Bio-identification using low resolution tandem mass spectrometry
Inventors
Glaros, Trevor C. • Mach, Phillip • Caprioli, Richard • Norris, Jeremy
Assignees
United States, Represented By Secretary Of Army Washington Dc AS • Vanderbilt University • Triad National Security LLC
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Publication Number
US-12590935-B2
Publication Date
2026-03-31
Expiration Date
Abstract
Systems and devices are disclosed to generate a multidimensional mass fingerprint that allows for identification on a low-resolution mass spectrometer equipped with post-ionization fragmentation. For this approach, rather than interrogating a sample that is processed into peptides using a single high resolution MS scan as in traditional fingerprinting, a raw unprocessed sample (containing all biochemical species: lipids, proteins, peptides, and metabolites) is analyzed by combining Matrix-Assisted Laser Dissociation/Ionization (MALDI) ionization with low resolution tandem mass spectrometry. The proposed system combines improvements in MS hardware and software with state-of-the-art machine learning (ML) approaches to usher in rapid biological detection. This technique does not require any prior separation (liquid or gas chromatography) and is therefore rapid (e.g. less than 5 sec) and amenable to high throughput (e.g. greater than 384 samples/hr).
Core Innovation
The invention relates to rapid biological/chemical identification using low-resolution tandem mass spectrometry to generate a multidimensional mass fingerprint. A raw or minimally processed biological analyte is ionized to produce a mixture of ions, and a parent mass spectrum (MS1) is recorded without fragmentation in a first stage of mass analysis.
A second stage of mass analysis obtains and records a series of mass spectra (MS2) with fragmentation using predefined mass windows within the parent spectrum. The MS1 and MS2 mass spectral data are combined to generate a multi-dimensional fingerprint from the combination of MS1 parent and MS2 fragment spectral data obtained with predefined mass windows.
The generated multidimensional mass fingerprint is used for machine-learning comparison against a fingerprint library to identify species/strain and to correlate fingerprints to a biological state. The biological state includes tissue disease or cancer, including optional spatial analysis derived from spatially-defined cell types from a region of interest.
Claims Coverage
The patent includes two independent claims. The core inventive features are the MS1/MS2 workflow that generates a multi-dimensional fingerprint using predefined mass windows, and the added selective ionization and spatial-information features for disease-state classification or tissue image rendering.
Ionizing a biological analyte to produce a mixture of ions
Ionize the biological analyte to produce a mixture of ions.
MS1 parent spectrum without fragmentation
Obtain and record a parent mass spectrum for the mixture of ions in a first stage of mass analysis (MS1) without fragmentation.
Windowed MS2 fragmentation using predefined mass windows
Obtain and record a series of mass spectra for the mixture of ions in a second stage of mass analysis (MS2) with fragmentation using predefined mass windows within the parent spectrum.
Multi-dimensional fingerprint from MS1 and MS2
Generate a multi-dimensional fingerprint from the combination of MS1 and MS2 mass spectral data.
Selective ionization of spatially-defined cell types
Selective ionization of spatially-defined cell types from a region of interest.
Disease-state analysis using spatial information
Use spatial information to classify the disease state of cells within a defined region of interest.
Tissue imagery rendering using spatial information
Use spatial information to render an image of the tissue based on the position of the molecules or disease state.
Across the independent claims, the core coverage is the combination of MS1 parent spectrum without fragmentation and MS2 fragmented spectra acquired with predefined mass windows, followed by generation of a multi-dimensional fingerprint from MS1+MS2 data. The spatially-defined cell type and spatial-information features further support disease-state classification or tissue image rendering.
Stated Advantages
Enables high throughput biological/chemical identification using low-resolution tandem mass spectrometry with predefined windowed MS2 acquisition.
Documented Applications
Identify species/strain and correlate fingerprints to a biological state, including tissue disease or cancer via optional spatial analysis.
Analyze biological analytes derived from pathogenic bacteria or virus.
Classify disease state of cells within a defined region of interest using spatial information.
Render an image of tissue based on the position of the molecules or disease state using spatial information.
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