Single-nucleus Multiomic Mapping of M6a Methylomes and Transcriptomes in Native Populations of Cells with Sn-M6a-ct.
MOLECULAR CELL（2023）
Cell Fate Engineering and Therapeutics Lab
Abstract
N6-methyladenosine (m6A) RNA modification plays important roles in the governance of gene expression and is temporally regulated in different cell states. In contrast to global m6A profiling in bulk sequencing, single-cell technologies for analyzing m6A heterogeneity are not extensively established. Here, we developed single-nucleus m6A-CUT&Tag (sn-m6A-CT) for simultaneous profiling of m6A methylomes and transcriptomes within a single nucleus using mouse embryonic stem cells (mESCs). m6A-CT is capable of enriching m6A-marked RNA molecules in situ, without isolating RNAs from cells. We adapted m6A-CT to the droplet-based single-cell omics platform and demonstrated high-throughput performance in analyzing nuclei isolated from thousands of cells from various cell types. We show that sn-m6A-CT profiling is sufficient to determine cell identity and allows the generation of cell-type-specific m6A methylome landscapes from heterogeneous populations. These indicate that sn-m6A-CT provides additional dimensions to multimodal datasets and insights into epitranscriptomic landscape in defining cell fate identity and states.
MoreTranslated text
Key words
m6A,RNA modification,single nucleus,CUT&Tag,multimodal,epitranscriptomics,in situ,droplet-based,embryonic stem cell
求助PDF
上传PDF
View via Publisher
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