Suppr超能文献

用于多组学测量的单细胞技术。

Single-cell technologies for multimodal omics measurements.

作者信息

Bai Dongsheng, Zhu Chenxu

机构信息

New York Genome Center, New York, NY, United States.

Deparatment of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, United States.

出版信息

Front Syst Biol. 2023 Apr 21;3:1155990. doi: 10.3389/fsysb.2023.1155990. eCollection 2023.

Abstract

The recent surge in single-cell genomics, including the development of a wide range of experimental and computational approaches, has provided insights into the complex molecular networks of cells during development and in human diseases at unprecedented resolution. Single-cell transcriptome analysis has enabled high-resolution investigation of cellular heterogeneity in a wide range of cell populations ranging from early embryos to complex tissues-while posing the risk of only capturing a partial picture of the cells' complex molecular networks. Single-cell multiomics technologies aim to bridge this gap by providing a more holistic view of the cell by simultaneously measuring multiple molecular types from the same cell and providing a more complete view of the interactions and combined functions of multiple regulatory layers at cell-type resolution. In this review, we briefly summarized the recent advances in multimodal single-cell technologies and discussed the challenges and opportunities of the field.

摘要

最近单细胞基因组学的迅速发展,包括一系列实验和计算方法的开发,以前所未有的分辨率揭示了细胞在发育过程中和人类疾病中的复杂分子网络。单细胞转录组分析能够对从早期胚胎到复杂组织的广泛细胞群体中的细胞异质性进行高分辨率研究,同时也存在只捕捉细胞复杂分子网络部分情况的风险。单细胞多组学技术旨在通过从同一细胞中同时测量多种分子类型,以细胞类型分辨率提供多个调控层的相互作用和联合功能的更完整视图,从而弥合这一差距。在本综述中,我们简要总结了多模态单细胞技术的最新进展,并讨论了该领域的挑战和机遇。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2480/12342015/084790b259e0/fsysb-03-1155990-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验