Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., Jülich, 52425, Germany.
Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, 52074, Germany.
BMC Bioinformatics. 2019 Sep 4;20(1):452. doi: 10.1186/s12859-019-3004-1.
Streptomycetes are filamentous microorganisms of high biotechnological relevance, especially for the production of antibiotics. In submerged cultures, the productivity of these microorganisms is closely linked to their growth morphology. Microfluidic lab-on-a-chip cultivation systems, coupled with automated time-lapse imaging, generate spatio-temporal insights into the mycelium development of streptomycetes, therewith extending the biotechnological toolset by spatio-temporal screening under well-controlled and reproducible conditions. However, the analysis of the complex mycelial structure formation is limited by the extent of manual interventions required during processing of the acquired high-volume image data. These interventions typically lead to high evaluation times and, therewith, limit the analytic throughput and exploitation of microfluidic-based screenings.
We present the tool mycelyso (MYCElium anaLYsis SOftware), an image analysis system tailored to fully automated hyphae-level processing of image stacks generated by time-lapse microscopy. With mycelyso, the developing hyphal streptomycete network is automatically segmented and tracked over the cultivation period. Versatile key growth parameters such as mycelium network structure, its development over time, and tip growth rates are extracted. Results are presented in the web-based exploration tool mycelyso Inspector, allowing for user friendly quality control and downstream evaluation of the extracted information. In addition, 2D and 3D visualizations show temporal tracking for detailed inspection of morphological growth behaviors. For ease of getting started with mycelyso, bundled Windows packages as well as Docker images along with tutorial videos are available.
mycelyso is a well-documented, platform-independent open source toolkit for the automated end-to-end analysis of Streptomyces image stacks. The batch-analysis mode facilitates the rapid and reproducible processing of large microfluidic screenings, and easy extraction of morphological parameters. The objective evaluation of image stacks is possible by reproducible evaluation workflows, useful to unravel correlations between morphological, molecular and process parameters at the hyphae- and mycelium-levels with statistical power.
链霉菌是具有高度生物技术相关性的丝状微生物，特别是在抗生素生产方面。在液体培养中，这些微生物的生产力与其生长形态密切相关。微流控芯片实验室培养系统与自动定时成像相结合，为研究链霉菌的菌丝体发育提供了时空见解，从而通过在可控和可重复的条件下进行时空筛选，扩展了生物技术工具集。然而，复杂的菌丝体结构形成的分析受到在处理获得的大容量图像数据时所需的人工干预程度的限制。这些干预通常会导致高评估时间，从而限制了基于微流控的筛选的分析吞吐量和利用。
我们介绍了工具 mycelyso（菌丝体分析软件），这是一个专门为通过定时显微镜生成的图像堆栈的全自动菌丝级处理量身定制的图像分析系统。使用 mycelyso，可以自动分割和跟踪培养过程中的发育菌丝体网络。提取了多种关键生长参数，如菌丝体网络结构、随时间的发展以及尖端生长速率。结果在基于网络的探索工具 mycelyso Inspector 中呈现，允许用户友好地进行质量控制和对提取信息的下游评估。此外，2D 和 3D 可视化显示了时间跟踪，用于详细检查形态生长行为。为了方便使用 mycelyso，我们提供了捆绑的 Windows 软件包以及 Docker 镜像，以及教程视频。
mycelyso 是一个文档齐全、与平台无关的开源工具包，用于自动化端到端分析链霉菌图像堆栈。批量分析模式便于快速、可重复地处理大型微流控筛选，并轻松提取形态参数。通过可重复的评估工作流程，可以对图像堆栈进行客观评估，这对于在菌丝体和菌丝体水平上用统计能力揭示形态、分子和过程参数之间的相关性非常有用。
