Analysing and meta-analysing time-series data of microbial growth and gene expression from plate readers.
Analysing and meta-analysing time-series data of microbial growth and gene expression from plate readers.
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DOI：
10.1371/journal.pcbi.1010138
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发表时间：
2022-05
影响因子：
4.3
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中科院分区：
文献类型：
--
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对变化的反应是生命的一个基本属性，这使得时间序列数据在生物学中具有无价的价值。对于微生物来说，平板读数是使用荧光记者测量生长和基因表达的一种流行、方便的手段。然而，分析由此产生的数据的困难可能是一个瓶颈，特别是在结合来自不同井和板块的测量时。在这里，我们介绍了Omniplate，这是一个Python模块，它可以校正和归一化平板阅读器数据，估计每个细胞的生长速度和荧光作为时间的函数，计算误差，以不同的格式导出，并启用多个平板的荟萃分析。该软件对自发荧光、光密度对细胞数量的非线性依赖以及介质的影响进行了校正。我们用全盘法测定了发芽酵母在棉子糖中生长的Monod关系，表明棉子糖是一种方便的碳源来控制生长速度。利用荧光标记技术，我们研究了酵母的葡萄糖转运。我们的结果与己糖转运蛋白(HXT)基因的调节大致是二分的一致：中亲和力和高亲和力转运蛋白主要由高亲和力葡萄糖传感器Snf3和激酶复合体SNF1通过抑制物MTH1、Mig1和MIG2调节；低亲和力转运体主要由低亲和力传感器Rgt2通过辅助阻遏物Std1调节。因此，我们证明了全能数据是利用时间序列数据在揭示生物规律方面提供的优势的一个强有力的工具。通过荧光记者的生长和基因表达的时间序列是表征细胞行为的丰富方法。使用平板阅读器，可以在一次实验中直接测量96个独立的时间序列，每10分钟获取一次读数，每个时间序列持续数十个小时。分析这样的数据可能会变得具有挑战性，特别是如果需要多个平板阅读器实验来描述一个现象，然后应该同时进行分析。利用Python中的现有包，我们编写了代码来自动执行此分析，但仍允许用户开发自定义例程。我们的全能软件将光密度测量更正为细胞数量的线性测量，并将自发荧光的荧光测量更正为线性。它估计每个细胞的生长率和荧光是时间的连续函数，并使数十个平板阅读器实验能够一起分析。数据可以在文本文件中以一种立即适用于公共存储库的格式导出。平板阅读器是研究细胞的一种便捷方式；全盘读取器提供了一种同样方便但功能强大的方式来分析结果数据。
Responding to change is a fundamental property of life, making time-series data invaluable in biology. For microbes, plate readers are a popular, convenient means to measure growth and also gene expression using fluorescent reporters. Nevertheless, the difficulties of analysing the resulting data can be a bottleneck, particularly when combining measurements from different wells and plates. Here we present omniplate, a Python module that corrects and normalises plate-reader data, estimates growth rates and fluorescence per cell as functions of time, calculates errors, exports in different formats, and enables meta-analysis of multiple plates. The software corrects for autofluorescence, the optical density’s non-linear dependence on the number of cells, and the effects of the media. We use omniplate to measure the Monod relationship for the growth of budding yeast in raffinose, showing that raffinose is a convenient carbon source for controlling growth rates. Using fluorescent tagging, we study yeast’s glucose transport. Our results are consistent with the regulation of the hexose transporter (HXT) genes being approximately bipartite: the medium and high affinity transporters are predominately regulated by both the high affinity glucose sensor Snf3 and the kinase complex SNF1 via the repressors Mth1, Mig1, and Mig2; the low affinity transporters are predominately regulated by the low affinity sensor Rgt2 via the co-repressor Std1. We thus demonstrate that omniplate is a powerful tool for exploiting the advantages offered by time-series data in revealing biological regulation. Time series of growth and of gene expression via fluorescent reporters are rich ways to characterise the behaviours of cells. With plate readers, it is straightforward to measure 96 independent time series in a single experiment, with readings taken every 10 minutes and each time series lasting tens of hours. Analysing such data can become challenging, particularly if multiple plate-reader experiments are required to characterise a phenomenon, which then should be analysed simultaneously. Taking advantage of existing packages in Python, we have written code that automates this analysis but yet still allows users to develop custom routines. Our omniplate software corrects both measurements of optical density to become linear in the number of cells and measurements of fluorescence for autofluorescence. It estimates growth rates and fluorescence per cell as continuous functions of time and enables tens of plate-reader experiments to be analysed together. Data can be exported in text files in a format immediately suitable for public repositories. Plate readers are a convenient way to study cells; omniplate provides an equally convenient yet powerful way to analyse the resulting data.
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影响因子：
5.8
作者：
Boyer, Frederic;Besson, Bruno;de Jong, Hidde
通讯作者：
de Jong, Hidde
影响因子：
9.9
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Keren, Leeat;Zackay, Ora;Lotan-Pompan, Maya;Barenholz, Uri;Dekel, Erez;Sasson, Vered;Aidelberg, Guy;Bren, Anat;Zeevi, Danny;Weinberger, Adina;Alon, Uri;Milo, Ron;Segal, Eran
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Segal, Eran
影响因子：
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作者：
Bren A;Hart Y;Dekel E;Koster D;Alon U
通讯作者：
Alon U
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作者：
Kaniak, A;Xue, ZX;Johnston, M
通讯作者：
Johnston, M
影响因子：
9.9
作者：
Gerosa, Luca;Kochanowski, Karl;Sauer, Uwe
通讯作者：
Sauer, Uwe