Automated High-Throughput Raman Spectral Framework for Cellular Differentiation Monitoring
Abstract
High-throughput, label-free monitoring of cellular differentiation remains a major challenge in stem cell biology and regenerative medicine. Raman spectroscopy offers rich molecular specificity without perturbing the cell state, but the analytical complexity of large, unlabeled spectral data sets has limited its adoption. Here, we introduce a scalable computational framework that adapts algorithms from single-cell genomics for the analysis of line-illumination Raman spectroscopy data. Applying this approach, we track the stepwise differentiation of human induced pluripotent stem cells into hepatocyte-like cells at single-cell resolution across more than 1.8 million spectra. By integration of unsupervised clustering with supervised learning, our pipeline enables rapid analysis (
Fields of Study & Subjects
Spectroscopy Techniques in Biomedical and Chemical Research
Microfluidic and Bio-sensing Technologies
Spectroscopy and Chemometric Analyses
Publication details
- Journal / source
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Volume
- 26
- Issue
- 4
- Pages
- 1357–1365
- Publication date
- 22 January 2026
- Language
- EN