2D classification plays a pivotal role in analyzing single particle cryo-electron microscopy images. Here, we introduce a simple and loss-less pre-processor that incorporates a fast dimension-reduction (2SDR) de-noiser to enhance 2D classification. By implementing this 2SDR pre-processor prior to a representative classification algorithm like RELION and ISAC, we compare the performances with and without the pre-processor. Tests on multiple cryo-EM experimental datasets show the pre-processor can make classification faster, improve yield of good particles and increase the number of class-average images to generate better initial models. Testing on the nanodisc-embedded TRPV1 dataset with high heterogeneity using a 3D reconstruction workflow with an initial model from class-average images highlights the pre-processor improves the final resolution to 2.82 Å, close to 0.9 Nyquist. Those findings and analyses suggest the 2SDR pre-processor, of minimal cost, is widely applicable for boosting 2D classification, while its generalization to accommodate neural network de-noisers is envisioned.

2D 分类在分析单粒子冷冻电子显微镜图像中起着关键作用。在这里，我们介绍了一个简单且无损的预处理器，它结合了快速降维 (2SDR) 降噪器来增强 2D 分类。通过在代表性分类算法（如 RELION 和 ISAC）之前实施此 2SDR 预处理器，我们比较了使用和不使用预处理器的性能。对多个冷冻电镜实验数据集的测试表明，预处理器可以加快分类速度，提高良好粒子的产量，并增加类平均图像的数量以生成更好的初始模型。使用 3D 重建工作流程对具有高异质性的纳米盘嵌入 TRPV1 数据集进行测试，其中初始模型来自类平均图像，突出显示预处理器将最终分辨率提高到 2.82 Å，接近 0.9 Nyquist。这些发现和分析表明，成本最低的 2SDR 预处理器广泛适用于促进 2D 分类，同时设想其泛化以适应神经网络降噪器。