Ekaterina V. Putintseva

MiXCR: software for comprehensive adaptive immunity profiling

MiXCR: software for comprehensive adaptive immunity profiling. MiXCR: software for comprehensive adaptive immunity profiling. MiXCR: software for comprehensive adaptive immunity profiling. MiXCR: software for comprehensive adaptive immunity profiling.MiXCR: software for comprehensive adaptive immunity profiling.

Towards error-free profiling of immune repertoires

Deep profiling of antibody and T cell–receptor repertoires by means of high-throughput sequencing has become an attractive approach for adaptive immunity studies, but its power is substantially compromised by the accumulation of PCR and sequencing errors. Here we report MIGEC (molecular identifier groups–based error correction), a strategy for high-throughput sequencing data analysis. MIGEC allows for nearly absolute error correction while fully preserving the natural diversity of complex immune repertoires.

Age-Related Decrease in TCR Repertoire Diversity Measured with Deep and Normalized Sequence Profiling

The decrease of TCR diversity with aging has never been studied by direct methods. In this study, we combined high-throughput Illumina sequencing with unique cDNA molecular identifier technology to achieve deep and precisely normalized profiling of TCR β repertoires in 39 healthy donors aged 6–90 y. We demonstrate that TCR β diversity per 106 T cells decreases roughly linearly with age, with significant reduction already apparent by age 40. The percentage of naive T cells showed a strong correlation with measured TCR diversity and decreased linearly up to age 70. Remarkably, the oldest group (average age 82 y) was characterized by a higher percentage of naive CD4+ T cells, lower abundance of expanded clones, and increased TCR diversity compared with the previous age group (average age 62 y), suggesting the influence of age selection and association of these three related parameters with longevity. Interestingly, cross-analysis of individual TCR β repertoires revealed a set >10,000 of the most representative public TCR β clonotypes, whose abundance among the top 100,000 clones correlated with TCR diversity and decreased with aging.

MiTCR: software for T-cell receptor sequencing data analysis

The approaches MiTCR uses to analyze TCR sequencing data have been shown to be efficient in previous studies5,6. The software performs CDR3 extraction, identifies V, D and J segments, assembles clonotypes, filters out or rescues low-quality reads5 and provides advanced correction of PCR and sequencing errors1,5 using either a predefined or user-specified strategy (Fig. 1a and Supplementary Notes 1–3). Simple command-line parameters, human-readable configuration files and a well-documented application programming interface (API) optimized for use in scripts make the software flexible enough for routine data extraction by immunologists as well as for more advanced analysis and customization by bioinformaticians (Supplementary Note 1 and Supplementary Data 1). We computationally optimized and parallelized the algorithms such that MiTCR can efficiently extract CDR3 information at a speed of more than 50,000 sequencing reads per second (0.3– 0.6 gigabases min–1) on standard PC hardware. For example, Illumina MiSeq run of 10 million reads can be analyzed in ~3 min, and a HiSeq lane of 100 million reads can be analyzed in ~20 min (Supplementary Note 4). Output is provided in a tab-delimited text file that contains exhaustive information regarding TCR clonotype composition, abundance and aggregated sequence quality (Supplementary Data 2). Additionally, we developed MiTCR Viewer software that works with a custom (*.cls) format produced by MiTCR, enabling convenient visualization, filtering and in silico spectratyping of the data (http://mitcr.milaboratory.com/viewer/; Supplementary Data 3). We demonstrated the accuracy and specificity of MiTCR for the analysis of both cDNA-based and genomic DNA–based high-throughput sequencing datasets (Supplementary Tables 1 and 2). To verify software performance with datasets of known clonotype composition, we generated Illumina-like data sets in silico, based on real rates of PCR and sequencing errors (Supplementary Note 5). We determined the efficiency of human TCR-α and TCR-β CDR3 extraction, clonotype generation and error correction for the model data (Fig. 1b,c). Accuracy of V and J segment identif ication was 97–99%. MiTCR efficiency was superior compared to that of existing CDR3extraction packages (Supplementary Note 6, Supplementary Table 3 and Supplementary Fig. 1). MiTCR: software for T-cell receptor sequencing data analysis

Pairing of T-cell receptor chains via emulsion PCR

Our ability to analyze adaptive immunity and engineer its activity has long been constrained by our limited ability to identify native pairs of heavy–light antibody chains and alpha–beta T‐cell receptor (TCR) chains — both of which comprise coupled “halves of a key”, collectively capable of recognizing specific antigens. Here, we report a cell‐based emulsion RT‐PCR approach that allows the selective fusion of the native pairs of amplified TCR alpha and beta chain genes for complex samples. A new type of PCR suppression technique was developed that makes it possible to amplify the fused library with minimal noise for subsequent analysis by high‐throughput paired‐end Illumina sequencing. With this technique, single analysis of a complex blood sample allows identification of multiple native TCR chain pairs. This approach may be extended to identify native antibody chain pairs and, more generally, pairs of mRNA molecules that are coexpressed in the same living cells.

VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires.

Despite the growing number of immune repertoire sequencing studies, the field still lacks software for analysis and comprehension of this high-dimensional data. Here we report VDJtools, a complementary software suite that solves a wide range of T cell receptor (TCR) repertoires post-analysis tasks, provides a detailed tabular output and publication-ready graphics, and is built on top of a flexible API. Using TCR datasets for a large cohort of unrelated healthy donors, twins, and multiple sclerosis patients we demonstrate that VDJtools greatly facilitates the analysis and leads to sound biological conclusions. VDJtools software and documentation are available at https://github.com/mikessh/vdjtools.

Local fitness landscape of the green fluorescent protein.

Fitness landscapes depict how genotypes manifest at the phenotypic level and form the basis of our understanding of many areas of biology, yet their properties remain elusive. Previous studies have analysed specific genes, often using their function as a proxy for fitness, experimentally assessing the effect on function of single mutations and their combinations in a specific sequence or in different sequences. However, systematic high-throughput studies of the local fitness landscape of an entire protein have not yet been reported. Here we visualize an extensive region of the local fitness landscape of the green fluorescent protein from Aequorea victoria (avGFP) by measuring the native function (fluorescence) of tens of thousands of derivative genotypes of avGFP. We show that the fitness landscape of avGFP is narrow, with 3/4 of the derivatives with a single mutation showing reduced fluorescence and half of the derivatives with four mutations being completely non-fluorescent. The narrowness is enhanced by epistasis, which was detected in up to 30% of genotypes with multiple mutations and mostly occurred through the cumulative effect of slightly deleterious mutations causing a threshold-like decrease in protein stability and a concomitant loss of fluorescence. A model of orthologous sequence divergence spanning hundreds of millions of years predicted the extent of epistasis in our data, indicating congruence between the fitness landscape properties at the local and global scales. The characterization of the local fitness landscape of avGFP has important implications for several fields including molecular evolution, population genetics and protein design.

Preparing Unbiased T-Cell Receptor and Antibody cDNA Libraries for the Deep Next Generation Sequencing Profiling

High-throughput sequencing has the power to reveal the nature of adaptive immunity as represented by the full complexity of T-cell receptor (TCR) and antibody (IG) repertoires, but is at present severely compromised by the quantitative bias, bottlenecks, and accumulated errors that inevitably occur in the course of library preparation and sequencing. Here we report an optimized protocol for the unbiased preparation of TCR and IG cDNA libraries for high-throughput sequencing, starting from thousands or millions of live cells in an investigated sample. Critical points to control are revealed, along with tips that allow researchers to minimize quantitative bias, accumulated errors, and cross-sample contamination at each stage, and to enhance the subsequent bioinformatic analysis. The protocol is simple, reliable, and can be performed in 1–2 days.

A mechanism for expansion of regulatory T-cell repertoire and its role in self-tolerance

T-cell receptor (TCR) signalling has a key role in determining T-cell fate. Precursor cells expressing TCRs within a certain low-affinity range for complexes of self-peptide and major histocompatibility complex (MHC) undergo positive selection and differentiate into naive T cells expressing a highly diverse self-MHC-restricted TCR repertoire. In contrast, precursors displaying TCRs with a high affinity for ‘self’ are either eliminated through TCR-agonist-induced apoptosis (negative selection) or restrained by regulatory T (Treg) cells, whose differentiation and function are controlled by the X-chromosome-encoded transcription factor Foxp3 (reviewed in ref. 2). Foxp3 is expressed in a fraction of self-reactive T cells that escape negative selection in response to agonist-driven TCR signals combined with interleukin 2 (IL-2) receptor signalling. In addition to Treg cells, TCR-agonist-driven selection results in the generation of several other specialized T-cell lineages such as natural killer T cells and innate mucosal-associated invariant T cells. Although the latter exhibit a restricted TCR repertoire, Treg cells display a highly diverse collection of TCRs. Here we explore in mice whether a specialized mechanism enables agonist-driven selection of Treg cells with a diverse TCR repertoire, and the importance this holds for self-tolerance. We show that the intronic Foxp3 enhancer conserved noncoding sequence 3 (CNS3) acts as an epigenetic switch that confers a poised state to the Foxp3 promoter in precursor cells to make Treg cell lineage commitment responsive to a broad range of TCR stimuli, particularly to suboptimal ones. CNS3-dependent expansion of the TCR repertoire enables Treg cells to control self-reactive T cells effectively, especially when thymic negative selection is genetically impaired. Our findings highlight the complementary roles of these two main mechanisms of self-tolerance.

Stability and function of regulatory T cells expressing the transcription factor T-bet

Adaptive immune responses are tailored to different types of pathogens through differentiation of naive CD4 T cells into functionally distinct subsets of effector T cells (T helper 1 (TH1), TH2, and TH17) defined by expression of the key transcription factors T-bet, GATA3, and RORγt, respectively. Regulatory T (Treg) cells comprise a distinct anti-inflammatory lineage specified by the X-linked transcription factor Foxp3 (refs 2, 3). Paradoxically, some activated Treg cells express the aforementioned effector CD4 T cell transcription factors, which have been suggested to provide Treg cells with enhanced suppressive capacity. Whether expression of these factors in Treg cells—as in effector T cells—is indicative of heterogeneity of functionally discrete and stable differentiation states, or conversely may be readily reversible, is unknown. Here we demonstrate that expression of the TH1-associated transcription factor T-bet in mouse Treg cells, induced at steady state and following infection, gradually becomes highly stable even under non-permissive conditions. Loss of function or elimination of T-bet-expressing Treg cells—but not of T-bet expression in Treg cells—resulted in severe TH1 autoimmunity. Conversely, following depletion of T-bet− Treg cells, the remaining T-bet+ cells specifically inhibited TH1 and CD8 T cell activation consistent with their co-localization with T-bet+ effector T cells. These results suggest that T-bet+ Treg cells have an essential immunosuppressive function and indicate that Treg cell functional heterogeneity is a critical feature of immunological tolerance.