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耦合深度测序的污染控制水平(CoLoC-seq)探究细胞器转录组的全局定位拓扑结构。

Controlled Level of Contamination Coupled to Deep Sequencing (CoLoC-seq) Probes the Global Localisation Topology of Organelle Transcriptomes.

作者信息

Smirnova Anna, Jeandard Damien, Smirnov Alexandre

机构信息

UMR7156-Génétique Moléculaire, Génomique, Microbiologie (GMGM), University of Strasbourg, CNRS, Strasbourg, France.

University of Strasbourg Institute for Advanced Study (USIAS), Strasbourg, France.

出版信息

Bio Protoc. 2023 Sep 20;13(18):e4820. doi: 10.21769/BioProtoc.4820.

Abstract

Information on RNA localisation is essential for understanding physiological and pathological processes, such as gene expression, cell reprogramming, host-pathogen interactions, and signalling pathways involving RNA transactions at the level of membrane-less or membrane-bounded organelles and extracellular vesicles. In many cases, it is important to assess the topology of RNA localisation, i.e., to distinguish the transcripts encapsulated within an organelle of interest from those merely attached to its surface. This allows establishing which RNAs can, in principle, engage in local molecular interactions and which are prevented from interacting by membranes or other physical barriers. The most widely used techniques interrogating RNA localisation topology are based on the treatment of isolated organelles with RNases with subsequent identification of the surviving transcripts by northern blotting, qRT-PCR, or RNA-seq. However, this approach produces incoherent results and many false positives. Here, we describe Controlled Level of Contamination coupled to deep sequencing (CoLoC-seq), a more refined subcellular transcriptomics approach that overcomes these pitfalls. CoLoC-seq starts by the purification of organelles of interest. They are then either left intact or lysed and subjected to a gradient of RNase concentrations to produce unique RNA degradation dynamics profiles, which can be monitored by northern blotting or RNA-seq. Through straightforward mathematical modelling, CoLoC-seq distinguishes true membrane-enveloped transcripts from degradable and non-degradable contaminants of any abundance. The method has been implemented in the mitochondria of HEK293 cells, where it outperformed alternative subcellular transcriptomics approaches. It is applicable to other membrane-bounded organelles, e.g., plastids, single-membrane organelles of the vesicular system, extracellular vesicles, or viral particles. Key features • Tested on human mitochondria; potentially applicable to cell cultures, non-model organisms, extracellular vesicles, enveloped viruses, tissues; does not require genetic manipulations or highly pure organelles. • In the case of human cells, the required amount of starting material is ~2,500 cm of 80% confluent cells (or ~3 × 10 HEK293 cells). • CoLoC-seq implements a special RNA-seq strategy to selectively capture intact transcripts, which requires RNases generating 5'-hydroxyl and 2'/3'-phosphate termini (e.g., RNase A, RNase I). • Relies on nonlinear regression software with customisable exponential functions.

摘要

RNA定位信息对于理解生理和病理过程至关重要,例如基因表达、细胞重编程、宿主-病原体相互作用以及涉及无膜或有膜细胞器和细胞外囊泡水平上RNA交易的信号通路。在许多情况下,评估RNA定位的拓扑结构很重要,即区分包裹在感兴趣细胞器内的转录本与仅附着在其表面的转录本。这有助于确定哪些RNA原则上可以参与局部分子相互作用,以及哪些RNA被膜或其他物理屏障阻止相互作用。最广泛使用的询问RNA定位拓扑结构的技术基于用RNase处理分离的细胞器,随后通过Northern印迹、qRT-PCR或RNA测序鉴定存活的转录本。然而,这种方法产生不一致的结果和许多假阳性。在这里,我们描述了与深度测序相结合的污染控制水平(CoLoC-seq),这是一种更精细的亚细胞转录组学方法,克服了这些缺陷。CoLoC-seq首先纯化感兴趣的细胞器。然后将它们保持完整或裂解,并置于RNase浓度梯度中,以产生独特的RNA降解动力学图谱,这可以通过Northern印迹或RNA测序进行监测。通过直接的数学建模,CoLoC-seq可以区分真正被膜包裹的转录本与任何丰度的可降解和不可降解污染物。该方法已在HEK293细胞的线粒体中实施,在那里它优于其他亚细胞转录组学方法。它适用于其他有膜细胞器,例如质体、囊泡系统的单膜细胞器、细胞外囊泡或病毒颗粒。关键特性 • 在人线粒体上进行了测试;可能适用于细胞培养物、非模式生物、细胞外囊泡、包膜病毒、组织;不需要基因操作或高度纯化的细胞器。 • 对于人类细胞,所需的起始材料量约为2500 cm的80%汇合细胞(或约3×10个HEK293细胞)。 • CoLoC-seq实施了一种特殊的RNA测序策略,以选择性捕获完整的转录本,这需要产生5'-羟基和2'/3'-磷酸末端的RNase(例如RNase A、RNase I)。 • 依赖于具有可定制指数函数的非线性回归软件。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd71/10518782/b1da635bf9d2/BioProtoc-13-18-4820-g001.jpg

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