MitoTrack, a user-friendly semi-automatic software for lineage tracking in living embryos

Area of Science:

• Developmental Biology
• Cell Biology
• Bioinformatics

Background:

• Faithful propagation of transcriptional programs is crucial during progenitor cell division in developing embryos.
• Analyzing the stochastic nature of transcriptional activation timing requires processing large datasets.
• Existing methods may lack automated, integrated solutions for lineage tracking and transcriptional timing analysis.

Purpose of the Study:

• To develop an automated software solution for analyzing transcriptional activation timing and lineage history in developing embryos.
• To provide a user-friendly tool that simplifies the analysis of complex biological data from raw imaging to final results.
• To enable precise reporting of transcriptional activation timing while maintaining lineage tree integrity.

Main Methods:

• Development of an integrated, open-source Python software named MitoTrack.
• Implementation of automated image analysis for segmenting and tracking both small and large objects.
• Integration of a graphical user interface for user supervision, correction, and validation of analysis steps.

Main Results:

• MitoTrack provides a user-friendly, open-access platform for dual analysis of transcriptional activation timing and lineage tree history.
• The software automates the process from raw data to final results, enabling precise reporting for each nucleus in living embryos.
• MitoTrack allows for easy supervision and correction, ensuring the reliability of the analysis.

Conclusions:

• MitoTrack offers a robust and accessible solution for studying transcriptional dynamics during embryonic development.
• The software facilitates high-throughput analysis of stochastic processes like transcriptional activation.
• MitoTrack enhances the ability to investigate cell lineage and gene expression timing in developmental biology research.