Label-free estimation of regulatory T cell activation markers using Raman spectroscopy with machine learning.

Regulatory T cells are a class of T lymphocytes which respond to activation signals by expanding their cell numbers, and whose culturing and expansion are of significant clinical interest. Cellular activation states are used to inform process control decisions such as restimulation and can be probed with experimental measurements of cell surface markers. However, these measurements are expensive, time-consuming, and invasive, and an urgent need exists for devising a non-invasive method for activation state monitoring that could be deployed on-line. Raman spectroscopy is a label-free and information-rich optical method that, when coupled to data analytical methods, can ameliorate these experimental issues. In this work, we quantitatively estimated experimental measurements of regulatory T cell activation markers with high accuracy. We simulated a clinical manufacturing setting by building an [Formula: see text]-regularized least-squares model with spectroscopic data from six regulatory T cell donors. Then, we validated the constructed model by accurately estimating different experimental measurements of biomarker values from two external donors, unseen by the model. We have devised a robust program to effectively estimate the activation state of regulatory T cells. We anticipate our method to be used with on-line Raman probes integrated into cell manufacturing devices for label-free monitoring of these processes.