Advances and opportunities in image analysis of bacterial cells and communities.
Advances and opportunities in image analysis of bacterial cells and communities.
复制标题
细菌细胞和群落图像分析的进展和机遇。
DOI：
10.1093/femsre/fuaa062
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发表时间：
2021-08-17
影响因子：
11.3
通讯作者：
Drescher K
中科院分区：
文献类型：
--
作者：
Jeckel H;Drescher K
细胞形态和亚细胞空间结构严重影响微生物细胞的功能。同样，微生物群落基因型和表型的空间排列对合作、竞争和群落功能具有重要影响。荧光显微镜技术广泛用于测量活细胞和群落内部的空间结构，这通常会产生大量难以或不可能手动分析的图像。计算图像分析的快速发展最近使得基于传统分析技术和卷积神经网络的单细胞和群落的大量特性得以量化。在这里，我们简要介绍自动化图像处理的核心概念、最新的软件工具以及如何验证图像分析结果。我们还讨论了微生物细胞和群落图像分析的最新进展，以及这些进展如何为基于图像细胞术和自适应显微镜控制的微生物学时空过程的定量研究提供机会。计算图像分析的进步为分析细菌细胞和群落的时空过程带来了新的机会，也许不久之后就会为微生物学中基于人工智能的自动自适应显微镜带来新的机会。
The cellular morphology and sub-cellular spatial structure critically influence the function of microbial cells. Similarly, the spatial arrangement of genotypes and phenotypes in microbial communities has important consequences for cooperation, competition, and community functions. Fluorescence microscopy techniques are widely used to measure spatial structure inside living cells and communities, which often results in large numbers of images that are difficult or impossible to analyze manually. The rapidly evolving progress in computational image analysis has recently enabled the quantification of a large number of properties of single cells and communities, based on traditional analysis techniques and convolutional neural networks. Here, we provide a brief introduction to core concepts of automated image processing, recent software tools and how to validate image analysis results. We also discuss recent advances in image analysis of microbial cells and communities, and how these advances open up opportunities for quantitative studies of spatiotemporal processes in microbiology, based on image cytometry and adaptive microscope control. Advances in computational image analysis result in new opportunities for the analysis of spatiotemporal processes in bacterial cells and communities, and perhaps in the near for artificial intelligence-based automated adaptive microscopy in microbiology.