Open AccessEnhancing the Throughput of FT Mass Spectrometry Imaging Using Joint Compressed Sensing and Subspace Modeling
Author(s) -
Yuxuan Richard Xie,
Daniel C. Castro,
Stanislav S. Rubakhin,
Jonathan V. Sweedler,
Fan Lam
Publication year - 2022
Publication title -
analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.117
H-Index - 332
eISSN - 1520-6882
pISSN - 0003-2700
DOI - 10.1021/acs.analchem.1c05279
Subject(s) - fourier transform ion cyclotron resonance , chemistry , mass spectrometry , mass spectrometry imaging , subspace topology , throughput , joint (building) , compressed sensing , temporal resolution , resolution (logic) , biological system , fourier transform , image resolution , analytical chemistry (journal) , algorithm , artificial intelligence , computer science , chromatography , optics , physics , architectural engineering , telecommunications , engineering , wireless , biology , quantum mechanics
Mass spectrometry imaging (MSI) allows for untargeted mapping of the chemical composition of tissues with attomole detection limits. MSI using Fourier transform (FT)-based mass spectrometers, such as FT-ion cyclotron resonance (FT-ICR), grants the ability to examine the chemical space with unmatched mass resolution and mass accuracy. However, direct imaging of large tissue samples using FT-ICR is slow. In this work, we present an approach that combines the subspace modeling of ICR temporal signals with compressed sensing to accelerate high-resolution FT-ICR MSI. A joint subspace and spatial sparsity constrained model computationally reconstructs high-resolution MSI data from the sparsely sampled transients with reduced duration, allowing a significant reduction in imaging time. Simulation studies and experimental implementation of the proposed method in investigation of brain tissues demonstrate a 10-fold enhancement in throughput of FT-ICR MSI, without the need for instrumental or hardware modifications.