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Abstract：
Common challenges in cryogenic electron microscopy, such as orientation bias, conformational diversity, and 3D misclassification, complicate single particle analysis and lead to significant resource expenditure. We previously introduced an in silico method using the maximum Feret diameter distribution, the Feret signature, to characterize sample heterogeneity of disc-shaped samples. Here, we expanded the Feret signature methodology to identify preferred orientations of samples containing arbitrary shapes with only about 1000 particles required. This method enables real-time adjustments of data acquisition parameters for optimizing data collection strategies or aiding in decisions to discontinue ineffective imaging sessions. Beyond detecting preferred orientations, the Feret signature approach can serve as an early-warning system for inconsistencies in classification during initial image processing steps, a capability that allows for strategic adjustments in data processing. These features establish the Feret signature as a valuable auxiliary tool in the context of single particle analysis, significantly accelerating the structure determination process.
摘要：
低温电子显微镜的共同挑战，例如取向偏差，构象多样性，和3D错误分类，复杂的单粒子分析，并导致大量的资源支出。我们之前介绍了一种使用最大费雷特直径分布的计算机模拟方法，费雷特的签名,表征圆盘形样品的样品异质性。这里,我们扩展了Feret签名方法，以确定包含任意形状且仅需要约1000个颗粒的样品的首选方向。该方法使得能够实时调整数据采集参数,以用于优化数据收集策略或帮助决定中断无效成像会话。除了检测首选方向，Feret签名方法可以作为初始图像处理步骤中分类不一致的早期预警系统，一种允许在数据处理中进行战略调整的能力。这些特征将Feret签名确立为在单粒子分析的背景下的有价值的辅助工具。显著加快了结构确定过程。
