In a recent study published in Scientific Reports, researchers introduced a novel method called atmospheric pressure photoionization mass spectrometry (APPI-MS) for detecting and identifying bacteria that cause lung infections in cystic fibrosis (CF) patients.
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They proposed a custom-made APPI source that can directly analyze volatile organic compounds (VOCs) in bacterial headspace samples without reagents. They aimed to identify and classify different types of bacteria related to CF infections.
Background
CF is a genetic disorder that causes thick mucus and poor mucociliary clearance, leading to chronic lung infections. Early diagnosis and treatment of these infections are crucial to reduce mortality. However, conventional tests for bacterial infections are often invasive, slow, expensive, or unreliable. Therefore, alternative methods that can quickly and accurately identify the bacteria causing infections in CF patients are needed.
One promising approach is to analyze the VOCs emitted by bacteria, which provide information about their metabolic activity and identity. VOCs can be detected in the headspace of bacterial cultures or the breath of infected patients.
MS is a powerful technique for VOC analysis, offering high sensitivity, specificity, and speed. However, most MS methods require extensive sample preparation, chromatographic separation, or reagent addition, limiting their use for online or real-time analysis.
About the Research
In this paper, the authors designed and developed a new APPI-MS method for direct, reagent-free, and real-time analysis of VOCs in bacterial headspace samples.
APPI uses ultraviolet (UV) photons to ionize molecules with lower ionization energy than the photon energy. This technique ionizes a wide range of compounds, including VOCs, with fewer matrix and ion suppression effects compared to other techniques. However, APPI requires a gaseous dopant like acetone or toluene to enhance ionization efficiency and sensitivity.
The study modified a commercial APPI source by adding a custom-made three-dimensional (3D) printed sampling chamber, a bias electrode, and a vaporizing heater.
The bias electrode applies a positive potential between the UV lamp and the MS inlet, increasing the transmission of positive ions to the MS. The vaporizing heater helps vaporize liquid analytes introduced into the chamber. The sampling chamber can accommodate gaseous and liquid samples and is easily adapted to fit any MS interface.
The researchers optimized the APPI-MS method parameters, including cone voltage, bias voltage, carrier gas flow rate, and vaporizing temperature, using VOC standards relevant to breath analysis. They obtained linear calibration curves and low detection limits for ethanol, 2-butanone, acetone, eucalyptol, and ethyl acetate.
The authors also characterized the ion distribution and beam diameter generated by the UV lamp under different electric field intensities. They also applied their method to analyze headspace samples of three types of bacteria: Escherichia coli (EC), Pseudomonas aeruginosa (PA), and Staphylococcus aureus (SA).
PA and SA are generally associated with lung infections in CF patients, while EC, unrelated to CF, served as a control. Headspace samples were collected in 1 L Tedlar bags and transferred to the APPI-MS interface for analysis.
Research Findings
The authors obtained distinct mass spectra for each type of bacteria, with several peaks appearing only in one of the samples. They performed principal component analysis (PCA) to visualize the spectral differences and reduce data dimensionality.
The first three principal components accounted for 86.9 % of the total variance, with samples well separated and grouped by bacterial type. The researchers also formed a linear discriminant classification model using the 247 peaks across all samples, achieving 100 % classification accuracy for the training and blind test data sets using a low-resolution mass spectrometer in full scan mode.
Applications
The novel APPI-MS method has several advantages and implications for bacterial detection and identification. It is rapid, sensitive, cost-effective, and potentially portable, as it does not require any reagents, chromatographic separation, or high-resolution MS.
It can directly analyze VOCs in gaseous or liquid samples, making it suitable for online or real-time analysis. The method can discriminate between different types of bacteria, aiding in the diagnosis and monitoring of CF patients. It can also be applied to other samples, such as breath, blood, urine, or environmental samples containing VOCs of interest.
Conclusion
The novel technique proved effective for bacterial headspace and breath analysis, classifying three types of bacteria with 100 % accuracy in a blind study using a low-resolution mass spectrometer. The method has potential applications for non-invasive, real-time, and cost-effective diagnosis and monitoring of CF patients, as well as other fields involving VOC analysis.
Moving forward, researchers acknowledged limitations and challenges, including the need for a standardized breath sampling methodology, the influence of environmental factors and human physiology on breath composition, and the difficulty of handling Class 2 bacterial cultures in the lab.
They suggested future directions such as developing a portable system, improving the breath sampling interface, adding flow controllers and gas sensors, and conducting clinical investigations with CF patients.
Additionally, they recommended validating their approach and comparing it with other existing methods, such as secondary electrospray ionization MS (SESI-MS) and selected ion flow tube MS (SIFT-MS). They suggested utilizing APPI-MS for other applications, such as food analysis and environmental monitoring.
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Journal Reference
Haworth-Duff, A., et al. (2024). Rapid differentiation of cystic fibrosis-related bacteria via reagentless atmospheric pressure photoionisation mass spectrometry. Sci Rep. DOI: 10.1038/s41598-024-66851-y
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