TY - JOUR
T1 - Accelerating Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry Imaging Using a Subspace Approach
AU - Xie, Yuxuan Richard
AU - Castro, Daniel C.
AU - Lam, Fan
AU - Sweedler, Jonathan V.
N1 - Funding Information:
This project was supported by the National Institute on Drug Abuse under Award No. P30DA018310 and the National Human Genome Research Institute under award No. R01HG010023. The content is solely the responsibility of the authors and does not necessarily represent the official views of the awarding agencies.
Publisher Copyright:
© 2020 American Society for Mass Spectrometry. Published by American Chemical Society. All rights reserved.
PY - 2020/11/4
Y1 - 2020/11/4
N2 - We present a subspace method that accelerates data acquisition using Fourier transform-ion cyclotron resonance (FT-ICR) mass spectrometry imaging (MSI). For MSI of biological tissue samples, there is a finite number of heterogeneous tissue types with distinct chemical profiles that introduce redundancy in the high-dimensional measurements. Our subspace model exploits the redundancy in data measured from whole-slice tissue samples by decomposing the transient signals into linear combinations of a set of basis transients with the desired spectral resolution. This decomposition allowed us to design a strategy that acquires a subset of long transients for basis determination and short transients for the remaining pixels, drastically reducing the acquisition time. The computational reconstruction strategy can maintain high-mass-resolution and spatial-resolution MSI while providing a 10-fold improvement in throughput. We validated the capability of the subspace model using a rat sagittal brain slice imaging data set. Comprehensive evaluation of the quality of the mass spectral and ion images demonstrated that the reconstructed data produced by the reported method required only 15% of the typical acquisition time and exhibited both qualitative and quantitative consistency when compared to the original data. Our method enables either higher sample throughput or higher-resolution images at similar acquisition lengths, providing greater flexibility in obtaining FT-ICR MSI measurements.
AB - We present a subspace method that accelerates data acquisition using Fourier transform-ion cyclotron resonance (FT-ICR) mass spectrometry imaging (MSI). For MSI of biological tissue samples, there is a finite number of heterogeneous tissue types with distinct chemical profiles that introduce redundancy in the high-dimensional measurements. Our subspace model exploits the redundancy in data measured from whole-slice tissue samples by decomposing the transient signals into linear combinations of a set of basis transients with the desired spectral resolution. This decomposition allowed us to design a strategy that acquires a subset of long transients for basis determination and short transients for the remaining pixels, drastically reducing the acquisition time. The computational reconstruction strategy can maintain high-mass-resolution and spatial-resolution MSI while providing a 10-fold improvement in throughput. We validated the capability of the subspace model using a rat sagittal brain slice imaging data set. Comprehensive evaluation of the quality of the mass spectral and ion images demonstrated that the reconstructed data produced by the reported method required only 15% of the typical acquisition time and exhibited both qualitative and quantitative consistency when compared to the original data. Our method enables either higher sample throughput or higher-resolution images at similar acquisition lengths, providing greater flexibility in obtaining FT-ICR MSI measurements.
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U2 - 10.1021/jasms.0c00276
DO - 10.1021/jasms.0c00276
M3 - Article
C2 - 33064944
AN - SCOPUS:85095666582
VL - 31
SP - 2338
EP - 2347
JO - Journal of the American Society for Mass Spectrometry
JF - Journal of the American Society for Mass Spectrometry
SN - 1044-0305
IS - 11
ER -