Issue 22, 2023

Specific recognition and sensitive quantification of mRNA splice variants via one-pot ligation-dependent loop-mediated isothermal amplification

Abstract

Specific recognition and sensitive quantification of mRNA alternative splice variants have been a necessity for exploring the regulatory mechanism of RNA splicing and revealing the association between pre-mRNA splicing and transcriptome function, as well as disease diagnosis. However, their wide abundance range and high sequence homology pose enormous challenges for high sensitivity and selectivity quantification of splice variants. Herein, taking advantage of the excellent specificity of ligation and the powerful nucleic acid replication feature of loop-mediated isothermal amplification (LAMP), we developed a one-pot method (termed one-pot ligation-LAMP) for specific recognition and sensitive quantification of mRNA splicing variants based on two splicing junction-specific stem-loop DNA probe ligation and the subsequently initiating LAMP. The one-pot ligation-LAMP can specifically detect as low as 100 aM mRNA splice variants without any nonspecific signals and quantify them with a wide dynamics range spanning at least six orders of magnitude. We have demonstrated that the one-pot ligation-LAMP is a versatile and practical strategy for accurately quantifying different splicing variants in complex biological samples with high sensitivity all in one tube within 90 min, thereby providing an attractive tool for mRNA splice variant-related studies.

Graphical abstract: Specific recognition and sensitive quantification of mRNA splice variants via one-pot ligation-dependent loop-mediated isothermal amplification

Supplementary files

Article information

Article type
Paper
Submitted
12 aug 2023
Accepted
05 sep 2023
First published
03 okt 2023

Analyst, 2023,148, 5605-5611

Specific recognition and sensitive quantification of mRNA splice variants via one-pot ligation-dependent loop-mediated isothermal amplification

M. Zhang, H. Wang, J. Han, H. Wang, Y. Jia, W. Hong, F. Tang and Z. Li, Analyst, 2023, 148, 5605 DOI: 10.1039/D3AN01382K

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