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Updated: Sep 15, 2025

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Published on: March 22, 2018

Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos.

Huihan Bao^1,2, Shihe Zhang^1,2, Zhiyang Yu^1,2

¹School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, China.

Nature Communications

|July 16, 2025

Summary

This summary is machine-generated.

Researchers developed a deep learning method to precisely determine developmental time in fixed fruit fly embryos. This approach reveals gene regulation dynamics without genetic modification, offering new insights into early development.

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Published on: March 22, 2017

Area of Science:

Developmental Biology
Computational Biology
Genetics

Background:

Embryo development relies on complex gene regulatory networks with spatiotemporal dynamics.
Traditional live-imaging methods struggle to track multiple molecular species over time.
Fixed-embryo imaging provides sensitivity but lacks temporal resolution.

Purpose of the Study:

To develop a novel computational approach for precise temporal inference in fixed embryos.
To investigate the spatiotemporal regulation of segmentation genes without genetic modification.
To uncover the kinetics of gene expression driven by transcription factor binding.

Main Methods:

A multi-scale ensemble deep learning model was developed to infer developmental time from nuclear morphology in fixed Drosophila embryos.
Quantitative imaging of fixed wild-type embryos was performed.
A time-resolved theoretical model of single-molecule mRNA statistics was integrated.

Main Results:

The deep learning approach achieved 1-minute resolution for absolute developmental time inference.
Spatiotemporal regulation of the Krüppel (Kr) gene by multiple transcription factors (TFs) was resolved.
Unsteady-state, bursty kinetics of the hunchback (hb) gene were uncovered, driven by dynamic TF binding.

Conclusions:

The developed deep learning framework accurately infers developmental time from fixed embryo images.
This method enables the study of gene regulatory network dynamics in genetically unmodified organisms.
The findings provide new insights into the complex kinetics of gene regulation during early development.