![]() ![]() Validation of SEQTR (A) Illustration of the different steps for TCR amplification using SEQTR. Data from patients 2 and 3 are shown in Figures S1E and S1F. The graphs depicted here show the data of patient 1. p values and R 2 were calculated using Spearman correlation after logarithmic transformation of the data. (F and G) Correlations between TCR mRNA frequencies (y axis) and clonotype frequencies (number of cells/clonotype, x axis) based on single-cell data. (E) All clonotypes with an average expression level of TCR mRNA >2-fold relative to medians (present in the gray zone of D) were deconvoluted according to the number of cells per clonotype. The graph depicted here shows data of patient 1 and is representative of the three patients. Red lines represent median expression levels dotted lines and gray zones represent the 2-fold variations from medians. (D) The average TCR expression levels (number of mRNA molecules per cell) were determined for each clonotype and plotted according to the number of cells per clonotype. ![]() The TCR expression distribution of all the cells of the clonotypes are shown in Figure S1B. For readability, only 20 cells of the clonotype are presented. Color scale highlights the number of mRNA molecules per cell. Each square represents one cell from each clonotype. (C) Heatmaps showing TCR mRNA expression levels of the 20 most frequent clonotypes (y axis). The number of single cells analyzed is indicated at the bottom of the graph. The numbers in violin plots indicate the average mRNA molecules per cell for each patient. (B) Violin plots showing the distribution of the number of RNA molecules per cell normalized to the median for TCR α and β chains in three patients. Variability of TCR expression levels (A) Schematic description of single-cell TCR-seq analysis performed on TILs from three melanoma patients and the analysis of the number of TCR RNA molecules per cell. holds patents around antibodies and receives royalties from the University of Pennsylvania regarding technology licensed to Novartis. has patents in the domain of antibodies and vaccines targeting the tumor vasculature as well as technologies related to T cell engineering for T cell therapy. The Center Hospitalier Universitaire Vaudois (CHUV) and the Ludwig Institute for Cancer Research have filed for patent protection on the technology related to T cell expansion. is affiliated has received fees for G.C.’s participation on advisory boards or for presentation at a company-sponsored symposium from Genentech, Roche, Bristol Myers Squibb, AstraZeneca, NextCure, Geneos Tx, and Sanofi/Avensis. has received grants from Celgene, Boehringer-Ingelheim, Roche, Bristol Myers Squibb, Iovance Therapeutics, and Kite Pharma. The University of Lausanne and Ludwig Institute for Cancer Research have filed for patent protection on the technology described herein. T cell engineering T cell receptor TCR cloning TCR repertoire cancer immune diversity immune repertoire immunotherapy repertoire profiling. Together, these methods will accelerate TCR repertoire analyses in discovery, translational, and clinical settings and permit fast TCR engineering for cellular therapies. Positioned downstream of single-cell or bulk TCR sequencing, it allows time- and cost-effective discovery, cloning, screening, and engineering of tumor-specific TCRs. We also present a TCR cloning strategy to specifically amplify TCRs from T cell populations. Here, we report on SEQTR, a high-throughput approach to analyze human and mouse repertoires that is more sensitive, reproducible, and accurate as compared with commonly used assays, and thus more reliably captures the complexity of blood and tumor TCR repertoires. However, sensitive and reliable methods for repertoire analyses and TCR cloning are still lacking. T cell receptor (TCR) technologies, including repertoire analyses and T cell engineering, are increasingly important in the clinical management of cellular immunity in cancer, transplantation, and other immune diseases. ![]()
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