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He H, Cao M, Gao Y, Zheng P, Yan S, Zhong JH, Wang L, Jin D, Ren B. Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy. Nat Commun 2024; 15:754. [PMID: 38272927 PMCID: PMC10810791 DOI: 10.1038/s41467-024-44864-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 01/05/2024] [Indexed: 01/27/2024] Open
Abstract
The low scattering efficiency of Raman scattering makes it challenging to simultaneously achieve good signal-to-noise ratio (SNR), high imaging speed, and adequate spatial and spectral resolutions. Here, we report a noise learning (NL) approach that estimates the intrinsic noise distribution of each instrument by statistically learning the noise in the pixel-spatial frequency domain. The estimated noise is then removed from the noisy spectra. This enhances the SNR by ca. 10 folds, and suppresses the mean-square error by almost 150 folds. NL allows us to improve the positioning accuracy and spatial resolution and largely eliminates the impact of thermal drift on tip-enhanced Raman spectroscopic nanoimaging. NL is also applicable to enhance SNR in fluorescence and photoluminescence imaging. Our method manages the ground truth spectra and the instrumental noise simultaneously within the training dataset, which bypasses the tedious labelling of huge dataset required in conventional deep learning, potentially shifting deep learning from sample-dependent to instrument-dependent.
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Affiliation(s)
- Hao He
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
- Department of Biomedical Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Maofeng Cao
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Yun Gao
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China
| | - Peng Zheng
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China
| | - Sen Yan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Jin-Hui Zhong
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Lei Wang
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China.
| | - Dayong Jin
- Department of Biomedical Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
- Institute for Biomedical Materials & Devices (IBMD), University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
- Tan Kah Kee Innovation Laboratory, Xiamen, 361104, China.
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Abstract
Lipids are essential cellular components forming membranes, serving as energy reserves, and acting as chemical messengers. Dysfunction in lipid metabolism and signaling is associated with a wide range of diseases including cancer and autoimmunity. Heterogeneity in cell behavior including lipid signaling is increasingly recognized as a driver of disease and drug resistance. This diversity in cellular responses as well as the roles of lipids in health and disease drive the need to quantify lipids within single cells. Single-cell lipid assays are challenging due to the small size of cells (∼1 pL) and the large numbers of lipid species present at concentrations spanning orders of magnitude. A growing number of methodologies enable assay of large numbers of lipid analytes, perform high-resolution spatial measurements, or permit highly sensitive lipid assays in single cells. Covered in this review are mass spectrometry, Raman imaging, and fluorescence-based assays including microscopy and microseparations.
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Affiliation(s)
- Ming Yao
- Department of Bioengineering, University of Washington, Seattle, Washington, USA; , ,
| | | | - Nancy L Allbritton
- Department of Bioengineering, University of Washington, Seattle, Washington, USA; , ,
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