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Quantitative Analysis of Protein Expression to Study Lineage Specification in Mouse Preimplantation Embryos

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Published on: February 22, 2016

A statistical framework for inferring genetic requirements from embryo-scale single-cell sequencing experiments.

Madeleine Duran^1,2, Eliza Barkan^1,3,2, Amy Tresenrider^1,2

¹Department of Genome Sciences, University of Washington, Seattle, WA.

Biorxiv : the Preprint Server for Biology

|April 16, 2025

Summary

New software tools, Hooke and Platt, analyze single-cell sequencing data to map gene and cell dependencies during development. This approach reveals new insights into transcription factor roles in vertebrate development.

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Area of Science:

Developmental Biology
Computational Biology
Genomics

Background:

Single-cell sequencing enables high-resolution phenotyping but presents computational challenges for data interpretation.
Identifying direct impacts of perturbations requires modeling complex lineage relationships over time from large datasets.

Purpose of the Study:

To develop computational tools for analyzing single-cell data to understand perturbation impacts.
To characterize direct molecular and cellular consequences of experimental perturbations.
To infer lineage dependencies and gene/cell relationships in development.

Main Methods:

Development of two software tools, "Hooke" and "Platt", designed to analyze single-cell datasets.
Exploitation of statistical patterns within single-cell data to model lineage dependencies.
Application of tools to a large-scale single-cell atlas of perturbed zebrafish embryos.

Main Results:

Hooke and Platt successfully characterized direct molecular and cellular consequences of perturbations.
A coherent map of lineage dependencies was synthesized from zebrafish embryo data.
Previously unappreciated roles for fate-determining transcription factors were revealed.

Conclusions:

Co-variation patterns in single-cell data are a powerful resource for inferring cellular dependencies.
The developed tools facilitate the understanding of gene and cell interactions in vertebrate development.
This work advances the computational analysis of large-scale single-cell perturbation experiments.