Multisite assessment of reproducibility in high-content cell migration imaging data

Area of Science:

• Life sciences
• Cell biology
• Microscopy

Background:

• High-content image-based cell phenotyping offers valuable insights across life science disciplines.
• Reproducibility is essential for accurate conclusions, data sharing, and meta-analysis.
• Systematic investigation of variability sources in live-cell microscopy data is lacking.

Purpose of the Study:

• To identify and quantify sources of biological and technical variability in high-content cell imaging.
• To assess the impact of variability on the reproducibility and meta-analysis of live-cell microscopy data.
• To determine strategies for improving the reliability of quantitative cell image analysis.

Main Methods:

• Utilized high-content imaging data focusing on cell migration and morphology.
• Analyzed variability across multiple scales: laboratories, persons, experiments, technical repeats, cells, and time points.
• Evaluated the effect of batch effect removal on combining image-based datasets.

Main Results:

• Significant technical variability was observed between laboratories and, to a lesser extent, between persons.
• Direct meta-analysis of data from different laboratories yielded limited value due to inter-laboratory variability.
• Batch effect removal substantially enhanced the ability to combine image-based datasets from perturbation experiments.

Conclusions:

• Reproducible quantitative high-content cell image analysis necessitates standardized experimental procedures.
• Batch correction is a critical step for enabling effective meta-analysis of image-based perturbation experiments.
• Addressing technical variability is key to maximizing the impact and utility of cell phenotyping data.