Total RNA was extracted from cells using RNeasy Micro Kit (QIAGEN 74004). For mRNA sequencing cDNA synthesis was performed using SMART-Seq Ultra Low Input RNA Kit for Sequencing (Takara 634889) from approximately 500 pg of total RNA. cDNA was validated using the High Sensitivity NGS Fragment Analysis Kit (Agilent formerly AATI DNF-474-0500) on a 12-Capillary Fragment Analyzer. Quantification was determined using the Quant-iT dsDNA Assay Kit, high sensitivity (Thermo Fisher Q33120), and 100 pg of cDNA was tagmented and ligated using the Nextera XT DNA Library Kit (Illumina FC-131-1024) at volumes to produce sequencing libraries. The resulting libraries were validated using the High Sensitivity NGS Fragment Analysis Kit on a 12-Capillary Fragment Analyzer and quantified using the Quant-iT dsDNA Assay Kit, high sensitivity. Equal concentrations of libraries were pooled, denatured, diluted, and subjected to paired-end sequencing using the Mid Output v2.5 kit (Illumina FC-404-2001) on a NextSeq550 following the manufacturer’s instructions.
Smart seq ultra low input rna kit for sequencing
Manufactured by Takara Bio
Sourced in United Kingdom
The SMART-Seq Ultra Low Input RNA Kit for Sequencing is a tool designed for the preparation of full-length cDNA from small amounts of total RNA. The kit utilizes SMART (Switching Mechanism at 5' end of RNA Template) technology to generate high-quality cDNA libraries suitable for next-generation sequencing.
Automatically generated - may contain errors
Lab products found in correlation
Rneasy micro kit, by Qiagen (3 mentions)
Nextera xt dna library preparation kit, by Illumina (3 mentions)
Nextseq 500, by Illumina (2 mentions)
Ingenuity pathway analysis (ipa), by Qiagen (2 mentions)
Quant it dsdna assay kit, by Thermo Fisher Scientific (1 mentions)
Nextera xt dna library kit, by Illumina (1 mentions)
High sensitivity ngs fragment analysis kit, by Agilent Technologies (1 mentions)
Mid output v2.5 kit, by Illumina (1 mentions)
Facsaria 2, by BD (1 mentions)
Nextseq 2000, by Illumina (1 mentions)
Basespace, by Illumina (1 mentions)
4 protocols using smart seq ultra low input rna kit for sequencing
1
Single-cell RNA-seq Library Preparation
2
RNA-seq Analysis of Sorted Cells
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Total RNA was extracted from sorted cells using an RNeasy Micro kit (Qiagen, Manchester, U.K.), and cDNA was amplified using a SMART-Seq ultra-low input RNA kit for sequencing (Takara Bio Europe/Clontech, Saint-Germain-en-Laye, France). Amplified cDNA (200 pg) was used as input for library preparation using a Nextera XT DNA library preparation kit (Illumina, Essex, U.K.) with 12 cycles of PCR. Samples were sequenced on a NextSeq 500 (Illumina). Alignments were performed using TopHat, and regularized log-transformed normalization was performed using DESeq2 (BaseSpace; Illumina). Expression of each gene was compared using a paired t test. Further analysis was undertaken by Ingenuity Pathway Analysis (Qiagen) with a fold change cutoff of 2 and a significance cutoff of 0.01.
3
Bulk RNA-seq Analysis of Cell Populations
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300 cells per population were sorted (FACS Aria II, Becton Dickinson) into lysis buffer, and cDNA was prepared using the SMART-Seq Ultra Low Input RNA Kit for Sequencing (Takara Bio). RNA-seq libraries were constructed using the NexteraXT DNA Library Preparation Kit (Illumina) with half the recommended volumes and reagents. Paired-end sequencing of pooled libraries was run on a NextSeq 2000 (Illumina) with 59-base reads and a target depth of 5 million reads per sample. After the run, base-calling and demultiplexing were performed automatically on BaseSpace (Illumina) to generate FASTQ files. The FASTQs were aligned to the University of California Santa Cruz (UCSC) Human Genome assembly version 19, using STAR v.2.4.2a, and gene counts were generated using htseq-count. QC and metrics analysis was performed using the Picard family of tools (v1.134). To detect differentially expressed genes between cell populations, the RNA-seq analysis functionality of the linear models for microarray data (Limma) R package was used, and the ROAST method within the Limma R package was used to perform gene set enrichment analysis (89 (link), 90 (link)). Expression counts were normalized using the TMM algorithm (91 (link)). A false discovery rate adjustment was applied to correct for multiple testing.
4
RNA-seq of Sorted Cells
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