Back

Modulating Cas13a trans-cleavage by double strand RNA: Application to the development of an autocatalytic sensor

Deng, F.; Gulati, S.; Sang, R.; Li, Y.; Goldys, E.

2024-03-05 molecular biology
10.1101/2024.03.05.583472 bioRxiv
Show abstract

Cas13a-based diagnostic systems have been widely utilized for the detection of RNA targets. However, without preamplification such systems have sensitivity in the picomolar range only. Here, we found that double strand RNA (dsRNA) over 20nt is able to effectively activate the trans-cleavage activity of Cas13a RNP, while the cleavage rates of dsRNA by activated Cas13a RNP are very low. In addition, specially designed small circular RNA constructs (Cir-mediators comprising a 20nt dsRNA trigger with a 5nt ssRNA linker) have limited ability to activate Cas13a RNP, but this activation is restored once the circular structures are cleaved and become linear. Based on this new method to control trans-cleavage activity of Cas13 RNP, we developed a Cas13a autocatalytic biosensing system assisted by Cir-mediators, which allows one target RNA to activate numerous Cas13a RNPs. With this approach we show ultrasensitive detection of 1aM of synthetic RNA targets without preamplification within 15min. The sensor was successfully applied to monitor miRNA-21 concentration in clinical plasma samples in colorectal cancer. This investigation yields novel insights into the properties of Cas13a RNPs, and the Cir-mediator-based autosensor introduces a novel method for detecting RNA targets with exceptional sensitivity. HighlightsO_LIdsRNA is able to trigger activation of Cas13a RNP. C_LIO_LIActivated Cas13a RNP do not cleave dsRNA. C_LIO_LICir-mediator induces low levels of Cas13a RNP activation. C_LIO_LICir-mediator based Cas13a auto-catalysis biosensor can detect 1aM RNA targets. C_LI

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.