library for Staphylococcus species gave a high positive predictive value (PPV 87.5–100%) when tested against known laboratory strains of Staphylococcus spp., while the PPV against non-Staphylococcus spp. was 0–38%. BARDOT detected S. aureus with 80–100% PPV from naturally contaminated cow milk and ready-to-eat chicken salad samples and the results were validated with PCR and 16S rRNA gene sequencing (Alsulami et al., 2018).

BARDOT was also used to study colonies formed by the members of the Enterobacteriaceae family, which comprises pathogens and commensals and has a significant impact on food safety, clinical microbiology, and public health. Their presence in foods/water indicates potential contamination with pathogens. Enterobacteriaceae (EB) detection has been used as an indicator for assessing the safety of food products or water. Various selective chromogenic media are used for quantification and analysis of colonies of EB; however, many produce similar chromogenic by-products thus they cannot be accurately visually identified on the Petri plate. Singh and Bhunia (Singh and Bhunia, 2016) applied BARDOT to screen colonies of the Enterobacteriaceae family including Klebsiella, Enterobacter, Citrobacter, Serratia, Proteus, Morganella, and Providencia cultured on CHROMagar™ Orientation medium ( Figure 5 ). A scatter image library (1683 scatter images) was made that contains colony scatter signatures of 36 isolates representing 12 genera and 15 species. This library helped BARDOT-based detection of colonies of members of Enterobacteriaceae and non-Enterobacteriaceae family (Pseudomonas aeruginosa, Acinetobacter spp., and Staphylococcus aureus) with high accuracy (83-100%) in 10-22 h or even before visible production of chromogens.

BARDOT was used in the screening of mutant strains that are deficient in several virulence genes essential for pathogenicity (Singh et al., 2016). During microbial pathogenesis studies, virulence-encoding genes are routinely disrupted by deletion or insertion to create mutant strains. Screening mutant strains is a laborious process involving plating on growth media containing antibiotics marker, replica plating, colony hybridization, DNA isolation, and PCR or immunoassays. BARDOT was used to screen virulence-gene-associated mutant colonies during microbial pathogenesis, co-infection, and genetic manipulation studies in L. monocytogenes. BARDOT generated differential scatter patterns in L. monocytogenes, deficient in Listeria adhesion protein (lap ^- ), Internalin A (ΔinlA), and an accessory secretory protein (ΔsecA2). Furthermore, the mutant strains complemented with respective genes were also able to restore the scatter signature to that of the WT (Singh et al., 2016). BARDOT was also useful in differential counting of mutant strains in the presence of WT strain in a co-infection experiment. These data demonstrate that BARDOT can be used as a label-free tool to aid researchers in screening virulence-gene-associated mutant colonies during microbial pathogenesis and genetic manipulation studies.

In another study, Singh et al. (Singh et al., 2015a) also investigated the streptomycin-induced stress response in Salmonella enterica serovars with BARDOT. Streptomycin-sensitive or streptomycin-resistant Salmonella serovars were exposed to various levels of streptomycin and grown on Petri plates and the colonies were screened by BARDOT to assess their stress responses and colony scatter signatures. A substantial qualitative and quantitative difference in the scatter signatures was observed for colonies that were grown in the presence of streptomycin than the colonies grown in the absence of antibiotics. Levels of a stress response protein, GroEL, were increased in the colony confirmed by mass-spec, quantitative RT-PCR, and immunoassays that were implicated to contribute to the differential scatter patterns. The study highlights the suitability of the BARDOT to investigate stress response in bacteria in conjunction with molecular or other analytical methods.

In another study, Zhu et al. (Zhu et al., 2018) used BARDOT to study the effect of tunicamycin, a cell wall teichoic acid (WTA) synthesis inhibitor on colony morphology and colony scatter patterns. WTA is a major component of the cell wall of Gram-positive bacteria and plays a significant role in physiology, biofilm formation, and pathogenesis (Brown et al., 2013).

BARDOT was evaluated for its ability to simultaneously detect colonies of three pathogens (L. monocytogenes, Salmonella enterica, and E. coli) from the same test sample, if present together on the same Petri plate (Abdelhaseib et al., 2019). Test samples were first enriched in a multi-pathogen enrichment broth, SEL (Salmonella, Escherichia, Listeria) (Kim and Bhunia, 2008) before plating onto SEL agar for BARDOT-based colony identification. The BARDOT sensor successfully detected Salmonella, Shiga-toxin-producing E. coli, and Listeria on the SEL agar plate with greater than 90% accuracy within 29–40 h demonstrating its simultaneous multi-pathogen detection potential (Abdelhaseib et al., 2019) ( Figure 6 ).

BARDOT coupled with 16s RNA sequencing was instrumental in identifying thermostable bacterial colonies in fluid milk subjected to a novel low temperature−short time (LTST) process for pasteurization (Myer et al., 2016).