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Early and accurate pathogen identification is essential to optimise patient care. This includes assigning he correct antimicrobial to treat a specific pathogen of concern. This is an area where rapid methods can prove highly beneficial. The conventional approach for pathogen identification involves adding blood samples to bottles of liquid media in which infectious microbes, if present, are amplified to a certain density.
Improving the time-to-result and doing so with greater accuracy has become a topic of concern for the microbiology laboratory as much as it has for other economic sectors. Achieving the result early may improve the chances of patient survival or it may help a firm to reduce inventory hold times.
There is a wide spectrum of rapid microbiological methods available, albeit some are at a more advanced stage than others. Technologies vary although the generally share the ability to capture data digitally and they offer greater accuracy and specificity. Such methods are also shifting the traditional skill sets found in microbiology laboratories towards biochemistry and biomedical engineering.
Focusing on innovations within the rapid microbiology space that have occurred during the past 12-18 months (2021 to 2022), this article surveys and explains some of the most promising rapid microbiological innovations.
These organisms are then grown on solid media and isolated colonies are identified using microbial identification systems like MALDI-TOF mass spectrometry (MS) (1).
Culture plates: Representative Image
A new innovation speeds up the above process. This process can be accelerated in some cases by using separation techniques designed to separate microorganisms from blood using host cell lysis and centrifugation. These processes can also release host proteins during the lysis that this can introduce reduce pathogen identification scores.
Researchers at Wyss Institute for Biologically Inspired Engineering at Harvard have re-engineered the process of microbial pathogen identification in blood samples from paediatric sepsis patients using broad-spectrum pathogen capture technology. The method enables accurate pathogen detection from patients with bloodstream infections and sepsis.
The new method uses FcMBL as the key component of a broad-spectrum pathogen capture technology (2). This consists of a genetically engineered human immune protein called mannose-binding lectin (MBL) that is fused to the Fc fragment of an antibody molecule to produce the resulting ‘FcMBL’ protein.
Inoculation :Representative Image
Native MBL is an ‘opsonin’ that binds to numerous pathogens from all microbial classes as well as many microbial toxins. Such toxins are Pathogen Associated Molecular Patterns (PAMPs), which trigger the inflammatory cascade that leads to organ injury and sepsis (3). With microbial cell binding, MBL binds various sugar motifs, including mannose, mannan, and N-acetylglucosamine, in a calcium-dependent manner (4).
In this configuration, the MBL portion of FcMBL can capture more than 100 different microbial species with high efficiency. This includes the primary bacterial and fungal pathogens responsible for sepsis. FcMBL’s Fc portion is used to couple microorganisms to magnetic beads, allowing the captured pathogens to be extracted more quickly. The identification result continues to be delivered by conventional identification methods.
In tests, the FcMBL approach has led to the identification of pathogenic organisms between 24 to 48 hours earlier than would be possible using standard culture techniques. In terms of accuracy, trials have demonstrated a 94.1% accuracy in terms of the capture and identification of microbial species isolated in clinical blood culture analysis from samples drawn from 68 paediatric patients.
Urinary tract infections are among the most common infections in the world and one point of origin is in the hospital setting where nosocomial infections pose a patient risk via the indwelling catheter route. There is a clinical advantage in having a rapid diagnostic tool to identify the causative agent and to understand its susceptibility to antimicrobials. Such a method is presented through Raman spectroscopy.
Representative Image
The potential for Raman spectroscopy has been evaluated against the most common microbes causing urinary tract infections (some 20 species and 254 strains). Raman spectroscopy is a non-destructive chemical analysis technique that generates information about chemical structure, phase and polymorphy, crystallinity and molecular interactions (5). Essentially, scattered light is used to measure the vibrational energy modes of a sample. The identification of a substance (including a microorganism) arises through their characteristic Raman ‘fingerprint’. The technology works well against specific pathogens.
The research assessed isolates grown on Mueller-Hinton agar plates and this showed it is possible to distinguish among the tested species using Raman spectroscopy through close to real-time analysis (6).
Further evaluation indicates that rapid identification should be possible (in less than 10 minutes) based on a single microbial cell found directly in urine. This is by deploying optical tweezers combined with Raman spectroscopy. Optical trapping (or optical tweezers is a technique that uses light scattering to hold an object in place). The main challenge is where there is only a weak Raman signal from bacterial cells and numerous the complications of differentiation caused by bacterial species and phenotypes.
Once isolation has been optimised, bacterial identification can take many hours or sometimes longer. In addition, accurate identification can be challenging where there are similarities between closely related species, which share the same morphological, biochemical, and genetic pattern. The overall microbial identification time can be shortened as demonstrated by teaching a deep learning algorithm to identify the ‘fingerprint’ spectra of the molecular components of various bacteria. So far, the data suggests it is possible to classify various bacteria in different media with accuracies up to 98%. The algorithm is based on the functionality of a dual-branch wide-kernel network.
This is possible by using surface-enhanced Raman spectroscopy (SERS) analysis, which is a a variant of Raman spectroscopy discussed above. This is used in conjunction with a deep learning model, based on optimising the route to classify the signals of two common bacteria and their resident media without the need for any separation procedures. This form of Raman spectroscopy functions by sending light through a sample to observe how it scatters.
This reveals structural information about the sample (referred to as the spectral fingerprint) and this ensures trained personnel are used to identify its molecules (7).
Also read:Importance of Pharmaceutical Microbiology
Current technologies can obtain difficulties especially with developing consistent and clear spectra of bacteria. This is due to numerous overlapping peak sources, such as proteins in cell walls. These approaches are also relatively time- consuming and tedious. The application of training a convolutional neural network (CNN) helps to classify noisy bacterial spectra by isolate, empiric treatment, and antibiotic resistance.
There is also the potential for Raman spectroscopy to be used to determine antimicrobial susceptibility through the detection of the metabolic incorporation of deuterium oxide (D2O), or heavy water in bacteria. With this technique, bacteria are incubated with a D2O-containing medium. From this, the characteristic metabolic activity of bacteria can be assessed by quantifying the conversion of D2O to C–D bonds of biomolecules. The accuracy of this approach has been enhanced by advances with Raman scattering technology (8).
To address the detection of identifying food poisoning-inducing bacteria, new technology has been developed based on colour differences in the scattered light of composite structures consisting of gold, silver, and copper nanoparticles and polymer particles. This allows for the simultaneous identification of multiple bacterial species within one hour, significantly shortening the usual 48-hour time requirement for conventional bacterial detection tests. The use of silver and copper builds upon earlier work that has evaluated gold nanoparticles. Such nanoparticles have a number of useful properties including chemical inertness, surface, high electron density and, most importantly, and strong optical absorption (9).
BioSensors: Representatiev Image
Scientists have demonstrated that by using these composites as test labels bound to specific bacteria, it is possible to detect food poisoning bacteria Escherichia coli O26, E. coli O157, and Staphylococcus aureus as white, red, and blue scattered light, respectively, under the microscope (identified within a single field of view under a dark field microscope). The colour differences were obtained via the scattered light from nanometre-scaled organic metal nanohybrid structures (NHs). The NHs bind via antibodies to specific bacteria.
NHs are composites consisting of polyaniline particles that encapsulate a large number of metal nanoparticles.
The basis of the technology is founded upon organic metal NHs producing strong, scattered light – stronger than metal nanoparticles of the same size.
As the scattered light of these NHs is stable in the air for a long period of time, they function as stable and highly sensitive labelling materials. The NHs exhibit different colours of scattered light (white, red, and blue) depending on the metal elements of the nanoparticles (gold, silver, and copper). Using this approach, scientists introduced antibodies that bind specifically to E. coli O26, E. coli O157, and S. aureus into the organic metal NHs. These NHs were used labels to evaluate the binding properties of the antibody-conjugated NHs to specific bacterial species (10).
Testing this approach under more challenging conditions, the scientists further established that by adding predetermined amounts of E. coli O26, E. coli O157, and S. aureus to rotten meat samples containing various species of bacteria, it was possible to use these labels to simultaneously identify each bacterial species added.
Since the process does not require microbial culturing, bacteria can be rapidly detected within one hour, increasing its practicality as a new testing method. Hence, the approach holds a promise of introducing portable and rapid detection methods that are cost-effective, quick and able to be used in a variety of different fields including quality control and clinical diagnostics.
Lab-on-a-chip technology has seen advances within the field of point-of-care (POC) devices. These devices have the potential to move laboratory analyses away from traditional diagnostic platform to new and advanced diagnostic technology. Devices consist of small chip-based, miniaturized platforms that can be used for the immediate detection of different pathogens using analytes of low volumes.
These devices are based on microfluidic technologies. Microfluidics integrated with biosensor technology allows the detection of small volumes of liquid in a channelled and automated means to improve diagnostics. Microfluidic devices work by exploiting the physical and chemical properties of liquids at a microscale.
One of the advantages, as well as using lower sample volumes, is the ability to screen samples quickly by running multiple tests in a single test session. One of the most challenging aspects with microfluidic design relates to the physics of the devices, which require an extremely low Reynolds number (a product of fluid inertia compared with the viscosity ratio), resulting in strictly laminar flow.
Biosensors can be developed to screen for selected microorganisms and biosensors using various technologies can reliably detect specific pathogens of interest to clinical and food preparation areas. An example is with microfluidic platforms based on immunomagnetic nanoparticles combined with urease and impedance measurement (11).
An alternative development is with a microfluidic fluorescence quantitative PCR system with pneumatic valve and a tree structure was developed by using 3D printing technology (12). This is necessary because the objective is to create well-defined microenvironments that mimic the natural habitats of bacteria (13).
Microbiologists have evaluated a new method for assessing the antimicrobial efficacy of detergents and textile additives. The approach is based on statistical analysis based on the results of an international ring trial designed to evaluate the robustness of a method s designed to test the efficacy of detergents against microorganisms in a domestic environment. The participating laboratories were equipped with five laboratory-scale devices simulating domestic washing machines, in which seven parameters — including the removal of Escherichia coli or Staphylococcus aureus adhering to fabrics — were evaluated at different levels of active substance and at different temperatures.
Of the different statistical measures, arguably the most important was robustness (14). This refers to the measure of the method’s ability not to be affected by small but deliberate variations in the experiment, such as using different washing machines or different temperatures. Also considered to be of importance was repeatability, which relates to demonstrating that if the same method is used with the same procedure in another laboratory, then similar results will be obtained (15).
To enable large quantities of data to be assessed, the researchers created a new library (the program that allows very fast calculations) in an R-language called Diagnobatch.
This created the tool that can be used in the industrial environment. R is a programming language for statistical computing. R’s data structures include vectors, arrays, lists, and data frames (16). The results indicate that the method is sensitive to disinfectant concentration. Here the higher the dose of active ingredient, then the greater the reduction in microbial numbers.
The faster method may be adopted by the European Committee for Standardisation (CEN) as a European standard.
Culture-based growth and identification methods lead to delays in taking the necessary corrective and preventative actions required to address contamination within an ecological niche or the built-environment. In contrast, developments with high-throughput methods provide culture-independent assessments of microbial communities from contaminated sites.
These are methods that use molecular tools, such as next-generation sequencing (NGS) approaches. NGS approaches can be superior to polymerase chain reaction (PCR) methods, since PCR is sometimes prone to false-positive results due to DNA contamination (17). However, in other applications PCR is very reliable. PCR targets and amplifies specific sections of microbial nucleic acids to provide a highly specific detection technique, and which, in the case of real-time PCR where amplification and detection are simultaneous, can produce results within a few hours.
Optimally, NGS technologies allow for a determination of the DNA sequence of a complete bacterial genome in a single sequence run. Different variants of the technology are used to determine the order of nucleotides in entire genomes or targeted regions of DNA or RNA. From such data, information on resistance and virulence can be obtained. Studies have shown that species of Acinetobacter, Arcobacter, and Clostridium can be identified from samples as potential pathogenic bacteria using 16S rRNA gene NGS. For these evaluations, Genomic DNA was cloned from different samples entering the 16S rRNA gene V4 region, and pathogenic species were described by comparing the sequences with a reference human pathogenic bacteria database (18).
Many medical and pharmaceutical processes require microbiologists to monitor or evaluate relevant microorganisms by using specific, sensitive, and reliable detection, identification, or enumeration methods. New microbiological methods are developed to improve upon some of the disadvantages of ‘conventional’ methods (which requires appropriate statistical techniques to demonstrate non-inferiority using Poisson distributed counts).
These are referred to as rapid or alternate methods. Before rapid methods can be used, they have to be properly validated to ensure that the performance is similar to, or better than, that of the standard method. This article has highlighted some of the advanced sin the past 18 months relating to rapid microbiological methods, indicating the progress away from culture-dependent techniques in some important areas.
