WeChat Mini Program
Old Version Features

Development of Proximity-Activated Programmable Multicomponent Nucleic Acid Enzymes for Simultaneous Visualization of Multiple Mrna Splicing Variants in Living Cells.

Wen-Jing Liu, Yun Han,Rui Song,Fei Ma,Chun-Yang Zhang

Analytical chemistry(2025)

Cited 0|Views1
Abstract
RNA splicing is a key regulatory process of gene expression that can increase the transcriptome complexity. Simultaneous monitoring of multiple splicing variants in living cells is critical for gaining new insight into cell development. Herein, we demonstrate the development of proximity-activated, programmable multicomponent nucleic acid enzymes (MNAzymes) for the simultaneous visualization of multiple RNA splicing variants (i.e., BRCA1 WT and BRCA1 Δ11q) in living cells. The presence of BRCA1 WT and BRCA1 Δ11q can specifically bring their corresponding partzymes into the proximity of each other to form two active MNAzyme motifs. Subsequently, the active sites of reporter probes 1 and 2 are cyclically cleaved by two activated MNAzyme motifs, respectively, to release abundant Cy3 and Cy5 fluorescent molecules, generating enhanced fluorescence signals for the simultaneous detection of BRCA1 WT and BRCA1 Δ11q in vitro and in vivo. Notably, this assay can be simply and isothermally conducted in a one-step format without the necessity for unstable protein enzymes, precise temperature control, and complex operation procedures. This method can sensitively detect 2.46 fM BRCA1 WT and 2.77 fM BRCA1 Δ11q and accurately distinguish breast cancer patients from healthy individuals by measuring target BRCA1 splicing variants from the tissue samples. Moreover, this method can real-time image BRCA1 splicing variants in living cells and can be extended to detect other cellular target RNAs (e.g., miRNAs, piRNAs, lncRNAs, and circRNAs) by simply changing the sequences of substrate arms, holding promising applications in clinical diagnosis and precise therapy.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined