Dataset Information

Targeted RNA sequencing enhances gene expression profiling of ultra-low input samples

ABSTRACT: RNA-seq is the standard method for profiling gene expression in many biological systems. Due to the wide dynamic range and complex nature of the transcriptome, RNA-seq provides an incomplete characterisation, especially of lowly expressed genes and transcripts. Targeted RNA sequencing (RNA CaptureSeq) focuses sequencing on genes of interest, providing exquisite sensitivity for transcript detection and quantification. However, uses of CaptureSeq have focused on bulk samples and its performance on very small populations of cells is unknown. Here we show CaptureSeq greatly enhances transcriptomic profiling of target genes in ultra-low-input samples and provides equivalent performance to that on bulk samples. We validate the performance of CaptureSeq using multiple probe sets on samples of iPSC-derived cortical neurons. We demonstrate up to 275-fold enrichment for target genes, the detection of 10% additional genes and a greater than 5-fold increase in identified gene isoforms. Analysis of spike-in controls demonstrated CaptureSeq improved both detection sensitivity and expression quantification. Comparison to the CORTECON database of cerebral cortex development revealed CaptureSeq enhanced the identification of sample differentiation stage. CaptureSeq provides sensitive, reliable and quantitative expression measurements on hundreds-to-thousands of target genes from ultra-low-input samples and has the potential to greatly enhance transcriptomic profiling when samples are limiting.

ORGANISM(S): Homo sapiens

PROVIDER: GSE140442 | GEO | 2019/11/16

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Purpose: The goals of this study is to determine the best method of gene expression quantification (RNA-seq, Microarray, NanoString) and amplification kits adapted to low-input and/or low-quality RNA samples (FFPE samples) Methods: Mouse bladder cancer cell line (mouse bladder cancer cell line, BC57) and mouse normal mouse normal urothelium were fixed in formalin and embedded in paraffin (FFPE), andfesh frozen (FF) in liquid nitrogen. The total RNA of these 4 samples were tested by 3 technologies (NanoString, RNA-seq and Microarray) and the results were compared to its reference (high-quality and high-input RNA of mouse bladder cancer cell line and mouse normal mouse normal urothelium). For NanoString with low-input RNA samples, each sample was tested by NanoString quantification after amplification by SMARTer Stranded Total RNA-Seq Kit - Pico Input mammelian and Ovation SoLo NuGEN RNA-seq System, and NanoString based on PCR approach with three input quantities: 50pg, 250pg and 2ng of total RNA, except for NanoString quantification after amplification by SMARTer Stranded Total RNA-Seq Kit - Pico Input mammelian kit for which the minimum recommended quantity was 250pg of total RNA. NanoString direct quantification was also done for FF and FFPE samples at high amount (50ng of total RNA) and results obtained from FF samples were considered as the reference. To determine which is the method for NanoString technology, low-input and low-quality RNA samples, we performed NanoString control quality metrics, principal component analysis, and a differential analysis between the mouse bladder cancer cell lines and the mouse normal mouse normal urothelium for each input quantity, amplification method and method of sample preservation (FF or FFPE). Results: The NanoString based PCR based approach is recommended for quantification of gene expression of FFPE and FF samples from 250pg of total RNA. However, NanoString quantification after amplification by SMARTer Stranded Total RNA-Seq Kit - Pico Input mammelian and Ovation SoLo NuGEN RNA-seq System is not recommended for FF and FFPE from low-input samples.

Dataset Information

Targeted RNA sequencing enhances gene expression profiling of ultra-low input samples

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets