Raman intragroup association analysis (IRCA) only requires one state of a cell population to predict its metabolite transformation network
Whether the intracellular metabolites are undergoing mutual transformation is one of the most important dynamic characteristics of cell metabolism
Recently, the Single Cell Center of Qingdao Institute of Bioenergy and Process , Chinese Academy of Sciences proposed a theoretical framework and algorithm called "Intra-Ramanome Correlation Analysis" (IRCA), and demonstrated the functional testing of cell factories.
Chinese Academy of SciencesChineseAcademy ofSciencesChineseAcademy of Sciences ChineseAcademy ofSciencesmBio It is reported that without the need to label or destroy cells, the "Raman intra-group association analysis" algorithm is only based on a Raman group data point, that is, a state of a sample, and the difference in the Raman spectra of different single cells can be used.
The reporter learned that the construction of metabolite mutual transformation network is traditionally based on metabolomics methods such as mass spectrometry or chromatography
Raman group is a collection of single-cell Raman spectra of a cell population in a specific state
Using this essential feature, the research team led by Dr.
According to reports, the research team used a variety of photosynthetic microalgae as a model to verify the accuracy and reliability of the IRCA prediction results through a series of systematic biochemical and genetic experiments
Finally, the researchers also used IRCA to construct a metabolite transformation network of microalgae, yeast, E.
Compared with analysis methods such as mass spectrometry and chromatography, IRCA has core advantages such as ultra-sensitive, fast, high-throughput, and low-cost (no reagents and consumables).
This work was presided over by Xu Jian, a researcher at the Single Cell Center, and was supported by the National Natural Science Foundation of China, the Pilot Project of the Chinese Academy of Sciences, the Natural Science Foundation of Shandong Province, and the Postdoctoral Science Foundation of China
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