A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification

Biosensors have been widely used for bacteria determination with great success. However, the "lock-and-key" methodology used by biosensors to identify bacteria has a significant limitation: it can only detect one species of bacteria. In recent years, optical (fluorescent and colorimetric) sensor arrays are gradually gaining attention from researchers as a new type of biosensor. They can acquire multiple features of a target simultaneously, form a feature pattern, and determine the bacteria species with the help of pattern recognition/machine learning algorithms. Previous reviews in this area have focused on the interaction between the sensor array and bacteria or the materials used to make the sensors. This review, on the other hand, will provide researchers with a better understanding of the field by discussing fluorescent and colorimetric sensor arrays based on the mechanism of optical signal generation. These sensor arrays will be compared based on the identified species. Finally, we will discuss the limitations of these sensor arrays and explore possible solutions.

Separation of bacteria smaller than 4 µm from other blood components using insulator-based dielectrophoresis: numerical simulation approach

Bloodstream infection (BSI) is a life-threatening infection that causes more than 80,000 deaths and more than 500,000 infections annually in North America. The rapid diagnosis of infection reduces BSI mortality. We proposed bacterial enrichment and separation approach in the current work that may reduce culturing time and accelerate the diagnosis of infection. Over the last two decades, multiple separation methods have been developed, and among these methods, insulator-based dielectrophoresis (iDEP) is considered a powerful technique for separating biological particles. Bacterial separation in the blood is challenging due to the presence of other blood cells, such as white blood cells, red blood cells, and platelets. In the present study, a model is presented which is capable of blood cells separation and directing each cell to a specific outlet using continuous flows of particles with sizes larger than 8 µm, 8-4 µm, and smaller than 4 µm. Compared to other methods, such as filtration, the main advantage of this model is that particles larger than 8 µm are separated from the flow before other particles, which prevents the accumulation of particles in the channel. The outcomes of simulations demonstrated that the factors such as applied voltage and channel dimensions significantly affect the separation efficiency. If these values are properly selected (for example voltage of 70 V that was causing an electric field of 200 V/cm), the proposed model can completely (100%) separate particles larger than 8 µm and smaller than 4 µm (8-4 µm particles separation efficiency is 95%).

Local fruit wastes driven benthic microbial fuel cell: a sustainable approach to toxic metal removal and bioelectricity generation

The present work focused on the utilization of three local wastes, i.e., rambutan (Nephelium lappaceum), langsat (Lansium parasiticum), and mango (Mangifera indica) wastes, as organic substrates in a benthic microbial fuel cell (BMFC) to reduce the cadmium and lead concentrations from synthetic water. Out of the three wastes, the mango waste promoted a maximum current density (87.71 mA/m²) along with 78% and 80% removal efficiencies for Cd²⁺ and Pb²⁺, respectively. The bacterial identification proved that Klebsiella pneumoniae, Enterobacter, and Citrobacter were responsible for metal removal and energy generation. In the present work, the BMFC mechanism, current challenges, and future recommendations are also enclosed.

Decision tree-based identification of Staphylococcus aureus via infrared spectral analysis of ambient gas

In this study, eight types of bacteria were cultivated, including Staphylococcus aureus. The infrared absorption spectra of the gas surrounding cultured bacteria were recorded at a resolution of 0.5 cm⁻¹ over the wavenumber range of 7500–500 cm⁻¹. From these spectra, we searched for the infrared wavenumbers at which characteristic absorptions of the gas surrounding Staphylococcus aureus could be measured. This paper reports two wavenumber regions, 6516–6506 cm⁻¹ and 2166–2158 cm⁻¹. A decision tree–based machine learning algorithm was used to search for these wavenumber regions. The peak intensity or the absorbance difference was calculated for each region, and the ratio between them was obtained. When these ratios were used as training data, decision trees were created to classify the gas surrounding Staphylococcus aureus and the gas surrounding other bacteria into different groups. These decision trees show the potential effectiveness of using absorbance measurement at two wavenumber regions in finding Staphylococcus aureus.

Human sensor-inspired supervised machine learning of smartphone-based paper microfluidic analysis for bacterial species classification

Bacteria identification has predominantly been conducted using specific bioreceptors such as antibodies or nucleic acid sequences. This approach may be inappropriate for environmental monitoring when the user does not know the target bacterial species and for screening complex water samples with many unknown bacterial species. In this work, we investigate the supervised machine learning of the bacteria-particle aggregation pattern induced by the peptide sets identified from the biofilm-bacteria interface. Each peptide is covalently conjugated to polystyrene particles and loaded together with bacterial suspensions onto paper microfluidic chips. Each peptide interacts with bacterial species to a different extent, leading to varying sizes of particle aggregation. This aggregation changes the surface tension and viscosity of the liquid flowing through the paper pores, altering the flow velocity at different extents. A smartphone camera captures this flow velocity without being affected by ambient and environmental conditions, towards a low-cost, rapid, and field-ready assay. A collection of such flow velocity data generates a unique fingerprinting profile for each bacterial species. Support vector machine is utilized to classify the species. At optimized conditions, the training model can predict the species at 93.3% accuracy out of five bacteria: Escherichia coli, Staphylococcus aureus, Salmonella Typhimurium, Enterococcus faecium, and Pseudomonas aeruginosa. Flow rates are monitored for less than 6 s and the sample-to-answer assay time is less than 10 min. The demonstrated method can open a new way of analyzing complex biological and environmental samples in a biomimetic manner with machine learning classification.

Rapid in vitro differentiation of bacteria by ion mobility spectrometry

Rapid screening of infected people plays a crucial role in interrupting infection chains. However, the current methods for identification of bacteria are very tedious and labor intense. Fast on-site screening for pathogens based on volatile organic compounds (VOCs) by ion mobility spectrometry (IMS) could help to differentiate between healthy and potentially infected subjects. As a first step towards this, the feasibility of differentiating between seven different bacteria including resistant strains was assessed using IMS coupled to multicapillary columns (MCC-IMS). The headspace above bacterial cultures was directly drawn and analyzed by MCC-IMS after 90 min of incubation. A cluster analysis software and statistical methods were applied to select discriminative VOC clusters. As a result, 63 VOC clusters were identified, enabling the differentiation between all investigated bacterial strains using canonical discriminant analysis. These 63 clusters were reduced to 7 discriminative VOC clusters by constructing a hierarchical classification tree. Using this tree, all bacteria including resistant strains could be classified with an AUC of 1.0 by receiver-operating characteristic analysis. In conclusion, MCC-IMS is able to differentiate the tested bacterial species, even the non-resistant and their corresponding resistant strains, based on VOC patterns after 90 min of cultivation. Although this result is very promising, in vivo studies need to be performed to investigate if this technology is able to also classify clinical samples. With a short analysis time of 5 min, MCC-IMS is quite attractive for a rapid screening for possible infections in various locations from hospitals to airports.

Key Points

• Differentiation of bacteria by MCC-IMS is shown after 90-min cultivation.

• Non-resistant and resistant strains can be distinguished.

• Classification of bacteria is possible based on metabolic features.

Releasing bacteria from functional magnetic beads is beneficial to MALDI-TOF MS based identification

Bacterial infections are the key cause of morbidity and mortality worldwide. Matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS)-based bacterial identification has been widely accepted in the clinic. Functional material, such as rabbit immunoglobulin G-modified Fe₃O₄ (IgG@Fe₃O₄) and fragment crystallizable mannose binding lectin-modified Fe₃O₄ (FcMBL@Fe₃O₄), is used to capture bacteria from biological samples for MALDI-TOF MS identification, and the bacteria MS signals are usually obtained by directly smearing enriched bacteria on a MALDI target with MALDI matrix solution. However, the accuracy of identification based on MALDI-TOF MS may be affected by the presence of functional molecules, especially proteins, resulting in errors in the comparison with the standard bacterial spectra in the database. Moreover, the long-term presence of the magnetic beads on the MALDI-TOF target may reduce the instrument service life. In this study, we constructed FcMBL@Fe₃O₄ and used it to capture bacteria from both aqueous solution and bovine blood, and the bacterial identification accuracy based on different target preparation methods was compared. In the presence of Ca²⁺, the similarity scores for bacteria identified with FcMBL@Fe₃O₄ were ~88% and ~82% for Staphylococcus. aureus and Escherichia coli, respectively. In the presence of ethylenediaminetetraacetic acid (EDTA), bacteria separate from FcMBL@Fe₃O₄, resulting in similarity scores of ~96% and ~92% for S. aureus and E. coli, respectively. These results indicate that the functional proteins on the surface of nanoparticles affect the accuracy of identification accuracy based on the MALDI-TOF MS database. Thus, the release of bacteria from the functional material could increase the identification accuracy and be beneficial for maintaining the instrument.

On the application of multi-parametric optical phenotyping of bacterial colonies for multipurpose microbiological diagnostics

The development of new diagnostics techniques and modalities is critical for early detection of microbial contamination. In this study, the novel integrated system for multi-parametric optical phenotyping and characterization of bacterial colonies, is presented. The system combines Mach-Zehnder interferometer with a spectral imaging system for capturing multispectral diffraction patterns and multispectral two-dimensional transmission maps of bacterial colonies, along with the simultaneous interferometric profilometry. The herein presented investigation was carried out on five representative bacteria species and nearly 3000 registered multispectral optical signatures. The interferograms were analyzed by four-step phase shift algorithm to reconstruct the colony profile to enable the obtaining of the comparable optical signatures. The dedicated image processing algorithms were used for extraction of quantitative features of these signatures. The random forest algorithm was applied for selection of the most predictive set of features, which were used in classification model based on Support-Vector Machine. Obtained results have shown that the use of multiple multispectral optical signatures provide a multi-parametric bacteria identification at an exceptionally high accuracy (99.4-100%), significantly better than in case of classification based on each of these signatures (multispectral diffraction patterns, two-dimensional transmission coefficient maps), separately. Obtained results revealed that analysis of multispectral signatures can also be applied for characterisation of physical, physicochemical and chemical properties of the bacterial colonies in the presence of the antimicrobial factors. Therefore, the proposed label-free, non-destructive optical technique has perspectives to be exploited in the multipurpose diagnostics and it can be used as a pre-screening tool in microbiological laboratories.

Ultrasensitive bio-detection using single-electron effect

Single-electron devices are capable of detecting changes of the electric field caused by the presence of one single electron in their environment. These devices are optimized to identify the material that is in close contact with them based on the material's internal charge distribution or dipole moment. As an important practical use, they present the possibility of detecting bacteria, viruses, or pathogens. However, their practical use is hampered by their nano-meter size, which is normally an order of magnitude smaller than that of detected species, their very complex fabrication techniques, their cryogenic operational temperature, and the problem of bringing the said species in contact with the single-electron structure. In this document, a large scaled room temperature single-electron structure is introduced, and its ability to distinguish liquids based on their internal dipole moments is demonstrated. The device is a Schottky junction made of PtSi, as the metal contact, and the walls and surfaces of the porous Si, as the semiconductor. The reverse bias current-voltage (IV) characteristic of this device is sensitive to 1 ppm change in the dipole moment of the liquid entering its pores. The simple fabrication, easy testing procedure, high sensitivity, and fast response can make this device an optimized testing kit to identify the given bacteria, viruses, or pathogens dissolved in liquids.

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Using single-electron phenomenon to detect minute changes in electric fields.

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Simple and fast Measurement for detection of liquids and suspensions.

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High sensitivity to electric fields at room temperature.

Application of MALDI-TOF MS to rapid identification of anaerobic bacteria

BACKGROUND

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been rapidly developed and widely used as an analytical technique in clinical laboratories with high accuracy in microorganism identification.

OBJECTIVE

To validate the efficacy of MALDI-TOF MS in identification of clinical pathogenic anaerobes.

METHODS

Twenty-eight studies covering 6685 strains of anaerobic bacteria were included in this meta-analysis. Fixed-effects models based on the P-value and the I-squared were used for meta-analysis to consider the possibility of heterogeneity between studies. Statistical analyses were performed by using STATA 12.0.

RESULTS

The identification accuracy of MALDI-TOF MS was 84% for species (I² = 98.0%, P < 0.1), and 92% for genus (I² = 96.6%, P < 0.1). Thereinto, the identification accuracy of Bacteroides was the highest at 96% with a 95% CI of 95-97%, followed by Lactobacillus spp., Parabacteroides spp., Clostridium spp., Propionibacterium spp., Prevotella spp., Veillonella spp. and Peptostreptococcus spp., and their correct identification rates were all above 90%, while the accuracy of rare anaerobic bacteria was relatively low. Meanwhile, the overall capabilities of two MALDI-TOF MS systems were different. The identification accuracy rate was 90% for VITEK MS vs. 86% for MALDI biotyper system.

CONCLUSIONS

Our research showed that MALDI-TOF-MS was satisfactory in genus identification of clinical pathogenic anaerobic bacteria. However, this method still suffers from different drawbacks in precise identification of rare anaerobe and species levels of common anaerobic bacteria.

Determination of live:dead bacteria as a function of antibiotic treatment

Antibiotics are drugs that react against, kill, or inhibit the growth of bacteria. The method most often employed to evaluate the effectiveness of an antibiotic to kill bacteria requires at least 16 to 24 h for bacterial incubation. The requirement of long periods of time for the determination of the number of bacteria still alive after antibiotic treatment, may, in many cases, be detrimental to the patient's health. In addition, with increasing of bacterial antibiotic resistance, the need to utilize methods for distinguishing between live and dead bacteria within a short period of time after treatment with antibiotic agents, is becoming more crucial. To that effect, we have utilized a hand-held double monochromator to record in situ and within minutes the synchronous and normal fluorescence spectra of bacteria and other species. The fluorescence spectra of bacterial components such as tryptophan, tyrosine and DNA are clearly displayed. In addition, principal component analysis, PCA, makes it possible to display live and dead bacteria separately and determine the ratio of live:dead bacteria before and after treatment with antibiotics.

Strain-level typing and identification of bacteria - a novel approach for SERS active plasmonic nanostructures

One of the potential applications of surface-enhanced Raman spectroscopy (SERS) is the detection of biological compounds and microorganisms. Here we demonstrate that SERS coupled with principal component analysis (PCA) serves as a perfect method for determining the taxonomic affiliation of bacteria at the strain level. We demonstrate for the first time that it is possible to distinguish different genoserogroups within a single species, Listeria monocytogenes, which is one of the most virulent foodborne pathogens and in some cases contact with which may be fatal. We also postulate that it is possible to detect additional proteins in the L. monocytogenes cell envelope, which provide resistance to benzalkonium chloride and cadmium. A better understanding of this infectious agent could help in selecting the appropriate pharmaceutical product for enhanced treatment. Graphical abstract ᅟ.

Influence of meteorological factors on the level and characteristics of culturable bacteria in the air in Gliwice, Upper Silesia (Poland)

Numerous studies have focused on occupational and indoor environments because people spend more than 90% of their time in them. Nevertheless, air is the main source of bacteria in indoors, and outdoor exposure is also crucial. Worldwide studies have indicated that bacterial concentrations vary among different types of outdoor environments, with considerable seasonal variations as well. Conducting comprehensive monitoring of atmospheric aerosol concentrations is very important not only for environmental management but also for the assessment of the health impacts of air pollution. To our knowledge, this is the first study to present outdoor and seasonal changes of bioaerosol data regarding an urban area of Poland. This study aimed to characterize culturable bacteria populations present in outdoor air in Gliwice, Upper Silesia Region, Poland, over the course of four seasons (spring, summer, autumn and winter) through quantification and identification procedures. In this study, the samples of bioaerosol were collected using a six-stage Andersen cascade impactor (with aerodynamic cut-off diameters of 7.0, 4.7, 3.3, 2.1, 1.1 and 0.65 μm). Results showed that the concentration of airborne bacteria ranged from 4 CFU m^-3, measured on one winter day, to a maximum equal to 669 CFU m^-3 on a spring day. The average size of culturable bacterial aerosol over the study period was 199 CFU m^-3. The maximal seasonally averaged concentration was found in the spring season and reached 306 CFU m^-3, and the minimal seasonally averaged concentration was found in the winter 49 CFU m^-3. The most prevalent bacteria found outdoors were gram-positive rods that form endospores. Statistically, the most important meteorological factors related to the viability of airborne bacteria were temperature and UV radiation. These results may contribute to the promotion and implementation of preventative public health programmes and the formulation of recommendations aimed at providing healthier outdoor environments.

MALDI-TOF mass spectrometry for the identification of lactic acid bacteria isolated from a French cheese: The Maroilles

In this study we identified the culturable population of mesophilic lactic acid bacteria (LAB) from a French cheese Maroilles made either with raw or pasteurized milk using MALDI-TOF mass spectrometry (MS). Samples from rind and heart of Maroilles cheese were used, the LAB were selected on MRS agar at 30°C and 197 Gram-positive and catalase-negative strains were subjected to identification by MALDI-TOF MS profiling. All strains were unambiguously identified: 105 strains from Maroilles made with raw milk (38 on the rind and 67 in the heart) and 92 strains from Maroilles made with pasteurized milk (39 on the rind and 53 in the heart). MALDI-TOF MS identification allowed identification of three genera belonging to LAB including Lactobacillus, Enterococcus and Leuconostoc. Lactobacillus was the most represented genus with seven species: Lactobacillus plantarum (L. plantarum), L. paracasei, L. curvatus, L. rhamnosus, L. fructivorans, L. parabuchneri, L. brevis found in Maroilles made with both kind of milk. The correlation between the 16S rDNA-based identification performed on selected strains and those obtained by MALDI-TOF-MS demonstrates that this fast, economically affordable, robust and reliable method for bacteria characterisation stands as an attractive alternative to the commonly-used methods and its application in food industry is discussed.

Medicago truncatula Gaertn. as a model for understanding the mechanism of growth promotion by bacteria from rhizosphere and nodules of alfalfa

MAIN CONCLUSION

The present study showed all the 16 strains isolated and identified from the alfalfa rhizosphere and nodules, and registered in GenBank, to be good candidates for targeted use in studies addressing the rather weak known mechanism of plant growth promotion, including that of Medicago truncatula, a molecular crop model. Based on physiological, biochemical and molecular analysis, the 16 isolates obtained were ascribed to the following five families: Bacillaceae, Rhizobiaceae, Xantomonadaceae, Enterobacteriaceae and Pseudomonadaceae, within which 9 genera and 16 species were identified. All these bacteria were found to significantly enhance fresh and dry weight of root, shoots and whole 5-week-old seedlings. The bacteria were capable of the in vitro use of tryptophan to produce indolic compounds at various concentrations. The ability of almost all the strains to enhance growth of seedlings and individual roots was positively correlated with the production of the indolic compounds (r = 0.69; P = 0.0001), but not with the 1-aminocyclopropane-1-carboxylate deaminase (ACCD) activity (no correlation). For some strains, it was difficult to conclude whether the growth promotion was related to the production of indolic compounds or to the ACCD activity. It is likely that promotion of M. truncatula root development involves also root interaction with pseudomonads, known to produce 2,4-diacetylphloroglucinol (DAPG), a secondary metabolite reported to alter the root architecture by interacting with an auxin-dependent signaling pathway. Inoculation of seedlings with Pseudomonas brassicacearum KK 5, a bacterium known for its lowest ability to produce indolic compounds, the highest ACCD activity and the presence of the phlD gene responsible for DAPG precursor synthesis, resulted in a substantial promotion of root development. Inoculation with the strain increased the endogenous IAA level in M. truncatula leaves after inoculation of 5-week-old seedlings. Three other strains examined in this study also increased the IAA level in the leaves upon inoculation. Moreover, several other factors such as mobilization of phosphorus and zinc to make them available to plants, iron sequestration by siderophore production and the ability to ammonia production also contributed substantially to the phytostimulatory biofertilizing potential of isolated strains. There is, thus, evidence that Medicago truncatula growth promotion by rhizobacteria involves more than one mechanism.

Effect of Sample Preparation on the Discrimination of Bacterial Isolates Cultured in Liquid Nutrient Media Using Laser-Induced Breakdown Spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) is used as the basis for discrimination between two genera of gram-negative bacteria and two genera of gram-positive bacteria representing pathogenic threats commonly found in poultry processing rinse waters. Because LIBS-based discrimination relies primarily upon the relative proportions of inorganic cell components including Na, K, Mg, and Ca, this study aims to determine the effects of trace mineral content and pH found in the water source used to isolate the bacteria upon the reliability of the resulting discriminant analysis. All four genera were cultured using tryptic soy agar (TSA) as the nutrient medium, and were grown under identical environmental conditions. The only variable introduced is the source water used to isolate the cultured bacteria. Cultures of each bacterium were produced using deionized (DI) water under two atmosphere conditions, reverse osmosis (RO) water, tap water, phosphate buffered saline (PBS) water, and TRIS buffered water. After 3 days of culture growth, the bacteria were centrifuged and washed three times in the same water source. Bacteria were then freeze dried, mixed with microcrystalline cellulose, and a pellet was made for LIBS analysis. Principal component analysis (PCA) was used to extract related variations in LIBS spectral data among the four bacteria genera and six water types used to isolate the bacteria, and Mahalanobis discriminant analysis (MDA) was used for classification. Results indicate not only that the four genera can be discriminated from each other in each water type, but that each genus can be discriminated by water type used for isolation. It is concluded that in order for LIBS to be a reliable and repeatable method for discrimination of bacteria grown in liquid nutrient media, care must be taken to insure that the water source used in purification of the culture be precisely controlled regarding pH, ionic strength, and proportionate amounts of mineral cations present.

Co-transmission of Rahnella aquatilis between hospitalized patients

Rahnella aquatilis is an environmental Gram-negative bacillus that is rarely reported as human pathogen, being mainly associated with infections in immunocompromised patients. Herein we describe two cases of R. aquatilis isolates recovered from endotracheal aspirate cultures of different patients in a tertiary hospital located in the city of São Paulo, Brazil. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and 16S rDNA gene sequencing were performed to confirm bacterial identification after the isolates being erroneously identified as Pantoea spp. by automated system. Both isolates showed the same PFGE pattern and presented the β-lactamase encoding gene blaRAHN-1, responsible for resistance to cephalothin. The isolates were susceptible to broad-spectrum cephalosporins, carbapenems, fluoroquinolones, aminoglycosides, and polymyxin B. This report shows the presence and transmission of uncommon bacteria in the nosocomial environment and alerts us about the need for new tools of correct microbiologic diagnosis.

Performance of MALDI-ToF MS for species identification of Burkholderia cepacia complex clinical isolates

We evaluated the performance of matrix-assisted laser desorption ionization-time of flight (MALDI-ToF) for identification of Bcc species compared with that of recA sequencing. MALDI-ToF was able of identifying 100% of Bcc isolates at the genus level, but 23.1% of Bcc isolates tested were not correctly identified at the species level. The misidentification occurred most frequently with Burkholderia contaminans (100%) and B. cepacia (33.3%).