Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues.

IF: 4.547

Cited by: 1

Abstract

Single-cell RNA sequencing (scRNA-seq) provides new insights to address biological and medical questions, and it will benefit more from the ultralow input RNA or subcellular sequencing. Here, we present a highly sensitive library construction protocol for ultralow input RNA sequencing (ulRNA-seq). We systematically evaluate experimental conditions of this protocol, such as reverse transcriptase, template-switching oligos (TSO), and template RNA structure. It was found that Maxima H Minus reverse transcriptase and rN modified TSO, as well as all RNA templates capped with m7G improved the sequencing sensitivity and low abundance gene detection ability. RNA-seq libraries were successfully prepared from total RNA samples as low as 0.5 pg, and more than 2000 genes have been identified. The ability of low abundance gene detection and sensitivity were largely enhanced with this optimized protocol. It was also confirmed in single-cell sequencing, that more genes and cell markers were identified compared to conventional sequencing method. We expect that ulRNA-seq will sequence and transcriptome characterization for the subcellular of disease tissue, to find the corresponding treatment plan.

Keywords

DBiT-seq

Gene Expression

Spatial Transcriptomics

Low abundance gene detection

Sensitivity

Subcellular

Template-switching oligos terminal modification

scRNA-seq

MeSH terms

Animals

Brain

Gene Expression Profiling

Gene Library

High-Throughput Nucleotide Sequencing

Mice

RNA-Seq

Sequence Analysis, RNA

Single-Cell Analysis

Transcriptome

Authors

Jia, Erteng

Shi, Huajuan

Wang, Ying

Zhou, Ying

Liu, Zhiyu

Pan, Min

Bai, Yunfei

Zhao, Xiangwei

Ge, Qinyu

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