A Meta-Learning Approach for Multicenter and Small-Data Single-Cell Image Analysis

作者：Lingzhi Ye, Wentao Wang, Hang Sun, Wei Ye, Yuting Hou, Yating Zhang, Yang Zhang, Guangli Ren, Zhifan Gao, Xiangmeng Qu · 发表于：Analytical Chemistry · 年份：2025 · DOI：10.1021/acs.analchem.5c01810 · 被引用次数：2 · 研究领域：Cell Image Analysis Techniques、Single-cell and spatial transcriptomics、Advanced Fluorescence Microscopy Techniques

The application of algorithm-based single-cell imaging techniques can visualize and analyze cellular heterogeneity. However, algorithm-based single-cell imaging techniques are severely limited by the high workload required to label single-cell images and the high variation of cells from different sources. Herein, we propose a meta-learning approach for multicenter and small-data single-cell image analysis. Meta-learning combines automated wide-field fluorescence microscopy to build a hardware and software system to analyze cellular heterogeneity. We verified that the meta-learning single-cell imaging platform extracts the relevant information between multiple data centers through training to reduce the need for workload required to label single-cell images. The results show that the classification accuracy of the target task can reach about 92% using only 60% data volume labeled single-cell images. However, to achieve the same recognition accuracy, we need to use 100% data volume labeled single-cell images for traditional deep learning. Moreover, the accuracy achieved by our platform surpasses that of traditional deep learning methods, even when the data volume is reduced to 5%, which means our platform can significantly reduce the volume of single-cell image data labeling and the manual data labeling workload, thereby enhancing work efficiency and reducing work costs. Furthermore, our platform's robustness against data from different sources of single-cell images has been ve...