# Deciphering Genetic and Epigenetic Regulatory Logic of Germ Layer Differentiation with Manifold Learning

> **NIH NIH R01** · YALE UNIVERSITY · 2022 · $404,931

## Abstract

Project Summary:
A deep understanding of the genetic and epigenetic regulatory logic that controls early development in hu-
mans is essential for uncovering the mechanisms of developmental diseases and designing new protocols
for regenerative medicine applications. Although over the years many developmentally important genes,
there has not been a systematic understanding of how these genes interact dynamically to create cellular
and organismal phenotype. For this purpose, we propose to combine experimental and computational
approaches to develop predictive models of early germ layer development from human embryonic stem
cell (hESC). In our preliminary work, we generated a single-cell RNA-sequencing (scRNA-seq) dataset of
31,000 hESCs, grown as embryoid bodies (EBs) over a period of 27 days to observe differentiation into
diverse cell lineages. We developed and applied a new dimensionality reduction and visualization method
called PHATE to this system and discovered that PHATE generates a comprehensive and interpretable
picture of differentiation. It captures all branches of early development, including ESCs, neural crest cells
and their derivatives, neural progenitors, and cells of the mesoderm and endoderm layers. Building upon
these findings, we propose to extend this study to a 60-day time course and rendering PHATE more scal-
able to capture differentiation to more mature lineages. Then we propose to integrate scRNA-seq and
epigenetic data, by interpolating bulk CHIP-seq measurements on sorted populations to a pseudo single-
cell resolution. Finally, in order to understand the gene regulatory logic that guides differentiation along
specific lineages, we will train a new neural network architecture known as DyMon (dynamics modeling
network), to walk through the data-manifold to learn a predictive computational model of germ layer de-
velopment in its hidden layers. Thus we will connect gene regulatory logic rewiring with developmental
cellular phenotypes and offer insights into reprogramming during this process.

## Key facts

- **NIH application ID:** 10394331
- **Project number:** 5R01GM130847-04
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Smita Krishnaswamy
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $404,931
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10394331

## Citation

> US National Institutes of Health, RePORTER application 10394331, Deciphering Genetic and Epigenetic Regulatory Logic of Germ Layer Differentiation with Manifold Learning (5R01GM130847-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10394331. Licensed CC0.

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