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TECH NOTE

A Complete Solution for Generating Stranded RNA-Seq Libraries from High-Input Total RNA

SMARTer Stranded Total RNA Sample Prep Kit - HI Mammalian

Overview

Streamlined methods provide the opportunities necessary to push each experiment to its fullest potential. By combining time-saving techniques with high-performance reagents, studies in transcriptomics can move forward efficiently and accurately. Expression analysis of the entire transcriptome by RNA-sequencing (RNA-seq) can reap great benefits from high sensitivity, wide range of sample input amounts, and easy-to-use protocols. Traditionally, generation of RNA-seq libraries from total RNA has been challenged by the high amounts of ribosomal RNA (rRNA) in the starting material, and lengthy protocols required to incorporate platform-specific adaptors via ligation. The SMARTer Stranded Total RNA Sample Prep Kit - HI Mammalian is a unique solution for generating indexed cDNA libraries suitable for next-generation sequencing (NGS) on any Illumina® platform, starting with 100 ng–1 µg of total mammalian RNA of any quality.

Fast, Accurate Technology for rRNA Removal and Library Generation

Our SMARTer RNA-seq kits are based on the core SMART (Switching Mechanism at 5' End of RNA Template) technology (1), a streamlined process that maintains strand information, and also eliminates tedious library preparation by incorporating adaptors in reverse transcription and PCR steps. The strand-specific reverse transcription reaction maintains close to 99% accurate strand of origin information, allowing for the identification of overlapping transcripts and antisense transcripts. Sequencer-ready libraries are generated during PCR amplification of the cDNA, using primers containing Illumina cluster-generating sequences and indexes.

Total RNA can consist of ≥90% rRNA, making it important to remove rRNA from samples before generating RNA-seq libraries. The RiboGone technology incorporated in the protocol uses hybridization technology and RNase H digestion to bind and specifically deplete nuclear rRNA sequences (5S, 5.8S, 18S, and 28S), as well as mitochondrial rRNA sequences (12S) from human, mouse, or rat total RNA (2). By depleting the rRNA in samples prior to library generation, sequencing costs are lowered and mapping statistics are improved.

With the combined power of SMART and RiboGone technologies, the SMARTer Stranded Total RNA Sample Prep Kit - HI Mammalian enables you to go from total RNA to Illumina-compatible RNA-seq libraries in around five hours.

Generate Illumina-specific, stranded RNA-seq libraries in 5 hours from total RNA

Flowchart of SMARTer Stranded Total RNA Sample Prep Kit - HI Mammalian library generation. Section A. Depletion of rRNA from total RNA samples with RiboGone technology. Section B. First-strand cDNA synthesis with SMART technology, incorporating Illumina Read Primers 1 and 2. Section C. Template switching and generation of sequencing libraries with Illumina cluster-generating sequences and indexes by PCR amplification.

Reproducible Sequencing Data

The SMARTer Stranded Total RNA Sample Prep Kit - HI Mammalian produces extremely reliable RNA-seq data. Two 100-ng samples of Human Universal Reference RNA (HURR; Agilent) were treated with this kit, and the data from the two resulting libraries were compared. The high correlation between them (R = 0.99) displays an impressive level of reproducibility and consistency across replicates.

High correlation of RNA-seq data between replicates

Reproducibility across replicates. RNA-seq libraries were generated from two samples of 100 ng of HURR. The scatterplot illustrates correlations between the FPKMs (Fragments Per Kilobase Of Exon Per Million Fragments Mapped) from the two libraries. See Methods >>

High-Quality Sequencing Data

With the SMARTer Stranded Total RNA Sample Prep Kit - HI Mammalian system, researchers can generate RNA-seq libraries from a variety of samples, including HURR and HBRR (Human Brain Reference RNA; Ambion), starting from 100 ng–1 µg of total RNA. Using this kit, rRNA content was depleted from samples prior to cDNA synthesis and library generation. When sequenced, both the HURR and HBRR libraries yielded a high number of quality reads, with 88–94% mapped, 84–91% uniquely mapped, and approximately 17,600 genes identified. Additionally, based on the ERCC Spike-In RNA, strand information was maintained at about 99% for both samples. The benefits of rRNA depletion are clear, with less than 0.5% of reads from the HURR library and less than 6% of reads from the HBRR library mapped to rRNA.

Sequence Alignment Metrics
RNA source Human Universal Human Brain
Input amount 400 ng
Number of reads (millions) 8.5 (paired end reads)
Percentage of reads (%):
rRNA 0.3% 5.3%
Mapped to genome 94% 88%
Mapped uniquely to genome 91% 84%
Exonic 43% 50%
Intronic 43% 33%
Intergenic 14% 12%
Number of genes identified 17,570 17,600
Percentage of ERCC transcripts with correct strand 99.3% 98.8%

Sequence Alignment Metrics. 400 ng of HURR and HBRR with ERCC Spike-In RNA were treated with this kit. Alignment data is displayed for both libraries, with the percentage of reads that mapped to rRNA, exonic regions, intronic regions, intergenic regions, and the correct strand, as defined by Picard analysis. See Methods >>

These same RNA-seq libraries, generated from HURR and HBRR samples, produced data that had a strong correlation (R = 0.927) with qPCR data for the same RNAs obtained through the MicroArray Quality Control (MAQC) analysis. This suggested that the RiboGone method of rRNA depletion and SMARTer cDNA synthesis and library preparation did not negatively affect the RNA-seq data and maintained exceptional accuracy.

MAQC analysis shows high accuracy and strong correlation with pPCR data

MAQC Analysis. RNA-seq libraries were generated with 400 ng of HURR and HBRR. The scatter plot shows the Log2 ratio of FPKMs of HURR/HBRR graphed against the Log2 of the ratio of HURR/HBRR derived from qPCR Taqman probes. See Methods >>

RNA-seq libraries produced with this kit provide an accurate representation of your sample. A 400-ng HBRR sample with ERCC (External RNA Controls Consortium) Spike-In RNA Mix (Life Technologies) was treated with this kit, and the libraries were sequenced, generating 8.5 million paired end reads. The FPKMs (Fragments Per Kilobase Of Exon Per Million Fragments Mapped) showed a strong correlation (R2 = 0.9199) and linearity (slope = 0.9988) to the input concentrations of the individual ERCC transcripts, indicating excellent accuracy and dynamic range.

ERCC spike-in data shows excellent accuracy and dynamic range

Dynamic range and linearity of RNA-seq data. Libraries were generated from Human Brain Reference RNA with ERCC Spike-In RNA Mix2. The above graph shows strong correlation between the Log2 of input concentrations of individual ERCC transcripts vs. the Log2 of FPKMs for those transcripts. See Methods >>

Reliable, Accurate Results across Varying Quality of Input RNA

Libraries can be quickly and precisely generated from input RNA of a wide range of quality. Mouse Liver RNA (Clontech) was chemically sheared to a RIN (RNA Integrity Number) of either 3 or 7 (3). Samples of each quality were used at both 100-ng and 1-µg levels and treated with this kit. All of the libraries generated from these samples had high mapping statistics with 81–88% mapped reads, 72–77% uniquely mapped reads, with over 12,000 genes identified. Stranded information of the biological RNA was maintained at high levels (95–98%), regardless of RIN value.

Sequence Alignment Metrics from RNA of Varying Quality
RNA source Mouse Liver
RNA quality (RIN) RIN 3 RIN 7
Input amount 100 ng 1 µg 100 ng 1 µg
Number of reads (millions) 1.7 (paired end reads)
Percentage of reads (%):
rRNA 2% 2% 1% 1%
Mapped to genome 82% 86% 81% 88%
Mapped uniquely to genome 73% 75% 72% 77%
Exonic 55% 53% 54% 54%
Intronic 32% 31% 33% 32%
Intergenic 12% 14% 12% 13%
Number of genes identified 12,079 12,172 12,099 12,212
Percent biological strandedness 95.5% 97.2% 95.6% 98.1%

High-quality libraries across varying levels of RNA quality. Libraries were generated from Mouse Liver RNA by chemically shearing until it had a RIN of 3 or 7. Sequencing data showed the percentage of reads that mapped to rRNA, exonic regions, intronic regions, intergenic regions, and the correct strand, as defined by Picard analysis. See Methods >>

The above data shows that the high reproducibility standards of this kit are not affected by the quality of input RNA. A comparison of data from the 1-µg libraries described above shows an extremely high correlation (R = 0.99), indicating the strong ability of the SMARTer Stranded Total RNA Sample Prep Kit - HI Mammalian kit to generate reliable, reproducible data across varying levels of RNA quality.

High quality, reproducible RNA-seq data from samples of varying RNA quality

Reproducibility across RNA quality. A scatterplot illustrates the correlations between the FPKMs from two libraries generated from 1 µg Mouse Liver RNA that was chemically sheared until it had a RIN of 3 or 7. See Methods >>

Summary

The SMARTer Stranded Total RNA Sample Prep Kit - HI Mammalian is a complete solution for preparing indexed Illumina sequencing libraries from 100 ng–1 µg of mammalian total RNA. This kit incorporates key RiboGone and SMART technologies, seamlessly blending abundant transcript (rRNA) removal and strand-specific library generation. SMART technology allows the addition of Illumina adaptors in a ligation-free manner, significantly reducing hands-on time while also increasing efficiency. The sequencing data obtained with this kit maintains high quality and reproducibility across sample replicates and RNA quality.

Methods and References

Reproducibility across replicates:

Reproducibility across replicates was illustrated with two samples of 100 ng of Human Universal Reference RNA (Agilent), treated with SMARTer Stranded Total RNA Sample Prep Kit - HI Mammalian. The two replicates underwent the same protocol, except Replica #1 used 13 PCR cycles and Replica #2 used 14 PCR cycles. The libraries were sequenced at 1.3 million single end reads (1 x 50 bp) on an Illumina MiSeq® instrument, and aligned with STAR against hg19 with Ensembl annotation. See data >>

MAQC and ERCC analysis:

The quality of sequencing data was demonstrated via MAQC analysis, dynamic range analysis, and sequence alignment metrics. For this purpose, RNA-seq libraries were generated from 400 ng of Human Universal Reference RNA (HURR; Agilent) and Human Brain Reference RNA (HBRR; Ambion) with ERCC Spike-In RNA, with Mix1 used for HURR and Mix2 used for HBRR. The libraries were sequenced at 8.5 million paired end reads (2 x 75 bp) on an Illumina MiSeq instrument, and aligned with STAR against hg19 with Ensembl annotation. The percentage of reads that mapped to rRNA, exonic regions, intronic regions, intergenic regions, and the correct strand were defined by Picard analysis. For MAQC analysis, the Log2 ratio of FPKMs from HURR/HBRR was graphed against the Log2 of the ratio of HURR/HBRR derived from qPCR Taqman probes. For the dynamic range study, the Log2 of input concentrations of individual ERCC transcripts was graphed against the Log2 of FPKMs for those transcripts in the HBRR sample. See data >>

Library generation across varying quality of input RNA:

To compare data across RNA quality, Mouse Liver RNA (Clontech) was chemically sheared until it had a RIN (RNA Integrity Number) of 3 or 7. Either 100 ng or 1 µg of each RIN was used with this kit to generate RNA-seq libraries. These libraries were sequenced at 1.7 million paired end reads (2 x 25 bp) on an Illumina MiSeq instrument, and aligned with STAR against mm10 with Ensembl annotation. The percentage of reads that mapped to rRNA, exonic regions, intronic regions, intergenic regions, and the correct strand were defined by Picard analysis. Correlations between the FPKMs of libraries generated from 1 µg of RNA with both RINs were illustrated in a scatterplot. See data >>

References:

  1. Chenchik, A., et al. (1998) RT-PCR Methods for Gene Cloning and Analysis. (BioTechniques Books, MA), pp. 305–319.
  2. Morlan, J.D., et al. (2012) PLOS One 7(8):e42882.
  3. Mortazavi, A., et al., (2008) Nature Methods 5(7): 621–628.

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