Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie cytometry data.
Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie cytometry data.
复制标题
使用细菌单细胞电影细胞术数据表征单细胞随机性的分析和可视化工具。
DOI：
10.1186/s12859-021-04409-9
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发表时间：
2021-10-29
影响因子：
3
通讯作者：
Manolakos ES
中科院分区：
文献类型：
--
作者：
Balomenos AD;Stefanou V;Manolakos ES
延时显微镜活细胞成像是必不可少的研究细菌群落在单细胞分辨率的演变。它可以在成像实验的每个时间实例中捕获有关单个细胞的形态学，基因表达和空间特征的详细信息。细菌“单细胞电影”（视频）的图像分析以测量细菌属性的多维时间序列的形式产生大数据。如果分析得当，这些数据集可以帮助我们破译细菌群落的生长动态，并确定亚群内和亚群间异质性的来源和潜在功能作用。最近的研究强调了研究生物“噪音”在基因调控、细胞生长、细胞分裂等方面的作用的重要性。对复杂的单细胞电影数据集进行单细胞分析，捕捉多个微菌落与数千个细胞的相互作用，可以揭示人类健康的基本现象，例如病原体和良性微生物组细胞的竞争、休眠细胞（“持续者”）的出现、不同应激条件下生物膜的形成等。然而，高度准确和自动化的细菌生物图像分析和单细胞分析方法仍然难以捉摸，即使在我们能够常规地利用单细胞电影产生的大量数据之前，它们也是必需的。我们提出了可视化和单细胞分析使用R (ViSCAR)，一套方法和相应的功能，可视化地探索和关联从复杂的细菌单细胞电影的图像处理产生的单细胞属性。它们可以用于模拟和可视化微生物群落组织不同层次（如细胞群体、菌落、世代等）属性的时空演化，发现细胞代际间可能的表观遗传信息传递，推断描述各种随机现象（如细胞生长、细胞分裂）的数学和统计模型；甚至识别和自动纠正在显微镜视野中有数千个过度拥挤的细胞的密集电影的生物图像分析中不可避免地引入的错误。ViSCAR使研究人员能够捕获和表征随机性，揭示导致感兴趣的细胞表型的机制，并破译大型异质微生物群落的动态行为。ViSCAR的源代码可从GitLab获取，网址为https://gitlab.com/ManolakosLab/viscar。在线版本包含补充材料，可在10.1186/s12859-021-04409-9获得。
Time-lapse microscopy live-cell imaging is essential for studying the evolution of bacterial communities at single-cell resolution. It allows capturing detailed information about the morphology, gene expression, and spatial characteristics of individual cells at every time instance of the imaging experiment. The image analysis of bacterial "single-cell movies" (videos) generates big data in the form of multidimensional time series of measured bacterial attributes. If properly analyzed, these datasets can help us decipher the bacterial communities' growth dynamics and identify the sources and potential functional role of intra- and inter-subpopulation heterogeneity. Recent research has highlighted the importance of investigating the role of biological "noise" in gene regulation, cell growth, cell division, etc. Single-cell analytics of complex single-cell movie datasets, capturing the interaction of multiple micro-colonies with thousands of cells, can shed light on essential phenomena for human health, such as the competition of pathogens and benign microbiome cells, the emergence of dormant cells (“persisters”), the formation of biofilms under different stress conditions, etc. However, highly accurate and automated bacterial bioimage analysis and single-cell analytics methods remain elusive, even though they are required before we can routinely exploit the plethora of data that single-cell movies generate. We present visualization and single-cell analytics using R (ViSCAR), a set of methods and corresponding functions, to visually explore and correlate single-cell attributes generated from the image processing of complex bacterial single-cell movies. They can be used to model and visualize the spatiotemporal evolution of attributes at different levels of the microbial community organization (i.e., cell population, colony, generation, etc.), to discover possible epigenetic information transfer across cell generations, infer mathematical and statistical models describing various stochastic phenomena (e.g., cell growth, cell division), and even identify and auto-correct errors introduced unavoidably during the bioimage analysis of a dense movie with thousands of overcrowded cells in the microscope's field of view. ViSCAR empowers researchers to capture and characterize the stochasticity, uncover the mechanisms leading to cellular phenotypes of interest, and decipher a large heterogeneous microbial communities' dynamic behavior. ViSCAR source code is available from GitLab at https://gitlab.com/ManolakosLab/viscar. The online version contains supplementary material available at 10.1186/s12859-021-04409-9.
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影响因子：
9.2
作者：
Carvalho G;Balestrino D;Forestier C;Mathias JD
通讯作者：
Mathias JD
影响因子：
3.6
作者：
Cass, Julie A.;Stylianidou, Stella;Wiggins, Paul A.
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影响因子：
5.6
作者：
Chiu SW;Leake MC
通讯作者：
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影响因子：
5.4
作者：
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通讯作者：
Booth, IR
影响因子：
5.3
作者：
Aspridou, Zafeiro;Balomenos, Athanasios;Koutsoumanis, Konstantinos
通讯作者：
Koutsoumanis, Konstantinos