Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands.
Faculty of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany.
Nat Commun. 2021 Jun 18;12(1):3791. doi: 10.1038/s41467-021-24106-8.
Particle fusion for single molecule localization microscopy improves signal-to-noise ratio and overcomes underlabeling, but ignores structural heterogeneity or conformational variability. We present a-priori knowledge-free unsupervised classification of structurally different particles employing the Bhattacharya cost function as dissimilarity metric. We achieve 96% classification accuracy on mixtures of up to four different DNA-origami structures, detect rare classes of origami occuring at 2% rate, and capture variation in ellipticity of nuclear pore complexes.
粒子融合用于单分子定位显微镜可以提高信噪比并克服标记不足的问题，但它忽略了结构异质性或构象变异性。我们提出了一种无先验知识的、基于 Bhattacharya 代价函数的、无需监督的结构不同粒子分类方法。我们在多达四种不同的 DNA 折纸结构混合物上实现了 96%的分类准确率，检测到了稀有折纸结构的出现概率为 2%的情况，并捕获了核孔复合物的椭圆率变化。
