Department of Physics, University of Colorado Boulder, Boulder, CO 80309, USA.
Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO 80309, USA.
Biomolecules. 2023 Jun 4;13(6):939. doi: 10.3390/biom13060939.
Fluorescently labeled proteins absorb and emit light, appearing as Gaussian spots in fluorescence imaging. When fluorescent tags are added to cytoskeletal polymers such as microtubules, a line of fluorescence and even non-linear structures results. While much progress has been made in techniques for imaging and microscopy, image analysis is less well-developed. Current analysis of fluorescent microtubules uses either manual tools, such as kymographs, or automated software. As a result, our ability to quantify microtubule dynamics and organization from light microscopy remains limited. Despite the development of automated microtubule analysis tools for in vitro studies, analysis of images from cells often depends heavily on manual analysis. One of the main reasons for this disparity is the low signal-to-noise ratio in cells, where background fluorescence is typically higher than in reconstituted systems. Here, we present the Toolkit for Automated Microtubule Tracking (TAMiT), which automatically detects, optimizes, and tracks fluorescent microtubules in living yeast cells with sub-pixel accuracy. Using basic information about microtubule organization, TAMiT detects linear and curved polymers using a geometrical scanning technique. Images are fit via an optimization problem for the microtubule image parameters that are solved using non-linear least squares in Matlab. We benchmark our software using simulated images and show that it reliably detects microtubules, even at low signal-to-noise ratios. Then, we use TAMiT to measure monopolar spindle microtubule bundle number, length, and lifetime in a large dataset that includes several mutants that affect microtubule dynamics and bundling. The results from the automated analysis are consistent with previous work and suggest a direct role for CLASP/Cls1 in bundling spindle microtubules. We also illustrate automated tracking of single curved astral microtubules in , with measurement of dynamic instability parameters. The results obtained with our fully-automated software are similar to results using hand-tracked measurements. Therefore, TAMiT can facilitate automated analysis of spindle and microtubule dynamics in yeast cells.
荧光标记的蛋白质吸收和发射光,在荧光成像中呈现为高斯斑点。当将荧光标记物添加到细胞骨架聚合物(如微管)中时,会产生一条荧光线,甚至是非线性结构。尽管在成像和显微镜技术方面取得了很大进展,但图像分析的发展却不尽如人意。目前,荧光微管的分析要么使用手动工具(如共聚焦显微镜),要么使用自动化软件。因此,我们从明场显微镜中定量测量微管动力学和组织的能力仍然受到限制。尽管已经开发出用于体外研究的自动化微管分析工具,但对细胞图像的分析通常仍然严重依赖于手动分析。造成这种差异的一个主要原因是细胞中的信号噪声比较低,细胞中的背景荧光通常比重构系统中的高。在这里,我们介绍了自动微管跟踪工具包(TAMiT),它可以以亚像素精度自动检测、优化和跟踪活酵母细胞中的荧光微管。TAMiT 使用关于微管组织的基本信息,使用几何扫描技术检测线性和弯曲聚合物。使用 Matlab 中的非线性最小二乘解来解决用于微管图像参数的优化问题,对图像进行拟合。我们使用模拟图像对我们的软件进行基准测试,结果表明它能够可靠地检测微管,即使在信号噪声比较低的情况下也是如此。然后,我们使用 TAMiT 在一个包括几个影响微管动力学和捆绑的 突变体的大型数据集上测量单核纺锤体微管束的数量、长度和寿命。自动化分析的结果与以前的工作一致,并表明 CLASP/Cls1 在捆绑纺锤体微管中起着直接作用。我们还说明了在 中自动跟踪单个弯曲星体微管的情况,测量了动态不稳定性参数。使用我们的全自动软件获得的结果与使用手动跟踪测量的结果相似。因此,TAMiT 可以促进酵母细胞中纺锤体和微管动力学的自动分析。