학술논문

Multisite assessment of reproducibility in high‐content cell migration imaging data.
Document Type
Article
Source
Molecular Systems Biology. 6/12/2023, Vol. 19 Issue 6, p1-15. 15p.
Subject
*CELL imaging
*CELL migration
*LIFE sciences
*IMAGE analysis
*CELL morphology
*CANCER cell culture
Language
ISSN
1744-4292
Abstract
High‐content image‐based cell phenotyping provides fundamental insights into a broad variety of life science disciplines. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, with particular relevance for high‐quality open‐access data sharing and meta‐analysis. However, the sources and degree of biological and technical variability, and thus the reproducibility and usefulness of meta‐analysis of results from live‐cell microscopy, have not been systematically investigated. Here, using high‐content data describing features of cell migration and morphology, we determine the sources of variability across different scales, including between laboratories, persons, experiments, technical repeats, cells, and time points. Significant technical variability occurred between laboratories and, to lesser extent, between persons, providing low value to direct meta‐analysis on the data from different laboratories. However, batch effect removal markedly improved the possibility to combine image‐based datasets of perturbation experiments. Thus, reproducible quantitative high‐content cell image analysis of perturbation effects and meta‐analysis depend on standardized procedures combined with batch correction. Synopsis: Analyses of the sources of variability of cell migration data obtained by live cell imaging produced by independent labs with identical protocol and key reagents show that the highest variability occurs between labs and the variance can be substantially reduced by batch effect removal. To quantify the sources of variability, a live cell imaging design of cell migration in 2D and 3D culture was replicated by expert labs, different team members and in independent replicates in a hierarchical structure.Lab to lab variance and, to lesser extent, variation between team members were key sources of technical variance, based on Linear Mixed Effect model analysis.Batch effect removal dramatically reduced the variance and was verified using a 3D cell migration dataset. [ABSTRACT FROM AUTHOR]