Scalable nanolaminated SERS multiwell cell culture assay

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dc.contributor.authorRen, Xiangen
dc.contributor.authorNam, Wonilen
dc.contributor.authorGhassemi, Parhamen
dc.contributor.authorStrobl, Jeannine S.en
dc.contributor.authorKim, Inyoungen
dc.contributor.authorZhou, Weien
dc.contributor.authorAgah, Masouden
dc.contributor.departmentElectrical and Computer Engineeringen
dc.contributor.departmentStatisticsen
dc.date.accessioned2020-08-06T17:58:50Zen
dc.date.available2020-08-06T17:58:50Zen
dc.date.issued2020en
dc.description.abstractThis paper presents a new cell culture platform enabling label-free surface-enhanced Raman spectroscopy (SERS) analysis of biological samples. The platform integrates a multilayered metal-insulator-metal nanolaminated SERS substrate and polydimethylsiloxane (PDMS) multiwells for the simultaneous analysis of cultured cells. Multiple cell lines, including breast normal and cancer cells and prostate cancer cells, were used to validate the applicability of this unique platform. The cell lines were cultured in different wells. The Raman spectra of over 100 cells from each cell line were collected and analyzed after 12 h of introducing the cells to the assay. The unique Raman spectra of each cell line yielded biomarkers for identifying cancerous and normal cells. A kernel-based machine learning algorithm was used to extract the high-dimensional variables from the Raman spectra. Specifically, the nonnegative garrote on a kernel machine classifier is a hybrid approach with a mixed nonparametric model that considers the nonlinear relationships between the higher-dimension variables. The breast cancer cell lines and normal breast epithelial cells were distinguished with an accuracy close to 90%. The prediction rate between breast cancer cells and prostate cancer cells reached 94%. Four blind test groups were used to evaluate the prediction power of the SERS spectra. The peak intensities at the selected Raman shifts of the testing groups were selected and compared with the training groups used in the machine learning algorithm. The blind testing groups were correctly predicted 100% of the time, demonstrating the applicability of the multiwell SERS array for analyzing cell populations for cancer research.en
dc.description.sponsorshipThe work was funded by the National Cancer Institute (NCI) under award number R21CA210216 (M.A.), the AFOSR Young Investigator Award under award number FA9550-18-1-0328 (W.N., W.Z.), and the Bradley Department of Electrical and Computer Engineering. The authors would like to thank the Micro and Nano Fabrication Laboratory at Virginia Tech for the equipment support.en
dc.format.extent11 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/s41378-020-0145-3en
dc.identifier.issue47en
dc.identifier.urihttp://hdl.handle.net/10919/99586en
dc.identifier.volume6en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleScalable nanolaminated SERS multiwell cell culture assayen
dc.title.serialMicrosystems & Nanoengineeringen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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