In vivo- and in vitro-analysis of single diatom cells/ cell walls by optical imaging using Raman spectroscopy and non-linear microscopy

拉曼光谱是一种重要的分析工具，可以应用于从整数细胞中无损地获取分子信息。基于我们正在进行的关于单个硅藻细胞壁研究的合作，将在拟议的项目中开发和使用拉曼光谱方法，以研究微米和纳米结构硅藻细胞壁的形成过程中的细胞壁组成和代谢过程。这些测量包括对单个提取的细胞壁的研究，以及通过将微操作系统与成像技术相结合来研究活细胞。此外，还将应用基于二次谐波、双光子荧光和相干反斯托克斯拉曼散射的非线性显微镜。该项目的目的是调查在细胞周期中发现的有机分子的性质和组成的变化。此外，还将监测细胞对生长介质成分的特定变化的反应。光学陷阱允许在活的单个硅藻细胞上进行拉曼实验。利用光学陷阱和拉曼光谱相结合的方法，在微流控环境中进行活体在线监测。此外，还将结合其他分析技术(电感耦合等离子体发射光谱、核磁共振)对提取的单个细胞壁进行拉曼显微镜检查，特别是对细胞壁中发现的无机成分进行分析。化学计量学将对数据的解释起到重要作用。

Raman spectroscopy is an important analytical tool which can be applied in order to non-destructively obtain molecular information from integer cells. Based on our ongoing collaboration concerning the study of single diatom cell walls, Raman spectroscopic methods will be developed and used within the proposed project in order to study the cell wall composition as well as the metabolic processes taking place during the formation of the micro- and nanostructured diatom cell walls. The measurements include the study of single, extracted cell walls as well as the study of living cells by the combination of micromanipulation systems with imaging techniques. Non-linear microscopy based on second harmonic generation, two-photon fluorescence and coherent anti-Stokes Raman scattering will be applied additionally. The aim of the project is the investigation of variations of the nature and composition of organic molecules found during the cell cycle. Furthermore, the response of the cells upon defined variations of the composition of the growth medium will be monitored. Optical traps allow Raman experiments on living single diatom cells. In vivo online monitoring will be carried out by combining optical traps and Raman spectroscopy in a microfluidics environment. Furthermore, Raman microscopy will also be performed on extracted single cell walls in combination with other analytical techniques (ICP-OES, NMR) especially with respect to the inorganic components found in the cell walls. Chemometrics will be important for the interpretation of the data.