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RAMAN SPECTROSCOPIC ANALYSIS OF CELL DIFFERENTIATION AND DEATH MODES

机译:细胞分化和死亡模式的拉曼光谱分析

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Raman spectroscopy provides opportunities for non-invasive, non-destructive, label-free analysis of cell states based on changes in the biochemical composition of cells. We are investigating the suitability of Raman spectroscopy to assess the stages of human embryonic stem cell (hESC) differentiation towards pancreatic insulin-positive cells. Raman microspectrometry analysis has revealed macromolecular composition differences over time that distinguished cell populations differentiating to pancreatic cell types, such as by an increase in the protein-to-nucleic acid signal ratio and to distinguish the presence of insulin. Added insight into these macromolecular changes were provided by principal component analysis (PCA) of the data. However, the application of PCA can be difficult to interpret. The usefulness of non-negative matrix factorization was explored to improve the interpretability of overlapping Raman bands. We demonstrated the utility of this procedure by analyzing spectra to determine the cellular insulin or glucagon content. Thus, Raman spectroscopy can detect such differences in cells to detect the desired product as well as the potential to detect residual hESCs or the emergence of unwanted cells. We also investigated the suitability of Raman spectroscopy to detect the onset and types of cell death. Apoptotic, necrotic or autophagic Chinese Hamster Ovary cells were compared to uninduced cultures using Raman spectroscopy and PCA. Furthermore, uninduced cells were compared to cells sorted at different stages of apoptosis to determine how early the onset of apoptosis could be detected. Changes were observed in several peaks during the course of cell death, with repeated changes observed in nucleic acid- and lipid-associated peaks, enabling the distinction of cell death modes. Application of such death monitoring capabilities to cellular therapy cultures should be even more useful, given the need for more process analytical technologies to address the often more variable performance of these cultures, especially when adaptive control is needed for primary cell derived manufacturing.
机译:拉曼光谱法为基于细胞生化成分变化的细胞状态无创,无损,无标记分析提供了机会。我们正在调查拉曼光谱的适用性,以评估人类胚胎干细胞(hESC)向胰腺胰岛素阳性细胞分化的阶段。拉曼光谱分析揭示了随时间变化的大分子组成差异,该差异区分了分化为胰腺细胞类型的细胞群,例如通过增加蛋白质与核酸信号之比并区分胰岛素的存在。通过数据的主成分分析(PCA)提供了对这些大分子变化的更多了解。但是,PCA的应用可能难以解释。探索了非负矩阵分解的有用性,以提高重叠拉曼谱带的可解释性。我们通过分析光谱以确定细胞胰岛素或胰高血糖素含量证明了该程序的实用性。因此,拉曼光谱法可以检测细胞中的此类差异以检测所需产物,还可以检测残留的hESC或有害细胞的出现。我们还研究了拉曼光谱法检测细胞死亡的发作和类型的适用性。使用拉曼光谱和PCA将凋亡,坏死或自噬的中国仓鼠卵巢细胞与未诱导培养物进行比较。此外,将未诱导的细胞与在凋亡不同阶段分选的细胞进行比较,以确定可以多早检测到凋亡的发生。在细胞死亡的过程中,在多个峰中观察到变化,并且在与核酸和脂质相关的峰中观察到重复变化,从而能够区分细胞死亡模式。考虑到需要更多的过程分析技术来解决这些培养物通常更可变的性能,将这种死亡监测功能应用于细胞治疗培养物应该更加有用,尤其是在原代细胞衍生生产需要自适应控制的情况下。

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