Mesenchymal stromal cells (MSCs) hold great potential in skeletal tissue engineering and regenerative medicine. However, conventional methods that are used in molecular biology to evaluate osteogenic differentiation of MSCs require a relatively large amount of cells. Cell lysis and cell fixation are also required and all these steps are time-consuming. Therefore, it is imperative to develop a facile technique which can provide real-time information with high sensitivity and selectivity to detect the osteogenic maturation of MSCs. In this study, we use Raman spectroscopy as a biosensor to monitor the production of mineralized matrices during osteogenic induction of MSCs. In summary, Raman spectroscopy is an excellent biosensor to detect the extent of maturation level during MSCs-osteoblast differentiation with a non-disruptive, real-time and label free manner. We expect that this study will promote further investigation of stem cell research and clinical applications.
Renal cell carcinoma (RCC) accounts for 85% of all primary renal cancers. The definitive diagnosis of RCC relies exclusively on the subjective pathological interpretation of the surgical specimen. In this study, we aimed to analyze renal tissue using objective Raman spectroscopy (RS). We obtained 15 pairs of RCC (T) and corresponding normal renal parenchymal tissues (N) from our biobank. There are three subtypes of RCC: clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (cRCC). Five pairs of tissue of each subtype were enrolled. Fresh-frozen sliced tissues were used for the RS detection. The Raman spectra between T and N were compared and analyzed using partial least squares (PLS) regression. Data for a total of 55 T and 58 N analyzable RS samples were obtained. The spectra were normalized by dividing the intensity of the characteristic peak at 1003 cm À1 using phenylalanine's Raman peak. After further analysis with PLS, the sensitivity and specificity for discriminating T from N were 95% and 93%, respectively. The RCC subtypes can be discriminated at an accuracy of 72% for ccRCC, 88% for cRCC, and 86% for pRCC. This study demonstrates the feasibility of analyzing renal tissue using RS. RS, with its advantages of easy and objective tissue assessment, may be applied to aid intraoperative decision making and pathological tissue assessment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.