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探索单细胞RNA测序:方案、工具、数据库及应用

Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.

作者信息

Arya Ankish, Tripathi Prabhat, Dubey Nidhi, Aier Imlimaong, Kumar Varadwaj Pritish

机构信息

Department of Applied Sciences, Indian Institute of Information Technology Allahabad, Jhalwa, Prayagraj, 211015, Uttar Pradesh, India.

出版信息

Genomics Inform. 2025 May 17;23(1):13. doi: 10.1186/s44342-025-00044-5.

DOI:10.1186/s44342-025-00044-5
PMID:40382658
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12085826/
Abstract

Single-cell RNA-sequencing (scRNA-seq) technology brought about a revolutionary change in the transcriptomic world, paving the way for comprehensive analysis of cellular heterogeneity in complex biological systems. It enabled researchers to see how different cells behaved at single-cell levels, providing new insights into the process. However, despite all these advancements, scRNA-seq also experiences challenges related to the complexity of data analysis, interpretation, and multi-omics data integration. In this review, these complications were discussed in detail, directly pointing at the optimization of scRNA-seq approaches and understanding the world of single-cell and its dynamics. Different protocols and currently functional single-cell databases were also covered. This review highlights different tools for the analysis of scRNA-seq and their methodologies, emphasizing innovative techniques that enhance resolution and accuracy at a single-cell level. Various applications were explored across domains including drug discovery, tumor microenvironment (TME), biomarker discovery, and microbial profiling, and case studies were discussed to explain the importance of scRNA-seq by uncovering novel and rare cell types and their identification. This review underlines a crucial aspect of scRNA-seq in the advancement of personalized medicine and highlights its potential to understand the complexity of biological systems.

摘要

单细胞RNA测序(scRNA-seq)技术给转录组学领域带来了革命性的变化,为复杂生物系统中细胞异质性的全面分析铺平了道路。它使研究人员能够在单细胞水平上观察不同细胞的行为,为这一过程提供了新的见解。然而,尽管有这些进展,scRNA-seq在数据分析、解释和多组学数据整合的复杂性方面也面临挑战。在这篇综述中,详细讨论了这些复杂问题,直接指向scRNA-seq方法的优化以及对单细胞世界及其动态的理解。还涵盖了不同的方案和当前可用的单细胞数据库。这篇综述重点介绍了用于scRNA-seq分析的不同工具及其方法,强调了在单细胞水平上提高分辨率和准确性的创新技术。探讨了scRNA-seq在药物发现、肿瘤微环境(TME)、生物标志物发现和微生物分析等领域的各种应用,并通过揭示新的和罕见的细胞类型及其鉴定来讨论案例研究,以解释scRNA-seq的重要性。这篇综述强调了scRNA-seq在个性化医学发展中的一个关键方面,并突出了其理解生物系统复杂性的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2a3/12085826/46d9b9624ec2/44342_2025_44_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2a3/12085826/46d9b9624ec2/44342_2025_44_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2a3/12085826/46d9b9624ec2/44342_2025_44_Fig1_HTML.jpg

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本文引用的文献

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An improved bacterial single-cell RNA-seq reveals biofilm heterogeneity.一种改进的细菌单细胞RNA测序揭示了生物膜的异质性。
Elife. 2024 Dec 17;13:RP97543. doi: 10.7554/eLife.97543.
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Single-cell RNA-sequencing identifies unique cell-specific gene expression profiles in high-grade cardiac allograft vasculopathy.单细胞RNA测序可识别高级别心脏移植血管病变中独特的细胞特异性基因表达谱。
J Heart Lung Transplant. 2024 Nov 22. doi: 10.1016/j.healun.2024.11.017.
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Single-Cell RNA Sequencing Reveals Cardiac Fibroblast-Specific Transcriptomic Changes in Dilated Cardiomyopathy.
单细胞 RNA 测序揭示扩张型心肌病中心房成纤维细胞特异性转录组变化。
Cells. 2024 Apr 26;13(9):752. doi: 10.3390/cells13090752.
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Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison.基于拆分池结扎的单细胞转录组测序(SPLiT-seq)数据处理流程比较。
BMC Genomics. 2024 Apr 12;25(1):361. doi: 10.1186/s12864-024-10285-3.
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Next-Generation Sequencing (NGS): Platforms and Applications.下一代测序(NGS):平台与应用
J Pharm Bioallied Sci. 2024 Feb;16(Suppl 1):S41-S45. doi: 10.4103/jpbs.jpbs_838_23. Epub 2024 Feb 29.
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Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender.基于 CellBender 的无监督去除液滴式单细胞实验系统背景噪声。
Nat Methods. 2023 Sep;20(9):1323-1335. doi: 10.1038/s41592-023-01943-7. Epub 2023 Aug 7.
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GRACE: a comprehensive web-based platform for integrative single-cell transcriptome analysis.GRACE:一个用于综合单细胞转录组分析的基于网络的综合平台。
NAR Genom Bioinform. 2023 Jun 9;5(2):lqad050. doi: 10.1093/nargab/lqad050. eCollection 2023 Jun.
8
SCAD-Brain: a public database of single cell RNA-seq data in human and mouse brains with Alzheimer's disease.SCAD-Brain:一个包含人类和患有阿尔茨海默病小鼠大脑单细胞RNA测序数据的公共数据库。
Front Aging Neurosci. 2023 May 12;15:1157792. doi: 10.3389/fnagi.2023.1157792. eCollection 2023.
9
Comparative analysis of cell-cell communication at single-cell resolution.单细胞分辨率下的细胞间通讯比较分析。
Nat Biotechnol. 2024 Mar;42(3):470-483. doi: 10.1038/s41587-023-01782-z. Epub 2023 May 11.
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Brief Bioinform. 2023 May 19;24(3). doi: 10.1093/bib/bbad157.