SMART-Seq v4 Ultra Low RNA Kit for the Fluidigm C1 System
SMART-Seq v4 Ultra Low RNA Kit for the Fluidigm C1 System
Brand: Takara Bio.
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SMART-Seq v4 Ultra Low RNA Kit for the Fluidigm C1 System
SMART-Seq v4 Ultra Low RNA Kit for the Fluidigm C1 System—high performance single-cell transcriptome analysis
Single-cell RNA-seq enables researchers to study how gene expression patterns vary within cell populations. Takara Bio has developed robust tools for performing RNA-seq on individual cells isolated in Fluidigm's integrated fluidic circuit (IFC) system. The sensitive SMARTer chemistry of our ultra-low input kits paired with the high-throughput automation capability of the Fluidigm C1 system offer high-resolution insights for transcriptome analysis.
We currently offer two generations of single-cell mRNA-seq kits for the Fluidigm C1 Single Cell Auto Prep system. The newest version, the SMART-Seq v4 Ultra Low Input RNA Kit for the Fluidigm C1 System, incorporates features from our industry-leading SMART-Seq v4 kit, including SMART (Switching Mechanism at 5’ End of RNA Template) and locked nucleic acid (LNA) technologies, which are collectively referred to as "SMARTer-seq® chemistry". This kit outperforms published protocols and our previous version, the SMARTer Ultra Low RNA Kit for the Fluidigm C1 System, by providing higher sensitivity, better reproducibility, and an improved representation of GC- and AT-rich genes. The new script for using this kit is supported by the mRNA Seq IFC and the Open App IFC, and can be downloaded through the Fluidigm Script Hub.
Overview
- Improved SMART-technology-based chemistry—LNA technology in the SMART-Seq v4 Oligo and improved chemistry lead to better performance (higher sensitivity, greater reproducibility, and more GC-rich genes detected).
- Library construction in 2 days—The cDNA synthesis protocol takes approximately two hours, followed by the eight-hour SMART-Seq v4 script on the C1 instrument. The entire library construction protocol can be completed within two working days.
- New script—The accompanying script is supported for use on both the mRNA Seq IFC and the Open App IFC. The SMART-Seq v4 script is available at the Fluidigm Script Hub. The user manual for this product is available on the Documents tab of this web page.
Applications
- cDNA synthesis for mRNA-seq from up to 96 single cells on the Fluidigm C1 System
- cDNA synthesis for qPCR analysis on the Fluidigm Biomark platform prior to Illumina sequencing
Better representation of GC-rich genes
Comparing expression levels by gene GC content for two cDNA synthesis protocols. Libraries made from single K562 cells using either the SS-v4 protocol or the UL-v1 protocol were compared. Genes were binned by GC content (Low: 0–36% GC; Medium: 37–62% GC; High: 63–100% GC) and correlation plots were used to evaluate the two protocols. The RPKM values were very reproducible for pairs of cells using the SS-v4 chemistry (Panel A) or the UL-v1 chemistry (Panel B), with even distribution of genes across all three GC-content categories. Panel C. When the two protocols were compared, genes with high GC content (shown in red) showed higher expression with the SS-v4 protocol, while genes with medium or low GC content (shown in gray and blue, respectively) showed an even distribution. A simple way to understand the reproducibility of the new method is to look at the Pearson correlation of sequencing data from replicates of libraries prepared with the UL-v1 and SS-v4 protocols. Transcriptomic data generated by SS-v4 were more consistent between replicates than the data generated by UL-v1. The SS-v4 protocol reduced technical variability, which may increase the opportunity to detect biologically relevant differences between samples.
Reduce technical variability
Reduced variability among cDNA libraries synthesized using the SS-v4 protocol. Sequencing data were generated with the SS-v4 method (11 libraries x 2 replicate experiments) and the UL-v1 method (11 libraries), as described above. The FASTQ files were aligned against the human reference genome (Gencode release GRCh38) using STAR v. 2.5 (Dobin et al. 2013) with default options. Gene counts generated by STAR were subjected to regularized log transformation (DESeq2; v.1.10.1; Love et al. 2014). These transformed gene expression values were then used in the gene expression analyses. The analysis results were visualized using R. Panel A. The Pearson correlation coefficients were higher and tighter in the cDNA libraries generated with SS-v4. Pane B. The hierarchical clustering heat map demonstrates that the Euclidean distances between the SS-v4 cDNA libraries were smaller than the distances between the UL-v1 cDNA libraries. This indicates that the cDNA libraries generated with SS-v4 were more similar to each other than the cDNA libraries generated with UL-v1.
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Better representation of GC-rich genes
Comparing expression levels by gene GC content for two cDNA synthesis protocols. Libraries made from single K562 cells using either the SS-v4 protocol or the UL-v1 protocol were compared. Genes were binned by GC content (Low: 0–36% GC; Medium: 37–62% GC; High: 63–100% GC) and correlation plots were used to evaluate the two protocols. The RPKM values were very reproducible for pairs of cells using the SS-v4 chemistry (Panel A) or the UL-v1 chemistry (Panel B), with even distribution of genes across all three GC-content categories. Panel C. When the two protocols were compared, genes with high GC content (shown in red) showed higher expression with the SS-v4 protocol, while genes with medium or low GC content (shown in gray and blue, respectively) showed an even distribution. A simple way to understand the reproducibility of the new method is to look at the Pearson correlation of sequencing data from replicates of libraries prepared with the UL-v1 and SS-v4 protocols. Transcriptomic data generated by SS-v4 were more consistent between replicates than the data generated by UL-v1. The SS-v4 protocol reduced technical variability, which may increase the opportunity to detect biologically relevant differences between samples.
Reduce technical variability
Reduced variability among cDNA libraries synthesized using the SS-v4 protocol. Sequencing data were generated with the SS-v4 method (11 libraries x 2 replicate experiments) and the UL-v1 method (11 libraries), as described above. The FASTQ files were aligned against the human reference genome (Gencode release GRCh38) using STAR v. 2.5 (Dobin et al. 2013) with default options. Gene counts generated by STAR were subjected to regularized log transformation (DESeq2; v.1.10.1; Love et al. 2014). These transformed gene expression values were then used in the gene expression analyses. The analysis results were visualized using R. Panel A. The Pearson correlation coefficients were higher and tighter in the cDNA libraries generated with SS-v4. Pane B. The hierarchical clustering heat map demonstrates that the Euclidean distances between the SS-v4 cDNA libraries were smaller than the distances between the UL-v1 cDNA libraries. This indicates that the cDNA libraries generated with SS-v4 were more similar to each other than the cDNA libraries generated with UL-v1.
