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Tipatet K, Du Boulay I, Muir H, Davison-Gates L, Ellederová Z, Downes A. Raman spectroscopy of brain and skin tissue in a minipig model of Huntington's disease. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:253-261. [PMID: 38108410 DOI: 10.1039/d3ay00970j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
We applied Raman spectroscopy to brain and skin tissues from a minipig model of Huntington's disease. Differences were observed between measured spectra of tissues with and without Huntington's disease, for both brain tissue and skin tissue. There are linked to changes in the chemical composition between tissue types. Using machine learning we correctly classified 96% of test spectra as diseased or wild type, indicating that the test would have a similar accuracy when used as a diagnostic tool for the disease. This suggests the technique has great potential in the rapid and accurate diagnosis of Huntington's and other neurodegenerative diseases in a clinical setting.
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Affiliation(s)
- Kevin Tipatet
- a, Institute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh EH9 3DW, UK.
| | - Isla Du Boulay
- a, Institute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh EH9 3DW, UK.
| | - Hamish Muir
- a, Institute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh EH9 3DW, UK.
| | - Liam Davison-Gates
- a, Institute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh EH9 3DW, UK.
| | - Zdenka Ellederová
- a, Institute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh EH9 3DW, UK.
- Institute of Animal Physiology and Genetics, Czech Academy of Sciences, Rumburská 89, 277 21 Liběchov, UK
| | - Andrew Downes
- a, Institute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh EH9 3DW, UK.
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Wang B, Xia X, Tang R, Jiang H, Qi M, Zhang X. Self-assembled Cr 2O 3@nanogel/Au nanozymes to simulate peroxidase activity as a H 2O 2 sensor. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121928. [PMID: 36191436 DOI: 10.1016/j.saa.2022.121928] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/14/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
The low temperature solvothermal method synthesized Cr2O3 NPs has not only peroxidase activity, but also oxidase activity. Then, the oxidase activity of Cr2O3 NPs is effectively shielded by nanogel immobilization using three monomers acrylamide, NIPAAM (N-isopropylacrylamide) and MBA (N,N'-methylene bisacrylamide) in HEPES (4-(2-hydroxyerhyl)piperazine-1-erhanesulfonic acid) buffer. Ultimately, the enzymatic activity of Cr2O3@nanogel/Au is significantly enhanced after doping Au NPs by SERS (Surface Enhanced Raman Spectroscopy) evaluation. A SERS strategy was proposed for the detection of H2O2 by Cr2O3@nanogel/Au. The linear range was 10-8 mol·L-1-10-1 mol·L-1.
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Affiliation(s)
- Baihui Wang
- School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Xuemin Xia
- School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Ruyi Tang
- School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Huan Jiang
- School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Mengyao Qi
- School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Xia Zhang
- School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
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Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging. BIOSENSORS 2022; 12:bios12040250. [PMID: 35448310 PMCID: PMC9031282 DOI: 10.3390/bios12040250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/10/2022] [Accepted: 04/12/2022] [Indexed: 11/17/2022]
Abstract
This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information–nucleic acids, proteins, and lipids—from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation.
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