细胞形态动力学作为体内常驻组织巨噬细胞功能状态的量化指标。

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Miriam Schnitzerlein, Eric Greto, Anja Wegner, Anna Möller, Oliver Aust, Oumaima Ben Brahim, David B Blumenthal, Vasily Zaburdaev, Stefan Uderhardt
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引用次数: 0

摘要

常驻组织巨噬细胞(RTMs)对组织稳态至关重要。它们的多种功能,从监测间质液到清除细胞碎片,都伴随着反映其功能状态的特征形态变化。虽然目前对巨噬细胞行为的了解主要来自体外研究,但它们在体内的动态行为是根本不同的,因此需要一种更生理学相关的方法来理解它们。在这项研究中,我们采用活体成像技术从不同条件下的小鼠腹膜RTM中生成动态数据,并开发了一个全面的图像处理管道,以量化RTM随时间的形态动力学,定义人类可解释的细胞大小和形状特征。这些特征允许细胞群在各种功能状态下进行定量和定性分化,包括促炎和抗炎激活以及内体功能障碍。研究表明,在稳态条件下,RTMs表现出广泛的形态动力学表型,构成了naïve行为基序的形态空间。在挑战后,形态动力模式在种群水平上均匀变化,但主要在这个naïve形态空间的约束下。值得注意的是,老年动物的naïve形态空间发生了显著变化,与年轻动物相比,这表明它们的行为模式截然不同。所开发的方法在优化外植组织设置方面也被证明是有价值的,使RTM行为更接近生理原生状态。因此,我们的多功能方法为体内真实巨噬细胞的动态行为提供了新的见解,能够区分生理和病理细胞状态,并在群体水平上评估功能组织年龄。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cellular morphodynamics as quantifiers for functional states of resident tissue macrophages in vivo.

Resident tissue macrophages (RTMs) are essential for tissue homeostasis. Their diverse functions, from monitoring interstitial fluids to clearing cellular debris, are accompanied by characteristic morphological changes that reflect their functional status. While current knowledge of macrophage behaviour comes primarily from in vitro studies, their dynamic behavior in vivo is fundamentally different, necessitating a more physiologically relevant approach to their understanding. In this study, we employed intravital imaging to generate dynamic data from peritoneal RTMs in mice under various conditions and developed a comprehensive image processing pipeline to quantify RTM morphodynamics over time, defining human-interpretable cell size and shape features. These features allowed for the quantitative and qualitative differentiation of cell populations in various functional states, including pro- and anti-inflammatory activation and endosomal dysfunction. The study revealed that under steady-state conditions, RTMs exhibit a wide range of morphodynamical phenotypes, constituting a naïve morphospace of behavioral motifs. Upon challenge, morphodynamic patterns changed uniformly at the population level but predominantly within the constraints of this naïve morphospace. Notably, aged animals displayed a markedly shifted naïve morphospace, indicating drastically different behavioral patterns compared to their young counterparts. The developed method also proved valuable in optimizing explanted tissue setups, bringing RTM behavior closer to the physiological native state. Our versatile approach thus provides novel insights into the dynamic behavior of bona fide macrophages in vivo, enabling the distinction between physiological and pathological cell states and the assessment of functional tissue age on a population level.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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