Single-cell T-cell Receptor Sequencing Research Articles

T Cell Determinants of Response and Resistance to PD-1 Blockade in Richter's Transformation

T Cell Determinants of Response and Resistance to PD-1 Blockade in Richter's Transformation

Identification of Specificity Groups in Myeloma Patients T Cell Receptor (TCR) Repertoire through Single Cell TCR Sequencing

Background: A renewed interest in immune and cellular based therapeutics in multiple myeloma was recently fueled by the development of CD38 targeting monoclonal antibodies as well as the introduction of engineered CAR-T cells. Daratumumab treatment in myeloma patients was demonstrated to expand clonal CD8+T cells and T cell clonality was correlated with the depth of response consistent with a daratumumab mediated cytotoxic T cell effect. However the mechanisms behind this adaptive immune response and the identity of the T cell receptor (TCR) interacting with MHC presented tumoral peptide (pMHC) remains elusive. The large diversity of TCR combinations generated though somatic recombination of the VDJ gene sequences (~ 1015combinations) represents a major challenge for the accurate characterization of the antigen specific TCR. The aim of this study was to define the identity of the adaptive immune repertoire of the bone marrow infiltrating T lymphocytes (single cell TCR α/β paired sequencing) in myeloma patients treated to daratumumab and IMiDs based therapies.Methods and Results: BM aspirates from patients (n=24) treated with daratumumab single agent or in combination with pomalidomide or lenalidomide (MM014, MM3008 and MMY3012 trials) were collected post initiation of therapy (cycle 3, day 1) followed by magnetic beads sorting of CD3pos T cells from Ficoll generated mononuclear cell fractions. Using the 10x Genomics Single cell VDJ solution which combines single cell droplet microfluidics with 5' molecular barcoding, T cells from each patient were partitioned into droplets containing individual cells with primers specific for the constant region of the V(D)J locus allowing the PCR amplification and enrichment of α and β TCR individual cell barcoded cDNA. Paired-end sequencing was performed on Illumina NEXTseq platform. Cell Ranger VDJ pipeline was used for sample de-multiplexing, barcode processing and grouping of T cells into clonotypes with shared TCR α/β sequences. Of note, generated sequences span the full length of V-J genes (including CDR3) allowing faithful reconstruction of TCR transcripts. Consistent with the known high TCR diverse repertoire, we identified 32322 individual clonotypes corresponding to an average of 1346 clonotypes with paired α/β TCR sequences per patient. Clonotypes proportion (> 2%) and number of individual clonotypes did correlate with the depth of response (≥VGPR vs PR vs PD). Analysis of clonotype TCRs and CDR3 sequences identified 11 clonotypes with the exact paired CDR3 αβ sequences that were shared by at least 2 patients. Of interest the CDR3 sequence of one shared clonotype is predicted (https://vdjdb.cdr3.net) to bind an epitope derived for CD317 (also know as BST2 or HM1.24) previously demonstrated to be highly expressed on myeloma cells (Jalili A et al. Blood 2005). Both patients harboring this CD317 reactive T cell clone are in sCR for more than 2 years. Of note, TCRs that recognize the same peptide-MHC complexes do not always share the exact CDR3 sequences but rather have conserved CDR3 sequence features, rendering possible to predictively model epitope specificity. Indeed, recent studies demonstrated that similarity in CDR3 sequences (CDR3 differing by up to one amino acid) or shared CDR3 motifs of 2-4 amino acids in length, define the TCR clusters that are often contact points with the antigenic peptides. Such features in CDR3 sequences facilitates T cell target antigen discovery. Therefore, we applied the GLIPH algorithm (https://github.com/immunoengineer/gliph) to cluster the sequenced TCRs based on their high probability of sharing pMHC specificity owing to both conserved CDR3 motifs and global similarity in their CDR3 sequences. GLIPH grouped the TCRs sequences identified in our study into 171 unique clusters (with a minimum of 3 clones each) that are predicted to recognize the same pMHC ligands. Furthermore, it identified 26 CDR3 motifs that are elevated at least 10-fold over expected frequency in a naïve TCR reference pool (p <0.001). Functional validation of this myeloma-targeting TCR clusters through pMHC tetramer binding is ongoing.Conclusion: Single cell TCR profiling identified unique clonotypes that are highly enriched in marrow infiltrating T cells and are predicted to be reactive with myeloma peptides. This work facilitates the future development of TCR engineered T cells targeting myeloma neoepitopes. DisclosuresNeri:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. McCulloch:Takeda: Other: Travel expenses; Celgene: Honoraria. Bahlis:Janssen: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding.

Abstract 5387: Novel targeted single-cell TCR sequencing method used for analysis of a T cell infiltrate from a glioblastoma patient

Abstract Obtaining paired alpha- and beta-chain information of the T cell receptor (TCR) enables identification of which TCR binds to a particular antigen, providing critical knowledge for understanding how a tumor interacts with its immune microenvironment and for monitoring responses to immunotherapy. We have developed a rapid, cost-effective single-cell TCR sequencing protocol (scTCRseq) that processes single T cells flow sorted into 384-well plates. Exploiting the added specificity provided by RNase H-dependent PCR (rhPCR; Dobosy et al., BMC Biotechnology 11:80, 2011), our scTCRseq protocol mixes primers for all known productive alpha and beta TCR alleles and obtains specific amplification of only the alleles present in a single-cell lysate (typically one alpha allele and one beta allele) without the use of nested PCR. Applying this protocol to T cell samples from normal donors, we detect TCRs arising from all productive TRAV and TRBV genes, and observe the expected highly diverse repertoires. Our method also includes targeted sequencing of 96 (or more) marker transcripts in order to obtain cell phenotype information. Our scTCRseq protocol has been adapted to analyze low-input samples (100 pg-10 ng) of PBMC RNA for characterization of the TCR repertoire and to improve the sensitivity of paired TCR detection in Smart-seq2 libraries prepared from single-cell RNA. We recently applied this approach to analyze T cell clonality in the T cell infiltrate from a glioblastoma patient receiving neoantigen-specific peptide vaccination. Fresh tumor obtained post-vaccination at the time of recurrence was dissociated and single CD3+ T cells were isolated by flow cytometry. Smart-seq2 analysis suggested the presence of clones, but this was based largely on inference from one-chain data. Using our scTCRseq protocol to re-amplify the TCR segments, paired alpha- and beta-chain information was obtained from 277 of 363 (76%) single cells that passed quality control. Out of 231 distinct clonotypes detected, 25 were identified as clones, where a clone is defined as two or more cells with identical alpha- and beta-CDR3 segments. GLIPH analysis (Glanville et al., Nature 547:94-98, 2017) was used to cluster infiltrate TCRs by specificity. A representative TCR from each specificity cluster was cloned in a TCR-negative Jurkat cell line. Cellular assays were used to determine which specificity groups react with epitopes from peptides used in the vaccination. TCRs specific for neoantigens included in the vaccine were detected. We have demonstrated a robust workflow that combines sensitive detection of paired alpha- and beta-chain TCR sequences with TCR cloning and expression to determine which TCRs in a tumor infiltrate have neoantigen specificity. Citation Format: Shuqiang Li, Jing Sun, Annabelle Anandappa, Oliver Spiro, Ignaty Leshchiner, Krishnalekha Datta, Yun Bao, Zhuting Hu, Nir Hacohen, Nathan D. Mathewson, Itay Tirosh, Kai Wucherpfennig, Aviv Regev, Mario Suva, Derin B. Keskin, Catherine J. Wu, Kenneth J. Livak. Novel targeted single-cell TCR sequencing method used for analysis of a T cell infiltrate from a glioblastoma patient [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5387.

Abstract 4678: High-throughput single-cell linking of antigen specificities with T cell receptor sequences using de novo generated DNA-linked MHC tetramers

Abstract Identification of correlated antigens recognized by T cell and its T cell receptor (TCR) sequences at single cell level is extremely useful to the understanding and treating immune-related diseases. Although mass cytometry, CyTOF, has pushed the number of antigenic peptides that can be interrogated at once to around 100, the destructive nature of CyTOF prohibits linking pMHCs recognized by a T cell with its TCR receptor sequences. In addition, the high cost associated with peptide synthesis process prevents the quick generation of a peptide library that can be tailored to any pathogens or diseases. Here we describe a new technology, TetATCR-Seq for Tetramer Associated TCR sequencing, that is capable of de novo generating a large amount of peptides and mapping antigen recognition with single cell TCR sequences by DNA barcoded tetramers. It evolves three major steps: 1) de novo peptide generation, 2) DNA barcoded tetramer generation, and 3) single cell sequencing of tetramer DNA barcode and TCR sequences. By spiking in 5 altered peptide ligands (APL) that are known to bind to a HCV T cell clone with other 90 irrelevant peptides, we showed that the binding of all these 5 APLs can be faithfully detected by sequencing while non-specific binding is minimum. Using a published set of cancer neo-antigen sequences, we showed that these peptides and their wild type counterparts can be quickly generated. Resulted tetramers can be used to clearly identify a population of neo-antigen only or wild type antigen only or double positive T cells. Subsequent single cell TCR sequencing showed that TCR paired α and β chain sequences can be obtained in more than 80% of single sorted T cells. Simultaneous tetramer DNA barcode sequencing showed that many T cells are found to cross-reactive to both neo-antigen and its wild type counterpart, suggesting the necessity of strict screening for neo-antigen only TCR in adoptive cell transfer therapy. In summary, the TetaTCR-seq tool we developed here enables one to quickly survey antigen repertoire again TCR repertoire at single cell level for a large number of peptides. This is an extremely useful tool in profiling T cell repertoire reactivity in infection and vaccination. It is also a useful tool to screen for T cell cross-reactivity and select neo-antigen specific TCR for cancer immunotherapy. Citation Format: Ning Jiang, Shu-qi Zhang, Chenfeng He, Eric Sun, Mingliang Zhang, Weiping Jia. High-throughput single-cell linking of antigen specificities with T cell receptor sequences using de novo generated DNA-linked MHC tetramers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4678.

High-throughput single-cell linking of antigen specificities with T cell receptor sequences using de novo generated DNA-linked MHC tetramers

Abstract Identification of correlated antigens recognized by T cell and its T cell receptor (TCR) sequences at single cell level is extremely useful to the understanding and treating immune-related diseases. Although CyTOF has pushed the number of antigenic peptides that can be interrogated at once to around 100, the destructive nature of CyTOF prohibits linking pMHCs recognized by a T cell with its TCR receptor sequences. Here we describe a new technology, TetATCR-Seq for Tetramer Associated TCR sequencing, that is capable of de novo generating a large amount of peptides and mapping antigen recognition with single cell TCR sequences by DNA barcoded tetramers. It evolves three major steps: 1) de novo peptide generation, 2) DNA barcoded tetramer generation, and 3) single cell sequencing of tetramer DNA barcode and TCR sequences. Using a published set of cancer neo-antigen sequences, we showed that these peptides and their wildtype counterparts can be quickly generated. Resulted tetramers can be used to clearly identify a population of neo-antigen only or wild type antigen only or double positive T cells. Subsequent single cell TCR sequencing showed that TCR paired a and b chain sequences can be obtained in more than 80% of single T cells. Simultaneous tetramer DNA barcode sequencing showed that many T cells are found to cross-reactive to both neo-antigen and its wild type counterpart, suggesting the necessity of strict screening for neo-antigen only TCR in adoptive cell transfer therapy. In summary, TetaTCR-seq enables one to quickly survey antigen repertoire again TCR repertoire at single cell level for a large number of peptides. It is a useful technology to screen for T cell cross-reactivity and select neo-antigen specific TCR for cancer immunotherapy.

Detecting neoepitope-specific intra-tumoral T cell responses in a glioblastoma patient treated with personal neoantigen vaccine

Abstract Recent advances in single cell T cell receptor (TCR) sequencing have allowed detailed analysis of TCRαβ pairs. However, efficient methods for probing the specificity of discovered TCRs remain limited. We developed a streamlined approach for cloning and expressing TCRs and screening against candidate antigens. A key innovation was the establishment of a plasmid library to encode all variable (V) regions of the TCR, from which any TCR of interest can be assembled with only custom synthesis of short CDR3 regions. A reporter cell system allows rapid detection of TCR antigen specificity and assessment of functional avidity based on cytokine production. We applied this pipeline to study the intra-tumoral TCR repertoire of a patient with glioblastoma treated with personal neoantigen vaccine. The vaccine consisted of up to 20 synthetic long peptides containing predicted neoepitopes and poly-ICLC adjuvant. Single cell TCR sequencing of CD3+ tumor infiltrating lymphocytes (TILs) and peripheral T cells in vitro expanded against immunizing peptides showed that 4 CD4+ and 2 CD8+ T cell clonotypes in peripheral blood were identical to TILs. The cloning and expression system was used to test their specificity. We identified a TCR from a CD4+ TIL specific for ARHGAP35, a neoantigen targeted by vaccination, and capable of discriminating between mutant and wildtype peptide. This result provides the first demonstration that neoepitope-sensitized T cells can traffic from the periphery to an intracranial GBM. Overall, we demonstrated our ability to identify TCRαβ pairs, express them on demand, and probe antigen specificity. Ongoing studies are characterizing the functional avidity of neoepitope-specific TCRs and reactivity against autologous tumor.

Revealing the specificity of regulatory T cells in murine autoimmune diabetes

Regulatory T cells (Tregs) control organ-specific autoimmunity in a tissue antigen-specific manner, yet little is known about their specificity in a natural repertoire. In this study, we used the nonobese diabetic (NOD) mouse model of autoimmune diabetes to investigate the antigen specificity of Tregs present in the inflamed tissue, the islets of Langerhans. Compared with Tregs present in spleen and lymph node, Tregs in the islets showed evidence of antigen stimulation that correlated with higher proliferation and expression of activation markers CD103, ICOS, and TIGIT. T cell receptor (TCR) repertoire profiling demonstrated that islet Treg clonotypes are expanded in the islets, suggesting localized antigen-driven expansion in inflamed islets. To determine their specificity, we captured TCRαβ pairs from islet Tregs using single-cell TCR sequencing and found direct evidence that some of these TCRs were specific for islet-derived antigens including insulin B:9-23 and proinsulin. Consistently, insulin B:9-23 tetramers readily detected insulin-specific Tregs in the islets of NOD mice. Lastly, islet Tregs from prediabetic NOD mice were effective at preventing diabetes in Treg-deficient NOD.CD28-/- recipients. These results provide a glimpse into the specificities of Tregs in a natural repertoire that are crucial for opposing the progression of autoimmune diabetes.

Unique glandular ex-vivo Th1 and Th17 receptor motifs in Sjögren's syndrome patients using single-cell analysis

Primary Sjögren's syndrome (pSS) is an autoimmune disease in which the underlying cause has yet to be elucidated. The main objective of this study was to determine the T cell receptor (TCR) repertoires of individual infiltrating T helper (Th)-1 and 17 cells of pSS patients using single-cell analysis. Single-cell analysis of ex-vivo infiltrating T cells demonstrated that pSS patients had higher frequencies of activated Th17 cells. Single-cell TCR sequencing revealed that TCRβ variable (TRBV)3-1/joint (J)1-2 (CLFLSMSACVW) and TRBV20-1/J1-1 (SVGSTAIPP*T) were expressed by activated Th1 and Th17 cells in both cohorts. Uniquely, TCRα variable (TRAV)8–2/J5 (VVSDTVLETAGE) was expressed by Th1 cells present only in patients and complementarity-determining region (CDR)3α-specific motif (LSTD*E) present in both Th1/Th17 cells. The study demonstrates that both activated Th1 and Th17 cells of pSS patients showed restricted clonal diversities of which two CDR3 motifs were present in controls and patients, with another two motifs unique to pSS.

Abstract LB-078: Improved single-cell mRNA sequencing for transcriptome and paired-chain TCR analysis of primary human CD3+ T cells

Abstract The low RNA content of single human T cells has limited the success of single-cell transcriptome profiling, an approach that can generate data to reconstruct paired α,β T cell receptor (TCR) chains¹. Single-cell TCR sequencing is a powerful molecular tool to analyze tumor-infiltrating lymphocytes and production of cellular therapeutics. We compared the sensitivity and overall performance of the new Clontech® SMART-Seq® v4 Kit to the previously validated SMART-Seq v1 Kit on single primary human CD3+ cells captured and processed on the C1™ system (Fluidigm®). Sequenced libraries were analyzed using the TraCeR bioinformatics pipeline¹ to identify the TCR α and β chains expressed in each T cell. Cryopreserved CD3+ T cells were prepared as a single-cell suspension for capture on C1 using SMART-Seq v1 and v4, according to manufacturer instructions (AllCells®, Fluidigm, Clontech). Single-cell libraries were sequenced at 1.9 million reads per single cell. After data processing, the aligned reads were analyzed using the TraCeR bioinformatics pipeline. Transcriptome-related metrics and paired-chain TCR information were compared to evaluate chemistry performance (Table 1). Results are comparable to single-cell data published by Clontech for cultured Jurkat cells², which do not pose as high a technical challenge to molecular profiling as the primary T cells used in this study. The v4 chemistry produced a higher number of single cells with paired α,β chains detected. These results show that combining the C1 system with SMART-Seq v4 chemistry for processing primary single human T cells provides a powerful tool to investigate and enumerate T cell clones and their phenotypes and functions. 1. Stubbington, M.J.T. et al. Nature Methods, 2016. 2. Bostick, M. et al. Poster, NIH Single Cell Analysis Investigators Meeting, 2016. Table 1.Metrics evaluated in the comparison of SMART-Seq v1 and v4 chemistry performance.MetricSMART-Seq v1 (98 cells)SMART-Seq v4 (122 cells)cDNA yield per single cell (ng)3.4 (±1.4)4.9 (±2.0)Reads aligned to the genome from total (%)82 (±3.2)77 (±6.4)Reads with unique alignment to the genome from total aligned (%)97 (±0.04)97 (±0.05)Reads aligned to RefSeq (%)38 (±0.7)56 (±0.06)Reads aligned to rRNA (%)0.61 (±0.03)1.31 (±0.05)Bases aligned to coding sequences (%)22 (±0.5)40 (±0.5)Bases aligned to intronic sequences (%)40 (±1.0)18 (±0.5)Genes detected per T cell in average (above 1 TPM)2,618 (±431)1,866 (±386)Number of cells for which paired α,β T cell receptor (TCR) chains were identified25 (25%)67 (55%) Citation Format: Camila Egidio, Michael Gonzales, Joel Brockman, Shuwen Chen, Robert Durruthy-Durruthy, Clare Rogers, Manisha Ray. Improved single-cell mRNA sequencing for transcriptome and paired-chain TCR analysis of primary human CD3+ T cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-078. doi:10.1158/1538-7445.AM2017-LB-078

Techniques in the Surgical Repair of Tracheal Stenosis

T cells exercise a multitude of functions such as cytotoxicity, secretion of immunomodulating cytokines or regulation of tolerance, collectively resulting in an effective control of immune-related disease. Through the unique mechanism of V(D)J recombination, T cells express a highly specific receptor complex known as the T-cell receptor (TCR). Single-cell sequencing technologies have paved the road for interrogating the transcriptome and the paired αβ TCR repertoire of a single T cell in tandem. In contrast, conventional bulk methods are restricted to only one layer of information. This combination of transcriptomic- and repertoire information can provide novel insight into the functional character of T cell immunity. Recently, single-cell technologies have gained in popularity due to improvements in throughput, decrease in cost and the ability for multimodal experiments that integrate different information layers. Consequently, this prompts the need for the development of novel computational tools that integrate transcriptomic profiles and corresponding features of the TCR repertoire. Here we discuss the current progress in the field of single-cell T cell sequencing, with a focus on the multimodality of new approaches that allow the paired profiling of cellular phenotype and clonotype information. In addition, this review provides detailed descriptions of recent computational developments for analyzing single-cell TCR sequencing data in an integrative manner using novel computational approaches. Finally, we present an overview of the available software tools that can be used to perform integrative analysis of gene expression and TCR profiles.