Progress and new challenges in image-based profiling

Area of Science:

• Computational biology
• Cellular imaging analysis

Background:

• Image-based profiling has been pivotal in cellular phenotype analysis for over 20 years.
• It transforms high-throughput microscopy data into unbiased measurements for various biological applications.

Purpose of the Study:

• To review the computational evolution of image-based profiling.
• To detail current methods, limitations, and future directions in the field.

Main Methods:

• Review of computational advancements, including deep learning for feature extraction and data integration.
• Incorporation of single-cell analysis and batch effect correction techniques.
• Analysis of open-source software ecosystems and community standards.

Main Results:

• Deep learning has significantly improved feature extraction, scalability, and multimodal data integration.
• Single-cell transcriptomics-inspired methods enhance analytical precision.
• Open-source platforms and community standards increase accessibility and collaboration.

Conclusions:

• Image-based profiling is rapidly advancing, driven by computational innovations.
• Despite progress, significant challenges require further research and development.
• This review provides a roadmap for researchers navigating the field's technical landscape.