IP Library Granted Patent US 12,308,222
Granted Patent B2
US 12,308,222 · App. 17/705,979 · Granted May 20, 2025

Subspace approach to accelerate Fourier transform mass spectrometry imaging

Inventors: Fan Lam (Champaign, IL); Jonathan V. Sweedler (Urbana, IL); Yuxuan Xie (Champaign, IL)
Assignee: THE BOARD OF TRUSTEES OF THE UNIVERSITY OF ILLINOIS
H01J49/0036G06T5/10G06T5/50G06T2207/20056G06T2207/20212
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Quick Facts
Patent No.
US 12,308,222
App. No.
17/705,979
Granted
May 20, 2025
Kind
B2
Abstract

Methods, apparatus, and storage medium for obtaining high-resolution mass spectra and chemical maps from a sample using a subspace Fourier transform mass spectrometry (FT-MS) approach are described. The method includes conducting a first set of image data corresponding to a first group of spatial positions on the sample and a second set of image data corresponding to a second group of spatial positions on the sample; conducting a decomposition process on the first set of image data to obtain a set of basis elements; performing a reconstruction process on a second set of image data to obtain a set of reconstructed image data; performing a Fourier transform on the first and second sets of image data to obtain a first and second sets of mass spectra, respectively; and obtaining a FT-MS image for the sample based on the first set of mass spectra and the second set of mass spectra.

Claims (79)

1. A method for obtaining high-resolution mass spectra and chemical maps from a sample using a subspace Fourier transform (FT) mass spectrometry (FT-MS) approach, the method comprising:

conducting, by a device comprising a memory storing instructions and a processor in communication with the memory, a data collection process that generates nonuniform lengths of FT-MS data across different spatial locations on a sample, the FT-MS data comprising a first set of image data corresponding to a first group of spatial positions on the sample and a second set of image data corresponding to a second group of spatial positions on the sample;

conducting, by the device, a decomposition process on the first set of image data to obtain a set of basis elements for the sample;

performing, by the device, a reconstruction process on a second set of image data based on the set of basis elements to obtain a set of reconstructed image data;

performing, by the device, a first Fourier transform on the first set of image data to obtain a first set of mass spectra, the first set of mass spectra corresponding to the first group of spatial positions on the sample;

performing, by the device, a second Fourier transform on the set of reconstructed image data to obtain a second set of mass spectra, the second set of mass spectra corresponding to the second group of spatial positions on the sample; and

obtaining, by the device, a FT-MS image for the sample based on the first set of mass spectra and the second set of mass spectra.

2. The method according to claim 1 , wherein:

the first set of image data corresponds to data with a first transient duration;

the second set of image data corresponds to data with a second transient duration; and

the second transient duration is shorter than the first transient duration.

3. The method according to claim 1 , wherein:

the first set of image data corresponds to a FT MS image with a first mass resolution;

the second set of image data corresponds to a FT MS image with a second mass resolution; and

the first mass resolution is finer than the second mass resolution.

4. The method according to claim 1 , wherein:

the first group of spatial positions comprises a first number of spatial positions;

the second group of spatial positions comprises a second number of spatial positions; and

the first number is smaller than the second number.

5. The method according to claim 1 , wherein:

the first group of spatial positions is randomly selected on the sample.

6. The method according to claim 1 , wherein:

the decomposition process comprises a singlular value decomposition or other types of matrix decomposition methods.

7. The method according to claim 1 , wherein:

the reconstruction process corresponding to the second set of mass spectra is performed in one of a location-by-location fashion or jointly for all the second group of spatial positions simultaneously.

8. The method according to claim 1 , wherein:

the FT MS image comprises at least one of the following:

a Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry image; or

a Fourier transform Orbitrap mass spectrometry image.

9. An apparatus for obtaining high-resolution mass spectra and chemical maps from a sample using a subspace Fourier transform (FT) mass spectrometry (FT-MS) approach, the apparatus comprising:

a memory storing instructions; and

a processor in communication with the memory, wherein, when the processor executes the instructions, the processor is configured to cause the apparatus to perform:

conducting a data collection process that generates nonuniform lengths of FT-MS data across different spatial locations on a sample, the FT-MS data comprising a first set of image data corresponding to a first group of spatial positions on the sample and a second set of image data corresponding to a second group of spatial positions on the sample,

conducting a decomposition process on the first set of image data to obtain a set of basis elements for the sample,

performing a reconstruction process on a second set of image data based on the set of basis elements to obtain a set of reconstructed image data,

performing a first Fourier transform on the first set of image data to obtain a first set of mass spectra, the first set of mass spectra corresponding to the first group of spatial positions on the sample,

performing a second Fourier transform on the set of reconstructed image data to obtain a second set of mass spectra, the second set of mass spectra corresponding to the second group of spatial positions on the sample, and

obtaining a FT-MS image for the sample based on the first set of mass spectra and the second set of mass spectra.

10. The apparatus according to claim 9 , wherein:

the first set of image data corresponds to data with a first transient duration;

the second set of image data corresponds to data with a second transient duration; and

the second transient duration is shorter than the first transient duration.

11. The apparatus according to claim 9 , wherein:

the first set of image data corresponds to a FT MS image with a first mass resolution;

the second set of image data corresponds to a FT MS image with a second mass resolution; and

the first mass resolution is finer than the second mass resolution.

12. The apparatus according to claim 9 , wherein:

the first group of spatial positions comprises a first number of spatial positions;

the second group of spatial positions comprises a second number of spatial positions; and

the first number is smaller than the second number.

13. The apparatus according to claim 9 , wherein:

the first group of spatial positions is randomly selected on the sample.

14. The apparatus according to claim 9 , wherein:

the decomposition process comprises a singlular value decomposition or other types of matrix decomposition methods.

15. The apparatus according to claim 9 , wherein:

the reconstruction process corresponding to the second set of mass spectra is performed in one of a location-by-location fashion or jointly for all the second group of spatial positions simultaneously.

16. The apparatus according to claim 9 , wherein:

the FT MS image comprises at least one of the following:

a Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry image; or

a Fourier transform Orbitrap mass spectrometry image.

17. A non-transitory computer readable storage medium storing computer readable instructions, wherein, the computer readable instructions, when executed by a processor, are configured to cause the processor to perform:

conducting a data collection process that generates nonuniform lengths of FT-MS data across different spatial locations on a sample, the FT-MS data comprising a first set of image data corresponding to a first group of spatial positions on the sample and a second set of image data corresponding to a second group of spatial positions on the sample;

conducting a decomposition process on the first set of image data to obtain a set of basis elements for the sample;

performing a reconstruction process on a second set of image data based on the set of basis elements to obtain a set of reconstructed image data;

performing a first Fourier transform on the first set of image data to obtain a first set of mass spectra, the first set of mass spectra corresponding to the first group of spatial positions on the sample;

performing a second Fourier transform on the set of reconstructed image data to obtain a second set of mass spectra, the second set of mass spectra corresponding to the second group of spatial positions on the sample; and

obtaining a FT-MS image for the sample based on the first set of mass spectra and the second set of mass spectra.

18. The non-transitory computer readable storage medium according to claim 17 , wherein:

the first set of image data corresponds to data with a first transient duration;

the second set of image data corresponds to data with a second transient duration; and

the second transient duration is shorter than the first transient duration.

19. The non-transitory computer readable storage medium according to claim 17 , wherein:

the first set of image data corresponds to a FT MS image with a first mass resolution;

the second set of image data corresponds to a FT MS image with a second mass resolution; and

the first mass resolution is finer than the second mass resolution.

20. The non-transitory computer readable storage medium according to claim 17 , wherein:

the first group of spatial positions comprises a first number of spatial positions;

the second group of spatial positions comprises a second number of spatial positions; and

the first number is smaller than the second number.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 12, 2022
From: LAM, FAN; SWEEDLER, JONATHAN V.; XIE, YUXUAN
To: THE BOARD OF TRUSTEES OF THE UNIVERSITY OF ILLINOIS
Reel/Frame 059572/0384 →
Continuity (2)
Provisional Application 63167370 · Mar 29, 2021
Related Publication 20220310374A1 · Sep 29, 2022
References Cited (48)
US 10338178B2 · Liang et al. · 2019 [cited by applicant]
US 10436871B2 · Li et al. · 2019 [cited by applicant]
US 20090136104A1 · Hajian · 2009 [cited by examiner]
US 20130195327A1 · Tanji · 2013 [cited by examiner]
US 20160202336A1 · Liang et al. · 2016 [cited by applicant]
US 20200049782A1 · Brender et al. · 2020 [cited by applicant]
Aichler, M.; Walch, A. MALDI Imaging Mass Spectrometry: Current Frontiers and Perspectives in Pathology Research and Practice. Lab. Investig. 2015, 95, 422-431. [cited by applicant]
Alexandrov, T. MALDI Imaging Mass Spectrometry: Statistical Data Analysis and Current Computational Challenges. BMC Bioinformatics 2012, 13, S11. [cited by applicant]
Astigarraga, E.; Barreda-Gómez, G.; Lombardero, L.; Fresnedo, O.; Castaño, F.; Giralt, M. T.; Ochoa, B.; Rodriguez-Puertas, R.; Fernandez, J. A. Profiling and Imaging of Lipids on Brain and Liver Tissue by Matrix-Assist… [cited by applicant]
Bartels, A.; Dulk, P.; Trede, D.; Alexandrov, T.; Maaß, P. Compressed Sensing in Imaging Mass Spectrometry. Inverse Probl. 2013, 29, 125015. [cited by applicant]
Bowman, A. P.; Blakney, G. T.; Hendrickson, C. L.; Ellis, S. R.; Heeren, R. M. A.; Smith, D. F. Ultra-High Mass Resolving Power, Mass Accuracy, and Dynamic Range MALDI Mass Spectrometry Imaging by 21-T FT-ICR MS. Anal. … [cited by applicant]
Buchberger, A.; DeLaney, K.; Johnson, J.; Li, L. Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights. Anal. Chem. 2018, 90, 240-265. [cited by applicant]
Chiron, Lionel, et al., “Efficient denoising algorithms for large experimental datasets and their applications in Fourier transform ion cyclotron resonance mass spectrometry,” [cited by applicant]
Chughtai, K.; Heeren, R. M. A. Mass Spectrometric Imaging for Biomedical Tissue Analysis. Chem. Rev. 2010, 110, 3237-3277. [cited by applicant]
Cornett, D. S.; Frappier, S. L.; Caprioli, R. M. MALDI-FTICR Imaging Mass Spectrometry of Drugs and Metabolites in Tissue. Anal. Chem. 2008, 80, 5648-5653. [cited by applicant]
Fonville, J. M.; Carter, C. L.; Pizarro, L.; Steven, R. T.; Palmer, A. D.; Griffiths, R. L.; Lalor, P. F.; Lindon, J. C.; Nicholson, J. K.; Holmes, E.; Bunch, J. Hyperspectral Visualization of Mass Spectrometry Imaging … [cited by applicant]
Gao et al., “Reconstruction and Feature Selection for Desorption Electrospray Ionization Mass Spectroscopy Imagery,” [cited by applicant]
Gemperline, E.; Chen, B.; Li, L. Challenges and Recent Advances in Mass Spectrometric Imaging of Neurotransmitters. Bioanalysis 2014, 6, 525-540. [cited by applicant]
Gessel, M. M.; Norris, J. L.; Caprioli, R. M. MALDI Imaging Mass Spectrometry: Spatial Molecular Analysis to Enable a New Age of Discovery. J. Proteomics 2014, 107, 71-82. [cited by applicant]
Haldar, J. P.; Liang, Z.-P. Spatiotemporal Imaging with Partially Separable Functions: A Matrix Recovery Approach. In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2010; pp. 716-719. [cited by applicant]
Halko, N.; Martinsson, P. G.; Tropp, J. A. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions. SIAM Rev. 2011, 53, 217-288. [cited by applicant]
Hendrickson, C. L.; Quinn, J. P.; Kaiser, N. K.; Smith, D. F.; Blakney, G. T.; Chen, T.; Marshall, A. G.; Weisbrod, C. R.; Beu, S. C. 21 Tesla Fourier Transform Ion Cyclotron Resonance Mass Spectrometer: A National Reso… [cited by applicant]
Kooijman, P. C.; Nagornov, K. O.; Kozhinov, A. N.; Kilgour, D. P. A.; Tsybin, Y. O.; Heeren, R. M. A.; Ellis, S. R. Increased Throughput and Ultra-High Mass Resolution in DESI FT-ICR MS Imaging through New-Generation Ex… [cited by applicant]
Kozhinov, A. N.; Tsybin, Y. O. Filter Diagonalization Method-Based Mass Spectrometry for Molecular and Macromolecular Structure Analysis. Anal. Chem. 2012, 84, 2850-2856. [cited by applicant]
Lam et al., “A Subspace Approach to High-Resolution Spectroscopic Imaging,” [cited by applicant]
Liang, Z. Spatiotemporal Imaging with Partially Separable Functions. In 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2007; pp. 988-991. [cited by applicant]
Nagornov, K. O.; Gorshkov, M. V.; Kozhinov, A. N.; Tsybin, Y. O. High-Resolution Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Increased Throughput for Biomolecular Analysis. Anal. Chem. 2014, 86, 902… [cited by applicant]
Neumann, E. K.; Comi, T. J.; Spegazzini, N.; Mitchell, J. W.; Rubakhin, S. S.; Gillette, M. U.; Bhargava, R.; Sweedler, J. V. Multimodal Chemical Analysis of the Brain by High Mass Resolution Mass Spectrometry and Infra… [cited by applicant]
Norris, J. L.; Caprioli, R. M. Imaging Mass Spectrometry: A New Tool for Pathology in a Molecular Age. Proteomics: Clin. Appl. 2013, 7, 733-738. [cited by applicant]
Palmer, A. D.; Bunch, J.; Styles, I. B. Randomized Approximation Methods for the Efficients Compression and Analysis of Hyperspectral Data. Anal. Chem. 2013, 85, 5078-5086. [cited by applicant]
Park, S.-G.; Anderson, G. A.; Bruce, J. E. Parallel Detection of Fundamental and Sixth Harmonic Signals Using an ICR Cell with Dipole and Sixth Harmonic Detectors. J. Am. Soc. Mass Spectrom. 2020, 31, 719-726. [cited by applicant]
Patterson, N. H.; Tuck, M.; Van de Plas, R.; Caprioli, R. M. Advanced Registration and Analysis of MALDI Imaging Mass Spectrometry Measurements through Autofluorescence Microscopy. Anal. Chem. 2018, 90, 12395-12403. [cited by applicant]
Qu, X.; Mayzel, M.; Cai, J.-F.; Chen, Z.; Orekhov, V. Accelerated NMR Spectroscopy with Low-Rank Reconstruction. Angew. Chem., Int. Ed. 2015, 54, 852-854. [cited by applicant]
Rubakhin, S. S.; Jurchen, J. C.; Monroe, E. B.; Sweedler, J. V. Imaging Mass Spectrometry: Fundamentals and Applications to Drug Discovery. Drug Discov. Today 2005, 10, 823-837. [cited by applicant]
Scigelova, M.; Hornshaw, M.; Giannakopulos, A.; Makarov, A. Fourier Transform Mass Spectrometry. Mol. Cell. Proteomics 2011, 10, M111.009431. [cited by applicant]
Ščupáková, K.; Balluff, B.; Tressler, C.; Adelaja, T.; Heeren, R. M. A.; Glunde, K.; Ertaylan, G. Cellular Resolution in Clinical MALDI Mass Spectrometry Imaging: The Latest Advancements and Current Challenges. Clin. Ch… [cited by applicant]
Shariatgorji, M.; Nilsson, A.; Fridjonsdottir, E.; Vallianatou, T.; Källback, P.; Katan, L.; Savmarker, J.; Mantas, I.; Zhang, X.; Bezard, E.; Svenningsson, P.; Odell, L. R.; Andrén, P. E. Comprehensive Mapping of Neuro… [cited by applicant]
Shaw, J. B.; Lin, T.-Y.; Leach, F. E.; Tolmachev, A. V.; Tolić, N.; Robinson, E. W.; Koppenaal, D. W.; Paša-Tolić, L. 21 Tesla Fourier Transform Ion Cyclotron Resonance Mass Spectrometer Greatly Expands Mass Spectrometr… [cited by applicant]
Smets, T.; Verbeeck, N.; Claesen, M.; Asperger, A.; Griffioen, G.; Tousseyn, T.; Waelput, W.; Waelkens, E.; De Moor, B. Evaluation of Distance Metrics and Spatial Autocorrelation in Uniform Manifold Approximation and Pr… [cited by applicant]
Smith, D. F.; Kilgour, D. P. A.; Konijnenburg, M.; O'Connor, P. B.; Heeren, R. M. A. Absorption Mode FTICR Mass Spectrometry Imaging. Anal. Chem. 2013, 85, 11180-11184. [cited by applicant]
Soltwisch, J.; Kettling, H.; Vens-Cappell, S ; Wiegelmann, M.; Müthing, J.; Dreisewerd, K. Mass Spectrometry Imaging with Laser-Induced Postionization. Science 2015, 348, 211-215. [cited by applicant]
Tang, Fei, et al.,“Application of super-resolution reconstruction of sparse representation in mass spectrometry imaging,” [cited by applicant]
Van de Plas, R.; Yang, J.; Spraggins, J.; Caprioli, R. M. Image Fusion of Mass Spectrometry and Microscopy: A Multimodality Paradigm for Molecular Tissue Mapping. Nat. Methods 2015, 12, 366-372. [cited by applicant]
Vaysse, P.-M.; A. Heeren, R. M. A.; Porta, T.; Balluff, B. Mass Spectrometry Imaging for Clinical Research—Latest Developments, Applications, and Current Limitations. Analyst 2017, 142, 2690-2712. [cited by applicant]
Verbeeck, N.; Caprioli, R. M.; Van de Plas, R. Unsupervised Machine Learning for Exploratory Data Analysis in Imaging Mass Spectrometry. Mass Spectrom. Rev. 2020, 39, 245-291. [cited by applicant]
Vollnhals, F.; Audinot, J.-N.; Wirtz, T.; Mercier-Bonin, M.; Fourquaux, I.; Schroeppel, B.; Kraushaar, U.; Lev-Ram, V.; Ellisman, M. H.; Eswara, S. Correlative Microscopy Combining Secondary Ion Mass Spectrometry and El… [cited by applicant]
Xian, F.; Hendrickson, C. L.; Marshall, A. G. High Resolution Mass Spectrometry. Anal. Chem. 2012, 84, 708-719. [cited by applicant]
Xie et al., “Accelerating Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry Imaging Using a Subspace Approach,” [cited by applicant]