Recovering mixtures of fast-diffusing states from short single-particle trajectories.
Recovering mixtures of fast-diffusing states from short single-particle trajectories.
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DOI：
10.7554/elife.70169
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发表时间：
2022-09-06
期刊：
影响因子：
7.7
通讯作者：
Darzacq, Xavier
中科院分区：
文献类型：
--
作者：
Heckert, Alec;Dahal, Liza;Tijan, Robert;Darzacq, Xavier
单粒子跟踪技术(SPT)直接测量活细胞中蛋白质的动态变化，是剖析细胞调控分子机制的有力工具。然而，在哺乳动物细胞中解释快速扩散蛋白质的SPT，由于快速图像采集带来的技术限制而变得复杂。这些限制包括由于光漂白和浅景深造成的短轨迹长度，由于短积分时间造成的低光子预算而导致的高定位误差，以及细胞到细胞的可变性。为了解决这些问题，我们研究了受贝叶斯非参数学启发的方法，该方法从SPT数据中推断状态参数的分布，具有轨迹短、定位精度可变以及缺乏关于潜在状态数量的先验知识。我们讨论了这些方法相对于其他SPT分析框架的优缺点。
Single-particle tracking (SPT) directly measures the dynamics of proteins in living cells and is a powerful tool to dissect molecular mechanisms of cellular regulation. Interpretation of SPT with fast-diffusing proteins in mammalian cells, however, is complicated by technical limitations imposed by fast image acquisition. These limitations include short trajectory length due to photobleaching and shallow depth of field, high localization error due to the low photon budget imposed by short integration times, and cell-to-cell variability. To address these issues, we investigated methods inspired by Bayesian nonparametrics to infer distributions of state parameters from SPT data with short trajectories, variable localization precision, and absence of prior knowledge about the number of underlying states. We discuss the advantages and disadvantages of these approaches relative to other frameworks for SPT analysis.