Bioimage Analysis and Cell Motility.
Bioimage Analysis and Cell Motility.
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DOI：
10.1016/j.patter.2020.100170
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发表时间：
2021-01-08
期刊：
影响因子：
--
通讯作者：
Guillén N
中科院分区：
文献类型：
--
作者：
Boquet-Pujadas A;Olivo-Marin JC;Guillén N
生物图像分析（BIA）历来有助于研究细胞如何以及为什么运动；生物实验是在最经典的图像处理技术的密切反馈中发展起来的，因为它们为一门卓越的定性科学贡献了客观性和可重复性。这里讨论了细胞分割、跟踪和形态描述符。以变形虫运动为例，这些方法帮助我们说明了适当的量化是如何增强生物数据的，例如，通过选择放大最初细微差异的数学表示，通过统计揭示一般规律或通过整合物理洞察力。最近，定量成像的非侵入性使两个蓬勃发展的领域得到了发展：机械生物学和微环境，在这两个领域，许多生物物理测量仍然无法实现，而在微环境中，对生理学相关性的追求已经产生了爆炸式的数据量。从救济到补救，这一趋势表明，随着人类视觉分析与越来越复杂的数据作斗争，BIA将成为生物学发现的主要载体。目前对细胞运动的研究正朝着更丰富的实验设置，目的是再现生理相关条件。为了应对随之而来的成像复杂性和数据吞吐量的增加，移动细胞的定量分析正在从长期以来被视为辅助作用转变为一种领先的发现工具，它不仅可以毫无负担地填补人类的劳动，而且还可以补充和扩展我们的直觉。在这篇综述中，我们解释了这一作用并不是新的，事实上，生物图像分析（BIA）已经有助于发现生物学中的许多多因素和非线性现象。我们利用生物学和BIA之间的这种持续相互作用，有机地激发了广泛的可用技术和概念框架，研究人员可以利用这些技术和概念框架来解决他们现在和不久的将来关于细胞运动的问题。这样，手稿就可以作为广泛的技术参考。
Bioimage analysis (BIA) has historically helped study how and why cells move; biological experiments evolved in intimate feedback with the most classical image processing techniques because they contribute objectivity and reproducibility to an eminently qualitative science. Cell segmentation, tracking, and morphology descriptors are all discussed here. Using ameboid motility as a case study, these methods help us illustrate how proper quantification can augment biological data, for example, by choosing mathematical representations that amplify initially subtle differences, by statistically uncovering general laws or by integrating physical insight. More recently, the non-invasive nature of quantitative imaging is fertilizing two blooming fields: mechanobiology, where many biophysical measurements remain inaccessible, and microenvironments, where the quest for physiological relevance has exploded data size. From relief to remedy, this trend indicates that BIA is to become a main vector of biological discovery as human visual analysis struggles against ever more complex data. Current research on cellular motility is moving toward richer experimental setups with the aim of reproducing physiologically relevant conditions. In response to the consequent increase in imaging complexity and data throughput, the quantitative analysis of moving cells is shifting from what has been long perceived as a supporting role to that of a leading vehicle of discovery that can not only fill in for human labor without burden but also complement and extend our intuition. In this review, we explain that this role is not new and that, in fact, bioimage analysis (BIA) has already been instrumental to the discovery of many multi-factor and non-linear phenomena in biology. We take advantage of this continued interplay between biology and BIA to organically motivate a wide range of available techniques and conceptual frameworks that researchers can leverage to tackle their questions on cell motility, both now and in the near future. In this way, the manuscript doubles as a broad technical reference.
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影响因子：
46.9
作者：
Becht, Etienne;McInnes, Leland;Newell, Evan W.
通讯作者：
Newell, Evan W.
DOI：
10.1073/pnas.0913669107
发表时间：
2010-08-31
影响因子：
11.1
作者：
Brugues, Jan;Maugis, Benoit;Sens, Pierre
通讯作者：
Sens, Pierre
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
5.8
作者：
Arganda-Carreras, Ignacio;Kaynig, Verena;Seung, H. Sebastian
通讯作者：
Seung, H. Sebastian
影响因子：
4
作者：
Blazquez, Samantha;Rigothier, Marie-Christine;Guillen, Nancy
通讯作者：
Guillen, Nancy