美国加州大学欧文分校Michelle A. Digman团队实现活细胞延时图像中线粒体的自动分割和追踪。这一研究成果于2021年8月19日在线发表在国际学术期刊《自然—方法学》上。

研究人员报道了Mitometer，一种用于快速、无偏差、自动分割和跟踪活细胞二维和三维延时图像中线粒体的算法。Mitometer只需要像素大小和帧之间的时间来识别线粒体运动和形态，包括融合和分裂事件。分割算法通过一个形状和尺寸保护的背景去除过程来分隔单个线粒体。追踪算法通过形态学特征和位移的差异将线粒体联系起来，然后采用间隙关闭方案。使用Mitometer，研究人员表明三阴性乳腺癌细胞的线粒体比其受体阳性细胞的线粒体更快、更有方向性、更细长。

此外，结果表明，乳腺癌中的线粒体运动和形态与代谢活动相关，而正常乳腺上皮细胞中的线粒体运动和形态则与之无关。Mitometer是一个无偏见的、用户友好的工具，将有助于解决有关线粒体形态和功能的基本问题。

据悉，线粒体显示出复杂的形态和运动，这使得它们在延时图像中的分割和追踪变得复杂。

附：英文原文

Title: Automated segmentation and tracking of mitochondria in live-cell time-lapse images

Author: Lefebvre, Austin E. Y. T., Ma, Dennis, Kessenbrock, Kai, Lawson, Devon A., Digman, Michelle A.

Issue&Volume: 2021-08-19

Abstract: Mitochondria display complex morphology and movements, which complicates their segmentation and tracking in time-lapse images. Here, we introduce Mitometer, an algorithm for fast, unbiased, and automated segmentation and tracking of mitochondria in live-cell two-dimensional and three-dimensional time-lapse images. Mitometer requires only the pixel size and the time between frames to identify mitochondrial motion and morphology, including fusion and fission events. The segmentation algorithm isolates individual mitochondria via a shape- and size-preserving background removal process. The tracking algorithm links mitochondria via differences in morphological features and displacement, followed by a gap-closing scheme. Using Mitometer, we show that mitochondria of triple-negative breast cancer cells are faster, more directional, and more elongated than those in their receptor-positive counterparts. Furthermore, we show that mitochondrial motility and morphology in breast cancer, but not in normal breast epithelia, correlate with metabolic activity. Mitometer is an unbiased and user-friendly tool that will help resolve fundamental questions regarding mitochondrial form and function.

DOI: 10.1038/s41592-021-01234-z

Source: https://www.nature.com/articles/s41592-021-01234-z