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Xu J, Chen H, Wang C, Ma Y, Song Y. Raman Flow Cytometry and Its Biomedical Applications. BIOSENSORS 2024; 14:171. [PMID: 38667164 PMCID: PMC11048678 DOI: 10.3390/bios14040171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024]
Abstract
Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.
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Affiliation(s)
- Jiayang Xu
- Zhejiang University-University of Edinburgh Institute, Zhejiang University, Hangzhou 310058, China;
- Edinburgh Medical School: Biomedical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Hongyi Chen
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou 215163, China
| | - Ce Wang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Yuting Ma
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Yizhi Song
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou 215163, China
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Esposito C, Janneh M, Spaziani S, Calcagno V, Bernardi ML, Iammarino M, Verdone C, Tagliamonte M, Buonaguro L, Pisco M, Aversano L, Cusano A. Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy. Cells 2023; 12:2645. [PMID: 37998378 PMCID: PMC10670489 DOI: 10.3390/cells12222645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells' Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.
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Affiliation(s)
- Concetta Esposito
- Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
| | - Mohammed Janneh
- Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
| | - Sara Spaziani
- Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
| | - Vincenzo Calcagno
- Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
| | - Mario Luca Bernardi
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
- Informatics Group, Engineering Department, University of Sannio, 82100 Benevento, Italy
| | - Martina Iammarino
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
- Informatics Group, Engineering Department, University of Sannio, 82100 Benevento, Italy
| | - Chiara Verdone
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
- Informatics Group, Engineering Department, University of Sannio, 82100 Benevento, Italy
| | - Maria Tagliamonte
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
- National Cancer Institute-IRCCS “Pascale”, Via Mariano Semmola, 52, 80131 Napoli, Italy
| | - Luigi Buonaguro
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
- National Cancer Institute-IRCCS “Pascale”, Via Mariano Semmola, 52, 80131 Napoli, Italy
| | - Marco Pisco
- Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
| | - Lerina Aversano
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
- Informatics Group, Engineering Department, University of Sannio, 82100 Benevento, Italy
| | - Andrea Cusano
- Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
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Xu Y, Hou X, Zhu Q, Mao S, Ren J, Lin J, Xu N. Phenotype Identification of HeLa Cells Knockout CDK6 Gene Based on Label-Free Raman Imaging. Anal Chem 2022; 94:8890-8898. [PMID: 35704426 DOI: 10.1021/acs.analchem.2c00188] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Identifying cell phenotypes is essential for understanding the function of biological macromolecules and molecular biology. We developed a noninvasive, label-free, single-cell Raman imaging analysis platform to distinguish between the cell phenotypes of the HeLa cell wild type (WT) and cyclin-dependent kinase 6 (CDK6) gene knockout (KO) type. Via large-scale Raman spectral and imaging analysis, two phenotypes of the HeLa cells were distinguished by their intrinsic biochemical profiles. A significant difference was found between the two cell lines: large lipid droplets formed in the knockout HeLa cells but were not observed in the WT cells, which was confirmed by Oil Red O staining. The band ratio of the Raman spectrum of saturated/unsaturated fatty acids was identified as the Raman spectral marker for HeLa cell WT or gene knockout type differentiation. The interaction between organelles involved in lipid metabolism was revealed by Raman imaging and Lorentz fitting, where the distribution intensity of the mitochondria and the endoplasmic reticulum membrane decreased. At the same time, lysosomes increased after the CDK6 gene knockout. The parameters obtained from Raman spectroscopy are based on hierarchical cluster analysis and one-way ANOVA, enabling highly accurate cell classification.
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Affiliation(s)
- Ying Xu
- Institute of Drug Development & Chemical Biology, College of Pharmaceutical Science, Institute of Drug Development & Chemical Biology, Zhejiang University of Technology, Huzhou, Zhejiang 313200, People's Republic of China
| | - Xiaoli Hou
- Academy of Chinese Medical Science, Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, People's Republic of China
| | - Qiaoqiao Zhu
- Institute of Drug Development & Chemical Biology, College of Pharmaceutical Science, Institute of Drug Development & Chemical Biology, Zhejiang University of Technology, Huzhou, Zhejiang 313200, People's Republic of China
| | - Shijie Mao
- Institute of Drug Development & Chemical Biology, College of Pharmaceutical Science, Institute of Drug Development & Chemical Biology, Zhejiang University of Technology, Huzhou, Zhejiang 313200, People's Republic of China
| | - Jie Ren
- Institute of Drug Development & Chemical Biology, College of Pharmaceutical Science, Institute of Drug Development & Chemical Biology, Zhejiang University of Technology, Huzhou, Zhejiang 313200, People's Republic of China
| | - Jidong Lin
- Institute of Drug Development & Chemical Biology, College of Pharmaceutical Science, Institute of Drug Development & Chemical Biology, Zhejiang University of Technology, Huzhou, Zhejiang 313200, People's Republic of China
| | - Ning Xu
- Institute of Drug Development & Chemical Biology, College of Pharmaceutical Science, Institute of Drug Development & Chemical Biology, Zhejiang University of Technology, Huzhou, Zhejiang 313200, People's Republic of China
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Mönch D, Koch J, Dahlke MH. Are Mesenchymal Stem Cells Fibroblasts with Benefits? CURRENT STEM CELL REPORTS 2022. [DOI: 10.1007/s40778-022-00210-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Fosca M, Basoli V, Della Bella E, Russo F, Vadala G, Alini M, Rau JV, Verrier S. Raman spectroscopy in skeletal tissue disorders and tissue engineering: present and prospective. TISSUE ENGINEERING PART B-REVIEWS 2021; 28:949-965. [PMID: 34579558 DOI: 10.1089/ten.teb.2021.0139] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Musculoskeletal disorders are the most common reason of chronic pain and disability representing worldwide an enormous socio-economic burden. In this review, new biomedical application fields for Raman spectroscopy (RS) technique related to skeletal tissues are discussed showing that it can provide a comprehensive profile of tissue composition in situ, in a rapid, label-free, and non-destructive manner. RS can be used as a tool to study tissue alterations associated to aging, pathologies, and disease treatments. The main advantage with respect to currently applied methods in clinics is its ability to provide specific information on molecular composition, which goes beyond other diagnostic tools. Being compatible with water, RS can be performed without pre-treatment on unfixed, hydrated tissue samples, without any labelling and chemical fixation used in histochemical methods. This review provides first the description of basic principles of RS as a biotechnology tool and introduces into the field of currently available RS based techniques, developed to enhance Raman signal. The main spectral processing statistical tools, fingerprint identification and available databases are mentioned. The recent literature has been analysed for such applications of RS as tendon and ligaments, cartilage, bone, and tissue engineered constructs for regenerative medicine. Several cases of proof-of-concept preclinical studies have been described. Finally, advantages, limitations, future perspectives, and challenges for translation of RS into clinical practice have been also discussed.
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Affiliation(s)
- Marco Fosca
- Istituto di Struttura della Materia Consiglio Nazionale delle Ricerche, 204549, Roma, Lazio, Italy;
| | - Valentina Basoli
- AO Research Institute Davos, 161930, Regenerative Orthopaedics, Davos, Graubünden, Switzerland;
| | - Elena Della Bella
- AO Research Institute Davos, 161930, Regenerative Orthopaedics, Davos, Graubünden, Switzerland;
| | - Fabrizio Russo
- Campus Bio-Medico University Hospital, 220431, Roma, Lazio, Italy;
| | - Gianluca Vadala
- Campus Bio-Medico University Hospital, 220431, Roma, Lazio, Italy;
| | - Mauro Alini
- AO Research Institute Davos, 161930, Regenerative Orthopaedics, Davos, Graubünden, Switzerland;
| | - Julietta V Rau
- Istituto di Struttura della Materia Consiglio Nazionale delle Ricerche, 204549, Roma, Lazio, Italy.,I M Sechenov First Moscow State Medical University, 68477, Moskva, Moskva, Russian Federation;
| | - Sophie Verrier
- AO Research Institute Davos, 161930, Regenerative Orthopaedics, Davos, Graubünden, Switzerland;
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