哥本哈根大学Nikos S. Hatzakis团队取得一项新突破。他们报道了深度学习辅助的单粒子跟踪分析，用于扩散和函数之间的自动关联。这一研究成果发表在2025年5月8日出版的国际学术期刊《自然—方法学》上。

在这里，该研究组介绍了DeepSPT，一个集成在分析软件中的深度学习框架，以一种快速有效的方式解释物体的二维或三维时间行为。为了证明其多功能性，该课题组人员将DeepSPT应用于病毒感染早期事件的自动映射，识别内体细胞器、网格蛋白包被的凹坑和囊泡等，F1得分分别为81%、82%和95%，并且在几秒钟内完成，而不是几周。DeepSPT仅从扩散中有效提取生物信息的事实说明，除了结构外，运动还在分子和亚细胞水平上编码功能。

据了解，生命系统中的亚细胞分裂反映了细胞过程和相互作用。光学显微镜的最新进展使该研究团队能够以前所未有的精度跟踪单个物体的纳米级扩散。然而，从亚细胞环境中分子和细胞器的分裂中提取功能信息的不可知和自动提取是劳动密集型的，并且提出了重大挑战。

附：英文原文

Title: Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function

Author: Kstel-Hansen, Jacob, de Sautu, Marilina, Saminathan, Anand, Scanavachi, Gustavo, Bango Da Cunha Correia, Ricardo F., Juma Nielsen, Annette, Bleshy, Sara Vogt, Tsolakidis, Konstantinos, Boomsma, Wouter, Kirchhausen, Tomas, Hatzakis, Nikos S.

Issue&Volume: 2025-05-08

Abstract: Subcellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with unprecedented precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the subcellular environment is labor intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework integrated in an analysis software, to interpret the diffusional two- or three-dimensional temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying endosomal organelles, clathrin-coated pits and vesicles among others with F1 scores of 81%, 82% and 95%, respectively, and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone illustrates that besides structure, motion encodes function at the molecular and subcellular level.

DOI: 10.1038/s41592-025-02665-8

Source: https://www.nature.com/articles/s41592-025-02665-8