小柯机器人

2023年2月27日，德国马克斯普朗克光科学研究所Vahid Sandoghdar团队在《自然—方法学》杂志在线发表论文。该研究表明，自我监督的机器学习推动10kDa以下单一蛋白质的无标签检测的灵敏度极限。

研究人员展示了一种用于异常检测的无监督机器学习隔离森林算法，将质量敏感度限制提高了4倍，达到10kDa以下。研究人员用一个用户定义的特征矩阵和一个自我监督的FastDVDNet来实现这个方案，并用全内反射模式下记录的相关荧光图像来验证了结果。这项工作为生物大分子和疾病标志物（如α-突触核蛋白、趋化因子和细胞因子）的微量光学调查打开了大门。

据悉，干涉散射（iSCAT）显微镜是一种无标签的光学方法，能够检测单个蛋白质，以纳米级的精度定位其结合位置，并测量其质量。在理想的情况下，iSCAT受到射出噪声的限制，因此收集更多的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，一些技术噪声源与斑点状背景波动相结合，限制了iSCAT的检测极限。

附：英文原文

Title: Self-supervised machine learning pushes the sensitivity limit in label-free detection of single proteins below 10 kDa

Author: Dahmardeh, Mahyar, Mirzaalian Dastjerdi, Houman, Mazal, Hisham, Kstler, Harald, Sandoghdar, Vahid

Issue&Volume: 2023-02-27

Abstract: 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 10kDa. 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.

DOI: 10.1038/s41592-023-01778-2

Source: https://www.nature.com/articles/s41592-023-01778-2

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex