TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences.

T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to misdiagnosis if diagnostically relevant TCRs remain undetected. To address this issue, we developed TCRpower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth and read cutoff. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones (TCCs) as spike-in TCRs. We sequenced the spike-in TCRs from TCCs, together with TCRs from peripheral blood, using a 5' RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cutoff, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0% and TCR ß-chain 92.4%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease-relevant TCRs for diagnostic applications.

Comprehensive Analysis of CDR3 Sequences in Gluten-Specific T-Cell Receptors Reveals a Dominant R-Motif and Several New Minor Motifs.

Gluten-specific CD4+ T cells are drivers of celiac disease (CeD). Previous studies of gluten-specific T-cell receptor (TCR) repertoires have found public TCRs shared across multiple individuals, biased usage of particular V-genes and conserved CDR3 motifs. The CDR3 motifs within the gluten-specific TCR repertoire, however, have not been systematically investigated. In the current study, we analyzed the largest TCR database of gluten-specific CD4+ T cells studied so far consisting of TCRs of 3122 clonotypes from 63 CeD patients. We established a TCR database from CD4+ T cells isolated with a mix of HLA-DQ2.5:gluten tetramers representing four immunodominant gluten epitopes. In an unbiased fashion we searched by hierarchical clustering for common CDR3 motifs among 2764 clonotypes. We identified multiple CDR3α, CDR3ß, and paired CDR3α:CDR3ß motif candidates. Among these, a previously known conserved CDR3ß R-motif used by TRAV26-1/TRBV7-2 TCRs specific for the DQ2.5-glia-α2 epitope was the most prominent motif. Furthermore, we identified the epitope specificity of altogether 16 new CDR3α:CDR3ß motifs by comparing with TCR sequences of 231 T-cell clones with known specificity and TCR sequences of cells sorted with single HLA-DQ2.5:gluten tetramers. We identified 325 public TCRα and TCRß sequences of which 145, 102 and 78 belonged to TCRα, TCRß and paired TCRαß sequences, respectively. While the number of public sequences was depended on the number of clonotypes in each patient, we found that the proportion of public clonotypes from the gluten-specific TCR repertoire of given CeD patients appeared to be stable (median 37%). Taken together, we here demonstrate that the TCR repertoire of CD4+ T cells specific to immunodominant gluten epitopes in CeD is diverse, yet there is clearly biased V-gene usage, presence of public TCRs and existence of conserved motifs of which R-motif is the most prominent.

T cell receptor repertoire as a potential diagnostic marker for celiac disease.

An individual's T cell repertoire is skewed towards some specificities as a result of past antigen exposure and subsequent clonal expansion. Identifying T cell receptor signatures associated with a disease is challenging due to the overall complexity of antigens and polymorphic HLA allotypes. In celiac disease, the antigen epitopes are well characterised and the specific HLA-DQ2-restricted T-cell repertoire associated with the disease has been explored in depth. By investigating T cell receptor repertoires of unsorted lamina propria T cells from 15 individuals, we provide the first proof-of-concept study showing that it could be possible to infer disease state by matching against a priori known disease-associated T cell receptor sequences.

Discriminative T-cell receptor recognition of highly homologous HLA-DQ2-bound gluten epitopes.

Celiac disease (CeD) provides an opportunity to study the specificity underlying human T-cell responses to an array of similar epitopes presented by the same human leukocyte antigen II (HLA-II) molecule. Here, we investigated T-cell responses to the two immunodominant and highly homologous HLA-DQ2.5-restricted gluten epitopes, DQ2.5-glia-α1a (PFPQPELPY) and DQ2.5-glia-ω1 (PFPQPEQPF). Using HLA-DQ2.5-DQ2.5-glia-α1a and HLA-DQ2.5-DQ2.5-glia-ω1 tetramers and single-cell αß T-cell receptor (TCR) sequencing, we observed that despite similarity in biased variable-gene usage in the TCR repertoire responding to these nearly identical peptide-HLA-II complexes, most of the T cells are specific for either of the two epitopes. To understand the molecular basis of this exquisite fine specificity, we undertook Ala substitution assays revealing that the p7 residue (Leu/Gln) is critical for specific epitope recognition by both DQ2.5-glia-α1a- and DQ2.5-glia-ω1-reactive T-cell clones. We determined high-resolution binary crystal structures of HLA-DQ2.5 bound to DQ2.5-glia-α1a (2.0 Å) and DQ2.5-glia-ω1 (2.6 Å). These structures disclosed that differences around the p7 residue subtly alter the neighboring substructure and electrostatic properties of the HLA-DQ2.5-peptide complex, providing the fine specificity underlying the responses against these two highly homologous gluten epitopes. This study underscores the ability of TCRs to recognize subtle differences in the peptide-HLA-II landscape in a human disease setting.

Soluble T-cell receptor design influences functional yield in an E. coli chaperone-assisted expression system.

There is a quest for production of soluble protein of high quality for the study of T-cell receptors (TCRs), but expression often results in low yields of functional molecules. In this study, we used an E. coli chaperone-assisted periplasmic production system and compared expression of 4 different soluble TCR formats: single-chain TCR (scTCR), two different disulfide-linked TCR (dsTCR) formats, and chimeric Fab (cFab). A stabilized version of scTCR was also included. Additionally, we evaluated the influence of host (XL1-Blue or RosettaBlueTM) and the effect of IPTG induction on expression profiles. A celiac disease patient-derived TCR with specificity for gluten was used, and we achieved detectable expression for all formats and variants. We found that expression in RosettaBlueTM without IPTG induction resulted in the highest periplasmic yields. Moreover, after large-scale expression and protein purification, only the scTCR format was obtained in high yields. Importantly, stability engineering of the scTCR was a prerequisite for obtaining reliable biophysical characterization of the TCR-pMHC interaction. The scTCR format is readily compatible with high-throughput screening approaches that may enable both development of reagents allowing for defined peptide MHC (pMHC) characterization and discovery of potential novel therapeutic leads.

A TCRα framework-centered codon shapes a biased T cell repertoire through direct MHC and CDR3ß interactions.

Selection of biased T cell receptor (TCR) repertoires across individuals is seen in both infectious diseases and autoimmunity, but the underlying molecular basis leading to these shared repertoires remains unclear. Celiac disease (CD) occurs primarily in HLA-DQ2.5+ individuals and is characterized by a CD4+ T cell response against gluten epitopes dominated by DQ2.5-glia-α1a and DQ2.5-glia-α2. The DQ2.5-glia-α2 response recruits a highly biased TCR repertoire composed of TRAV26-1 paired with TRBV7-2 harboring a semipublic CDR3ß loop. We aimed to unravel the molecular basis for this signature. By variable gene segment exchange, directed mutagenesis, and cellular T cell activation studies, we found that TRBV7-3 can substitute for TRBV7-2, as both can contain the canonical CDR3ß loop. Furthermore, we identified a pivotal germline-encoded MHC recognition motif centered on framework residue Y40 in TRAV26-1 engaging both DQB1*02 and the canonical CDR3ß. This allowed prediction of expanded DQ2.5-glia-α2-reactive TCR repertoires, which were confirmed by single-cell sorting and TCR sequencing from CD patient samples. Our data refine our understanding of how HLA-dependent biased TCR repertoires are selected in the periphery due to germline-encoded residues.

BLASTGrabber: a bioinformatic tool for visualization, analysis and sequence selection of massive BLAST data.

BACKGROUND: Advances in sequencing efficiency have vastly increased the sizes of biological sequence databases, including many thousands of genome-sequenced species. The BLAST algorithm remains the main search engine for retrieving sequence information, and must consequently handle data on an unprecedented scale. This has been possible due to high-performance computers and parallel processing. However, the raw BLAST output from contemporary searches involving thousands of queries becomes ill-suited for direct human processing. Few programs attempt to directly visualize and interpret BLAST output; those that do often provide a mere basic structuring of BLAST data. RESULTS: Here we present a bioinformatics application named BLASTGrabber suitable for high-throughput sequencing analysis. BLASTGrabber, being implemented as a Java application, is OS-independent and includes a user friendly graphical user interface. Text or XML-formatted BLAST output files can be directly imported, displayed and categorized based on BLAST statistics. Query names and FASTA headers can be analysed by text-mining. In addition to visualizing sequence alignments, BLAST data can be ordered as an interactive taxonomy tree. All modes of analysis support selection, export and storage of data. A Java interface-based plugin structure facilitates the addition of customized third party functionality. CONCLUSION: The BLASTGrabber application introduces new ways of visualizing and analysing massive BLAST output data by integrating taxonomy identification, text mining capabilities and generic multi-dimensional rendering of BLAST hits. The program aims at a non-expert audience in terms of computer skills; the combination of new functionalities makes the program flexible and useful for a broad range of operations.

BLAST output visualization in the new sequencing era.

The Basic Local Alignment Search Tool (BLAST) algorithm remains one of the most widely used bioinformatic programs. For many projects, new sequencing technologies and increased database sizes will increase the BLAST output significantly. Frequently, this output is so large that it is no longer able to be processed manually. As BLAST users are increasingly recruited from mainstream biology without any bioinformatic background, user-friendly programs capable of BLAST output visualization, analysis and post-processing are in demand. In this review, freely available BLAST output processing programs are categorized as BLAST output interpreters, BLAST environments, BLAST output parsers or specialized tools. They are evaluated according to their user-friendliness, analysis features and high-throughput data processing capabilities.