Cargando…

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,...

Descripción completa

Detalles Bibliográficos
Autores principales: Hu, Hang, Helminiak, David, Yang, Manxi, Unsihuay, Daisy, Hilger, Ryan T., Ye, Dong Hye, Laskin, Julia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
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
_version_ 1784813536292110336
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
work_keys_str_mv AT huhang highthroughputmassspectrometryimagingwithdynamicsparsesampling
AT helminiakdavid highthroughputmassspectrometryimagingwithdynamicsparsesampling
AT yangmanxi highthroughputmassspectrometryimagingwithdynamicsparsesampling
AT unsihuaydaisy highthroughputmassspectrometryimagingwithdynamicsparsesampling
AT hilgerryant highthroughputmassspectrometryimagingwithdynamicsparsesampling
AT yedonghye highthroughputmassspectrometryimagingwithdynamicsparsesampling
AT laskinjulia highthroughputmassspectrometryimagingwithdynamicsparsesampling