Ilgar Z. Mamedov

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.

Next generation sequencing for TCR repertoire profiling: platform-specific features and correction algorithms.

The TCR repertoire is a mirror of the human immune system that reflects processes caused by infections, cancer, autoimmunity, and aging. Next generation sequencing (NGS) is becoming a powerful tool for deep TCR profiling; yet, questions abound regarding the methodological approaches for sample preparation and correct data interpretation. Accumulated PCR and sequencing errors along with library preparation bottlenecks and uneven PCR efficiencies lead to information loss, biased quantification, and generation of huge artificial TCR diversity. Here, we compare Illumina, 454, and Ion Torrent platforms for individual TCR profiling, evaluate the rate and character of errors, and propose advanced platform‐specific algorithms to correct massive sequencing data. These developments are applicable to a wide variety of next generation sequencing applications. We demonstrate that advanced correction allows the removal of the majority of artificial TCR diversity with concomitant rescue of most of the sequencing information. Thus, this correction enhances the accuracy of clonotype identification and quantification as well as overall TCR diversity measurements.

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

High-throughput identification of antigen-specific TCRs by TCR gene capture

The transfer of T cell receptor (TCR) genes into patient T cells is a promising approach for the treatment of both viral infections and cancer. Although efficient methods exist to identify antibodies for the treatment of these diseases, comparable strategies to identify TCRs have been lacking. We have developed a high-throughput DNA-based strategy to identify TCR sequences by the capture and sequencing of genomic DNA fragments encoding the TCR genes. We establish the value of this approach by assembling a large library of cancer germline tumor antigen–reactive TCRs. Furthermore, by exploiting the quantitative nature of TCR gene capture, we show the feasibility of identifying antigen-specific TCRs in oligoclonal T cell populations from either human material or TCR-humanized mice. Finally, we demonstrate the ability to identify tumor-reactive TCRs within intratumoral T cell subsets without knowledge of antigen specificities, which may be the first step toward the development of autologous TCR gene therapy to target patient-specific neoantigens in human cancer.

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.

Optogenetic in vivo cell manipulation in KillerRed-expressing zebrafish transgenics

BackgroundKillerRed (KR) is a novel photosensitizer that efficiently generates reactive oxygen species (ROS) in KR-expressing cells upon intense green or white light illumination in vitro, resulting in damage to their plasma membrane and cell death.ResultsWe report an in vivo modification of this technique using a fluorescent microscope and membrane-tagged KR (mem-KR)-expressing transgenic zebrafish. We generated several stable zebrafish Tol2 transposon-mediated enhancer-trap (ET) transgenic lines expressing mem-KR (SqKR series), and mapped the transposon insertion sites. As mem-KR accumulates on the cell membrane and/or Golgi, it highlights cell bodies and extensions, and reveals details of cellular morphology. The photodynamic property of KR made it possible to damage cells expressing this protein in a dose-dependent manner. As a proof-of-principle, two zebrafish transgenic lines were used to affect cell viability and function: SqKR2 expresses mem-KR in the hindbrain rhombomeres 3 and 5, and elsewhere; SqKR15 expresses mem-KR in the heart and elsewhere. Photobleaching of KR by intense light in the heart of SqKR15 embryos at lower levels caused a reduction in pumping efficiency of the heart and pericardial edema and at higher levels - in cell death in the hindbrain of SqKR2 and in the heart of SqKR15 embryos.ConclusionsAn intense illumination of tissues expressing mem-KR affects cell viability and function in living zebrafish embryos. Hence, the zebrafish transgenics expressing mem-KR in a tissue-specific manner are useful tools for studying the biological effects of ROS.

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.

tcR: an R package for T cell receptor repertoire advanced data analysis

BackgroundThe Immunoglobulins (IG) and the T cell receptors (TR) play the key role in antigen recognition during the adaptive immune response. Recent progress in next-generation sequencing technologies has provided an opportunity for the deep T cell receptor repertoire profiling. However, a specialised software is required for the rational analysis of massive data generated by next-generation sequencing.ResultsHere we introduce tcR, a new R package, representing a platform for the advanced analysis of T cell receptor repertoires, which includes diversity measures, shared T cell receptor sequences identification, gene usage statistics computation and other widely used methods. The tool has proven its utility in recent research studies.ConclusionstcR is an R package for the advanced analysis of T cell receptor repertoires after primary TR sequences extraction from raw sequencing reads. The stable version can be directly installed from The Comprehensive R Archive Network (http://cran.r-project.org/mirrors.html). The source code and development version are available at tcR GitHub (http://imminfo.github.io/tcr/) along with the full documentation and typical usage examples.