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 behavior 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.
Cellular morphodynamics as quantifiers for functional states of resident tissue macrophages in vivo
细胞形态动力学作为体内驻留组织巨噬细胞功能状态的量化指标
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作者:Miriam Schnitzerlein ,Eric Greto ,Anja Wegner ,Anna Möller ,Oliver Aust ,Oumaima Ben Brahim ,David B Blumenthal ,Vasily Zaburdaev ,Stefan Uderhardt
| 期刊: | PLoS Computational Biology | 影响因子: | 3.800 |
| 时间: | 2025 | 起止号: | 2025 May 29;21(5):e1011859. |
| doi: | 10.1371/journal.pcbi.1011859 | 研究方向: | 细胞生物学 |
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