Dynamic, single-cell monitoring of CAR T cell identity and activation with Raman spectroscopy
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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.
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Fig. 3b
style note on plot: does the heatmap offer different information than the line plot? the thin vertical lines in the heatmap are difficult to see
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Discriminating peaks correspond with known Raman biochemical features resulting fromblood cell identity (Supplementary Fig. 5; Supplementary Table 4),20,29–35 including those fromDNA/RNA (adenine ring breathing 725-730 cm-1,
do you see spatial differences in the prominence of the different peaks? e.g. DNA/RNA-related peaks should be most prominent in the nucleus, etc.
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biological fingerprint region on arectangular scan grid with ~30 μm spacing, exceeding both the laser spot size (~1.2-1.5 μm) andtypical cell diameters (7-12 μm
Given the small spot size, the laser is sampling different parts of the cells (e.g. nucleus, cytoplasm) which would result in different spectra (presumably). do you observe significant variations in spectra within cells?
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All spectra were acquired with 100% power output: 30.47 mW at the sample for the 20xobjective and 26.34 mW for the 50x.
has autofluorescence from the glass coverslip and microscope slide been problematic, especially for low-mag objectives and at 785 nm excitation where glass is highly absoptive?
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