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J Immunol. 2015 Jun 15;194(12):6177-89. doi: 10.4049/jimmunol.1400958. Epub 2015 May 8.

Generation of TCR-engineered T cells and their use to control the performance of T cell assays.

Author information

1
Translational Oncology, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany;
2
Translational Oncology, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany; University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany;
3
Department of Immunology, University of Tuebingen, 72076 Tuebingen, Germany; and.
4
Department of Clinical Oncology, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands.
5
Translational Oncology, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany; cedrik.x.britten@gsk.com.

Abstract

The systematic assessment of the human immune system bears huge potential to guide rational development of novel immunotherapies and clinical decision making. Multiple assays to monitor the quantity, phenotype, and function of Ag-specific T cells are commonly used to unravel patients' immune signatures in various disease settings and during therapeutic interventions. When compared with tests measuring soluble analytes, cellular immune assays have a higher variation, which is a major technical factor limiting their broad adoption in clinical immunology. The key solution may arise from continuous control of assay performance using TCR-engineered reference samples. We developed a simple, stable, robust, and scalable technology to generate reference samples that contain defined numbers of functional Ag-specific T cells. First, we show that RNA-engineered lymphocytes, equipped with selected TCRs, can repetitively deliver functional readouts of a controlled size across multiple assay platforms. We further describe a concept for the application of TCR-engineered reference samples to keep assay performance within or across institutions under tight control. Finally, we provide evidence that these novel control reagents can sensitively detect assay variation resulting from typical sources of error, such as low cell quality, loss of reagent stability, suboptimal hardware settings, or inaccurate gating.

PMID:
25957167
DOI:
10.4049/jimmunol.1400958
[Indexed for MEDLINE]
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