Solvent effects on differentiation of mouse brain tissue using laser microdissection ‘cut and drop’ sampling with direct mass spectral analysis
Abstract
Rationale: Laser microdissection-liquid vortex capture/electrospray ionization mass spectrometry (LMD-LVC/ESI-MS) has potential for on-line classification of tissue but an investigation into what analytical conditions provide best spectral differentiation has not been conducted. The effects of solvent, ionization polarity, and spectral acquisition parameters on differentiation of mouse brain tissue regions are described.Methods: Individual 40 × 40 μm microdissections from cortex, white, grey, granular, and nucleus regions of mouse brain tissue were analyzed using different capture/ESI solvents, in positive and negative ion mode ESI, using time-of-flight (TOF)-MS and sequential window acquisitions of all theoretical spectra (SWATH)-MS (a permutation of tandem-MS), and combinations thereof. Principal component analysis-linear discriminant analysis (PCA-LDA), applied to each mass spectral dataset, was used to determine the accuracy of differentiation of mouse brain tissue regions. Results: Mass spectral differences associated with capture/ESI solvent composition manifested as altered relative distributions of ions rather than the presence or absence of unique ions. In negative ion mode ESI, 80/20 (v/v) methanol/water yielded spectra with low signal/noise ratios relative to other solvents. PCA-LDA models acquired using 90/10 (v/v) methanol/chloroform differentiated tissue regions with 100% accuracy while data collected using methanol misclassified some samples. The combination of SWATH-MS and TOF-MS data improved differentiation accuracy.Conclusions: Combinedmore »
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Chemical Sciences Division, Mass Spectrometry and Laser Spectroscopy Group
- Maastricht Univ., Maastricht (The Netherlands). Maastricht MultiModal Molecular Imaging (M4I) inst., Division of Imaging Mass Spectrometry
- Sciex, Concord, ON (Canada)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1436950
- Alternate Identifier(s):
- OSTI ID: 1419991
- Grant/Contract Number:
- AC05-00OR22725; CRADA NFE‐10‐02966; CRADA NFE-10-02966
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Rapid Communications in Mass Spectrometry
- Additional Journal Information:
- Journal Volume: 32; Journal Issue: 5; Journal ID: ISSN 0951-4198
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 47 OTHER INSTRUMENTATION; Mass spectrometry; PCA; liquid capture; laser microdissection
Citation Formats
Cahill, John F., Kertesz, Vilmos, Porta, Tiffany, LeBlanc, J. C. Yves, Heeren, Ron M. A., and Van Berkel, Gary J. Solvent effects on differentiation of mouse brain tissue using laser microdissection ‘cut and drop’ sampling with direct mass spectral analysis. United States: N. p., 2018.
Web. doi:10.1002/rcm.8053.
Cahill, John F., Kertesz, Vilmos, Porta, Tiffany, LeBlanc, J. C. Yves, Heeren, Ron M. A., & Van Berkel, Gary J. Solvent effects on differentiation of mouse brain tissue using laser microdissection ‘cut and drop’ sampling with direct mass spectral analysis. United States. https://doi.org/10.1002/rcm.8053
Cahill, John F., Kertesz, Vilmos, Porta, Tiffany, LeBlanc, J. C. Yves, Heeren, Ron M. A., and Van Berkel, Gary J. Thu .
"Solvent effects on differentiation of mouse brain tissue using laser microdissection ‘cut and drop’ sampling with direct mass spectral analysis". United States. https://doi.org/10.1002/rcm.8053. https://www.osti.gov/servlets/purl/1436950.
@article{osti_1436950,
title = {Solvent effects on differentiation of mouse brain tissue using laser microdissection ‘cut and drop’ sampling with direct mass spectral analysis},
author = {Cahill, John F. and Kertesz, Vilmos and Porta, Tiffany and LeBlanc, J. C. Yves and Heeren, Ron M. A. and Van Berkel, Gary J.},
abstractNote = {Rationale: Laser microdissection-liquid vortex capture/electrospray ionization mass spectrometry (LMD-LVC/ESI-MS) has potential for on-line classification of tissue but an investigation into what analytical conditions provide best spectral differentiation has not been conducted. The effects of solvent, ionization polarity, and spectral acquisition parameters on differentiation of mouse brain tissue regions are described.Methods: Individual 40 × 40 μm microdissections from cortex, white, grey, granular, and nucleus regions of mouse brain tissue were analyzed using different capture/ESI solvents, in positive and negative ion mode ESI, using time-of-flight (TOF)-MS and sequential window acquisitions of all theoretical spectra (SWATH)-MS (a permutation of tandem-MS), and combinations thereof. Principal component analysis-linear discriminant analysis (PCA-LDA), applied to each mass spectral dataset, was used to determine the accuracy of differentiation of mouse brain tissue regions. Results: Mass spectral differences associated with capture/ESI solvent composition manifested as altered relative distributions of ions rather than the presence or absence of unique ions. In negative ion mode ESI, 80/20 (v/v) methanol/water yielded spectra with low signal/noise ratios relative to other solvents. PCA-LDA models acquired using 90/10 (v/v) methanol/chloroform differentiated tissue regions with 100% accuracy while data collected using methanol misclassified some samples. The combination of SWATH-MS and TOF-MS data improved differentiation accuracy.Conclusions: Combined TOF-MS and SWATH-MS data differentiated white, grey, granular, and nucleus mouse tissue regions with greater accuracy than when solely using TOF-MS data. Using 90/10 (v/v) methanol/chloroform, tissue regions were perfectly differentiated. Lastly, these results will guide future studies looking to utilize the potential of LMD-LVC/ESI-MS for tissue and disease differentiation.},
doi = {10.1002/rcm.8053},
journal = {Rapid Communications in Mass Spectrometry},
number = 5,
volume = 32,
place = {United States},
year = {2018},
month = {2}
}
Web of Science
Figures / Tables:

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