Integrating Biological Knowledge for Robust Microscopy Image Profiling on De Novo Cell Lines

TL;DR

This paper introduces a framework that integrates external biological knowledge, such as protein interactions and transcriptomics, into microscopy image profiling models to improve their robustness and generalization to new cell lines.

Contribution

The novel approach explicitly disentangles perturbation-specific and cell line-specific features using biological knowledge, enhancing model performance on de novo cell lines.

Findings

Improved generalization to new cell lines in microscopy profiling.

Enhanced performance in one-shot and few-shot fine-tuning scenarios.

Effective integration of protein interaction data and transcriptomic features.

Abstract

High-throughput screening techniques, such as microscopy imaging of cellular responses to genetic and chemical perturbations, play a crucial role in drug discovery and biomedical research. However, robust perturbation screening for \textit{de novo} cell lines remains challenging due to the significant morphological and biological heterogeneity across cell lines. To address this, we propose a novel framework that integrates external biological knowledge into existing pretraining strategies to enhance microscopy image profiling models. Our approach explicitly disentangles perturbation-specific and cell line-specific representations using external biological information. Specifically, we construct a knowledge graph leveraging protein interaction data from STRING and Hetionet databases to guide models toward perturbation-specific features during pretraining. Additionally, we incorporate transcriptomic features from single-cell foundation models to capture cell line-specific representations. By learning these disentangled features, our method improves the generalization of imaging models to \textit{de novo} cell lines. We evaluate our framework on the RxRx database through one-shot fine-tuning on an RxRx1 cell line and few-shot fine-tuning on cell lines from the RxRx19a dataset. Experimental results demonstrate that our method enhances microscopy image profiling for \textit{de novo} cell lines, highlighting its effectiveness in real-world phenotype-based drug discovery applications.