LAMP-MS for Locus-Specific Visual Quantification of DNA 5 Mc and RNA M6a Using Ultra-Low Input.

Runyu Xie, Xiaotong Yang,Weizhi He,Zhongguang Luo,Wenshuai Li, Chu Xu,Xiaolong Cui,Wei Zhang, Ning Wei, Xiaolan Wang,Yixiang Shi,Chuan He,Jie Liu,Lulu Hu

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION（2025）

Fudan University | Fudan Univ | Univ Chicago | Northwestern Univ | Bionova Shanghai Med Technol Co Ltd

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Abstract

Enhancing the effectiveness of utilizing circulating cell-free DNA (cfDNA) for disease screening remains a challenge, necessitating improved sensitivity, specificity, cost-efficiency, and patient adherence. We present here LAMP-MS, an innovative technology that integrates linear amplification with single-base quantitative nucleic acid mass spectrometry on silicon chips. This approach overcomes several limitations in utilizing cfDNA 5-methylcytosine (5mC) status for colorectal cancer (CRC) screening. LAMP-MS enables unbiased amplification of as little as 1ng of cfDNA, site-specifically quantify methylation levels of multiple 5mC sites, thereby facilitating cost-effective, high-resolution quantitative detection of cfDNA methylation markers. We have validated the accuracy of DNA methylation determination using DNA probes and cfDNA from patient plasma samples, confirmed by mass spectrometric peak areas. Additionally, we have further shown this Mass Array technology could be expanded to also quantify RNA m(6)A modification sites. Combining the ability to work with ultra-low input materials and a visually interpretable output, LAMP-MS stands out as a promising method for real-world applications in clinics and laboratories for nucleic acid methylation detection and quantification.

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Key words

DNA 5mC,RNA m(6)A,cell-free DNA (cfDNA),colorectal cancer (CRC) screening,LAMP-MS

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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

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