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Surface-enhanced Raman spectroscopy of degranulation response to C48/80 in mast cells

机译:肥大细胞对C48 / 80脱颗粒反应的表面增强拉曼光谱

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Mast cell (MCs) researches have received worldwide attention and achieved great achievements. Degranulation of MCsis not only related to anaphylaxis, but also plays an important role in the formation and progression of tumor. Theexisting detection methods could not fully reflect the degree of cell degranulation. In this paper, surface-enhancedRaman scattering (SERS) was used to detect and analyze the degranulation degree of MCs treated with differentconcentrations of C48/80 (compound 48/80, a mast cell activator). The culture supernatants of cells treated with differentconcentrations of C48/80 (0 μg/mL, 2 μg/mL and 10 μg/mL) were mixed with Ag colloids and high quality SERS spectrawere acquired. The assignment of SERS bands combined with differential spectra analysis indicated that biomoleculesassociated with cell degranulation in the C48/80 treated groups were changed compared with the control group,including a decrease in the percentage of lipid content and an increase in the relative contents of collegen andphosphatidylserine. Furthermore, principal component analysis (PCA) and linear discriminant analysis (LDA) diagnosticalgorithms were employed to analyze and distinguish the SERS spectra of different cell degranulation groups with highsensitivity, specificity and accuracy. The larger value of the integration area under the ROC curve also suggested thegreater forecast accuracy. This exploratory work demonstrates that the combination of SERS technology and PCA-LDAalgorithm has great potential for developing a label-free, comprehensive and accurate method for detecting celldegranulation.
机译:肥大细胞(MCs)的研究已引起全世界的关注并取得了巨大的成就。 MC的去粒 它不仅与过敏反应有关,而且在肿瘤的形成和发展中也起着重要的作用。这 现有的检测方法不能完全反映细胞脱粒的程度。在本文中,表面增强 拉曼散射(SERS)用于检测和分析不同处理的MC的脱粒程度 浓度为C48 / 80(化合物48/80,肥大细胞激活剂)。经不同处理的细胞的培养上清液 浓度为C48 / 80(0μg/ mL,2μg/ mL和10μg/ mL)与Ag胶体混合并获得高质量的SERS光谱 被收购。 SERS谱带的分配与差分光谱分析相结合表明,生物分子 与对照组相比,C48 / 80处理组中与细胞脱粒相关的改变了, 包括血脂含量百分比的降低和大学生血脂的相对含量的增加 磷脂酰丝氨酸。此外,主成分分析(PCA)和线性判别分析(LDA)诊断 算法被用来分析和区分不同细胞脱颗粒组的SERS光谱。 敏感性,特异性和准确性。 ROC曲线下较大的积分面积值也表明 更高的预测准确性。这项探索性工作证明了SERS技术与PCA-LDA的结合 算法具有开发无标记,全面,准确的细胞检测方法的巨大潜力 脱粒。

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