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High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy
[Image: see text] Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286315/ https://www.ncbi.nlm.nih.gov/pubmed/34797972 http://dx.doi.org/10.1021/acs.analchem.1c02178 |
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author | Horgan, Conor C. Jensen, Magnus Nagelkerke, Anika St-Pierre, Jean-Philippe Vercauteren, Tom Stevens, Molly M. Bergholt, Mads S. |
author_facet | Horgan, Conor C. Jensen, Magnus Nagelkerke, Anika St-Pierre, Jean-Philippe Vercauteren, Tom Stevens, Molly M. Bergholt, Mads S. |
author_sort | Horgan, Conor C. |
collection | PubMed |
description | [Image: see text] Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep-learning-enabled Raman spectroscopy, termed DeepeR, trained on a large data set of hyperspectral Raman images, with over 1.5 million spectra (400 h of acquisition) in total. We first perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 10× improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2–4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40–90×, enabling good-quality cellular imaging with a high-resolution, high signal-to-noise ratio in under 1 min. We further demonstrate Raman imaging speed-up of 160×, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine. |
format | Online Article Text |
id | pubmed-9286315 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-92863152022-07-16 High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy Horgan, Conor C. Jensen, Magnus Nagelkerke, Anika St-Pierre, Jean-Philippe Vercauteren, Tom Stevens, Molly M. Bergholt, Mads S. Anal Chem [Image: see text] Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep-learning-enabled Raman spectroscopy, termed DeepeR, trained on a large data set of hyperspectral Raman images, with over 1.5 million spectra (400 h of acquisition) in total. We first perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 10× improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2–4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40–90×, enabling good-quality cellular imaging with a high-resolution, high signal-to-noise ratio in under 1 min. We further demonstrate Raman imaging speed-up of 160×, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine. American Chemical Society 2021-11-19 2021-12-07 /pmc/articles/PMC9286315/ /pubmed/34797972 http://dx.doi.org/10.1021/acs.analchem.1c02178 Text en © 2021 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Horgan, Conor C. Jensen, Magnus Nagelkerke, Anika St-Pierre, Jean-Philippe Vercauteren, Tom Stevens, Molly M. Bergholt, Mads S. High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy |
title | High-Throughput Molecular Imaging via Deep-Learning-Enabled
Raman Spectroscopy |
title_full | High-Throughput Molecular Imaging via Deep-Learning-Enabled
Raman Spectroscopy |
title_fullStr | High-Throughput Molecular Imaging via Deep-Learning-Enabled
Raman Spectroscopy |
title_full_unstemmed | High-Throughput Molecular Imaging via Deep-Learning-Enabled
Raman Spectroscopy |
title_short | High-Throughput Molecular Imaging via Deep-Learning-Enabled
Raman Spectroscopy |
title_sort | high-throughput molecular imaging via deep-learning-enabled
raman spectroscopy |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286315/ https://www.ncbi.nlm.nih.gov/pubmed/34797972 http://dx.doi.org/10.1021/acs.analchem.1c02178 |
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