1
|
Yang Z, Zheng X, Gao X, Zeng Q, Yang C, Luo J, Zhan C, Lin Y. Deep Learning Methodology for Obtaining Ultraclean Pure Shift Proton Nuclear Magnetic Resonance Spectra. J Phys Chem Lett 2023; 14:3397-3402. [PMID: 36999661 DOI: 10.1021/acs.jpclett.3c00455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Nuclear magnetic resonance (NMR) is one of the most powerful analytical techniques. In order to obtain high-quality NMR spectra, a real-time Zangger-Sterk (ZS) pulse sequence is employed to collect low-quality pure shift NMR data with high efficiency. Then, a neural network named AC-ResNet and a loss function named SM-CDMANE are developed to train a network model. The model with excellent abilities of suppressing noise, reducing line widths, discerning peaks, and removing artifacts is utilized to process the acquired NMR data. The processed spectra with noise and artifact suppression and small line widths are ultraclean and high-resolution. Peaks overlapped heavily can be resolved. Weak peaks, even hidden in the noise, can be discerned from noise. Artifacts, even as high as spectral peaks, can be removed completely while not suppressing peaks. Eliminating perfectly noise and artifacts and smoothing baseline make spectra ultraclean. The proposed methodology would greatly promote various NMR applications.
Collapse
Affiliation(s)
- Zhengxian Yang
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, No. 422, Siming South Road, Siming District, Xiamen, Fujian 361005, People's Republic of China
| | - Xiaoxu Zheng
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, No. 422, Siming South Road, Siming District, Xiamen, Fujian 361005, People's Republic of China
| | - Xinjing Gao
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, No. 422, Siming South Road, Siming District, Xiamen, Fujian 361005, People's Republic of China
| | - Qing Zeng
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, No. 422, Siming South Road, Siming District, Xiamen, Fujian 361005, People's Republic of China
| | - Chuang Yang
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, No. 422, Siming South Road, Siming District, Xiamen, Fujian 361005, People's Republic of China
| | - Jie Luo
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, No. 422, Siming South Road, Siming District, Xiamen, Fujian 361005, People's Republic of China
| | - Chaoqun Zhan
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, No. 422, Siming South Road, Siming District, Xiamen, Fujian 361005, People's Republic of China
| | - Yanqin Lin
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, No. 422, Siming South Road, Siming District, Xiamen, Fujian 361005, People's Republic of China
| |
Collapse
|
2
|
Abstract
Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.
Collapse
|
3
|
Wu K, Luo J, Zeng Q, Dong X, Chen J, Zhan C, Chen Z, Lin Y. Improvement in Signal-to-Noise Ratio of Liquid-State NMR Spectroscopy via a Deep Neural Network DN-Unet. Anal Chem 2020; 93:1377-1382. [DOI: 10.1021/acs.analchem.0c03087] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ke Wu
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Jie Luo
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Qing Zeng
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Xi Dong
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Jinyong Chen
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Chaoqun Zhan
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Zhong Chen
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Yanqin Lin
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| |
Collapse
|
4
|
Luo J, Zeng Q, Wu K, Lin Y. Fast reconstruction of non-uniform sampling multidimensional NMR spectroscopy via a deep neural network. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2020; 317:106772. [PMID: 32589585 DOI: 10.1016/j.jmr.2020.106772] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 06/07/2020] [Accepted: 06/10/2020] [Indexed: 05/25/2023]
Abstract
Multidimensional nuclear magnetic resonance (NMR) spectroscopy is used to examine the chemical structures of the studied systems. Unfortunately, the application of NMR spectra is limited by their long acquisition time, especially for 3D, 4D, and higher dimensional spectra. Non-uniform sampling (NUS) has been widely recognized as a powerful tool to reduce the NMR experimental time. But the quality of NUS spectra depends on appropriate reconstruction algorithms. As an effective data processing method, deep learning has been widely used in many fields in recent years. In this work, a deep learning-based strategy for fast reconstruction of non-uniform sampling NMR spectra is proposed. In our experiments, the proposed deep neural network has better performance in removing artifacts and preserving weak peaks than typical convolutional neural networks of U-Net and DenseNet. Besides, a novel approach of generating training data is utilized to reduce the computational burden of neural networks, and thus training our network can be easier and faster than previous deep learning-based works. Compared with the two currently available methods, SMILE and hmsIST, our strategy can provide comparable reconstruction quality in terms of peak intensities and the fidelity of peak shape. The reconstruction time of our methods is also comparable to or faster than the two methods, especially for 3D spectra.
Collapse
Affiliation(s)
- Jie Luo
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Qing Zeng
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Ke Wu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Yanqin Lin
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China; Shenzhen Research Institute of Xiamen University, Shenzhen 518057, China.
| |
Collapse
|
5
|
Song L, Wang J, Su X, Zhang X, Li C, Zhou X, Yang D, Jiang B, Liu M. REAL‐
t
1
, an Effective Approach for
t
1
‐Noise Suppression in NMR Spectroscopy Based on Resampling Algorithm. CHINESE J CHEM 2019. [DOI: 10.1002/cjoc.201900389] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Linhong Song
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan National Laboratory for Optoelectronics, National Center for Magnetic Resonance in Wuhan, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences Wuhan Hubei 430071 China
- University of Chinese Academy of Sciences Beijing 100049 China
| | - Jiannan Wang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan National Laboratory for Optoelectronics, National Center for Magnetic Resonance in Wuhan, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences Wuhan Hubei 430071 China
- University of Chinese Academy of Sciences Beijing 100049 China
| | - Xuncheng Su
- State Key Laboratory of Elemento‐Organic Chemistry, Collaborative Innovation Center of Chemical Science and Engineering, College of Chemistry, Nankai University Tianjin 300071 China
| | - Xu Zhang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan National Laboratory for Optoelectronics, National Center for Magnetic Resonance in Wuhan, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences Wuhan Hubei 430071 China
- University of Chinese Academy of Sciences Beijing 100049 China
| | - Conggang Li
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan National Laboratory for Optoelectronics, National Center for Magnetic Resonance in Wuhan, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences Wuhan Hubei 430071 China
- University of Chinese Academy of Sciences Beijing 100049 China
| | - Xin Zhou
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan National Laboratory for Optoelectronics, National Center for Magnetic Resonance in Wuhan, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences Wuhan Hubei 430071 China
- University of Chinese Academy of Sciences Beijing 100049 China
| | - Daiwen Yang
- Department of Biological SciencesNational University of Singapore 14 Science Drive 4 Singapore 117543 Singapore
| | - Bin Jiang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan National Laboratory for Optoelectronics, National Center for Magnetic Resonance in Wuhan, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences Wuhan Hubei 430071 China
- University of Chinese Academy of Sciences Beijing 100049 China
| | - Maili Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan National Laboratory for Optoelectronics, National Center for Magnetic Resonance in Wuhan, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences Wuhan Hubei 430071 China
- University of Chinese Academy of Sciences Beijing 100049 China
| |
Collapse
|
6
|
A noise and artifact suppression using resampling (NASR) method to facilitate de novo protein structure determination. RADIATION DETECTION TECHNOLOGY AND METHODS 2019. [DOI: 10.1007/s41605-019-0127-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
7
|
Le Guennec A, Dumez JN, Giraudeau P, Caldarelli S. Resolution-enhanced 2D NMR of complex mixtures by non-uniform sampling. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2015; 53:913-20. [PMID: 26053155 DOI: 10.1002/mrc.4258] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Revised: 03/28/2015] [Accepted: 04/08/2015] [Indexed: 05/20/2023]
Abstract
NMR is a powerful tool for the analysis of complex mixtures and the identification of individual components. Two-dimensional (2D) NMR potentially offers a wealth of information, but resolution is often sacrificed in order to contain experimental times. We explore the use of non-uniform sampling (NUS) to increase substantially the resolution of 2D NMR spectra of complex mixtures of small molecules, with no increase in experimental time. Two common pulse sequences for metabolomics applications are analysed, HSQC and TOCSY. Specific attention is paid to sensitivity in resolution-enhanced NUS spectra, using the signal-to-maximum-noise ratio as a metric. With a careful choice of sampling schedule and reconstruction algorithm, resolution in the (13) C dimension for HSQC is increased by a factor of at least 32, with no loss in sensitivity and no spurious peaks. For TOCSY, multiplets can be resolved in the indirect dimension in a reasonable experimental time. These properties should increase the usefulness of 2D NMR for metabolomics applications by, for example, increasing the chances of metabolite identification.
Collapse
Affiliation(s)
- Adrien Le Guennec
- Institut de Chimie des Substances Naturelles, CNRS UPR 2301, Avenue de la Terrasse, 91190, Gif-sur-Yvette, France
- Université de Nantes, CNRS, CEISAM UMR 6230, BP92208, 2, rue de la Houssinière, F-44322, Nantes Cedex 03, France
| | - Jean-Nicolas Dumez
- Institut de Chimie des Substances Naturelles, CNRS UPR 2301, Avenue de la Terrasse, 91190, Gif-sur-Yvette, France
| | - Patrick Giraudeau
- Université de Nantes, CNRS, CEISAM UMR 6230, BP92208, 2, rue de la Houssinière, F-44322, Nantes Cedex 03, France
- Institut Universitaire de France, 103 Boulevard St. Michel, 75005, Paris Cedex 5, France
| | - Stefano Caldarelli
- Institut de Chimie des Substances Naturelles, CNRS UPR 2301, Avenue de la Terrasse, 91190, Gif-sur-Yvette, France
- Aix Marseille Université, Centrale Marseille, CNRS, iSm2 UMR 7313, 13397, Marseille, France
| |
Collapse
|
8
|
Lin L, Wei Z, Lin Y, Chen Z. A single-scan method for NMR 2D J-resolved spectroscopy. Chem Commun (Camb) 2015; 51:1234-6. [DOI: 10.1039/c4cc07751b] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A single-scan NMR 2D J-resolved method is proposed for obtaining decoupled proton spectra and fine scalar-coupling splitting patterns.
Collapse
Affiliation(s)
- Liangjie Lin
- Department of Electronic Science
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance
- State Key Laboratory for Physical Chemistry of Solid Surfaces
- Xiamen University
- Xiamen
| | - Zhiliang Wei
- Department of Electronic Science
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance
- State Key Laboratory for Physical Chemistry of Solid Surfaces
- Xiamen University
- Xiamen
| | - Yanqin Lin
- Department of Electronic Science
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance
- State Key Laboratory for Physical Chemistry of Solid Surfaces
- Xiamen University
- Xiamen
| | - Zhong Chen
- Department of Electronic Science
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance
- State Key Laboratory for Physical Chemistry of Solid Surfaces
- Xiamen University
- Xiamen
| |
Collapse
|