# Single-cell gene expression dynamics during neurogenesis

> **NIH NIH F31** · YALE UNIVERSITY · 2021 · $7,246

## Abstract

PROJECT SUMMARY/ABSTRACT
The proper specification of the neuroectoderm, neural crest, and neural progenitor populations dictates the
overall success of neurodevelopment, and disruption of these populations is at the root of many developmental
diseases. Recent advances in single cell transcriptomics allow for the study of this process in tens of thousands
of cells across the transcriptome. Unlike bulk sequencing, which averages expression across a population, single
cell approaches are essential to studying a multifaceted, asynchronous process like neurodevelopment.
However, several computational barriers exist to analyzing scRNA-seq data, including dropout, technical noise,
and batch effects. Furthermore, studying developing neural tissue provides its own challenges because in many
contexts, progenitor cells and mature progeny are present at the same time. Thus, gaining biological insight from
these data requires development of novel computational methods. The overall goal of this proposal is to establish
computational methods to study single cell gene expression during neurodevelopment, focusing on human
embryoid bodies (EBs). In aim 1, I will characterize the branching structure of germ layer and neural specification
in a 27-day time course of EB culture. In my preliminary results, I used PHATE, a dimensionality reduction tool I
co-developed, to describe a set of smooth transitions from stem cell through the three germ layers to several
derivatives including progenitors of the cardiac, bone, and neural lineages. I will validate this structure of
differentiation by FACS sorting and bulk RNA-sequencing of predicted neuroectoderm and neural progenitor
cells. In aim 2, I will describe the smooth changes of gene expression the drive specification of the neural
lineages by inferring the latent developmental time in the EB time course. To assign each cell a unique single
developmental time label, I developed a method called MELD (Manifold Enhancement of Latent Dimensions). In
my preliminary work, I use MELD to recapitulate known patterns of gene expression during the specification of
neural crest cells. In this aim, I will expand my analysis to the neuroectoderm and neural progenitor populations
and confirm that MELD predicts intermediate cell states present during time points not sampled in the original
time course. The work in this aim will generate a continuous roadmap of gene expression from stem cell through
neural progenitor in EBs. In the third aim I will use mutual information to study edge-rewiring of transcription
factors and their targets (how the regulatory relationships change during neurogenesis). By modelling the
statistical dependency between regulatory factors and their targets as a continuous process, it will be possible
to infer the crucial windows during which regulatory factors expression has the strongest influence on
downstream targets. To confirm the timing of these windows, I will use doxycycline induction to show induction
ha...

## Key facts

- **NIH application ID:** 10128483
- **Project number:** 5F31HD097958-03
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Daniel Burkhardt
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $7,246
- **Award type:** 5
- **Project period:** 2019-04-01 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10128483

## Citation

> US National Institutes of Health, RePORTER application 10128483, Single-cell gene expression dynamics during neurogenesis (5F31HD097958-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10128483. Licensed CC0.

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