Label-Free Raman Spectral Identification of NK and NKT Cells in Immune Heterogeneous Cell Populations.

Area of Science:

• Immunology
• Biotechnology
• Spectroscopy

Background:

• Natural Killer (NK) and NK T (NKT) cells are crucial immune effector cells.
• Accurate identification of these cells is vital for immunotherapy development and quality control.
• Current methods like flow cytometry have limitations in speed and require labeling.

Purpose of the Study:

• To develop a rapid, label-free method for classifying NK and NKT cells within heterogeneous immune cell populations (HCPs).
• To leverage Raman spectroscopy and convolutional neural networks (CNNs) for enhanced cell identification.
• To establish spectral biomarkers for distinguishing NKT from NK cells.

Main Methods:

• Coupling Raman spectroscopy with a convolutional neural network (CNN) for label-free cell classification.
• Analyzing diverse immune HCPs, including NK and CIK (Cytokine-Induced Killer) cells.
• Utilizing spectral analysis and a visualization module to identify characteristic Raman peaks.

Main Results:

• Achieved rapid and accurate classification of NKT and NK cells without antibody labeling.
• Identified specific Raman peaks at 558 and 938 cm⁻¹ as biomarkers for NKT cells.
• Demonstrated significant improvements in classification speed and accuracy compared to traditional methods.

Conclusions:

• The developed Raman spectroscopy-CNN strategy offers a robust, label-free alternative for immune cell identification.
• This approach enhances quality monitoring for immune effector cell products in immunotherapy.
• The findings have significant implications for advancing mechanistic studies and immunotherapy development.