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Exploiting antigen receptor information to quantify index switching in single-cell transcriptome sequencing experiments.

ABSTRACT: By offering high sequencing speed and ultra-high-throughput at a low price, Illumina next-generation sequencing platforms have been widely adopted in recent years. However, an experiment with multiplexed library could be at risk of molecular recombination, known as "index switching", which causes a proportion of the reads to be assigned to an incorrect sample. It is reported that a new advance, exclusion amplification (ExAmp) in conjunction with the patterned flow cell technology introduced on HiSeq 3000/HiSeq 4000/HiSeq X sequencing systems, potentially suffers from a higher rate of index switching than conventional bridge amplification. We took advantage of the diverse but highly cell-specific expression of antigen receptors on immune cells to quantify index switching on single cell RNA-seq data that were sequenced on HiSeq 3000 and HiSeq 4000. By utilizing the unique antigen receptor expression, we could quantify the spread-of-signal from many different wells (n = 55 from total of three batches) due to index switching. Based on full-length T cell receptor (TCR) sequences from all samples reconstructed by TraCeR and TCR gene expression quantified by Kallisto, we found index switching in all three batches of experiments investigated. The median percentage of incorrectly detected markers was estimated to be 3.9% (interquartile range (IQR): 1.7%-7.3%). We did not detect any consistent patterns of certain indices to be more prone to switching than others, suggesting that index switching is a stochastic process. Our results confirm that index switching is a problem that affects samples run in multiplexed libraries on Illumina HiSeq 3000 and HiSeq 4000 platforms.

SUBMITTER: Yao Y

PROVIDER: S-EPMC6281226 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Exploiting antigen receptor information to quantify index switching in single-cell transcriptome sequencing experiments.

Yao Ying Y Zia Asima A Wyrożemski Łukasz Ł Lindeman Ida I Sandve Geir Kjetil GK Qiao Shuo-Wang SW

PloS one 20181205 12

By offering high sequencing speed and ultra-high-throughput at a low price, Illumina next-generation sequencing platforms have been widely adopted in recent years. However, an experiment with multiplexed library could be at risk of molecular recombination, known as "index switching", which causes a proportion of the reads to be assigned to an incorrect sample. It is reported that a new advance, exclusion amplification (ExAmp) in conjunction with the patterned flow cell technology introduced on H ...[more]

PMID: 30517183

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Project description:BackgroundThe ability to generate recombinant drug target proteins is important for drug discovery research as it facilitates the investigation of drug-target-interactions in vitro. To accomplish this, the target's exact protein sequence is required. Public databases, such as Ensembl, UniProt and RefSeq, are extensive protein and nucleotide sequence repositories. However, many sequences for non-human organisms are predicted by computational pipelines and may thus be incomplete or incorrect. This could lead to misinterpreted experimental outcomes due to gaps or errors in orthologous drug target sequences. Transcriptome analysis by RNA-Seq has been established as a standard method for gene expression analysis. Apart from this common application, paired-end RNA-Seq data can also be used to obtain full coverage cDNA sequences via de novo transcriptome assembly.MethodsTo assess whether de novo transcriptome assemblies can be used to determine a protein's sequence by searching the assembly for a known orthologous sequence, we generated 3 × 6 = 18 tissue specific assemblies (three organs: brain, kidney and liver; six species: human, mouse, rat, dog, pig and cynomolgus monkey). These assemblies and the manually curated human protein sequences from UniProtKB/Swiss-Prot were used in a reciprocal BLAST search to identify best matching hits. We automated and generalised our approach and present the a&o-tool, a workflow which exploits de novo assemblies of paired-end RNA-Seq data and orthology information for target sequence validation and refinement across related species. Furthermore, the a&o-tool extracts best hits' sequences from a reciprocal BLAST search, translates them into protein sequences, computes a multiple sequence alignment and quantifies the refinement.ResultsFor the three human assemblies we observed a hit rate greater than 60% with 100% sequence coverage and identity. For assemblies from the other species we observed similar hit rates and coverage with highest identities for cynomolgus monkey.ConclusionsIn summary, we show how to refine protein sequences using RNA-Seq data and sequence information from closely related species. With the a&o-tool we provide a fully automated pipeline to perform refinement including cDNA translation and multiple sequence alignment for visual inspection. The major prerequisite for applying the a&o-tool is high quality sequencing data.

Dataset Information

Exploiting antigen receptor information to quantify index switching in single-cell transcriptome sequencing experiments.

Publications

Exploiting antigen receptor information to quantify index switching in single-cell transcriptome sequencing experiments.

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