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细胞三维成像技术的新进展
作者:小柯机器人 发布时间:2019/9/10 15:19:24

美国德克萨斯大学西南医学中心Gaudenz Danuser课题组,发现利用3D显微镜可以稳定且自动检测亚细胞的形态。相关论文2019年9月9日在线发表于《自然—方法学》。

研究人员介绍了u-shape3D的方法,这种方法将计算机作图和机器学习相结合,用于探测3D细胞形态发生的分子机制,并测试形态发生本身影响细胞内信号传导的可能性。研究者展示了一种通用的形态学图案检测器,可以自动发现片状伪足,丝状伪足,囊泡和其他组织。结合基序检测和分子定位,研究者测量PIP2和KrasV12与囊泡的差异关联。正如膜定位蛋白所预期的那样,两种信号都与泡胛边缘相关,但只有PIP2在囊泡上增多。这表明亚细胞信号传导过程受局部形态学基序的差异调节。总的来说,研究人员提出的计算工作流程,可以实现细胞形状和信号耦合的客观3D分析。

据了解,活细胞三维显微镜的快速发展,使得利用成像技术观察细胞形态和信号的细节达到前所未有的程度。然而,系统地测量和可视化细胞内信号传导、细胞骨架组织和下游细胞形态学之间复杂关系的工具,还没有出现。

附:英文原文

Title: Robust and automated detection of subcellular morphological motifs in 3D microscopy images

Author: Meghan K. Driscoll, Erik S. Welf, Andrew R. Jamieson, Kevin M. Dean, Tadamoto Isogai, Reto Fiolka, Gaudenz Danuser

Issue&Volume: 2019-09-09

Abstract: Rapid developments in live-cell three-dimensional (3D) microscopy enable imaging of cell morphology and signaling with unprecedented detail. However, tools to systematically measure and visualize the intricate relationships between intracellular signaling, cytoskeletal organization and downstream cell morphological outputs do not exist. Here, we introduce u-shape3D, a computer graphics and machine-learning pipeline to probe molecular mechanisms underlying 3D cell morphogenesis and to test the intriguing possibility that morphogenesis itself affects intracellular signaling. We demonstrate a generic morphological motif detector that automatically finds lamellipodia, filopodia, blebs and other motifs. Combining motif detection with molecular localization, we measure the differential association of PIP2 and KrasV12 with blebs. Both signals associate with bleb edges, as expected for membrane-localized proteins, but only PIP2 is enhanced on blebs. This indicates that subcellular signaling processes are differentially modulated by local morphological motifs. Overall, our computational workflow enables the objective, 3D analysis of the coupling of cell shape and signaling. A computational workflow combining image segmentation, computer graphics and supervised machine learning enables automated and robust 3D analysis of the coupling of cell shape and signaling.

DOI: 10.1038/s41592-019-0539-z

Source:https://www.nature.com/articles/s41592-019-0539-z

期刊信息

Nature Methods:《自然—方法学》,创刊于2004年。隶属于施普林格·自然出版集团,最新IF:28.467
官方网址:https://www.nature.com/nmeth/
投稿链接:https://mts-nmeth.nature.com/cgi-bin/main.plex