# Deep learning models to predict primitive streak formation in human development

> **NIH NIH F31** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2022 · $38,266

## Abstract

Project Summary
Congenital birth defects affect an estimated 3% of live births. To develop effective treatment strategies, a
thorough understanding of early human development is necessary. Our lab recently developed and in vitro
model of human gastrulation, the process by which the three germ layers (endoderm, ectoderm, mesoderm)
are formed around week three of gestation. This so-called “gastruloid” model is formed by treating human
embryonic stem cells with purified differentiation factors that cause them to self-organize into a pattern
resembling a gastrulating embryo. One of the key events during this process is formation of the primitive
streak—a migration of specialized mesenchymal stem cells along the embryonic midline that will form all
mesodermal tissues including the heart, lungs, blood vessels, and cells of the circulatory system. At the same
time, cells on the periphery of the embryo begin forming extraembryonic mesoderm, which will ultimately
become placental tissue. Despite the critical importance of these cell fate changes, it is currently unclear which
population of embryonic stem cells will differentiate to form primitive streak or extraembryonic mesoderm and
how these cell fate decisions are determined. The research objective of this fellowship proposal is to
understand when and how human stem cells differentiate into primitive streak and extraembryonic mesoderm
during gastrulation. My overall approach is to use time-lapse fluorescence imaging to monitor differentiation
decisions in real time and at single-cell solution. I will then employ a specialized type of machine learning
known as deep learning to accurately track the movement and signaling behavior of individual cells. Next, I will
develop a computational model that uses a cell’s image patterns to accurately predict how each cell “chooses”
between differentiation fates. The two specific research aims are: 1) to identify the subpopulation of human
embryonic stem cells that will commit to primitive streak; and 2) to determine the combination of intracellular
and extracellular signaling events that govern differentiation to extraembryonic mesoderm. The proposed work
includes novel experimental procedures (specifically, real-time imaging of gastruloids formation in Aim 1) as
well as unique neural network architectures that accurately predict binary cell fate outcomes of individual stem
cells based on their signaling history. These methods will be generalizable to other biological systems. The
proposed training plan focuses on generating and applying cutting-edge statistical methods tasked with full
single-cell feature data incorporation in order to make robust, theoretically and biologically sound predictions
about human stem cell fate decisions. A better understanding of early human development will inform future
cellular therapies to prevent and treat congenital birth defects. To support my training, I have assembled a
strong mentorship team with expertise in stem cell biolo...

## Key facts

- **NIH application ID:** 10532136
- **Project number:** 5F31HL156464-02
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Tarek M Zikry
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $38,266
- **Award type:** 5
- **Project period:** 2021-01-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10532136

## Citation

> US National Institutes of Health, RePORTER application 10532136, Deep learning models to predict primitive streak formation in human development (5F31HL156464-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10532136. Licensed CC0.

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