Summary

遺伝子発現およびゲノム変化を特徴づけるために、RNAシーケンシングアッセイをターゲットに

Published: August 04, 2016
doi:

Summary

We describe a targeted RNA sequencing-based method that includes preparation of indexed cDNA libraries, hybridization and capture with custom probes and data analysis to interrogate selected transcripts for gene expression, mutations, and gene fusions. Targeted RNAseq permits cost-effective, rapid evaluation of selected transcripts on a desktop sequencer.

Abstract

RNA配列(RNAseq)は、遺伝子発現、突然変異、遺伝子融合、および非コードRNAを検出し、特徴付けるために使用することができる汎用性の高い方法です。億シーケンシング読み取り、そのようなmRNAおよび非コードRNAなどの複数のRNA産物を含めることができます – 標準RNAseq 30が必要です。我々は、ターゲットを絞ったRNAseq(キャプチャ)は、デスクトップ・シーケンサーを使用して、選択したRNA産物にフォーカスされた研究を可能にする方法を示します。 RNAseqキャプチャは、そうでなければ、伝統的なRNAseq方法を使用してミスの可能性があることを、注釈なしの低い、または一時的に発現転写産物を特徴づけることができます。ここでは、細胞株からのRNAの抽出、リボソームRNAの枯渇、cDNA合成、バーコードのライブラリ、ハイブリダイゼーションおよびデスクトップ・シーケンサー上のターゲット転写産物と多重シーケンシングのキャプチャの準備について説明します。我々はまた、品質管理評価、整列、融合検出、遺伝子発現の定量化および単NUCの識別を含むコンピュータ解析パイプラインを、概要leotide変種。このアッセイは、遺伝子発現、遺伝子融合、および変異を特徴付ける標的転写物配列決定を可能にします。

Introduction

Whole transcriptome or RNA sequencing (RNAseq) is an unbiased sequencing method to assess all RNA products. The goal of targeted RNAseq (Capture) is a focused evaluation of selected transcripts with increased sensitivity, dynamic range, reduced cost or scale, and increased throughput compared to standard RNAseq. Similar to standard RNAseq, targeted enrichment approaches can be used to evaluate gene expression, multiple RNA species such as mRNA, microRNA (miRNA), lncRNA1, other noncoding RNAs2, gene fusions3, and mutations4-6.

Capture involves hybridization of complementary oligonucleotides to enrich cDNA libraries for sequencing. The rationale for RNAseq Capture is similar to microarray approaches where complementary oligonucleotides or probes are hybridized to samples and then measured for relative abundance. For microarray technologies, expression is based on relative signal measured for transcripts binding to these probes. Microarrays are thus limited by range, potential background noise from non-specific binding, and cross-hybridization of probes. Furthermore, arrays have limited dynamic range for low and highly expressed transcripts compared to RNAseq1. Microarrays are widely utilized due to their reduced cost and high throughput capacity compared to RNAseq.

Here, we demonstrate a method for RNAseq Capture that offers a middle ground between RNAseq and microarray approaches for evaluating the transcriptome. RNAseq Capture has intermediate throughput, greater dynamic range and sensitivity, and is scaled for fast turnaround on desktop sequencers. RNAseq Capture also requires reduced computational resources in terms of storage space and data processing.

Protocol

注:このプロトコルは、4つのサンプルの同時処理および分析について説明します。この方法は、細胞から単離されたRNA、新鮮凍結組織及びホルマリン固定パラフィン包埋組織(FFPE)と互換性があります。各試料についてのRNA入力を開始するの千ngの(250推奨NG) – このプロトコルは50で始まります。 RNA手順の1. rRNAの枯渇とフラグメンテーション rRNAを枯渇</…

Representative Results

RNAseqキャプチャの模式的ハイライトの重要なステップは、図1に示されている。既知の変異を有する四癌細胞株がRNAseqキャプチャ手法の有効性を実証するために使用された(K562 ABL1融合 、RET融合とLC2と、EOL1 PDGFRalpha融合およびRT-でFGFR3融合 4)。 4つのサンプルを一緒にプールし、100 bpのはFASTQファイルを生成し、デスクトップ・…

Discussion

RNAseq CaptureはRNAseqとマイクロアレイとの間の中間の戦略は、トランスクリプトームの選択した部分を評価するためのアプローチです。キャプチャの利点は、デスクトップ・シーケンサー、高スループット、およびゲノム変化の検出にコスト削減、迅速なターンアラウンドタイムを含みます。この方法は、RNAスプライシングを調べ、単一ヌクレオチド変異体を4-6を検出 、遺?…

Disclosures

The authors have nothing to disclose.

Acknowledgements

We give special thanks to Ezra Lyon, Eliot Zhu, Michele Wing, Esko Kautto and Eric Samorodnitsky for technical support. We would also like to thank Jenny Badillo for her administrative support for our team. We acknowledge the Ohio Supercomputer Center (OSC) for providing disk space, processing capacity, and support to run our analyses. We thank the Comprehensive Cancer Center (CCC) at The Ohio State University Wexner Medical Center for their administrative support of this work. S.R. and Team are supported by the American Cancer Society (MRSG-12-194-01-TBG), a Prostate Cancer Foundation Young Investigator Award, NHGRI (UM1HG006508-01A1), Fore Cancer Research Foundation, American Lung Association, and Pelotonia.

Materials

Thermomixer R Eppendorf 21516-166
Centrifuge 5417R Eppendorf 5417R
miRNeasy Mini Kit Qiagen 217004
Molecular Biology Grade Ethanol Sigma Aldrich E7023-6X500ML
Thermoblock 24 X 1.5ml Eppendorf 21516-166
MiSeq Reagent Kit v2 (300-cycles) Illumina MS-102-2002
MiSeq Desktop Sequencer Illumina
PhiX Control v3 Illumina FC-110-3001
TruSeq Stranded Total RNA Kit with RiboZero Gold SetA Illumina RS-122-2301
25 rxn xGen® Universal Blocking Oligo – TS-p5 IDT 127040822
25 rxn xGen® Universal Blocking Oligo – TS-p7(6nt) IDT 127040823
25 rxn xGen® Universal Blocking Oligo – TS-p7(8nt) IDT 127040824
Agencourt® AMPure® XP – PCR Purification beads  Beckman-Coulter A63880
Dynabeads® M-270 Streptavidin Life Technologies 65305
COT Human DNA, Fluorometric Grade, 1mg Roche Applied Science 05480647001
Qubit® Assay Tubes  Life Technologies Q32856
Qubit® dsDNA HS Assay Kit Life Technologies Q32851
SeqCap® EZ Hybridization and Wash Kits  (24 or 96 reactions) Roche NimbleGen  05634261001 or 05634253001 
Qubit® 2.0 Fluorometer  Life Technologies Q32866
10 x 2 ml IDTE pH 8.0 (1X TE Solution) IDT
Tween20 BioXtra Sigma P7949-500ML
Nuclease Free Water Life Technologies AM9937
C1000 Touch™ Thermal Cycler with 96–Well Fast Rection Module Biorad 185-1196
SeqCap EZ Hybridization and Wash Kits Roche Applied Science 05634253001
SuperScript II Reverse Transcription 200U/ul Life Technologies 18064-014
D1000 ScreenTape Agilent Technol. Inc. 5067-5582
Agencourt RNAClean XP -40ml Beckman Coulter Inc A63987
RNA ScreenTape Agilent Technol. Inc. 5067-5576
RNA ScreenTape Ladder Agilent Technol. Inc. 5067-5578
RNA ScreenTape Sample Buffer Agilent Technol. Inc. 5067-5577
Sodium Hydroxide Sigma 72068-100ML
DynaBeads MyOne Streptavidin T1 Life Technologies 65602
DYNAMAG -96 SIDE EACH Life Technologies 12331D
Chloroform Sigma C2432-1L
KAPA HotStart ReadyMix KAPA Biosystems KK2602
NanoDrop 2000 Spectrophotometer Thermo Scientific
My Block Mini Dry Bath Benchmark BSH200
D1000 Reagents Agilent Technol. Inc. 5067- 5583
Vacufuge Plus Eppendorf 022829861 

References

  1. Wang, Z., Gerstein, M., Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 10, 57-63 (2009).
  2. Mercer, T. R., et al. Targeted sequencing for gene discovery and quantification using RNA CaptureSeq. Nat Protoc. 9, 989-1009 (2014).
  3. Maher, C. A., et al. Chimeric transcript discovery by paired-end transcriptome sequencing. Proc Natl Acad Sci U S A. 106, 12353-12358 (2009).
  4. Piskol, R., Ramaswami, G., Li, J. B. Reliable identification of genomic variants from RNA-seq data. Am J Hum Genet. 93, 641-651 (2013).
  5. Quinn, E. M., et al. Development of strategies for SNP detection in RNA-seq data: application to lymphoblastoid cell lines and evaluation using 1000 Genomes data. PLoS One. 8, e58815 (2013).
  6. Tang, X., et al. The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data. Nucleic Acids Res. 42, e172 (2014).
  7. . Preparing Libraries for Sequencing on the MiSeq® Available from: https://support.illumina.com/content/dam/illumina-support/documents/documentation/system_documentation/miseq/preparing-libraries-for-sequencing-on-miseq-15039740-d.pdf (2013)
  8. . MiSeq® Reagent Kit v2 Reagen Preparation Guide Available from: https://support.illumina.com/downloads/miseq_reagent_kit_reagent_preparation_guide.html (2012)
  9. Kim, D., et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
  10. Li, H., et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 25, 2078-2079 (2009).
  11. DeLuca, D. S., et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics. 28, 1530-1532 (2012).
  12. Dobin, A., et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 29, 15-21 (2013).
  13. Van der Auwera, G. A., et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics. 11, 11.10.1-11.10.33 (2013).
  14. Trapnell, C., et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 28, 511-515 (2010).
  15. Iyer, M. K., Chinnaiyan, A. M., Maher, C. A. ChimeraScan: a tool for identifying chimeric transcription in sequencing data. Bioinformatics. 27, 2903-2904 (2011).
  16. Kim, D., Salzberg, S. L. TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol. 12, R72 (2011).
  17. Fernandez-Cuesta, L., et al. Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data. Genome Biol. 16, 7 (2015).
  18. Shugay, M., Ortiz de Mendibil, ., Vizmanos, I., L, J., Novo, F. J. Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions. Bioinformatics. 29, 2539-2546 (2013).
  19. Clark, M. B., et al. Quantitative gene profiling of long noncoding RNAs with targeted RNA sequencing. Nat Methods. 12, 339-342 (2015).
  20. Cieslik, M., et al. The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing. Genome Res. , (2015).
  21. Cabanski, C. R., et al. cDNA hybrid capture improves transcriptome analysis on low-input and archived samples. J Mol Diagn. 16, 440-451 (2014).
  22. Costa, C., Gimenez-Capitan, A., Karachaliou, N., Rosell, R. Comprehensive molecular screening: from the RT-PCR to the RNA-seq. Transl Lung Cancer Res. 2, 87-91 (2013).
  23. Zhao, W., et al. Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling. BMC Genomics. 15, 419 (2014).
check_url/54090?article_type=t

Play Video

Cite This Article
Martin, D. P., Miya, J., Reeser, J. W., Roychowdhury, S. Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations. J. Vis. Exp. (114), e54090, doi:10.3791/54090 (2016).

View Video