BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing

Despite its widespread use, RNA-seq is still too laborious and expensive to replace RT-qPCR as the default gene expression analysis method. We present a novel approach, BRB-seq, which uses early multiplexing to produce 3′ cDNA libraries for dozens of samples, requiring just 2 hours of hands-on time. BRB-seq has a comparable performance to the standard TruSeq approach while showing greater tolerance for lower RNA quality and being up to 25 times cheaper. We anticipate that BRB-seq will transform basic laboratory practice given its capacity to generate genome-wide transcriptomic data at a similar cost as profiling four genes using RT-qPCR.

[1]  James A. Thomson,et al.  A cost-effective RNA sequencing protocol for large-scale gene expression studies , 2015, Scientific Reports.

[2]  Paul Theodor Pyl,et al.  HTSeq—a Python framework to work with high-throughput sequencing data , 2014, bioRxiv.

[3]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[4]  Harald Binder,et al.  Feasibility of sample size calculation for RNA‐seq studies , 2017, Briefings Bioinform..

[5]  Petra C. Schwalie,et al.  Dissecting the brown adipogenic regulatory network using integrative genomics , 2017, Scientific Reports.

[6]  O. Delaneau,et al.  Population Variation and Genetic Control of Modular Chromatin Architecture in Humans , 2015, Cell.

[7]  Pedro G. Ferreira,et al.  Transcriptome and genome sequencing uncovers functional variation in humans , 2013, Nature.

[8]  T. Borodina,et al.  Transcriptome analysis by strand-specific sequencing of complementary DNA , 2009, Nucleic acids research.

[9]  P. Sorger,et al.  Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs , 2016, Nature Methods.

[10]  O. Delaneau,et al.  Intra- and inter-chromosomal chromatin interactions mediate genetic effects on regulatory networks , 2017, bioRxiv.

[11]  B. Hoffman,et al.  A simple and very efficient method for generating cDNA libraries. , 1983, Gene.

[12]  Michele A. Busby,et al.  Simultaneous generation of many RNA-seq libraries in a single reaction , 2015, Nature Methods.

[13]  Wei Li,et al.  RSeQC: quality control of RNA-seq experiments , 2012, Bioinform..

[14]  Jacob F. Degner,et al.  Genetic variants regulating expression levels and isoform diversity during embryogenesis , 2016, Nature.

[15]  S. Lukyanov,et al.  An improved PCR method for walking in uncloned genomic DNA. , 1995, Nucleic acids research.

[16]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[17]  Christoph Ziegenhain,et al.  powsimR: Power analysis for bulk and single cell RNA-seq experiments , 2017, bioRxiv.

[18]  N. Friedman,et al.  Comprehensive comparative analysis of strand-specific RNA sequencing methods , 2010, Nature Methods.

[19]  Melis N. Anahtar,et al.  Cervicovaginal bacteria are a major modulator of host inflammatory responses in the female genital tract. , 2015, Immunity.

[20]  Lucas E. Wange,et al.  Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq , 2018, Nature Communications.

[21]  Petra C. Schwalie,et al.  A stromal cell population that inhibits adipogenesis in mammalian fat depots , 2018, Nature.

[22]  Michael J. Ziller,et al.  Integrative Analyses of Human Reprogramming Reveal Dynamic Nature of Induced Pluripotency , 2015, Cell.

[23]  Pawel Zajac,et al.  Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing , 2012, Nature Protocols.

[24]  D. Cacchiarelli,et al.  Characterization of directed differentiation by high-throughput single-cell RNA-Seq , 2014, bioRxiv.

[25]  Piero Carninci,et al.  Suppression of artifacts and barcode bias in high-throughput transcriptome analyses utilizing template switching , 2012, Nucleic acids research.

[26]  Shuqiang Li,et al.  CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq , 2016, Genome Biology.

[27]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[28]  Ravi Iyengar,et al.  A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries , 2017, Scientific Reports.

[29]  I. Hellmann,et al.  Comparative Analysis of Single-Cell RNA Sequencing Methods , 2016, bioRxiv.

[30]  Ryan T Fuchs,et al.  Bias in Ligation-Based Small RNA Sequencing Library Construction Is Determined by Adaptor and RNA Structure , 2015, PloS one.

[31]  Joakim Lundeberg,et al.  Sequencing Degraded RNA Addressed by 3' Tag Counting , 2014, PloS one.

[32]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[33]  E. Shapiro,et al.  Single-cell sequencing-based technologies will revolutionize whole-organism science , 2013, Nature Reviews Genetics.

[34]  Davis J. McCarthy,et al.  Common genetic variation drives molecular heterogeneity in human iPSCs , 2017, Nature.

[35]  Daniel J. O'Connell,et al.  Simultaneous Pathway Activity Inference and Gene Expression Analysis Using RNA Sequencing. , 2016, Cell systems.

[36]  T. Mikkelsen,et al.  Parallel derivation of isogenic human primed and naive induced pluripotent stem cells , 2018, Nature Communications.

[37]  Vincent Gardeux,et al.  ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data , 2016, bioRxiv.

[38]  Christoph Ziegenhain,et al.  The impact of amplification on differential expression analyses by RNA-seq , 2016, Scientific Reports.

[39]  B. Faircloth,et al.  Primer3—new capabilities and interfaces , 2012, Nucleic acids research.

[40]  Åsa K. Björklund,et al.  Tn5 transposase and tagmentation procedures for massively scaled sequencing projects , 2014, Genome research.