Deep Learning-Decoded Raman Spectroscopy for Hour-Scale iPSC Pluripotency Assessment via Lipid–Protein Biomarkers

Rapid and label-free evaluation of induced pluripotent stem cell (iPSC) pluripotency is critical for advancing regenerative medicine and clinical applications. Although traditional genomics- and proteomics-based pluripotency assessment methods are reliable, their invasive nature, reliance on labeling, and time-intensive workflows limit their suitability for dynamic monitoring. Here, we present a method combining deep learning with Raman spectroscopy for an hour-scale label-free pluripotency assessment in iPSCs. By inducing pluripotency modulation through a culture medium alteration and simultaneously acquiring correlated Raman spectra, we established spectral data sets of iPSCs at distinct pluripotent states. Using these data sets as input, we trained a one-dimensional convolutional neural network (1D-CNN) to classify pluripotent states with an average accuracy of 97.10%. Remarkably, the model achieved 98.00% accuracy in detecting pluripotency anomalies at the 1 h time point of medium perturbation─prior to observable morphological changes─establishing the fastest reported detection framework for iPSC quality control. Gradient-weighted class activation mapping (Grad-CAM) shows that lipids and proteins (with Raman peaks at 1440 and 1660 cm –1, respectively) are biomarker signatures directly linked to pluripotency states. Further, the pluripotency of iPSCs was confirmed to be related to PI3K/AKT pathway activity. This integration of Raman spectroscopy and interpretable deep learning bypasses prior biomarker knowledge requirements, offering a paradigm shift toward clinical-grade noninvasive stem cell diagnostics.