Arvindekar Shreyas, Golatkar Omkar, Viswanath Shruthi
National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India 560065.
bioRxiv. 2025 Aug 21:2025.08.20.671250. doi: 10.1101/2025.08.20.671250.
Cryo-electron tomography (cryo-ET) datasets are rich sources of information capable of describing the localizations, structures, and interactions of macromolecules. However, most current methods for localizing particles in cryo-electron tomograms are limited to macromolecules with known structure, require extensive manual annotations, and/or are computationally expensive. Here, we present PickET, a method for localizing macromolecules in tomograms that does not rely on expert annotations and prior structures. Its performance is demonstrated on a diverse dataset comprising over a hundred tomograms from publicly available datasets, varying in sample types, sample preparation conditions, microscope hardware, and image processing workflows. We demonstrate that PickET can simultaneously localize macromolecules of various shapes, sizes, and abundance. The predicted particle localizations can be used for 3D classification and structural characterization. Our fully unsupervised approach is efficient and scalable, and enables high-throughput analysis of cryo-ET data.
冷冻电子断层扫描（cryo-ET）数据集是丰富的信息来源，能够描述大分子的定位、结构和相互作用。然而，目前大多数在冷冻电子断层图像中定位颗粒的方法仅限于具有已知结构的大分子，需要大量的人工注释，和/或计算成本高昂。在这里，我们提出了PickET，一种在断层图像中定位大分子的方法，该方法不依赖专家注释和先前的结构。我们在一个多样的数据集上展示了它的性能，该数据集包含来自公开可用数据集的一百多个断层图像，样本类型、样本制备条件、显微镜硬件和图像处理工作流程各不相同。我们证明PickET可以同时定位各种形状、大小和丰度的大分子。预测的颗粒定位可用于三维分类和结构表征。我们的完全无监督方法高效且可扩展，能够对冷冻电子断层扫描数据进行高通量分析。
