Suppr超能文献

揭示酵母膜蛋白 Pma1 的扩散状态,以及标记方法如何改变扩散行为。

Uncovering diffusive states of the yeast membrane protein, Pma1, and how labeling method can change diffusive behavior.

机构信息

Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT, 06511, USA.

Department of Applied Physics, Yale University, New Haven, CT, 06511, USA.

出版信息

Eur Phys J E Soft Matter. 2023 Jun 9;46(6):42. doi: 10.1140/epje/s10189-023-00301-x.

Abstract

We present and analyze video-microscopy-based single-particle-tracking measurements of the budding yeast (Saccharomyces cerevisiae) membrane protein, Pma1, fluorescently labeled either by direct fusion to the switchable fluorescent protein, mEos3.2, or by a novel, light-touch, labeling scheme, in which a 5 amino acid tag is directly fused to the C-terminus of Pma1, which then binds mEos3.2. The track diffusivity distributions of these two populations of single-particle tracks differ significantly, demonstrating that labeling method can be an important determinant of diffusive behavior. We also applied perturbation expectation maximization (pEMv2) (Koo and Mochrie in Phys Rev E 94(5):052412, 2016), which sorts trajectories into the statistically optimum number of diffusive states. For both TRAP-labeled Pma1 and Pma1-mEos3.2, pEMv2 sorts the tracks into two diffusive states: an essentially immobile state and a more mobile state. However, the mobile fraction of Pma1-mEos3.2 tracks is much smaller ([Formula: see text]) than the mobile fraction of TRAP-labeled Pma1 tracks ([Formula: see text]). In addition, the diffusivity of Pma1-mEos3.2's mobile state is several times smaller than the diffusivity of TRAP-labeled Pma1's mobile state. Thus, the two different labeling methods give rise to very different overall diffusive behaviors. To critically assess pEMv2's performance, we compare the diffusivity and covariance distributions of the experimental pEMv2-sorted populations to corresponding theoretical distributions, assuming that Pma1 displacements realize a Gaussian random process. The experiment-theory comparisons for both the TRAP-labeled Pma1 and Pma1-mEos3.2 reveal good agreement, bolstering the pEMv2 approach.

摘要

我们展示并分析了基于视频显微镜的单个粒子跟踪测量,研究对象是荧光标记的 budding yeast(酿酒酵母)膜蛋白 Pma1,其荧光标记方法有两种:一种是直接融合到可切换荧光蛋白 mEos3.2 上,另一种是一种新的、轻触式的标记方案,其中一个 5 个氨基酸的标签直接融合到 Pma1 的 C 末端,然后与 mEos3.2 结合。这两种单粒子轨迹的轨迹扩散分布有显著差异,这表明标记方法可以是扩散行为的一个重要决定因素。我们还应用了扰动期望最大化(pEMv2)(Koo 和 Mochrie 在 Phys Rev E 94(5):052412, 2016),它将轨迹分为统计上最优数量的扩散状态。对于 TRAP 标记的 Pma1 和 Pma1-mEos3.2,pEMv2 将轨迹分为两种扩散状态:一种是基本上不动的状态和一种更活跃的状态。然而,Pma1-mEos3.2 轨迹的活跃部分 ([Formula: see text]) 远小于 TRAP 标记的 Pma1 轨迹的活跃部分 ([Formula: see text])。此外,Pma1-mEos3.2 的活跃状态的扩散系数比 TRAP 标记的 Pma1 的活跃状态的扩散系数小几个数量级。因此,两种不同的标记方法导致了非常不同的整体扩散行为。为了严格评估 pEMv2 的性能,我们将实验 pEMv2 排序的种群的扩散率和协方差分布与相应的理论分布进行比较,假设 Pma1 位移实现了高斯随机过程。TRAP 标记的 Pma1 和 Pma1-mEos3.2 的实验-理论比较都显示出很好的一致性,支持了 pEMv2 方法。

相似文献

本文引用的文献

4
Covariance distributions in single particle tracking.单颗粒追踪中的协方差分布。
Phys Rev E. 2021 Mar;103(3-1):032405. doi: 10.1103/PhysRevE.103.032405.
9
Analysis of the Diffusivity Change from Single-Molecule Trajectories on Living Cells.活细胞中单分子轨迹扩散率变化的分析。
Anal Chem. 2019 Nov 5;91(21):13390-13397. doi: 10.1021/acs.analchem.9b01005. Epub 2019 Oct 17.
10
Fungal plasma membrane domains.真菌质膜域。
FEMS Microbiol Rev. 2019 Nov 1;43(6):642-673. doi: 10.1093/femsre/fuz022.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验