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Updated: May 14, 2026
Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy
Published on: May 29, 2012
Dynamic, single-cell monitoring of CAR T cell identity and activation with Raman spectroscopy
Ariel Stiber1, Boi Quach2,3,4, Babatunde Ogunlade1
1Department of Materials Science and Engineering, Stanford University, Stanford, California, USA.
Abstract:
Chimeric antigen receptor (CAR) T cell therapies have reshaped treatment for cancers and immune-mediated diseases, yet their safety and efficacy depend on both the proliferation of engineered cells and their dynamic functional state - features that remain challenging to monitor in real-time clinical settings. Current methods require labels, extensive processing, and provide only static snapshots of cell identity and activation. Here, we introduce a surface-enhanced Raman spectroscopy and machine learning approach that enables label-free single-cell identification of engineered CAR T cells and time-resolved, semi-continuous monitoring of their functional activation state. Using the intrinsic vibrational signatures from live cells, we detect spectral differences resulting from engineered receptor expression in donor-derived CD19- and GD2-targeted CAR T cells (nine and five donors, respectively) with 81-85% donor-level accuracy and resolve dynamic antigen-specific activation trajectories with temporal precision. These capabilities stem from biochemical signatures consistent with processes such as receptor expression, tonic signalling, and immune synapse formation, demonstrating a single method that reports both cellular identity and activation state with biochemical specificity. Our results extend CAR T cell monitoring beyond static phenotyping and establish the potential of SERS-ML analysis for rapid, point-of-care assessment of engineered immune cells.
Insights
We developed a label-free method using surface-enhanced Raman spectroscopy and machine learning to identify engineered CAR T cells and monitor their activation state in real-time. This advance offers precise, continuous tracking of cell function for improved therapies.
Area of Science:
- Biotechnology
- Immunology
- Spectroscopy
Background:
- Chimeric antigen receptor (CAR) T cell therapies are revolutionizing disease treatment.
- Monitoring CAR T cell proliferation and functional status in real-time is crucial but challenging.
- Current methods for CAR T cell analysis are often label-dependent, require extensive processing, and provide only static data.
Purpose of the Study:
- To develop a label-free, real-time method for identifying engineered CAR T cells.
- To enable semi-continuous monitoring of CAR T cell functional activation state.
- To establish a single approach for assessing both cellular identity and activation state with biochemical specificity.
Main Methods:
- Utilized surface-enhanced Raman spectroscopy (SERS) to capture intrinsic vibrational signatures from live cells.
- Applied machine learning (ML) algorithms for label-free single-cell identification and analysis.
- Analyzed spectral differences corresponding to engineered receptor expression and cellular activation processes.
Main Results:
- Achieved 81-85% donor-level accuracy in identifying CD19- and GD2-targeted CAR T cells.
- Resolved dynamic, antigen-specific activation trajectories with temporal precision.
- Detected biochemical signatures linked to receptor expression, tonic signaling, and immune synapse formation.
Conclusions:
- The SERS-ML approach enables label-free identification and real-time functional monitoring of CAR T cells.
- This method moves beyond static phenotyping to provide dynamic insights into engineered immune cell behavior.
- The technology holds potential for rapid, point-of-care assessment of CAR T cells in clinical settings.
