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dc.contributor.authorSuhito, Intan Rosalina-
dc.contributor.authorHan, Yoojoong-
dc.contributor.authorRyu, Yong-Sang-
dc.contributor.authorSon, Hyungbin-
dc.contributor.authorKim, Tae-Hyung-
dc.date.accessioned2024-01-19T15:01:25Z-
dc.date.available2024-01-19T15:01:25Z-
dc.date.created2022-01-10-
dc.date.issued2021-04-15-
dc.identifier.issn0956-5663-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/117124-
dc.description.abstractStem cell-based therapies have recently emerged to treat various incurable diseases and disorders. Types of stem cell-derived cells and their functions should be intensively analyzed before therapy. However, current pretreatment steps for biological analysis are mostly destructive. Here, we report a novel optical method that enables ultra-fast and label-free characterization of cells, eliminating invasive, destructive steps. The technique, referred to as "autofluorescence-Raman mapping integration (ARMI)" analysis uses cell autofluorescence (AF) to reveal cellular morphology and cytosolic microstructures, while Raman mapping allows site-specific intensive analysis of target molecules, which enables ultra-fast identification of cell types. We used human mesenchymal stem cells (MSCs) as a model and induced adipogenesis. Lipid droplets in cells appeared as "blanks" in three-dimensional AF images and site-specific Raman mapping guided by AF identified the structure and components of the CH2 stretch. Adipogenesis could be rapidly and precisely analyzed, not only for the same batch but also for different batches. Therefore, the developed tool is highly useful for the accurate screening of stem cell differentiation and implementation in biomedical and clinical applications.-
dc.languageEnglish-
dc.publisherELSEVIER ADVANCED TECHNOLOGY-
dc.titleAutofluorescence-Raman Mapping Integration analysis for ultra-fast label-free monitoring of adipogenic differentiation of stem cells-
dc.typeArticle-
dc.identifier.doi10.1016/j.bios.2021.113018-
dc.description.journalClass1-
dc.identifier.bibliographicCitationBIOSENSORS & BIOELECTRONICS, v.178-
dc.citation.titleBIOSENSORS & BIOELECTRONICS-
dc.citation.volume178-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.identifier.wosid000621208000004-
dc.identifier.scopusid2-s2.0-85099941250-
dc.relation.journalWebOfScienceCategoryBiophysics-
dc.relation.journalWebOfScienceCategoryBiotechnology & Applied Microbiology-
dc.relation.journalWebOfScienceCategoryChemistry, Analytical-
dc.relation.journalWebOfScienceCategoryElectrochemistry-
dc.relation.journalWebOfScienceCategoryNanoscience & Nanotechnology-
dc.relation.journalResearchAreaBiophysics-
dc.relation.journalResearchAreaBiotechnology & Applied Microbiology-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaElectrochemistry-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.type.docTypeArticle-
dc.subject.keywordPlusPLURIPOTENCY-
dc.subject.keywordPlusSPECTROSCOPY-
dc.subject.keywordPlusMETABOLISM-
dc.subject.keywordPlusSTRATEGIES-
dc.subject.keywordPlusDENSITY-
dc.subject.keywordAuthorAutofluorescence microscopy-
dc.subject.keywordAuthorRaman spectroscopy-
dc.subject.keywordAuthorStem cells-
dc.subject.keywordAuthorDifferentiation variability-
dc.subject.keywordAuthorLabel-free method-
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KIST Article > 2021
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