Dynamic, single-cell monitoring of CAR T cell identity and activation with Raman spectroscopy
Year: 2026
Type: preprint
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 targeted labels, extensive processing, and provide only static snapshots of cell identity and activation. Here, we introduce a surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) approach that enables single-cell identification of engineered CAR T cells without molecularly-targeted labels and time-resolved, semi-continuous monitoring of their functional activation state through a single physical readout spanning donor-derived cells and patient blood. From intrinsic vibrational signatures of 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. Applying this same SERS-ML method to longitudinal samples from a four-patient CD19-CAR T therapy cohort, we classify patient peripheral blood mononuclear cells across pre-and post-infusion timepoints with a mean patient-level accuracy of 84%, and show that isolated CAR-positive T cells are distinguishable from both CAR-negative T cells and background populations with average 86-88% accuracies. 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 across cell engineering and clinical monitoring contexts. Our results extend CAR T cell monitoring beyond static phenotyping and support the potential of SERS-ML analysis for rapid, point-of-care assessment of engineered immune cells across the therapeutic lifecycle.
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
Awards: Learning features of optimal CAR T cells for LBCL from patient data, G5510899569, Next Generation Immunotherapies for Pediatric Cancers, Learning features of optimal CAR T cells for LBCL from patient data, Stanford Cancer Institute, Monitoring CAR T Cell Therapeutic Response and Toxicity with Real-time, Label-free Cell Quantification, Bone Marrow Grafting and Cellular Therapy for Leukemia and Lymphoma, Multimodal AI modeling of T cell therapies to predict patient response and nominate advanced cell design strategies
OpenAlex ID: W7131215430