Enhancing the Throughput of FT Mass Spectrometry Imaging Using Joint Compressed Sensing and Subspace Modeling

Xie, Yuxuan Richard; Castro, Daniel C.; Rubakhin, Stanislav S.; Sweedler, Jonathan V.; Lam, Fan

doi:10.1021/acs.analchem.1c05279

Cited by 15 publications

(11 citation statements)

References 34 publications

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“…Xie et al. [ 255 ] have integrated a compressed sensing approach with subspace modeling to accelerate FT‐ICR MALDI MSI experiments. In this work, a joint subspace and spatial sparsity constrained model enabled computational reconstruction of high‐resolution MSI images using short FT‐ICR transient signals from randomly sampled pixels covering 40% of the sample.…”

Section: Computational Methods For Msi Experimentsmentioning

confidence: 99%

“…Compressed sensing is an approach, which relies on sparse sampling from selected locations on a sample followed by image reconstruction using a uniform virtual grid. Xie et al [255] have integrated a compressed sensing approach with subspace modeling to accelerate FT-ICR MALDI MSI experiments. In this work, a joint subspace and spatial sparsity constrained model enabled computational reconstruction of high-resolution MSI images using short FT-ICR transient signals from randomly sampled pixels covering 40% of the sample.…”

Section: "Smart" Samplingmentioning

confidence: 99%

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Emerging Computational Methods in Mass Spectrometry Imaging

Laskin

2022

Advanced Science

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Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

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Section: Computational Methods For Msi Experimentsmentioning

confidence: 99%

Section: "Smart" Samplingmentioning

confidence: 99%

Emerging Computational Methods in Mass Spectrometry Imaging

Laskin

2022

Advanced Science

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“…Moreover, the implementation of tandem mass spectrometry further limits imaging throughput. Efforts have been directed towards accelerating high-resolution MSI, such as the implementation of compressed sensing, and dynamic sparse sampling [ 55 , 56 ], with the aim of improving throughput. However, the imaging process still requires the comprehensive scanning of all pixels on the slides, even though only a fraction of these pixels correspond to the cells or organelles of interest.…”

Section: Limitations and Future Perspectivesmentioning

confidence: 99%

Mass Spectrometry Imaging for Single-Cell or Subcellular Lipidomics: A Review of Recent Advancements and Future Development

Ouyang

2023

Molecules

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Mass Spectrometry Imaging (MSI) has emerged as a powerful imaging technique for the analysis of biological samples, providing valuable insights into the spatial distribution and structural characterization of lipids. The advancements in high-resolution MSI have made it an indispensable tool for single-cell or subcellular lipidomics. By preserving both intracellular and intercellular information, MSI enables a comprehensive analysis of lipidomics in individual cells and organelles. This enables researchers to delve deeper into the diversity of lipids within cells and to understand the role of lipids in shaping cell behavior. In this review, we aim to provide a comprehensive overview of recent advancements and future prospects of MSI for cellular/subcellular lipidomics. By keeping abreast of the cutting-edge studies in this field, we will continue to push the boundaries of the understanding of lipid metabolism and the impact of lipids on cellular behavior.

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“…For example, a subspace modeling approach has been used to accelerate FT-ICR MSI by reconstructing high-resolution mass spectral data from short transients . A follow-up study coupled the subspace modeling method with compressed sensing to reconstruct MSI images from a sparse set of randomly selected locations, reducing the total number of pixels to be sampled and thereby, the acquisition time . However, current compressed sensing methods based on stochastic process are computationally expensive, which limits their applicability to on-the-fly implementations.…”

Section: Introductionmentioning

confidence: 99%

High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling

Helminiak

Yang

et al. 2022

ACS Meas. Sci. Au

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Mass spectrometry imaging (MSI) enables label-free mapping of hundreds of molecules in biological samples with high sensitivity and unprecedented specificity. Conventional MSI experiments are relatively slow, limiting their utility for applications requiring rapid data acquisition, such as intraoperative tissue analysis or 3D imaging. Recent advances in MSI technology focus on improving the spatial resolution and molecular coverage, further increasing the acquisition time. Herein, a deep learning approach for dynamic sampling (DLADS) was employed to reduce the number of required measurements, thereby improving the throughput of MSI experiments in comparison with conventional methods. DLADS trains a deep learning model to dynamically predict molecularly informative tissue locations for active mass spectra sampling and reconstructs high-fidelity molecular images using only the sparsely sampled information. Experimental hardware and software integration of DLADS with nanospray desorption electrospray ionization (nano-DESI) MSI is reported for the first time, which demonstrates a 2.3-fold improvement in throughput for a linewise acquisition mode. Meanwhile, simulations indicate that a 5–10-fold throughput improvement may be achieved using the pointwise acquisition mode.

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Enhancing the Throughput of FT Mass Spectrometry Imaging Using Joint Compressed Sensing and Subspace Modeling

Cited by 15 publications

References 34 publications

Emerging Computational Methods in Mass Spectrometry Imaging

Emerging Computational Methods in Mass Spectrometry Imaging

Mass Spectrometry Imaging for Single-Cell or Subcellular Lipidomics: A Review of Recent Advancements and Future Development

High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling

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