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High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy

Research output: Contribution to journalArticlepeer-review

Conor C Horgan, Magnus Jensen, Anika Nagelkerke, Jean-Philippe St-Pierre, Tom Vercauteren, Molly M Stevens, Mads S Bergholt

Original languageEnglish
Pages (from-to)15850-15860
Number of pages11
JournalAnalytical Chemistry
Issue number48
Early online date19 Nov 2021
Accepted/In press2021
E-pub ahead of print19 Nov 2021
Published7 Dec 2021

Bibliographical note

Funding Information: This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 802778). This work is supported by the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z]. C.C.H. acknowledges funding from the NanoMed Marie Skłodowska-Curie ITN from the H2020 programme under grant number 676137. T.V. is supported by a Medtronic/Royal Academy of Engineering Research Chair [RCSRF1819/7/34]. A.N. and M.M.S. acknowledge support from the GlaxoSmithKline Engineered Medicines Laboratory. M.M.S. acknowledges a Wellcome Trust Senior Investigator Award (098411/Z/12/Z). We acknowledge the use of the JADE HPC facility, which has received funding through the Engineering and Physical Sciences Research Council (EPSRC). Publisher Copyright: © 2021 The Authors. Published by American Chemical Society

King's Authors


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.

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