scholarly journals Phenotypic characteristics and T cell receptor properties in melanoma: deciphering the correlation at single-cell resolution

2022 ◽  
Vol 7 (1) ◽  
Author(s):  
Yier Lu ◽  
Chenyang Ye ◽  
Ying Yuan
Keyword(s):  
T Cell ◽  
Single Cell ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
Phenotypic Characteristics

scholarly journals 192 Pooled T cell receptor screening (POTS) provides unbiased, high-throughput method for TCR discovery

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A204-A204
Author(s):  
Jack Reid ◽  
Shihong Zhang ◽  
Ariunaa Munkhbat ◽  
Matyas Ecsedi ◽  
Megan McAfee ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
T Cell Receptor ◽  
High Throughput ◽  
Ex Vivo ◽  
Cell Receptor ◽  
High Avidity ◽  
Reporter System ◽  
High Throughput Method

BackgroundT Cell Receptor (TCR)-T cell therapies have shown some promising results in cancer clinical trials, however the efficacy of treatment remains suboptimal. Outcomes could potentially be improved by utilizing highly functional TCRs for future trials. Current TCR discovery methods are relatively low throughput and rely on synthesis and screening of individual TCRs based on tetramer binding and peptide specificity, which is costly and labor intensive. We have developed and validated a pooled approach relying on directly cloned TCRs transduced into a fluorescent Jurkat reporter system (figure 1). This approach provides an unbiased, high-throughput method for TCR discovery.MethodsAs a model for POTS, T cells specific for a peptide derived adenovirus structural protein were sorted on tetramer and subjected to 10x single cell VDJ analysis. Pools of randomly paired TCR alpha and beta chains were cloned from the 10x cDNA into a lentiviral vector and transduced into a Jurkat reporter cells. Consecutive stimulations with cognate antigen followed by cell sorts were performed to enrich for functional TCRs. Full length TCRab pools were sequenced by Oxford Nanopore Technologies (ONT) and compared to a 10x dataset to find naturally paired TCRs.ResultsComparison between the ex vivo single cell VDJ sequencing and ONT sequencing of the transduced antigen specific TCRs showed more than 99% of the TCR pairs found in reporter positive Jurkat cells were naturally paired TCRs. The functionality of 8 TCR clonotypes discovered using POTS were compared and clone #2 showed the strongest response. Of the selected clonotypes, clone #2 showed a low frequency of 0.9% in the ex vivo single cell VDJ sequencing. After the first round of stimulation and sequencing, clone #2 takes up of 5% of all reporter-positive clones. The abundance of clone #2 further increased to 17% after another round of stimulation, sorting and sequencing, suggesting this method can retrieve and enrich for highly functional antigen specific TCRs.Abstract 192 Figure 1Outline of the POTS workflow.ConclusionsPOTS provides a high-throughput method for discovery of naturally paired, high-avidity T cell receptors. This method mitigates bias introduced by T cell differentiation state by screening TCRs in a clonal reporter system. Additionally, POTS allows for screening of low abundance clones when compared with traditional TCR discovery techniques. Pooled TCRs could also be screened in vivo with primary T cells in a mouse model to screen for the most functional and physiologically fit TCR for cancer treatment.

scholarly journals 096 Identification of T cell receptor α and β chains responsible for AA pathogenesis via single cell TCR sequencing

2019 ◽  
Vol 139 (5) ◽  
pp. S16
Author(s):  
G.C. Monnot ◽  
Z. Dai ◽  
A. Han ◽  
A. de Jong ◽  
A. Christiano
Keyword(s):  
T Cell ◽  
Single Cell ◽  
T Cell Receptor ◽  
Cell Receptor

scholarly journals Validation of a Combined Transcriptome and T Cell Receptor Alpha/Beta (TRA/TRB) Repertoire Assay at the Single Cell Level for Paucicellular Samples

2020 ◽  
Vol 11 ◽  
Author(s):  
Nicolle H. R. Litjens ◽  
Anton W. Langerak ◽  
Amy C. J. van der List ◽  
Mariska Klepper ◽  
Maaike de Bie ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
Single Cell Level ◽  
Cell Level ◽  
Receptor Alpha ◽  
Alpha Beta ◽  
Cell Receptor Alpha

scholarly journals Single Cell T Cell Receptor Sequencing: Techniques and Future Challenges

2018 ◽  
Vol 9 ◽  
Author(s):  
Marco De Simone ◽  
Grazisa Rossetti ◽  
Massimiliano Pagani
Keyword(s):  
T Cell ◽  
Single Cell ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
Future Challenges

scholarly journals Linking T-cell receptor sequence to functional phenotype at the single-cell level

2014 ◽  
Vol 32 (7) ◽  
pp. 684-692 ◽  
Author(s):  
Arnold Han ◽  
Jacob Glanville ◽  
Leo Hansmann ◽  
Mark M Davis
Keyword(s):  
T Cell ◽  
Single Cell ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
Single Cell Level ◽  
Cell Level ◽  
Functional Phenotype

scholarly journals Differential expression profile of gluten-specific T cells identified by single-cell RNA-seq

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258029
Author(s):  
Ying Yao ◽  
Łukasz Wyrozżemski ◽  
Knut E. A. Lundin ◽  
Geir Kjetil Sandve ◽  
Shuo-Wang Qiao
Keyword(s):  
T Cells ◽  
Celiac Disease ◽  
T Cell ◽  
Single Cell ◽  
Differential Expression ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
Effector Memory ◽  
Fatty Acid Transport ◽  
Rna Seq

Gluten-specific CD4+ T cells drive the pathogenesis of celiac disease and circulating gluten-specific T cells can be identified by staining with HLA-DQ:gluten tetramers. In this first single-cell RNA-seq study of tetramer-sorted T cells from untreated celiac disease patients blood, we found that gluten-specific T cells showed distinct transcriptomic profiles consistent with activated effector memory T cells that shared features with Th1 and follicular helper T cells. Compared to non-specific cells, gluten-specific T cells showed differential expression of several genes involved in T-cell receptor signaling, translational processes, apoptosis, fatty acid transport, and redox potentials. Many of the gluten-specific T cells studied shared T-cell receptor with each other, indicating that circulating gluten-specific T cells belong to a limited number of clones. Moreover, the transcriptional profiles of cells that shared the same clonal origin were transcriptionally more similar compared with between clonally unrelated gluten-specific cells.

scholarly journals Linking T cell receptor sequence to transcriptional profiles with clonotype neighbor graph analysis (CoNGA)

2020 ◽  
Author(s):  
Stefan A. Schattgen ◽  
Kate Guion ◽  
Jeremy Chase Crawford ◽  
Aisha Souquette ◽  
Alvaro Martinez Barrio ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
T Cell Subsets ◽  
Graph Analysis ◽  
Theoretic Approach ◽  
Neighbor Graph

AbstractMulti-modal single-cell technologies capable of simultaneously assaying gene expression and surface phenotype across large numbers of immune cells have described extensive heterogeneity within these complex populations, in healthy and diseased states. In the case of T cells, these technologies have made it possible to profile clonotype, defined by T cell receptor (TCR) sequence, and phenotype, as reflected in gene expression (GEX) profile, surface protein expression, and peptide:MHC (pMHC) binding, across large and diverse cell populations. These rich, high-dimensional datasets have the potential to reveal new relationships between TCR sequence and T cell phenotype that go beyond identification of features shared by clonally related cells. In order to uncover these connections in an unbiased way, we developed a graph-theoretic approach---clonotype neighbor-graph analysis or “CoNGA”---that identifies correlations between GEX profile and TCR sequence through statistical analysis of a pair of T cell similarity graphs, one in which cells are linked based on gene expression similarity and another in which cells are linked by similarity of TCR sequence. Applying CoNGA across diverse human and mouse T cell datasets uncovered known and novel associations between TCR sequence features and cellular phenotype including the classical invariant T cell subsets; a novel defined population of human blood CD8+ T cells expressing the transcription factors HOBIT and HELIOS, NK-associated receptors, and a biased TCR repertoire, representing a potential previously undescribed lineage of “natural lymphocytes”; a striking association between usage of a specific V-beta gene segment and expression of the EPHB6 gene that is conserved between mouse and human; and TCR sequence determinants of differentiation in developing thymocytes. As the size and scale of single-cell datasets continue to grow, we expect that CoNGA will prove to be a useful tool for deconvolving complex relationships between TCR sequence and cellular state in single-cell applications.

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