Tsui, K. C. Y. | bioRxiv Science

bioRxiv2026

All-by-All Cytokine Receptor Pairing Network Unlocks Coding of Non-Natural T Cell States

Cytokine receptor pairing rules, set by evolution, confine JAK-STAT signaling to a narrow region of a far larger combinatorial space. Of more than 1,200 pairings theoretically possible among the [~]36 JAK-associated human cytokine receptors, only [~]30-40 exist in nature. Using a double-orthogonal platform, we enforced pairings across the full all-by-all receptor matrix and resolved a fine-grained STAT atlas richer than the natural repertoire. Selected non-natural pairings generated emergent T cell states unpredictable from either parental receptor, with pairing orientation encoding signaling specificity. A synthetic IL-21R>IL-2R{beta} pairing, but not its reciprocal, drove a cytotoxic Tc17-like state, whereas natural IL-9R/{gamma}c drove a Tc1 fate despite similar STAT activation, showing that rebalancing quantitative STAT combinatorics can modulate T cell fate. Recombining IL-31R, not expressed in T cells, with STAT-biased receptor partners generated diverse states, several with superior antitumor efficacy. These findings define a non-natural pairing code for engineering synthetic T cell fates.

immunology↗

bioRxiv2026

Dynamic, single-cell monitoring of CAR T cell identity and activation with Raman spectroscopy

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.

bioengineering↗