文章基本信息

标题：A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons
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作者：Chia-Chen Hsu ; Jiabao Xu ; Bas Brinkhof 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2020
卷号：117
期号：31
页码：18412-18423
DOI：10.1073/pnas.2001906117
出版社：The National Academy of Sciences of the United States of America
摘要：Stem cells with the capability to self-renew and differentiate into multiple cell derivatives provide platforms for drug screening and promising treatment options for a wide variety of neural diseases. Nevertheless, clinical applications of stem cells have been hindered partly owing to a lack of standardized techniques to characterize cell molecular profiles noninvasively and comprehensively. Here, we demonstrate that a label-free and noninvasive single-cell Raman microspectroscopy (SCRM) platform was able to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). By analyzing the intrinsic biochemical profiles of single cells at a large scale (8,774 Raman spectra in total), iPSCs and iPSC-derived neural cells can be distinguished by their intrinsic phenotypic Raman spectra. We identified a Raman biomarker from glycogen to distinguish iPSCs from their neural derivatives, and the result was verified by the conventional glycogen detection assays. Further analysis with a machine learning classification model, utilizing t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, clearly categorized hiPSCs in different developmental stages with 97.5% accuracy. The present study demonstrates the capability of the SCRM-based platform to monitor cell development using high content screening with a noninvasive and label-free approach. This platform as well as our identified biomarker could be extensible to other cell types and can potentially have a high impact on neural stem cell therapy.
关键词：neural stem cell ; Raman spectroscopy ; differentiation ; machine learning ; biomarker