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.
Label-free, image-based cellular mechanophenotyping in microfluidic devices provides a high-throughput method for single-cell profiling. However, while complex microchannels (e.g., hyperbolic geometries) reveal transient deformation dynamics under continuous extensional stress, the resulting high-dimensional feature spaces are highly susceptible to hydrodynamic artifacts. Flow rate variations often distort discriminative boundaries, linking feature distributions to fluid conditions rather than intrinsic biology. To overcome this, we introduce a stability-guided analytical framework that decouples flow-induced noise from authentic mechanobiological signatures. We tracked the morphodynamic, kinematic, and intracellular optical-density trajectories of healthy and malignant ovarian cells to build a 93-dimensional feature space. Using a cross-flow screening strategy based on structural consistency and statistical persistence, we isolated robust descriptors, creating task-adapted subsets (20 features for binary classification; 25 for cancer subtyping). Variance-attribution analysis confirmed the neutralization of flow-conditioned artifacts; notably, flow-associated variance in the primary principal component fell from 69.9% to 9.3% in the subtyping task. We also found that macroscopic binary discrimination depends on bulk kinematic transitions, while clonal subtyping requires localized intracellular optical heterogeneity. These optimized subsets maintained diagnostic fidelity across multiple machine learning architectures and restricted sampling conditions. This framework establishes a robust, flow-independent foundation for continuous dynamic phenotyping.