An Open-Source Image Analysis Method for Quantifying Reporter Fluorescence
Ball, K. F.; Perez, J. A.; Cooper, A. M.; Pira, C. U.; Oberg, K. C.; Wilson, C. G.
Show abstract
Image analysis is a rapidly developing field that provides unique opportunities to characterize and quantify spatial information in images. We study cis-regulatory modules (CRMs), non-coding DNA regions that regulate gene expression, during development using fluorescent reporters in vivo. Characterizing CRM activity during development presents challenges including image segmentation into biologically relevant regions of interest that are not easily distinguishable via common segmentation methods, fluorophore band passing, and variable transfection undermine standardized analysis. To quantify and compare CRM activity levels, we compiled an open-source computer vision tool stack in the form of a Python-based Jupyter notebook and tested four analysis methods to assess their efficacy in quantifying CRM expression in limb development. This Jupyter notebook provides a reproducible, standardized workflow that can be adapted to numerous image analysis applications.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.