Optimized smFISH Pipeline for Studying Nascent Transcription in Mouse Embryonic Tissue Samples

Area of Science:

• Developmental Biology
• Molecular Biology
• Genetics

Background:

• Understanding gene expression dynamics is key to deciphering biological processes.
• Traditional methods often overlook nascent transcription, focusing only on mature mRNA.
• Current live-cell approaches for nascent transcription are limited in mammalian embryos.

Purpose of the Study:

• To develop and optimize a method for visualizing and quantifying nascent transcription in mammalian embryos.
• To address the limitations of existing techniques in studying early developmental gene expression.
• To enable precise analysis of transcriptional dynamics in mouse embryonic tissue.

Main Methods:

• Optimization of single molecule fluorescent in situ hybridization (smFISH).
• Integration of deep learning algorithms for automated detection of nascent transcripts.
• Application to mouse embryonic tissue samples for spatial and temporal analysis.

Main Results:

• Successful adaptation and optimization of smFISH for nascent transcription detection in mouse embryos.
• Development of a deep learning pipeline for automated and accurate quantification of nascent transcripts.
• Demonstration of reproducible results for analyzing gene expression dynamics.

Conclusions:

• The optimized smFISH and deep learning method provides a powerful tool for studying nascent transcription in mammalian embryos.
• This approach facilitates precise quantification and comparison of transcriptional dynamics across developmental stages.
• The technique holds significant potential for advancing research in developmental biology and gene regulation.