TCR sequencing paired with massively parallel 3' RNA-seq reveals clonotypic T cell signatures.

High-throughput 3' single-cell RNA-sequencing (scRNA-seq) allows cost-effective, detailed characterization of individual immune cells from tissues. Current techniques, however, are limited in their ability to elucidate essential immune cell features, including variable sequences of T cell antigen receptors (TCRs) that confer antigen specificity. Here, we present a strategy that enables simultaneous analysis of TCR sequences and corresponding full transcriptomes from 3'-barcoded scRNA-seq samples. This approach is compatible with common 3' scRNA-seq methods, and adaptable to processed samples post hoc. We applied the technique to identify transcriptional signatures associated with T cells sharing common TCRs from immunized mice and from patients with food allergy. We observed preferential phenotypes among subsets of expanded clonotypes, including type 2 helper CD4+ T cell (TH2) states associated with food allergy. These results demonstrate the utility of our method when studying diseases in which clonotype-driven responses are critical to understanding the underlying biology.

A method for learning a sparse classifier in the presence of missing data for high-dimensional biological datasets.

MOTIVATION: This work addresses two common issues in building classification models for biological or medical studies: learning a sparse model, where only a subset of a large number of possible predictors is used, and training in the presence of missing data. This work focuses on supervised generative binary classification models, specifically linear discriminant analysis (LDA). The parameters are determined using an expectation maximization algorithm to both address missing data and introduce priors to promote sparsity. The proposed algorithm, expectation-maximization sparse discriminant analysis (EM-SDA), produces a sparse LDA model for datasets with and without missing data. RESULTS: EM-SDA is tested via simulations and case studies. In the simulations, EM-SDA is compared with nearest shrunken centroids (NSCs) and sparse discriminant analysis (SDA) with k-nearest neighbors for imputation for varying mechanism and amount of missing data. In three case studies using published biomedical data, the results are compared with NSC and SDA models with four different types of imputation, all of which are common approaches in the field. EM-SDA is more accurate and sparse than competing methods both with and without missing data in most of the experiments. Furthermore, the EM-SDA results are mostly consistent between the missing and full cases. Biological relevance of the resulting models, as quantified via a literature search, is also presented. AVAILABILITY AND IMPLEMENTATION: A Matlab implementation published under GNU GPL v.3 license is available at http://web.mit.edu/braatzgroup/links.html . CONTACT: braatz@mit.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reversal of DNA damage induced Topoisomerase 2 DNA-protein crosslinks by Tdp2.

Mammalian Tyrosyl-DNA phosphodiesterase 2 (Tdp2) reverses Topoisomerase 2 (Top2) DNA-protein crosslinks triggered by Top2 engagement of DNA damage or poisoning by anticancer drugs. Tdp2 deficiencies are linked to neurological disease and cellular sensitivity to Top2 poisons. Herein, we report X-ray crystal structures of ligand-free Tdp2 and Tdp2-DNA complexes with alkylated and abasic DNA that unveil a dynamic Tdp2 active site lid and deep substrate binding trench well-suited for engaging the diverse DNA damage triggers of abortive Top2 reactions. Modeling of a proposed Tdp2 reaction coordinate, combined with mutagenesis and biochemical studies support a single Mg(2+)-ion mechanism assisted by a phosphotyrosyl-arginine cation-π interface. We further identify a Tdp2 active site SNP that ablates Tdp2 Mg(2+) binding and catalytic activity, impairs Tdp2 mediated NHEJ of tyrosine blocked termini, and renders cells sensitive to the anticancer agent etoposide. Collectively, our results provide a structural mechanism for Tdp2 engagement of heterogeneous DNA damage that causes Top2 poisoning, and indicate that evaluation of Tdp2 status may be an important personalized medicine biomarker informing on individual sensitivities to chemotherapeutic Top2 poisons.