Methods and systems for detecting aerosol particles without using complex organic MALDI matrices
Inventors
McLoughlin, Michael • Bryden, Wayne A. • Call, Charles J. • Chen, Dapeng
Assignees
Publication Number
US-11996280-B2
Publication Date
2024-05-28
Expiration Date
2040-06-27
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Abstract
Disclosed are systems are methods for identifying the composition of single aerosol particles, particularly that of bioaerosol particles, without pre-treatment using complex organic MALDI matrices. A continuous timing laser may be used to index aerosol particles, measure particle properties, and trigger a pulse ionization laser. Ionized fragments and optionally photons associated with each particle producing by the ionization laser may be analyzed using one or more detectors including a TOF-MS detector and an optical detector. The laser pulse may comprise a simultaneous IR and UV laser pulse when fragments comprise predominantly of UV chromophores. Unique spectral data associated with each indexed particle from each detector may be compiled using data fusion to generate compiled spectral data. Machine learning methods may be used to improve the prediction of composition over time.
Core Innovation
The invention provides methods and systems for the identification of the composition of single aerosol particles, particularly bioaerosol particles, without requiring complex sample pre-treatment using organic MALDI matrices. Instead, it employs the use of a continuous timing laser to index aerosol particles, measure their properties (such as size, shape, and fluorescence), and trigger a pulse ionization laser that simultaneously produces both IR and UV laser pulses when appropriate. This dual-laser approach generates ionized fragments and photons from each indexed particle for subsequent detection.
These fragments, along with associated optical signals, are analyzed using detectors including time-of-flight mass spectrometers (TOF-MS) and optical detectors. The unique spectral data gathered from each particle is compiled using data fusion methods to form comprehensive compiled spectral data. Machine learning, including both supervised and unsupervised techniques, is then applied to this data to predict and improve identification of each particle's composition over time.
The problem addressed is the lack of real-time detection and identification of aerosol analytes, especially for bioaerosols such as bacteria, viruses, fungi, and toxins. Existing methods require lengthy and complex sample processing, like culturing or extraction, often taking many hours or days, which is not feasible for timely response in biodefense or other urgent applications. The disclosed invention enables rapid or real-time analysis and accurate identification of aerosol analyte particles without the need for complex pre-treatment.
Claims Coverage
The patent includes two independent claims, covering a system and a method for identifying the composition of bioaerosol particles without pre-treatment using complex organic MALDI matrices.
System for identifying bioaerosol particle composition without complex organic MALDI matrices
The system comprises: - An aerosol beam generator producing a beam of single particles. - A continuous laser generator, integrated with a data analysis system, that indexes each particle, optically characterizes properties including particle size, shape, and fluorescence, and selects which particles to ionize. - A pulse ionization laser generator, triggered by the continuous laser, that simultaneously generates IR and UV laser pulses with an ionization region less than about 150 μm in diameter, exposing selected particles to produce ionized fragments (with molecular weight between about 1 kDa and 150 kDa) and photons. - A TOFMS detector analyzing the ionized fragments of each selected particle to generate unique mass spectral data. - A data analysis system that compiles optical data and mass spectral data using data fusion, compares this compiled data with a training knowledge base of known biological matter, and predicts bioaerosol particle composition.
Method for identifying bioaerosol particle composition without complex organic MALDI matrices
The method involves: 1. Generating a beam of single particles using an aerosol beam generator. 2. Using a single continuous laser, in association with a data analysis system, to index each particle, optically characterize their size, shape, and fluorescence, and select which indexed particles are to be ionized. 3. Triggering an ionization pulse laser generator using the continuous laser to simultaneously generate IR and UV laser pulses when a selected indexed particle reaches the ionization region, exposing the particle and producing ionized fragments (with molecular weight between about 1 kDa and about 150 kDa) and photons. 4. Analyzing the ionized fragments using a TOFMS detector to generate unique mass spectral data for each selected indexed particle. 5. Determining the composition by compiling the optical and mass spectral data using data fusion and comparing with a training data set of known biological matter to predict the composition.
Together, these inventive features establish a system and method for real-time identification of bioaerosol particle composition that eliminates the need for complex organic MALDI matrices by combining dual-laser ionization, particle property analysis, and data-driven interpretation.
Stated Advantages
Enables rapid or real-time analysis and identification of aerosol analyte particles including bacteria, fungi, viruses, and toxins, without requiring complex organic MALDI matrices.
Eliminates the need for time-consuming sample processing steps such as culturing or chemical extraction, reducing analysis times from hours or days to minutes.
Allows for high accuracy, sensitivity, and specificity in identification by integrating mass spectrometry, optical detection, data fusion, and machine learning.
Permits real-time detection and identification in applications where rapid response is critical, such as biodefense and point-of-care healthcare.
Improves prediction capability over time through the use of machine learning methods that update with new data.
Documented Applications
Real-time identification of biological threat agents in air for biodefense, including agents like anthrax, Ebola virus, ricin, and botulinum toxin.
Monitoring of air in enclosed structures, such as office buildings, airports, and mass transit facilities, for contaminant detection and safety response.
Wide area monitoring across inhabited areas, such as towns or cities, to provide timely information on the type, quantity, and location of contaminants.
Speciation of microbes in food or healthcare facilities to detect contamination by microbes such as viruses, bacteria, algae, or fungi.
Analysis of exhaled breath particles from humans or animals for healthcare diagnostics.
Analysis of liquid samples by aerosolizing the sample for identification of bacterial, viral, or toxic analytes in real-time.
Detection and monitoring of environmental contaminants and anomalies through continual air sampling and machine learning-assisted anomaly detection.
Analysis of headspace in fermenters for contaminants.
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