Universal Cell Image Embeddings

Self-supervised representation learning of cellular morphology / perturbation state across cell types and image modalities to build a foundational vision model capable of universally embedding all in vitro cellular image modalities.

Overview

This project aims to develop a universal foundation model for cellular imaging that can:

• Learn robust representations of cellular morphology across different cell types
• Generalize across diverse imaging modalities and experimental conditions
• Capture perturbation-induced phenotypic changes
• Enable transfer learning for downstream biological tasks

Motivation

Current cell image analysis methods often require task-specific models trained on limited datasets. A universal embedding model trained via self-supervised learning on diverse cellular imaging data can provide:

• Better generalization to new cell types and conditions
• Reduced need for labeled training data
• Consistent representations across different imaging platforms
• Foundation for multiple downstream applications

Approach

We employ self-supervised learning techniques to train vision models on large-scale cellular image datasets:

1. Contrastive Learning: Learn to distinguish between different cellular states
2. Multi-modal Training: Incorporate data from diverse imaging modalities
3. Cross-cell-type Generalization: Train on multiple cell types to learn universal features
4. Perturbation-aware Representations: Capture both baseline morphology and perturbation effects

Technical Details

• Vision transformer architectures for flexible image encoding
• Self-supervised pretraining on unlabeled cellular image datasets
• Fine-tuning strategies for specific biological applications
• Evaluation on downstream tasks including cell type classification, compound mechanism prediction, and phenotype clustering

Applications

• Drug Discovery: Rapidly screen compounds based on phenotypic similarity
• Cell Biology: Identify novel cellular states and transitions
• Quality Control: Detect anomalous cells or imaging artifacts
• Data Integration: Harmonize datasets across labs and platforms