Rapid and accurate identification of stem cell differentiation stages via SERS and convolutional neural networks

Abstract

Monitoring the transition of cell states during induced pluripotent stem cell (iPSC) differentiation is crucial for clinical medicine and basic research. However, both identification category and prediction accuracy need further improvement. Here, we propose a method combining surface-enhanced Raman spectroscopy (SERS) with convolutional neural networks (CNN) to precisely identify and distinguish cell states during stem cell differentiation. First, mitochondria-targeted probes were synthesized by combining AuNRs and mitochondrial localization signal (MLS) peptides to obtain effective and stable SERS spectra signals at various stages of cell differentiation. Then, the SERS spectra served as input datasets, and their distinctive features were learned and distinguished by CNN. As a result, rapid and accurate identification of six different cell states, including the embryoid body (EB) stage, was successfully achieved throughout the stem cell differentiation process with an impressive prediction accuracy of 98.5%. Furthermore, the impact of different spectral feature peaks on the identification results was investigated, which provides a valuable reference for selecting appropriate spectral bands to identify cell states. This is also beneficial for shortening the spectral acquisition region to enhance spectral acquisition speed. These results suggest the potential for SERS-CNN models in quality monitoring of stem cells, advancing the practical applications of stem cells.

Fig. 1.

(a) Construction of the CNN-S model. (b) Diagram of the 10-fold cross-validation process.

Fig. 2.

(a) Transmission electron microscopy (TEM) of AuNRs-MLS probe. (b) UV–vis spectra of AuNRs and AuNRs-MLS. (c) Raman spectra of iPSC samples with (red line) and without AuNRs-MLS probe added (black line). (d-f) Fluorescent images of iPSC cells added with AuNRs-MLS probes. AuNRs-MLS probes were stained green with FITC (d) and mitochondria of iPSCs were stained red with Mito Tracker red CMXRos (e). (f) Fluorescence merges the image of d and e.

Fig. 3.

(a) Process and timeline of iPSC differentiation into NPC using a single inhibition method. (b) In the phase contrast images of cells at different differentiation stages, the red line in EB10d is the dividing line between EB and early NPC. (c) Immunostaining was performed with different protein markers in iPSCs and NPCs. The nuclei are blue by Hoechst staining. Scale bar:100 µm.

Fig. 4.

(a) SERS spectra of cells at six states during stem cell differentiation. Note: The average SERS spectra are shown as solid lines. (b) The evolving trend of the characteristic peak of the six stages.

Fig. 5.

(a) Accuracy and loss function curve versus different epochs; (b) ROC curves for the model. The average AUC values for each class are all greater than 0.99. (c) The confusion matrix of the CNN-S model for each cell states on the test dataset. (d) Performance of the CNN-S model under different evaluation metrics. (e) The confusion matrix of the CNN-S model for 12 cell states on the test dataset.

Fig. 6.

(a) Prediction accuracy curve of CNN-S model using SRES spectra including different spectral peaks as input. (b) Prediction accuracy curve of CNN-S model for different spectral lengths. Note: Error lines in the curve are standard deviation values through tenfold cross-validation.