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Troncoso-Afonso L, Vinnacombe-Willson GA, García-Astrain C, Liz-Márzan LM. SERS in 3D cell models: a powerful tool in cancer research. Chem Soc Rev 2024; 53:5118-5148. [PMID: 38607302 PMCID: PMC11104264 DOI: 10.1039/d3cs01049j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Indexed: 04/13/2024]
Abstract
Unraveling the cellular and molecular mechanisms underlying tumoral processes is fundamental for the diagnosis and treatment of cancer. In this regard, three-dimensional (3D) cancer cell models more realistically mimic tumors compared to conventional 2D cell cultures and are more attractive for performing such studies. Nonetheless, the analysis of such architectures is challenging because most available techniques are destructive, resulting in the loss of biochemical information. On the contrary, surface-enhanced Raman spectroscopy (SERS) is a non-invasive analytical tool that can record the structural fingerprint of molecules present in complex biological environments. The implementation of SERS in 3D cancer models can be leveraged to track therapeutics, the production of cancer-related metabolites, different signaling and communication pathways, and to image the different cellular components and structural features. In this review, we highlight recent progress in the use of SERS for the evaluation of cancer diagnosis and therapy in 3D tumoral models. We outline strategies for the delivery and design of SERS tags and shed light on the possibilities this technique offers for studying different cellular processes, through either biosensing or bioimaging modalities. Finally, we address current challenges and future directions, such as overcoming the limitations of SERS and the need for the development of user-friendly and robust data analysis methods. Continued development of SERS 3D bioimaging and biosensing systems, techniques, and analytical strategies, can provide significant contributions for early disease detection, novel cancer therapies, and the realization of patient-tailored medicine.
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Affiliation(s)
- Lara Troncoso-Afonso
- BioNanoPlasmonics Laboratory, CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain.
- Department of Applied Chemistry, University of the Basque Country, 20018 Donostia-San Sebastián, Gipuzkoa, Spain
| | - Gail A Vinnacombe-Willson
- BioNanoPlasmonics Laboratory, CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain.
| | - Clara García-Astrain
- BioNanoPlasmonics Laboratory, CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain.
- Centro de Investigación Biomédica en Red de Bioingeniería Biomateriales, y Nanomedicina (CIBER-BBN), Paseo de Miramón 182, 20014 Donostia-San Sebastián, Spain
| | - Luis M Liz-Márzan
- BioNanoPlasmonics Laboratory, CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain.
- Centro de Investigación Biomédica en Red de Bioingeniería Biomateriales, y Nanomedicina (CIBER-BBN), Paseo de Miramón 182, 20014 Donostia-San Sebastián, Spain
- Ikerbasque Basque Foundation for Science, 48013 Bilbao, Spain
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Kuznetsova V, Coogan Á, Botov D, Gromova Y, Ushakova EV, Gun'ko YK. Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2308912. [PMID: 38241607 PMCID: PMC11167410 DOI: 10.1002/adma.202308912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/10/2024] [Indexed: 01/21/2024]
Abstract
Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.
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Affiliation(s)
- Vera Kuznetsova
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
| | - Áine Coogan
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
| | - Dmitry Botov
- Everypixel Media Innovation Group, 021 Fillmore St., PMB 15, San Francisco, CA, 94115, USA
- Neapolis University Pafos, 2 Danais Avenue, Pafos, 8042, Cyprus
| | - Yulia Gromova
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford St., Cambridge, MA, 02138, USA
| | - Elena V Ushakova
- Department of Materials Science and Engineering, and Centre for Functional Photonics (CFP), City University of Hong Kong, Hong Kong SAR, 999077, P. R. China
| | - Yurii K Gun'ko
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
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