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细胞感染甲型流感病毒时生物分子凝聚物成像分析的挑战。

Challenges in Imaging Analyses of Biomolecular Condensates in Cells Infected with Influenza A Virus.

机构信息

Cell Biology of Viral Infection Lab (CBV), Instituto Gulbenkian de Ciência (IGC), Fundação Calouste Gulbenkian, R. Quinta Grande, 6, 2780-156 Oeiras, Portugal.

Cell Biology of Viral Infection Lab (CBV), Católica Biomedical Research Centre (CBR), Católica Medical School, Universidade Católica Portuguesa, Palma de Cima, 1649-023 Lisboa, Portugal.

出版信息

Int J Mol Sci. 2023 Oct 17;24(20):15253. doi: 10.3390/ijms242015253.

Abstract

Biomolecular condensates are crucial compartments within cells, relying on their material properties for function. They form and persist through weak, transient interactions, often undetectable by classical biochemical approaches. Hence, microscopy-based techniques have been the most reliable methods to detail the molecular mechanisms controlling their formation, material properties, and alterations, including dissolution or phase transitions due to cellular manipulation and disease, and to search for novel therapeutic strategies targeting biomolecular condensates. However, technical challenges in microscopy-based analysis persist. This paper discusses imaging, data acquisition, and analytical methodologies' advantages, challenges, and limitations in determining biophysical parameters explaining biomolecular condensate formation, dissolution, and phase transitions. In addition, we mention how machine learning is increasingly important for efficient image analysis, teaching programs what a condensate should resemble, aiding in the correlation and interpretation of information from diverse data sources. Influenza A virus forms liquid viral inclusions in the infected cell cytosol that serve as model biomolecular condensates for this study. Our previous work showcased the possibility of hardening these liquid inclusions, potentially leading to novel antiviral strategies. This was established using a framework involving live cell imaging to measure dynamics, internal rearrangement capacity, coalescence, and relaxation time. Additionally, we integrated thermodynamic characteristics by analysing fixed images through Z-projections. The aforementioned paper laid the foundation for this subsequent technical paper, which explores how different modalities in data acquisition and processing impact the robustness of results to detect phase transitions by measuring thermodynamic traits in fixed cells. Using solely this approach would greatly simplify screening pipelines. For this, we tested how single focal plane images, Z-projections, or volumetric analyses of images stained with antibodies or live tagged proteins altered the quantification of thermodynamic measurements. Customizing methodologies for different biomolecular condensates through advanced bioimaging significantly contributes to biological research and potential therapeutic advancements.

摘要

生物分子凝聚物是细胞内至关重要的隔室,其功能依赖于物质特性。它们通过弱的、短暂的相互作用形成并持续存在,这些相互作用通常无法通过经典的生化方法检测到。因此,基于显微镜的技术一直是详细描述控制其形成、物质特性和变化的分子机制的最可靠方法,包括由于细胞操作和疾病导致的凝聚物的溶解或相转变,以及寻找针对生物分子凝聚物的新型治疗策略。然而,基于显微镜的分析技术仍然存在技术挑战。本文讨论了成像、数据采集和分析方法在确定解释生物分子凝聚物形成、溶解和相转变的生物物理参数方面的优势、挑战和局限性。此外,我们还提到了机器学习在高效图像分析中的重要性,它可以为程序提供凝聚物应该具有的特征,帮助相关人员对来自不同数据源的信息进行关联和解释。甲型流感病毒在感染细胞的细胞质中形成液态病毒包含体,作为本研究的模型生物分子凝聚物。我们之前的工作展示了使这些液态包含体固化的可能性,这可能为新型抗病毒策略提供了思路。这是通过一个涉及活细胞成像以测量动力学、内部重排能力、聚结和弛豫时间的框架来实现的。此外,我们通过分析 Z 投影中的固定图像来整合热力学特性。之前的论文为后续的技术论文奠定了基础,该论文探讨了不同的数据获取和处理方式如何影响通过测量固定细胞中的热力学特征来检测相转变的结果的稳健性。仅使用这种方法将大大简化筛选管道。为此,我们测试了仅使用单焦平面图像、Z 投影或对用抗体或活标记蛋白染色的图像进行体积分析如何改变热力学测量的定量。通过高级生物成像为不同的生物分子凝聚物定制方法学,为生物研究和潜在的治疗进展做出了重要贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626e/10607852/31f1e52e501b/ijms-24-15253-g001.jpg

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