A Novel Automated High-Content Analysis Workflow Capturing Cell Population Dynamics from Induced Pluripotent Stem...

Area of Science:

• Cell Biology
• Bioinformatics
• Microscopy

Background:

• Traditional image analysis requires multiple channels and reference points, posing challenges for single-channel imaging.
• Induced pluripotent stem cells (iPSCs) exhibit unfavorable morphology, complicating segmentation in phase-contrast microscopy.
• Live imaging introduces temporal complexity, hindering integration with static endpoint data.

Purpose of the Study:

• To develop a robust image analysis workflow for segmenting single-channel phase-contrast images, particularly for iPSCs.
• To enable seamless integration of live imaging data with endpoint analyses.
• To facilitate automated high-content analysis of cells with challenging morphology.

Main Methods:

• A novel CellProfiler-based image analysis pipeline was developed for segmenting single-channel images.
• An R-based software solution was employed to reduce temporal data from live imaging to a single data point.
• The combined workflow allows segmentation of iPSCs using only phase-contrast microscopy.

Main Results:

• Robust segmentation of iPSCs was achieved using solely single-channel phase-contrast images.
• The workflow successfully integrates live imaging dynamics with endpoint data.
• Cell line-specific phenotypic signatures were defined for live-imaged iPSCs responding to stimuli.

Conclusions:

• The presented workflow provides an efficient toolset for automated high-content analysis of challenging cell types.
• This approach is suitable for human pluripotent stem cells and other cell types with difficult morphologies.
• The method enhances the characterization of cellular responses to external stimuli in live imaging studies.