Clustering of proteins is crucial for many cellular processes and can be imaged at nanoscale resolution using single-molecule localization microscopy (SMLM). Ideally, molecular clustering in regions of interest (ROIs) from SMLM images would be assessed using computational methods that are robust to sample and experimental heterogeneity, account for uncertainties in localization data, can analyze both 2D and 3D data, and have practical computational requirements in terms of time and hardware. While analyzing surface protein clustering on B lymphocytes using SMLM, we encountered limitations with existing cluster analysis methods. This inspired us to develop StormGraph, an algorithm using graph theory and community detection to identify clusters in heterogeneous sets of 2D and 3D SMLM data while accounting for localization uncertainties. StormGraph generates both multi-level and single-level clusterings and can quantify cluster overlap for two-color SMLM data. Importantly, StormGraph automatically determines scale-dependent thresholds from the data using scale-independent input parameters. This makes identical choices of input parameter values suitable for disparate ROIs, eliminating the need to tune parameters for different ROIs in heterogeneous SMLM datasets. We show that StormGraph outperforms existing algorithms at analyzing heterogeneous sets of simulated SMLM ROIs where ground-truth clusters are known. Applying StormGraph to real SMLM data in 2D, we reveal that B-cell antigen receptors (BCRs) reside in a heterogeneous combination of small and large clusters following stimulation, which suggests for the first time that two conflicting models of BCR activation are not mutually exclusive. We also demonstrate application of StormGraph to real two-color and 3D SMLM data.

中文翻译：

---

StormGraph：基于图的算法，用于对各种单分子定位显微镜数据进行定量聚类分析

蛋白质的聚集对于许多细胞过程至关重要，可以使用单分子定位显微镜（SMLM）以纳米级的分辨率成像。理想情况下，SMLM图像中感兴趣区域（ROI）的分子聚类将使用对样本和实验异质性鲁棒的计算方法进行评估，考虑到本地化数据的不确定性，可以分析2D和3D数据，并且对时间和硬件方面。当使用SMLM分析B淋巴细胞上的表面蛋白簇时，我们遇到了现有簇分析方法的局限性。这启发了我们开发StormGraph，这是一种使用图论和社区检测的算法，可以在考虑定位不确定性的同时识别2D和3D SMLM异构数据集中的聚类。StormGraph可以生成多级和单级聚类，并且可以量化两色SMLM数据的聚类重叠。重要的是，StormGraph使用与比例无关的输入参数自动从数据确定与比例相关的阈值。这样就可以对输入参数值进行相同的选择，以适合不同的ROI，从而无需为异构SMLM数据集中的不同ROI调整参数。我们显示，在分析地面真相群集的情况下，StormGraph在分析模拟SMLM ROI的异构集方面优于现有算法。将StormGraph应用于2D的真实SMLM数据中，我们揭示了刺激后，B细胞抗原受体（BCR）驻留在大小簇的异质组合中，这首次表明，两个相互冲突的BCR激活模型不是互斥的。我们还演示了StormGraph在真实的两色和3D SMLM数据中的应用。