Generating realistic single-cell images from CellProfiler representations.

Area of Science:

• Computational Biology
• Bioimage Analysis
• Machine Learning in Biology

Background:

• High-throughput imaging generates vast biological data, often analyzed via quantitative representation vectors.
• Current methods for extracting cellular information into representations include hand-crafted and machine-learning approaches.
• Machine-learning representations offer high reconstruction but lack biological interpretability, while hand-crafted ones are interpretable but uncertain in image generation.

Purpose of the Study:

• To develop a model that generates realistic cell images directly from CellProfiler representations.
• To ensure biological interpretability is maintained during the image generation process.
• To explore the model's capability in generating conditional phenotypes for applications in diagnostics and drug screening.

Main Methods:

• Proposed a novel CellProfiler to image (CP2Image) model.
• Evaluated model robustness across various architectures: ResNet, InceptionNet, and Transformer.
• Demonstrated preservation of biological information by correlating changes in CellProfiler features with generated image alterations.

Main Results:

• The CP2Image model successfully generates realistic cell images from CellProfiler representations.
• Biological information encoded in CellProfiler features is well-preserved in the generated images.
• The model exhibits robustness across different neural network architectures.

Conclusions:

• The CP2Image model bridges the gap between interpretable hand-crafted features and realistic image generation.
• This approach facilitates the use of biologically meaningful representations for image synthesis.
• The ability to generate conditional phenotypes holds significant potential for advancing cell-based diagnostics and drug discovery.