ESC-Track: A computer workflow for 4-D segmentation, tracking, lineage tracing and dynamic context analysis of ESCs

Area of Science:

• Stem cell biology
• Developmental biology
• Biotechnology

Background:

• Embryonic stem cells (ESCs) are vital for studying differentiation and tissue repair.
• Advances in live imaging allow observation of ESCs, but low resolution hinders detailed analysis.
• Understanding lineage and microenvironmental factors is key to maintaining ESC fitness.

Purpose of the Study:

• To present ESC-Track (ESC-T), a novel workflow for analyzing 4-D confocal image data of ESCs.
• To enable automated cell segmentation, tracking, and lineage reconstruction.
• To facilitate quantitative analysis of cell dynamics and microenvironmental influences.

Main Methods:

• Developed ESC-Track (ESC-T) workflow with automated cell/nuclear segmentation and tracking.
• Incorporated manual editing for error correction and visual inspection.
• Applied ESC-T to analyze Myc intensity in mouse ESC (mESC) lineage and neighborhood.

Main Results:

• ESC-T successfully reconstructs lineage trees and cell neighborhoods from 4-D data.
• The tool quantifies cell features, including fluorescence dynamics, morphology, and motion.
• Demonstrated utility by examining Myc intensity fluctuations in mESC context.

Conclusions:

• ESC-T is a powerful tool for high-resolution, long-term imaging of ESCs.
• Enables evaluation of genealogical and microenvironmental cues essential for ESC fitness.
• Advances the study of stem cell dynamics and tissue repair strategies.