Transcriptional fingerprinting of regulatory T cells: ensuring quality in cell therapy applications

TL;DR

This paper introduces a new method to ensure the quality of regulatory T cell therapies by using molecular fingerprints to distinguish and assess Treg cells.

Contribution

The study introduces a novel framework using next-generation sequencing and non-parametric algorithms to define molecular fingerprints for Treg cell identity and expansion.

Findings

The identity fingerprint distinguishes Treg from Teff cells with 100% sensitivity and specificity.

The expansion fingerprint differentiates expanded from endogenous Treg or Teff cells.

The method predicts Treg stability and was validated in a clinical trial for type 1 diabetes.

Abstract

The success of regulatory T cell (Treg) therapies depends on the source of Treg and the quality of the Treg manufacturing product that maintains Treg identity. Commonly used methods to identify Treg, including assessment of FOXP3 expression and demethylation of the Treg-specific demethylated region (TSDR), may not be sufficient on their own to ensure that Treg cell therapy drug products have an optimal identity and phenotype prior to infusion into patients.

To address this critical need, we developed a robust framework to molecularly characterize Treg products using next-generation sequencing. By systematically profiling Treg and effector T cells (Teff) pre- and post-expansion, we defined the molecular fingerprints for expanded Treg products. We employed a non-parametric algorithm to score Treg manufacturing products for their cell identity and expansion fingerprints.

The identity fingerprint reflects Treg cell identity by effectively distinguishing Treg from Teff cells irrespective of their activation status, with 100% sensitivity and specificity, while the expansion fingerprint discriminates expanded versus endogenous Treg or Teff cells. We also showed that the identity fingerprint predicts Treg stability in in vitro settings and can be used to illustrate differences in drug products generated using distinct strategies. We further applied fingerprinting to bulk RNA sequencing (RNA-seq) data from endogenous and expanded Treg cells in a Phase 2 clinical trial for type 1 diabetes (T1D), demonstrating its ability to capture Treg identity and expansion in an independent study.

This Treg fingerprinting method provides a powerful tool to molecularly characterize Treg products, potentially enabling correlative analysis with the safety and efficacy outcomes of Treg-based cell therapies.

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