Applying Machine Learning for Multi-Individual Raman Spectroscopic Data to Identify Different Stages of Proliferating Human Hepatocytes

Scientists developed a machine learning framework to integrate single-cell Raman spectroscopy from multiple donors and identify different stages of ProliHHs. A repository of more than 14,000 Raman spectra, consisting of primary human hepatocytes (PHHs) and different passages of ProliHHs from six donors, was generated.