Tracking the Differentiation Status of Human Neural Stem Cells through Label-Free Raman Spectroscopy and Machine Learning-Based Analysis

• Published in: Analytical chemistry (Washington) 2021-08, Vol.93 (30), p.10453-10461
• Main Authors: Geng, Junnan, Zhang, Wei, Chen, Cheng, Zhang, Han, Zhou, Anhong, Huang, Yu
• Format: Article
• Language: English
• Subjects: Analytical chemistry; Artificial neural networks; Biomarkers; Cell differentiation; Chemistry; Classification; Cluster analysis; Data points; Differentiation; Discriminant analysis; Image acquisition; Immunocytochemistry; In vivo methods and tests; Learning algorithms; Learning theory; Machine learning; Neural networks; Neural stem cells; Principal components analysis; Raman spectra; Raman spectroscopy; Regression models; Spectroscopic analysis; Spectroscopy; Spectrum analysis; Stem cells; Trend analysis
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.0c04941

The ability to noninvasively monitor stem cells’ differentiation is important to stem cell studies. Raman spectroscopy is a non-harmful imaging approach that acquires the cellular biochemical signatures. Herein, we report the first use of label-free Raman spectroscopy to characterize the gradual change during the differentiation process of live human neural stem cells (NSCs) in the in vitro cultures. Raman spectra of 600–1800 cm–1 were measured with human NSC cultures from the undifferentiated stage (NSC-predominant) to the highly differentiated one (neuron-predominant) and subsequently analyzed using various mathematical methods. Hierarchical cluster analysis distinguished two cell types (NSCs and neurons) through the spectra. The subsequently derived differentiation rate matched that measured by immunocytochemistry. The key spectral biomarkers were identified by time-dependent trend analysis and principal component analysis. Furthermore, through machine learning-based analysis, a set of eight spectral data points were found to be highly accurate in classifying cell types and predicting the differentiation rate. The predictive accuracy was the highest using the artificial neural network (ANN) and slightly lowered using the logistic regression model and linear discriminant analysis. In conclusion, label-free Raman spectroscopy with the aid of machine learning analysis can provide the noninvasive classification of cell types at the single-cell level and thus accurately track the human NSC differentiation. A set of eight spectral data points combined with the ANN method were found to be the most efficient and accurate. Establishing this non-harmful and efficient strategy will shed light on the in vivo and clinical studies of NSCs.