Raman spectroscopy combined with multiple technologies for label-free identification of immune cells: An overview.

Abstract

Traditional immune cell identification and sorting methods rely on antibodies and fluorophores, which may compromise cell viability and functionality. Raman spectroscopy, a label-free and highly sensitive technique, enables precise differentiation of immune cell subtypes based on their intrinsic biochemical composition. When integrated with chemometrics, microfluidics, and machine learning (ML)/deep learning (DL) approaches, Raman spectroscopy significantly enhances the accuracy, throughput, and efficiency of immune cell sorting. This review systematically analyzes the principles and advantages of these integrated strategies and explores their potential applications in immunological research, clinical diagnostics, and precision medicine, paving the way for non-invasive and high-efficiency immune cell analysis.