AI 中文总结
研究针对单细胞光学显微镜图像分析难题，提出scMIR模型，通过自监督图像重建与文本引导跨模态对齐，在多图像文本对上预训练，在多种复杂任务中表现出色，优于现有方法，具备强泛化能力，能推动高通量表型分析工作流程标准化和自动化。
AI 中文摘要
单细胞光学显微镜图像已成为表征细胞表型的重要数据源，但其复杂性和异质性给高通量自动分析带来挑战。现有表示学习方法多依赖任务导向建模，受特定数据集和预定义任务限制。通用方法虽有改进，但对实验背景和生物上下文信息利用有限。本文提出scMIR，通过自监督图像重建与文本引导跨模态对齐，在统一表示空间编码形态和生物语义信息。它在207,957个图像文本对上预训练，在16个基准数据集的多种复杂任务中优于现有模型和方法，具有强泛化能力，有望推动高通量表型分析工作流程的标准化和自动化。
英文摘要
Single-cell light microscopy images have become an important data source for characterizing cell phenotypes, but their complexity and heterogeneity pose challenges to high-throughput automated analysis. Existing representation learning methods mostly rely on task-oriented modeling, which is limited by specific datasets and predefined tasks, making them difficult to generalize across different cell types and microscopy modalities, and experimental conditions. Although general-purpose methods have improved the generalization ability of image representation in recent years, their limited utilization of experimental background and biological context information still poses challenges in complex phenotypic analysis. Here, we propose scMIR, a vision-language foundation model for single-cell light microscopy image representation. By synergistically combining self-supervised image reconstruction with text-guided cross-modal alignment, scMIR can simultaneously encode morphological and biological semantic information in a unified representation space. scMIR is pre-trained on 207,957 image-text pairs, covering various cell types, microscopy modalities, and perturbation conditions. scMIR outperforms existing general models and task-oriented methods as systematically evaluated on various complex tasks using 16 benchmark datasets, including cell classification, clustering, phenotype inference, and batch effect correction tasks. Furthermore, scMIR shows a strong generalization ability across various tasks without requiring task-specific fine-tuning. With its unique advantages, we envision scMIR may promote the standardization and automation of high-throughput phenotyping workflows through supporting various downstream analysis tasks.
