Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany.
Nat Methods. 2023 Jan;20(1):139-148. doi: 10.1038/s41592-022-01676-z. Epub 2022 Dec 15.
Quantitative data analysis is important for any single-molecule localization microscopy (SMLM) workflow to extract biological insights from the coordinates of the single fluorophores. However, current approaches are restricted to simple geometries or require identical structures. Here, we present LocMoFit (Localization Model Fit), an open-source framework to fit an arbitrary model to localization coordinates. It extracts meaningful parameters from individual structures and can select the most suitable model. In addition to analyzing complex, heterogeneous and dynamic structures for in situ structural biology, we demonstrate how LocMoFit can assemble multi-protein distribution maps of six nuclear pore components, calculate single-particle averages without any assumption about geometry or symmetry, and perform a time-resolved reconstruction of the highly dynamic endocytic process from static snapshots. We provide extensive simulation and visualization routines to validate the robustness of LocMoFit and tutorials to enable any user to increase the information content they can extract from their SMLM data.
定量数据分析对于任何单分子定位显微镜（SMLM）工作流程都很重要，它可以从单个荧光团的坐标中提取生物学见解。然而，目前的方法仅限于简单的几何形状或需要相同的结构。在这里，我们提出了 LocMoFit（定位模型拟合），这是一个开源框架，可以将任意模型拟合到定位坐标上。它从单个结构中提取有意义的参数，并可以选择最合适的模型。除了分析原位结构生物学中的复杂、异质和动态结构外，我们还展示了 LocMoFit 如何组装六个核孔组件的多蛋白分布图谱，在不假设任何几何形状或对称性的情况下计算单粒子平均值，并从静态快照中进行高度动态的内吞过程的时分辨重构。我们提供了广泛的模拟和可视化例程来验证 LocMoFit 的稳健性，并提供了教程，使任何用户都能够增加他们从 SMLM 数据中提取的信息量。
