Functionally specialized human CD4+ T-cell subsets express physicochemically distinct TCRs

The organizational integrity of the adaptive immune system is determined by functionally discrete subsets of CD4+ T cells, but it has remained unclear to what extent lineage choice is influenced by clonotypically expressed T-cell receptors (TCRs). To address this issue, we used a high-throughput approach to profile the αβ TCR repertoires of human naive and effector/memory CD4+ T-cell subsets, irrespective of antigen specificity. Highly conserved physicochemical and recombinatorial features were encoded on a subset-specific basis in the effector/memory compartment. Clonal tracking further identified forbidden and permitted transition pathways, mapping effector/memory subsets related by interconversion or ontogeny. Public sequences were largely confined to particular effector/memory subsets, including regulatory T cells (Tregs), which also displayed hardwired repertoire features in the naive compartment. Accordingly, these cumulative repertoire portraits establish a link between clonotype fate decisions in the complex world of CD4+ T cells and the intrinsic properties of somatically rearranged TCRs

Application of nonsense-mediated primer exclusion (NOPE) for preparation of unique molecular barcoded libraries

Abstract Background Recently we proposed efficient method to exclude undesirable primers at any stage of amplification reaction, here termed NOPE (NOnsense-mediated Primer Exclusion). According to this method, added oligonucleotide overlapping with the 3′-end of unwanted amplification primer (NOPE oligo) simultaneously provides a template for its elongation. This elongation disrupts specificity of unwanted primer, preventing its further participation in PCR. The suggested approach allows to rationally manage the course of PCR reactions in order to facilitate analysis of complex DNA mixtures as well as to perform multistage PCR bypassing intermediate purification steps. Results Here we apply NOPE method to DNA library preparation for the high-throughput sequencing (HTS) with the PCR-based introduction of unique molecular identifiers (UMI). We show that NOPE oligo efficiently neutralizes UMI-containing oligonucleotides after introduction of UMI into sample DNA molecules, thus allowing to proceed with further amplification steps without purification and associated loss of starting material. At the same time, NOPE oligo does not affect the efficiency of target PCR amplification. Conclusion We describe a simple, robust and cheap modification of UMI-labeled HTS libraries preparation procedure, that allows to bypass purification step and thus to preserve starting material which may be limited, e.g. circulating tumor DNA, circulating fetal DNA, or small amounts of isolated cells of interest. Furthermore, demonstrated simplicity and robustness of NOPE method should make it popular in various PCR protocols

MAGERI: Computational pipeline for molecular-barcoded targeted resequencing

<div><p>Unique molecular identifiers (UMIs) show outstanding performance in targeted high-throughput resequencing, being the most promising approach for the accurate identification of rare variants in complex DNA samples. This approach has application in multiple areas, including cancer diagnostics, thus demanding dedicated software and algorithms. Here we introduce MAGERI, a computational pipeline that efficiently handles all caveats of UMI-based analysis to obtain high-fidelity mutation profiles and call ultra-rare variants. Using an extensive set of benchmark datasets including gold-standard biological samples with known variant frequencies, cell-free DNA from tumor patient blood samples and publicly available UMI-encoded datasets we demonstrate that our method is both robust and efficient in calling rare variants. The versatility of our software is supported by accurate results obtained for both tumor DNA and viral RNA samples in datasets prepared using three different UMI-based protocols.</p></div

MAGERI software benchmark using Tru-Q 7 reference standard and control donor DNA.

<p><b>a</b> Number of detected variant for each variant frequency tier across two independent experiments with the reference standard. Shaded areas show the 95% confidence intervals for expected fraction of recovered variants, i.e. binomial proportion confidence intervals built using known variant frequency and template coverage. <b>b</b> Frequency distribution of known Tru-Q 7 variants coming from each frequency tier and errors in the control donor DNA. <b>c</b> MAGERI Q score and the empirical P-values of erroneous variants detected in control donor DNA. <b>d</b> Comparison of Q score distribution of erroneous variants and variants of each frequency tier. Dotted and dashed lines show P < 0.05 and P < 0.01 thresholds respectively. <b>e</b> Receiver operation characteristic (ROC) curve comparing the sensitivity and specificity of MAGERI Q scores (blue line) and frequency-based thresholding (red line) in the task of classification of errors and 0.1% tier variants.</p

MAGERI performance on different types of UMI-tagged data.

<p><b>a.</b> Analysis of single-strand consensuses from duplex sequencing data. Q scores of detected variants are plotted against empirical P-values, a smoothed fitting is shown with red line, ABL variant known to be present in the sample at ~1% frequency is shown with black dot. <b>b</b>. Analysis of UMI-tagged HIV cDNA sequencing data. MAGERI Q scores are plotted against empirical P-values for a control unmutated HIV cDNA from 8E5 cell line (red) and HIV+ donor plasma sample (blue). <b>c</b>. Indel variants detected in Tru-Q 7 reference standard and PBMC DNA of a healthy donor. Indel frequency is plotted against its size (number of added/deleted nucleotides). The figure shows known EGFR deletion (ΔE746 − A750) in two independent experiments with a known frequency of 1% (original Tru-Q 7 reference standard) and 0.1% (Tru-Q 7 reference standard diluted in 1:9 ratio with healthy donor DNA), erroneous variants present in healthy donor DNA are shown with empty circles.</p

Datasets used for MAGERI benchmark.

<p>Datasets used for MAGERI benchmark.</p

Decoding the fine-scale structure of a breast cancer genome and transcriptome

A comprehensive understanding of cancer is predicated upon knowledge of the structure of malignant genomes underlying its many variant forms and the molecular mechanisms giving rise to them. It is well established that solid tumor genomes accumulate a large number of genome rearrangements during tumorigenesis. End Sequence Profiling (ESP) maps and clones genome breakpoints associated with all types of genome rearrangements elucidating the structural organization of tumor genomes. Here we extend the ESP methodology in several directions using the breast cancer cell line MCF-7. First, targeted ESP is applied to multiple amplified loci, revealing a complex process of rearrangement and coamplification in these regions reminiscent of breakage/fusion/bridge cycles. Second, genome breakpoints identified by ESP are confirmed using a combination of DNA sequencing and PCR. Third, in vitro functional studies assign biological function to a rearranged tumor BAC clone, demonstrating that it encodes antiapoptotic activity. Finally, ESP is extended to the transcriptome identifying four novel fusion transcripts and providing evidence that expression of fusion genes may be common in tumors. These results demonstrate the distinct advantages of ESP including: (1) the ability to detect all types of rearrangements and copy number changes; (2) straightforward integration of ESP data with the annotated genome sequence; (3) immortalization of the genome; (4) ability to generate tumor-specific reagents for in vitro and in vivo functional studies. Given these properties, ESP could play an important role in a tumor genome project

A new set of markers for human identification based on 32 polymorphic Alu insertions

A number of genetic systems for human genetic identification based on short tandem repeats or single nucleotide polymorphisms are widely used for crime detection, kinship studies and in analysis of victims of mass disasters. Here, we have developed a new set of 32 molecular genetic markers for human genetic identification based on polymorphic retroelement insertions. Allele frequencies were determined in a group of 90 unrelated individuals from four genetically distant populations of the Russian Federation. The mean match probability and probability of paternal exclusion, calculated based on population data, were 5.53 × 10−14 and 99.784%, respectively. The developed system is cheap and easy to use as compared to all previously published methods. The application of fluorescence-based methods for allele discrimination allows to use the human genetic identification set in automatic and high-throughput formats