Rapid and accurate identification of marine bacteria spores at a single-cell resolution by laser tweezers Raman spectroscopy and deep learning

Marine bacteria have been considered as important participants in revealing various carbon/sulfur/nitrogen cycles of marine ecosystem. Thus, how to accurately identify rare marine bacteria without a culture process is significant and valuable. In this work, we constructed a single-cell Raman spectra dataset from five living bacteria spores and utilized convolutional neural network to rapidly, accurately, nondestructively identify bacteria spores. The optimal CNN architecture can provide a prediction accuracy of five bacteria spore as high as 94.93% ± 1.78%. To evaluate the classification weight of extracted spectra features, we proposed a novel algorithm by occluding fingerprint Raman bands. Based on the relative classification weight arranged from large to small, four Raman bands located at 1518, 1397, 1666, and 1017 cm-1 mostly contribute to producing such high prediction accuracy. It can be foreseen that, LTRS combined with CNN approach have great potential for identifying marine bacteria, which cannot be cultured under normal condition.

MALDI-TOF MS: A Reliable Tool in the Real Life of the Clinical Microbiology Laboratory

Matrix-Assisted Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) in the last decade has revealed itself as a valid support in the workflow in the clinical microbiology laboratory for the identification of bacteria and fungi, demonstrating high reliability and effectiveness in this application. Its use has reduced, by 24 h, the time to obtain a microbiological diagnosis compared to conventional biochemical automatic systems. MALDI-TOF MS application to the detection of pathogens directly in clinical samples was proposed but requires a deeper investigation, whereas its application to positive blood cultures for the identification of microorganisms and the detection of antimicrobial resistance are now the most useful applications. Thanks to its rapidity, accuracy, and low price in reagents and consumables, MALDI-TOF MS has also been applied to different fields of clinical microbiology, such as the detection of antibiotic susceptibility/resistance biomarkers, the identification of aminoacidic sequences and the chemical structure of protein terminal groups, and as an emerging method in microbial typing. Some of these applications are waiting for an extensive evaluation before confirming a transfer to the routine. MALDI-TOF MS has not yet been used for the routine identification of parasites; nevertheless, studies have been reported in the last few years on its use in the identification of intestinal protozoa, Plasmodium falciparum, or ectoparasites. Innovative applications of MALDI-TOF MS to viruses' identification were also reported, seeking further studies before adapting this tool to the virus's diagnostic. This mini-review is focused on the MALDI-TOF MS application in the real life of the diagnostic microbiology laboratory.

Nano-Biotechnology for Bacteria Identification and Potent Anti-bacterial Properties: A Review of Current State of the Art

Sepsis is a critical disease caused by the abrupt increase of bacteria in human blood, which subsequently causes a cytokine storm. Early identification of bacteria is critical to treating a patient with proper antibiotics to avoid sepsis. However, conventional culture-based identification takes a long time. Polymerase chain reaction (PCR) is not so successful because of the complexity and similarity in the genome sequence of some bacterial species, making it difficult to design primers and thus less suitable for rapid bacterial identification. To address these issues, several new technologies have been developed. Recent advances in nanotechnology have shown great potential for fast and accurate bacterial identification. The most promising strategy in nanotechnology involves the use of nanoparticles, which has led to the advancement of highly specific and sensitive biosensors capable of detecting and identifying bacteria even at low concentrations in very little time. The primary drawback of conventional antibiotics is the potential for antimicrobial resistance, which can lead to the development of superbacteria, making them difficult to treat. The incorporation of diverse nanomaterials and designs of nanomaterials has been utilized to kill bacteria efficiently. Nanomaterials with distinct physicochemical properties, such as optical and magnetic properties, including plasmonic and magnetic nanoparticles, have been extensively studied for their potential to efficiently kill bacteria. In this review, we are emphasizing the recent advances in nano-biotechnologies for bacterial identification and anti-bacterial properties. The basic principles of new technologies, as well as their future challenges, have been discussed.

Disintegration and Machine-Learning-Assisted Identification of Bacteria on Antimicrobial and Plasmonic Ag-CuxO Nanostructures

Bacteria cause many common infections and are the culprit of many outbreaks throughout history that have led to the loss of millions of lives. Contamination of inanimate surfaces in clinics, the food chain, and the environment poses a significant threat to humanity, with the increase in antimicrobial resistance exacerbating the issue. Two key strategies to address this issue are antibacterial coatings and effective detection of bacterial contamination. In this study, we present the formation of antimicrobial and plasmonic surfaces based on Ag-Cu_xO nanostructures using green synthesis methods and low-cost paper substrates. The fabricated nanostructured surfaces exhibit excellent bactericidal efficiency and high surface-enhanced Raman scattering (SERS) activity. The Cu_xO ensures outstanding and rapid antibacterial activity within 30 min, with a rate of >99.99% against typical Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus bacteria. The plasmonic Ag nanoparticles facilitate the electromagnetic enhancement of Raman scattering and enables rapid, label-free, and sensitive identification of bacteria at a concentration as low as 10³ cfu/mL. The detection of different strains at this low concentration is attributed to the leaching of the intracellular components of the bacteria caused by the nanostructures. Additionally, SERS is coupled with machine learning algorithms for the automated identification of bacteria with an accuracy that exceeds 96%. The proposed strategy achieves effective prevention of bacterial contamination and accurate identification of the bacteria on the same material platform by using sustainable and low-cost materials.

Targeted Sequencing Approach and Its Clinical Applications for the Molecular Diagnosis of Human Diseases

The outbreak of COVID-19 has positively impacted the NGS market recently. Targeted sequencing (TS) has become an important routine technique in both clinical and research settings, with advantages including high confidence and accuracy, a reasonable turnaround time, relatively low cost, and fewer data burdens with the level of bioinformatics or computational demand. Since there are no clear consensus guidelines on the wide range of next-generation sequencing (NGS) platforms and techniques, there is a vital need for researchers and clinicians to develop efficient approaches, especially for the molecular diagnosis of diseases in the emergency of the disease and the global pandemic outbreak of COVID-19. In this review, we aim to summarize different methods of TS, demonstrate parameters for TS assay designs, illustrate different TS panels, discuss their limitations, and present the challenges of TS concerning their clinical application for the molecular diagnosis of human diseases.

Label-free bacteria identification for clinical applications

We have developed a system for bacteria identification based on absorption spectroscopy in the mid-infrared spectral range. The data collected are analyzed with a deep learning algorithm. It is based on a neural-network model which takes one-dimensional signal vectors and outputs a probability score of identification of a bacterium type by extracting micro and macro scale features, using convolutions and nonlinear operations. The results are achieved in real time and do not require any offline postprocessing. The study was done on 12 of the most common bacteria usually seen in clinical microbiology laboratories. The system sensitivity is 0.94 ± 0.04, with a specificity of 0.95 ± 0.02. The system can be extended to additional bacterium types and variants with no change to its hardware or software, but only updating the model's parameters. The system's accuracy, size, ease of operation and low cost make it suitable for use in any type of clinical setting.

Rapid identification of methicillin-resistant Staphylococcus aureus by MALDI-TOF MS: A meta-analysis

Invasive infections caused by methicillin-resistant Staphylococcus aureus (MRSA) are associated with high mortality and morbidity. The sooner the pathogen is determined, the better it is beneficial to patient. However, routine laboratory inspections are time-consuming and laborious. A thorough research was conducted in PubMed and Web of Science (until June 2021) to identify studies evaluating the accuracy of MRSA identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). STATA 15.0 software was used to analyze the pooled results of sensitivity, specificity, and 95% confidence intervals (CI). The summary receiver operating characteristic curves (SROC) and area under the curve (AUC) were utilized to show the overall performance of MALDI-TOF MS. Fifteen studies involving 2471 isolates were included in this study after the final selection in this meta-analysis. Using the random effects model forest plot to summarize the overall statistics, the sensitivity of MALDI-TOF MS for identifying MRSA was 92% (95% CI: 81%-97%), and the specificity was 97% (95% CI: 89%-99%). In the SROC curve, the AUC reached 0.99 (95% CI: 97%-99%). Deeks' test showed no significant publication bias in this meta-analysis. Compared with clinical reference methods, MALDI-TOF MS identification of MRSA shows a higher degree of sensitivity and specificity.

A Lab-Made E-Nose-MOS Device for Assessing the Bacterial Growth in a Solid Culture Medium

The detection and level assessment of microorganisms is a practical quality/contamination indicator of food and water samples. Conventional analytical procedures (e.g., culture methods, immunological techniques, and polymerase chain reactions), while accurate and widely used, are time-consuming, costly, and generate a large amount of waste. Electronic noses (E-noses), combined with chemometrics, provide a direct, green, and non-invasive assessment of the volatile fraction without the need for sample pre-treatments. The unique olfactory fingerprint generated during each microorganism's growth can be a vehicle for its detection using gas sensors. A lab-made E-nose, comprising metal oxide semiconductor sensors was applied, to analyze solid medium containing Gram-positive (Enterococcus faecalis and Staphylococcus aureus) or Gram-negative (Escherichia coli and Pseudomonas aeruginosa) bacteria. The electrical-resistance signals generated by the E-nose coupled with linear discriminant analysis allowed the discrimination of the four bacteria (90% of correct classifications for leave-one-out cross-validation). Furthermore, multiple linear regression models were also established allowing quantifying the number of colony-forming units (CFU) (0.9428 ≤ R² ≤ 0.9946), with maximum root mean square errors lower than 4 CFU. Overall, the E-nose showed to be a powerful qualitative-quantitative device for bacteria preliminary analysis, being envisaged its possible application in solid food matrices.

First preliminary study on identification of bacterial fish pathogens with Raman spectroscopy

Accurate and rapid determination of bacterial disease agents of fish is an important step for sustainable and efficient aquaculture production. In general, biochemical and molecular methods are used for pathogen detection but they are usually time-consuming and required qualified personnel. Recently spectroscopic methods are preferred in clinical and food microbiology and declared as a promising alternative method for pathogens diagnosis with many advantages. In this study, the significant spectra of three important bacterial fish pathogens (Lactococcus garvieae, Vibrio anguillarum and Yersinia ruckeri) were determined by Raman spectroscopy. The first data of the pathogens were obtained and the distinctive differences in polysaccharides, nucleic acids, fatty acids and amino acids were identified. This preliminary study aimed to be pioneer for further studies in aquaculture and veterinary microbiology toward developing an alternative method for routine identification.

Development of the Correction Algorithm to Limit the Deformation of Bacterial Colonies Diffraction Patterns Caused by Misalignment and Its Impact on the Bacteria Identification in the Proposed Optical Biosensor

Recently proposed methods of bacteria identification in optical biosensors based on the phenomenon of light diffraction on macro-colonies offer over 98% classification accuracy. However, such high accuracy relies on the comparable and repeatable spatial intensity distribution of diffraction patterns. Therefore, it is essential to eliminate all non-species/strain-dependent factors affecting the diffraction patterns. In this study, the impact of the bacterial colony and illuminating beam misalignment on the variation of classification features extracted from diffraction patterns was examined. It was demonstrated that misalignment introduced by the scanning module significantly affected diffraction patterns and extracted classification features used for bacteria identification. Therefore, it is a crucial system-dependent factor limiting the identification accuracy. The acceptable misalignment level, when the accuracy and quality of the classification features are not affected, was determined as no greater than 50 µm. Obtained results led to development of image-processing algorithms for determination of the direction of misalignment and concurrent alignment of the bacterial colonies’ diffraction patterns. The proposed algorithms enable the rigorous monitoring and controlling of the measurement’s conditions in order to preserve the high accuracy of bacteria identification.

Fluorescent Materials With Aggregation-Induced Emission Characteristics for Array-Based Sensing Assay

Array-based sensing is a powerful tool for identifying analytes in complex environments with unknown interferences. In array-based sensing, the sensors, which transduce binding details to signal outputs, are of crucial importance for identifying analytes. Aggregation-induced emission luminogens (AIEgens) enjoy the advantages of easy synthesis and high sensitivity, which enable them to facilely form a sensor pool through structural modifications and sensitively reflect the subtle changes associated with binding events. All these features make AIEgens excellent candidates for array-based sensing, and attempts have been made by several research groups to explore their potentials in array-based sensing. In this review, we introduce the recent progresses of employing AIEgens as sensors in sensing assays and in building up sensor arrays for identification of varied biological analytes, including biomolecules and bacteria. Examples are selected to illustrate the working mechanism, probe design and selection, capability of the sensor array, and implications of these sensing methods.

Rapid Identification of Bacteria by Membrane-Responsive Aggregation of a Pyrene Derivative

An imidazolium-derived pyrene aggregation was developed to rapidly identify and quantify different bacteria species. When the nonemissive aggregates bound to the anionic bacteria surface, the sensor disassembled to turn on significant fluorescence. At the same time, ratiometric signals between pyrene monomer and excimer emission were controlled by different interactions with various bacteria surfaces. The resulted different fluorescent emission profiles then were obtained as fingerprints for various bacterial species. By converting emission profiles directly into output signals of two channels, fluorescence increase and ratiometric change, a two-dimensional analysis map was generated for bacteria identification. We demonstrated that our sensor rapidly identified 10 species of bacteria and 14 clinical isolated multidrug-resistant bacteria, and we determined their staining properties (Gram-positive or Gram-negative).

Detection and identification of bacterial pathogens directly from sputum samples by pyrosequencing

PURPOSE

The standard culture findings for detecting and identifying bacterial pathogens in patients with lower respiratory tract infections (LRTIs) are usually not available for two to three days, which delays the initiation of appropriate antibiotic therapies. We aimed to develop a faster method of identification of bacterial pathogens in LRTIs which would offer a timelier guide to initial antibiotic choices.

METHODOLOGY

The developed PCR-pyrosequencing-based method was defined as mask PCR-pyrosequencing (MPP). This method uses primer pairs deliberately designed to mask the interference of colonised bacteria in sputum to detect and identify bacterial pathogens directly from LRTI patient sputum samples within 5 h. Accordingly, the standard PCR-pyrosequencing method was defined as normal PCR-pyrosequencing (NPP) here. The clinical performance of the novel system was evaluated by comparing with traditional semi-quantitative culture and identification results.

RESULTS

The coincidence for culture and MPP was 91.3 %. Compared with the semi-quantitative culture results, NPP identified 89.9 % strains of grade 3+ (corresponding to 1.0×10⁶ c.f.u ml^-1) and 100 % of grade 4+ (corresponding to 1.0×10⁷ c.f.u ml^-1), both of which were considered to be the presumptive pathogens in the clinics. MPP identified 98.9 % strains of grade 3+ and 100 % of grade 4+. Additionally, PCR-pyrosequencing could detect a minimum concentration of 1.0×10⁶ c.f.u ml^-1 of bacteria in sputum, with no significant difference between NPP and MPP.

CONCLUSION

The PCR-pyrosequencing technique developed in this study is an accurate, fast, and high throughput method for the direct detection and identification of bacterial pathogens from sputum.

Identification and Antibiotic-Susceptibility Profiling of Infectious Bacterial Agents: A Review of Current and Future Trends

Antimicrobial resistance is one of the most worrying threats to humankind with extremely high healthcare costs associated. The current technologies used in clinical microbiology to identify the bacterial agent and profile antimicrobial susceptibility are time-consuming and frequently expensive. As a result, physicians prescribe empirical antimicrobial therapies. This scenario is often the cause of therapeutic failures, causing higher mortality rates and healthcare costs, as well as the emergence and spread of antibiotic resistant bacteria. As such, new technologies for rapid identification of the pathogen and antimicrobial susceptibility testing are needed. This review summarizes the current technologies, and the promising emerging and future alternatives for the identification and profiling of antimicrobial resistance bacterial agents, which are expected to revolutionize the field of clinical diagnostics.

Fluorescent nanodiamond-bacteriophage conjugates maintain host specificity

Rapid identification of specific bacterial strains within clinical, environmental, and food samples can facilitate the prevention and treatment of disease. Fluorescent nanodiamonds (FNDs) are being developed as biomarkers in biology and medicine, due to their excellent imaging properties, ability to accept surface modifications, and lack of toxicity. Bacteriophages, the viruses of bacteria, can have exquisite specificity for certain hosts. We propose to exploit the properties of FNDs and phages to develop phages conjugated with FNDs as long-lived fluorescent diagnostic reagents. In this study, we develop a simple procedure to create such fluorescent probes by functionalizing the FNDs and phages with streptavidin and biotin, respectively. We find that the FND-phage conjugates retain the favorable characteristics of the individual components and can discern their proper host within a mixture. This technology may be further explored using different phage/bacteria systems, different FND color centers and alternate chemical labeling schemes for additional means of bacterial identification and new single-cell/virus studies.

Development of a multispectral light-scatter sensor for bacterial colonies

We report a multispectral elastic-light-scatter instrument that can simultaneously detect three-wavelength scatter patterns and associated optical densities from individual bacterial colonies, overcoming the limits of the single-wavelength predecessor. Absorption measurements on liquid bacterial samples revealed that the spectroscopic information can indeed contribute to sample differentiability. New optical components, including a pellicle beam splitter and an optical cage system, were utilized for robust acquisition of multispectral images. Four different genera and seven shiga toxin producing E. coli serovars were analyzed; the acquired images showed differences in scattering characteristics among the tested organisms. In addition, colony-based spectral optical-density information was also collected. The optical model, which was developed using diffraction theory, correctly predicted wavelength-related differences in scatter patterns, and was matched with the experimental results. Scatter-pattern classification was performed using pseudo-Zernike (GPZ) polynomials/moments by combining the features collected at all three wavelengths and selecting the best features via a random-forest method. The data demonstrate that the selected features provide better classification rates than the same number of features from any single wavelength. Three wavelength-merged scatter pattern from E. coli.

Laboratory identification of anaerobic bacteria isolated on Clostridium difficile selective medium

Despite increasing interest in the bacterium, the methodology for Clostridium difficile recovery has not yet been standardized. Cycloserine-cefoxitin fructose taurocholate (CCFT) has historically been the most used medium for C. difficile isolation from human, animal, environmental, and food samples, and presumptive identification is usually based on colony morphologies. However, CCFT is not totally selective. This study describes the recovery of 24 bacteria species belonging to 10 different genera other than C. difficile, present in the environment and foods of a retirement establishment that were not inhibited in the C. difficile selective medium. These findings provide insight for further environmental and food studies as well as for the isolation of C. difficile on supplemented CCFT.

Direct Identification of Aerobic Bacteria by Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry Is Accurate and Robust

BACKGROUND

Bacterial identification in the clinical laboratory can be laborious and expensive. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a rapid and cost-effective diagnostic tool for the identification of organisms routinely found in the microbiology laboratory. The objective of this study was to demonstrate that identification of aerobic Gram-positive and Gram-negative organisms could be performed accurately and efficiently by MALDI-TOF MS and the Bruker Biotyper system without the use of time-consuming extraction methodologies.

METHODS

Isolates previously recovered by routine culture and workup from clinical specimens were cultured to appropriate media, identified directly by MALDI-TOF MS, and compared to results from various biochemical identification methods.

RESULTS

Using the direct-smear method, 99.5% and 98.0% of aerobic Gram-negative and Gram-positive bacteria, respectively, were identified to the genus level. At a score of ≥1.9, 97.6% Gram-negative organisms and 94.6% Gram-positive organisms were correctly identified to the species level by direct-smear method. Only 1.1% of isolates required further reflex to direct-plate extraction. The direct-smear method proved to be robust, as various growth temperatures, media, culture age, and different operators had no notable impact on the bacterial identification rate.

CONCLUSION

The direct-smear method is an accurate and time-saving method for routine species-level bacterial identification.

Comparative study of MALDI-TOF MS and VITEK 2 in bacteria identification

BACKGROUND

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has recently been introduced in diagnostic microbiology laboratories for the identification of bacterial and yeast strains isolated from clinical samples. This study aimed to evaluate the accuracy of MALDI-TOF MS in clinical microbiology diagnosis by comparing it with commonly-used VITEK 2 or gene sequencing.

METHODS

The performances of MALDI-TOF MS and VITEK 2 were compared retrospectively for identifying routine isolates. Discrepancies were analyzed by gene sequencing analysis of the 16S genes.

RESULTS

For 1,025 isolates, classified as 55 species of 25 genera, 1,021 (99.60%) isolates were accurately identified at the genus level, and 957 (93.37%) isolates at the species level by using MALDI-TOF MS. A total of 949 (92.59%) isolates were completely matched by both methods. Both methods found 76 unmatched isolates among which one strain had no definite identification by MALDI-TOF MS and VITEK 2 respectively. However, MALDI-TOF MS made no errors at the genus level while VITEK 2 made 6 (0.58%) errors at the genus level. At the species level, the identification error rates for MALDI-TOF MS and VITEK 2 were 5.56% and 6.24%, respectively.

CONCLUSIONS

With a lower identification error rate, MALDI-TOF MS has better performance than VITEK 2 in identifying bacteria found routinely in the clinical laboratory. It is a quick and cost-effective technique, and has the potential to replace conventional phenotype methods in identifying common bacterial isolates in clinical microbiology laboratories.