Interferometric scattering (iSCAT) microscopy is a label-free optical method capable of detecting single proteins, localizing their binding positions with nanometer precision, and measuring their mass. In the ideal case, iSCAT is limited by shot noise such that collection of more photons should extend its detection sensitivity to biomolecules of arbitrarily low mass. However, a number of technical noise sources combined with speckle-like background fluctuations have restricted the detection limit in iSCAT. Here, we show that an unsupervised machine learning isolation forest algorithm for anomaly detection pushes the mass sensitivity limit by a factor of 4 to below 10 kDa. We implement this scheme both with a user-defined feature matrix and a self-supervised FastDVDNet and validate our results with correlative fluorescence images recorded in total internal reflection mode. Our work opens the door to optical investigations of small traces of biomolecules and disease markers such as α-synuclein, chemokines and cytokines.

干涉散射 (iSCAT) 显微镜是一种无标记光学方法，能够检测单个蛋白质，以纳米精度定位其结合位置，并测量其质量。在理想情况下，iSCAT 受到散粒噪声的限制，因此收集更多光子应该将其检测灵敏度扩展到任意低质量的生物分子。然而，许多技术噪声源与类似散斑的背景波动相结合，限制了 iSCAT 的检测极限。在这里，我们展示了用于异常检测的无监督机器学习隔离森林算法将质量灵敏度限制提高了 4 倍至 10 kDa 以下。我们使用用户定义的特征矩阵和自我监督的 FastDVDNet 来实现该方案，并使用以全内反射模式记录的相关荧光图像验证我们的结果。我们的工作为对微量生物分子和疾病标志物（如 α-突触核蛋白、趋化因子和细胞因子）进行光学研究打开了大门。