# Spatial genomics single cell analysis of aging brains

> **NIH NIH R01** · CALIFORNIA INSTITUTE OF TECHNOLOGY · 2021 · $868,124

## Abstract

Summary
We recently demonstrated a method called seqFISH+ that profiles >10,000 genes in single cells in intact brain
samples. seqFISH+ provides 10-fold or more improvement over existing methods in the number of mRNAs
profiled and barcodes detected per cell, providing a method to generate spatial atlas of cell. In this project, we
will apply seqFISH+ to the aging brain at P56, 9 month and 18 month of age for both males and females. We
will use the seqFISH+ data to map out cell-to-cell signaling interactions and their effects on cell fate decisions
directly in situ. With the genome coverage and spatial resolution of seqFISH+, it is now possible to perform
discovery-driven studies independent of scRNA-seq, allowing the interrogation of molecular processes in the
aging brain directly in situ. At the same time, we will develop the computational infrastructure to understand the
transition between different developmental time points at the single cell level. This highly innovative and
multidisciplinary approach will allow us to systematically generate a spatial atlas of the aging brain based on
anatomy and molecular identities. These collaborative efforts will allow us to break technological barriers and
develop an unprecedentedly comprehensive open resource for brain research.

## Key facts

- **NIH application ID:** 10196928
- **Project number:** 5R01AG066028-03
- **Recipient organization:** CALIFORNIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Long Cai
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $868,124
- **Award type:** 5
- **Project period:** 2019-09-30 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10196928

## Citation

> US National Institutes of Health, RePORTER application 10196928, Spatial genomics single cell analysis of aging brains (5R01AG066028-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10196928. Licensed CC0.

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