Position ß57 of I-Ag7 controls early anti-insulin responses in NOD mice, linking an MHC susceptibility allele to type 1 diabetes onset.

The class II region of the major histocompatibility complex (MHC) locus is the main contributor to the genetic susceptibility to type 1 diabetes (T1D). The loss of an aspartic acid at position 57 of diabetogenic HLA-DQß chains supports this association; this single amino acid change influences how TCRs recognize peptides in the context of HLA-DQ8 and I-Ag7 using a mechanism termed the P9 switch. Here, we built register-specific insulin peptide MHC tetramers to examine CD4+ T cell responses to Ins12-20 and Ins13-21 peptides during the early prediabetic phase of disease in nonobese diabetic (NOD) mice. A single-cell analysis of anti-insulin CD4+ T cells performed in 6- and 12-week-old NOD mice revealed tissue-specific gene expression signatures. TCR signaling and clonal expansion were found only in the islets of Langerhans and produced either classical TH1 differentiation or an unusual Treg phenotype, independent of TCR usage. The early phase of the anti-insulin response was dominated by T cells specific for Ins12-20, the register that supports a P9 switch mode of recognition. The presence of the P9 switch was demonstrated by TCR sequencing, reexpression, mutagenesis, and functional testing of TCRαß pairs in vitro. Genetic correction of the I-Aß57 mutation in NOD mice resulted in the disappearance of D/E residues in the CDR3ß of anti-Ins12-20 T cells. These results provide a mechanistic molecular explanation that links the characteristic MHC class II polymorphism of T1D with the recognition of islet autoantigens and disease onset.

Using single cell analysis for translational studies in immune mediated diseases: Opportunities and challenges.

The difficulty of studying small tissue samples and rare cell populations have been some of the main limitations in performing efficient translational studies of immune mediated diseases. Many of these conditions are grouped under the name of a single disease whilst there are strong suggestions that disease heterogeneity leads to variable disease progression as well as therapeutic responses. The recent development of single cell techniques, such as single cell RNA sequencing, gene expression profiling, or multiparametric cytometry, is likely to be a turning point. Single cell approaches provide researchers the opportunity to finally dissect disease pathology at a level that will allow mechanistic classifications and precision therapeutic strategies. In this review, we will give an overview of the current and developing repertoire of single cell techniques, the benefits and limitations of each, and provide an example of how single cell techniques can be utilized to understand complex immune mediated diseases and their translation from mouse to human.

A genome-wide survey of mutations in the Jurkat cell line.

BACKGROUND: The Jurkat cell line has an extensive history as a model of T cell signaling. But at the turn of the 21st century, some expression irregularities were observed, raising doubts about how closely the cell line paralleled normal human T cells. While numerous expression deficiencies have been described in Jurkat, genetic explanations have only been provided for a handful of defects. RESULTS: Here, we report a comprehensive catolog of genomic variation in the Jurkat cell line based on whole-genome sequencing. With this list of all detectable, non-reference sequences, we prioritize potentially damaging mutations by mining public databases for functional effects. We confirm documented mutations in Jurkat and propose links from detrimental gene variants to observed expression abnormalities in the cell line. CONCLUSIONS: The Jurkat cell line harbors many mutations that are associated with cancer and contribute to Jurkat's unique characteristics. Genes with damaging mutations in the Jurkat cell line are involved in T-cell receptor signaling (PTEN, INPP5D, CTLA4, and SYK), maintenance of genome stability (TP53, BAX, and MSH2), and O-linked glycosylation (C1GALT1C1). This work ties together decades of molecular experiments and serves as a resource that will streamline both the interpretation of past research and the design of future Jurkat studies.

Gene Profiling and T Cell Receptor Sequencing from Antigen-Specific CD4 T Cells.

The paucity of pathogenic T cells in circulating blood limits the information delivered by bulk analysis. Toward diagnosis and monitoring of treatments of autoimmune diseases, we have devised single-cell analysis approaches capable of identifying and characterizing rare circulating CD4 T cells.

OncoRep: an n-of-1 reporting tool to support genome-guided treatment for breast cancer patients using RNA-sequencing.

BACKGROUND: Breast cancer comprises multiple tumor entities associated with different biological features and clinical behaviors, making individualized medicine a powerful tool to bring the right drug to the right patient. Next generation sequencing of RNA (RNA-Seq) is a suitable method to detect targets for individualized treatment. Challenges that arise are i) preprocessing and analyzing RNA-Seq data in the n-of-1 setting, ii) extracting clinically relevant and actionable targets from complex data, iii) integrating drug databases, and iv) reporting results to clinicians in a timely and understandable manner. RESULTS: To address these challenges, we present OncoRep, an RNA-Seq based n-of-1 reporting tool for breast cancer patients. It reports molecular classification, altered genes and pathways, gene fusions, clinically actionable mutations and drug recommendations. It visualizes the data in an approachable html-based interactive report and a PDF clinical report, providing the clinician and tumor board with a tool to guide the treatment decision making process. CONCLUSIONS: OncoRep is free and open-source ( https://bitbucket.org/sulab/oncorep/ ), thereby offering a platform for future development and innovation by the community.

Omics Pipe: a community-based framework for reproducible multi-omics data analysis.

MOTIVATION: Omics Pipe (http://sulab.scripps.edu/omicspipe) is a computational framework that automates multi-omics data analysis pipelines on high performance compute clusters and in the cloud. It supports best practice published pipelines for RNA-seq, miRNA-seq, Exome-seq, Whole-Genome sequencing, ChIP-seq analyses and automatic processing of data from The Cancer Genome Atlas (TCGA). Omics Pipe provides researchers with a tool for reproducible, open source and extensible next generation sequencing analysis. The goal of Omics Pipe is to democratize next-generation sequencing analysis by dramatically increasing the accessibility and reproducibility of best practice computational pipelines, which will enable researchers to generate biologically meaningful and interpretable results. RESULTS: Using Omics Pipe, we analyzed 100 TCGA breast invasive carcinoma paired tumor-normal datasets based on the latest UCSC hg19 RefSeq annotation. Omics Pipe automatically downloaded and processed the desired TCGA samples on a high throughput compute cluster to produce a results report for each sample. We aggregated the individual sample results and compared them to the analysis in the original publications. This comparison revealed high overlap between the analyses, as well as novel findings due to the use of updated annotations and methods. AVAILABILITY AND IMPLEMENTATION: Source code for Omics Pipe is freely available on the web (https://bitbucket.org/sulab/omics_pipe). Omics Pipe is distributed as a standalone Python package for installation (https://pypi.python.org/pypi/omics_pipe) and as an Amazon Machine Image in Amazon Web Services Elastic Compute Cloud that contains all necessary third-party software dependencies and databases (https://pythonhosted.org/omics_pipe/AWS_installation.html).