Accession PRJCA029459
Title NEAT-seq: A NSR Primed and Transposase Tagmentation Mediated Strand-specific Total RNA Sequencing in Single Cell
Relevance Model organism
Data types Transcriptome or Gene expression
Organisms Mus musculus
Homo sapiens
Description Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular diversity with unprecedented resolution. However, many current methods are limited in capturing full-length transcripts and discerning strand orientation. We present NEAT-seq, an innovative strand-specific total RNA sequencing technique that combines not-so-random (NSR) primers with Tn5 transposase-mediated tagmentation. NEAT-seq overcomes previous limitations by delivering comprehensive transcript coverage and maintaining strand orientation, which is essential for accurate quantification of overlapping genes and detection of antisense transcripts. Through optimized reverse transcription with NSR primers, rRNA depletion via DASH (Depletion of Abundant Sequences by Hybridization), and linear amplification, NEAT-seq enhances sensitivity and reproducibility, especially for low-input samples and single cells. Application to mouse oocytes and early embryos highlights NEAT-seq's superior performance in identifying stage-specific antisense transcripts, shedding light on their regulatory roles during early development. This advancement represents a significant leap in transcriptome analysis within complex biological contexts.
Sample scope Monoisolate
Release date 2024-10-04
Grants
Agency program Grant ID Grant title
Chinese Academy of Sciences (CAS) Strategic Priority Research Program of Chinese Academy of Sciences (CAS) GJTD-2020-06 Molecular Mechanisms of Heterochromatin Establishment and Maintenance
Submitter Yang Yu (yulabngs@163.com)
Organization Institute of Biophysics, Chinese Academy of Sciences
Submission date 2024-08-25

Project Data

Resource name Description
BioSample (140)  show -
GSA (1) -
CRA018566 NEAT-seq: A NSR Primed and Transposase Tagmentation Mediated Strand-specific Total RNA Sequencing in Single Cell