Cell Growth Analysis Method Based on Spectral Clustering and Single-Cell Raman Spectroscopy
LI Xin-li1, CONG Li-li2, XU Shu-ping2, LI Su-yi1*
1. College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China
2. State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, China
Abstract：Single-cell Raman spectroscopy (SCRS) technology has the advantages of being rapid, sensitive, and label-free to study cell structure at the single-cell level. A cell growth detection method based on Spectral Clustering and SCRS was proposed in this paper. SCRS data of 600 synchronous culture fermentation-engineered bacteria E. Coli were collected as experimental data, and SCRS data of 300 fermentation-probiotic bacteria-Bacillus subtilis, were collected to verify the method's applicability. Firstly, the growth curve of OD600 was measured for the synchronously cultured colonies as growth period labels at the microbial population level. Secondly, t-SNE was applied to visualize the SCRS data of the population cells, guiding Spectral Clustering to cluster the high-dimensional SCRS data. Silhouette Coefficient and CH index were applied to evaluate the best clusters and assign labels to each SCRS data cluster. Finally, the intersection of SCRS data cluster labels and growth period labels was fitted by cubic spline interpolation to accurately identify the heterogeneous growth period data co-existing in the population and achieve accurate identification of growth periods of single-celled microorganisms. The results showed that the cell growth analysis method based on spectral clustering and SCRS could effectively detect 9% and 4.3% heterogeneous data of the optimal clusters in the three growth periods by using a 2-dimensional embedding space dimension and nearest neighbor-based spectral clustering similarity calculation method according to the cell growth curve of synchronous culture population. The study proposed a method of unsupervised detection of single-cell growth, with the help of spectral clustering without tags, can directly according to the features of SCRS data modeling, and can be of the arbitrary shape of high-dimensional SCRS data clustering and the advantages of fast convergence, realized with two kinds of fermentation engineering bacteria and probiotic fermentation cells lag, the accuracy of logarithmic phase and stable phase identification. In a real sense, it can detect cell growth from the single cell level and provide more accurate and real-time control guidance for fermentation engineering, which has important engineering application value.
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