# Resolving Spatiotemporal Determinants of Cell Specification in Corticogenesis with Latent Space Methods

> **NIH NIH R00** · JOHNS HOPKINS UNIVERSITY · 2024 · $224,100

## Abstract

Project Summary
High-throughput profiling of hundreds of thousands of cells in the central nervous system (CNS) is currently
underway. One of the goals of the BRAIN initiative is to build a census of cell types in the CNS, however
previous work in single cell RNA sequencing (scRNAseq) has demonstrated that reliance on small collections
of marker genes for cell type/state/position classification is insufficient to account for the dynamic nature of and
variation in cellular classes/states. Previous work from both myself and others has demonstrated that latent
space methods identify low dimensional patterns from high dimensional profiling data can discover molecular
drivers of cell types and states in scRNAseq. However, the use of algorithms untethered to biological
constraints or not extensively functionally validated can lead to the arbitrary delineation of cell class/state and
the trivial designation of “novel” cell types. As proper development of the CNS requires precise regulation and
coordination of spatial and temporal cues, the overall objective of this application is to develop analytic and
experimental methods that integrate spatiotemporal information with scRNAseq to learn meaningful latent
spaces. Specifically, I will 1) generate a comprehensive collection of transcriptional signatures for spatial
features of the brain, 2) build dimension reduction software to encode spatial and cell cycle information to
account for the highly specific organization of cells in the CNS, 3) derive a statistic, projectionDrivers, that
allows for quantification of the gene drivers of differential latent space usage, and 4) define a statistic,
proMapR, that will tell you the probability of a cell existing in a particular location in the brain at a given point in
time from the cell's transcriptional signature. The ability to define and validate biologically meaningful latent
spaces not only enables multiOmic data integration and exploratory analysis of scRNA-seq data via the
massive amount of publicly available data, but also lays the groundwork for multimodal data integration—a
necessary next step to characterize how individual cells and complex neural circuits interact in both time and
space.

## Key facts

- **NIH application ID:** 10848501
- **Project number:** 5R00NS122085-04
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Genevieve Lauren Stein-O'Brien
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $224,100
- **Award type:** 5
- **Project period:** 2021-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10848501

## Citation

> US National Institutes of Health, RePORTER application 10848501, Resolving Spatiotemporal Determinants of Cell Specification in Corticogenesis with Latent Space Methods (5R00NS122085-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10848501. Licensed CC0.

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