Dataset Information

ScRNA-seq revealed the special TCR β & α V(D)J allelic inclusion rearrangement and the high proportion dual (or more) TCR-expressing cells.

ABSTRACT: Allelic exclusion, one lymphocyte expresses one antigen receptor, is a fundamental mechanism of immunological self-tolerance and highly specific immune responses to pathogens. However, the phenomenon of V(D)J allelic inclusion (incomplete allelic exclusion or allelic escape) rearrangement and dual TCR T cells have been discovered by multiple laboratories. Despite continuous new discoveries, the proportion and underlying mechanism of dual TCR has been puzzling immunologists. In this study, we observed the presence of single T cells expressing multiple TCR chains in all samples, with the proportion of 15%, 10%, and 20% in the human thymus, human peripheral blood, and mouse lymphoid organs, respectively. The proportion of T cells possessing multiple T-cell receptors (TCR) varied significantly in different physiological states and developmental stages. By analyzing RSS category, RSS direction, and V(D)J gene position at TR locus of T cells which contain multiple TCR chains, we creatively found that one of TCR β (or TCR α) should originate from the transcription of V(D)J combination in T-cell receptor excision circle (TREC) formed after the twice successful rearrangement in the same chromosome. Moreover, human V30 (or mouse V31) gene may participate in reverse recombination and transcription to prevent allelic exclusion. In general, high proportion of T cells with multiple TCR at the transcriptome level was first made public, and we proposed a novel mechanism of secondary (or more) TCR rearrangement on a single chromosome. Our findings also indicated that the single-cell sequencing data should be classified according to the single, multiple, and abnormal TCR when analyzing the T-cell repertoire.

SUBMITTER: Zhu L

PROVIDER: S-EPMC10390570 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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scRNA-seq revealed the special TCR β & α V(D)J allelic inclusion rearrangement and the high proportion dual (or more) TCR-expressing cells.

Zhu Lanwei L Peng Qi Q Li Jun J Wu Yingjie Y Wang Jiayi J Zhou Dewei D Ma Long L Yao Xinsheng X

Cell death & disease 20230731 7

Allelic exclusion, one lymphocyte expresses one antigen receptor, is a fundamental mechanism of immunological self-tolerance and highly specific immune responses to pathogens. However, the phenomenon of V(D)J allelic inclusion (incomplete allelic exclusion or allelic escape) rearrangement and dual TCR T cells have been discovered by multiple laboratories. Despite continuous new discoveries, the proportion and underlying mechanism of dual TCR has been puzzling immunologists. In this study, we obs ...[more]

PMID: 37524693

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Project description:BackgroundSingle-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq data and its significant sparsity accentuated by frequent dropout events, introducing false zero count observations, make the clustering analysis computationally challenging. Even though multiple scRNA-seq clustering techniques have been proposed, there is no consensus on the best performing approach. On a parallel research track, self-supervised contrastive learning recently achieved state-of-the-art results on images clustering and, subsequently, image classification.ResultsWe propose contrastive-sc, a new unsupervised learning method for scRNA-seq data that perform cell clustering. The method consists of two consecutive phases: first, an artificial neural network learns an embedding for each cell through a representation training phase. The embedding is then clustered in the second phase with a general clustering algorithm (i.e. KMeans or Leiden community detection). The proposed representation training phase is a new adaptation of the self-supervised contrastive learning framework, initially proposed for image processing, to scRNA-seq data. contrastive-sc has been compared with ten state-of-the-art techniques. A broad experimental study has been conducted on both simulated and real-world datasets, assessing multiple external and internal clustering performance metrics (i.e. ARI, NMI, Silhouette, Calinski scores). Our experimental analysis shows that constastive-sc compares favorably with state-of-the-art methods on both simulated and real-world datasets.ConclusionOn average, our method identifies well-defined clusters in close agreement with ground truth annotations. Our method is computationally efficient, being fast to train and having a limited memory footprint. contrastive-sc maintains good performance when only a fraction of input cells is provided and is robust to changes in hyperparameters or network architecture. The decoupling between the creation of the embedding and the clustering phase allows the flexibility to choose a suitable clustering algorithm (i.e. KMeans when the number of expected clusters is known, Leiden otherwise) or to integrate the embedding with other existing techniques.

Project description:MotivationQuantification estimates of gene expression from single-cell RNA-seq (scRNA-seq) data have inherent uncertainty due to reads that map to multiple genes. Many existing scRNA-seq quantification pipelines ignore multi-mapping reads and therefore underestimate expected read counts for many genes. alevin accounts for multi-mapping reads and allows for the generation of 'inferential replicates', which reflect quantification uncertainty. Previous methods have shown improved performance when incorporating these replicates into statistical analyses, but storage and use of these replicates increases computation time and memory requirements.ResultsWe demonstrate that storing only the mean and variance from a set of inferential replicates ('compression') is sufficient to capture gene-level quantification uncertainty, while reducing disk storage to as low as 9% of original storage, and memory usage when loading data to as low as 6%. Using these values, we generate 'pseudo-inferential' replicates from a negative binomial distribution and propose a general procedure for incorporating these replicates into a proposed statistical testing framework. When applying this procedure to trajectory-based differential expression analyses, we show false positives are reduced by more than a third for genes with high levels of quantification uncertainty. We additionally extend the Swish method to incorporate pseudo-inferential replicates and demonstrate improvements in computation time and memory usage without any loss in performance. Lastly, we show that discarding multi-mapping reads can result in significant underestimation of counts for functionally important genes in a real dataset.Availability and implementationmakeInfReps and splitSwish are implemented in the R/Bioconductor fishpond package available at https://bioconductor.org/packages/fishpond. Analyses and simulated datasets can be found in the paper's GitHub repo at https://github.com/skvanburen/scUncertaintyPaperCode.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

ScRNA-seq revealed the special TCR β & α V(D)J allelic inclusion rearrangement and the high proportion dual (or more) TCR-expressing cells.

Publications

scRNA-seq revealed the special TCR β & α V(D)J allelic inclusion rearrangement and the high proportion dual (or more) TCR-expressing cells.

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