Accurate Raman indicators of protein synthesis through sparse classification
Accurate Raman indicators of protein synthesis through sparse classification
复制标题
通过稀疏分类获得准确的蛋白质合成拉曼指标
DOI：
10.1117/12.2609775
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Smith Nicholas
中科院分区：
文献类型：
--
作者：
Pavillon Nicolas;Smith Nicholas
拉曼光谱（RS）具有以非侵入性方式从细胞和组织中检索分子信息的能力，允许检测各种生物反应。它通常依赖于多变量分析来检测感兴趣的变化。我们在这里展示了正则化方法如何在用于检测炎症或不同表型的单细胞测量的背景下提高检测的准确性和稳定性。然后，我们使用蛋白质合成作为一个案例研究，以评估哪些拉曼带是最重要的，并发现，小波段以外的主要拉曼区域往往是更准确的检测。
Raman spectroscopy (RS) has the ability to retrieve in a non-invasive way molecular information from cells and tissues, allowing the detection of various biological responses. It often relies on multivariate analysis to detect the changes of interest. We show here how regularized methods can improve the accuracy and stability of detection in the context of single-cell measurements for the detection of inflammation, or different phenotypes. We then use protein synthesis as a case study to assess which Raman bands are the most significant, and find that small bands outside of the main Raman regions are often more accurate for detection.