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High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling
[Image: see text] 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,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585637/ https://www.ncbi.nlm.nih.gov/pubmed/36281292 http://dx.doi.org/10.1021/acsmeasuresciau.2c00031 |
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author | Hu, Hang Helminiak, David Yang, Manxi Unsihuay, Daisy Hilger, Ryan T. Ye, Dong Hye Laskin, Julia |
author_facet | Hu, Hang Helminiak, David Yang, Manxi Unsihuay, Daisy Hilger, Ryan T. Ye, Dong Hye Laskin, Julia |
author_sort | Hu, Hang |
collection | PubMed |
description | [Image: see text] 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. |
format | Online Article Text |
id | pubmed-9585637 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-95856372022-10-22 High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling Hu, Hang Helminiak, David Yang, Manxi Unsihuay, Daisy Hilger, Ryan T. Ye, Dong Hye Laskin, Julia ACS Meas Sci Au [Image: see text] 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. American Chemical Society 2022-08-15 /pmc/articles/PMC9585637/ /pubmed/36281292 http://dx.doi.org/10.1021/acsmeasuresciau.2c00031 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Hu, Hang Helminiak, David Yang, Manxi Unsihuay, Daisy Hilger, Ryan T. Ye, Dong Hye Laskin, Julia High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling |
title | High-Throughput
Mass Spectrometry Imaging with Dynamic
Sparse Sampling |
title_full | High-Throughput
Mass Spectrometry Imaging with Dynamic
Sparse Sampling |
title_fullStr | High-Throughput
Mass Spectrometry Imaging with Dynamic
Sparse Sampling |
title_full_unstemmed | High-Throughput
Mass Spectrometry Imaging with Dynamic
Sparse Sampling |
title_short | High-Throughput
Mass Spectrometry Imaging with Dynamic
Sparse Sampling |
title_sort | high-throughput
mass spectrometry imaging with dynamic
sparse sampling |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585637/ https://www.ncbi.nlm.nih.gov/pubmed/36281292 http://dx.doi.org/10.1021/acsmeasuresciau.2c00031 |
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