# High-throughput approaches to local and long-range synaptic connectivity

> **NIH NIH RF1** · COLD SPRING HARBOR LABORATORY · 2020 · $3,325,699

## Abstract

Project Summary/Abstract
The overarching objective of this proposal is to develop a robust approach to map the brain's connections
quickly, accurately, and cost-effectively. Past efforts to address the challenge of teasing apart the complex
connectome of the mammalian brain were subject to a steep trade-off between throughput/efficiency and
resolution. Two cutting-edge neuronal mapping techniques—barcoding based connection mapping (BARseq)
and expansion microscopy (ExM)—have proven they can achieve efficient and high-resolution connection
mapping within mammalian neural tissue. We will optimize and then integrate these two techniques to map
both local and long-range circuitry with a single-synapse resolution. In ExM, neural tissue is physically
expanded, making it easier to disambiguate neural fibers in close proximity and to detect the precise location of
synapse-associated proteins. This approach is ideally suited to teasing apart the paths and connections among
densely packed local circuits. Using BARseq, neurons express unique barcoded tags, which allows even
distant processes to be accurately traced to their somatic origins. Combining BARseq with in situ
immunolabeling techniques, we can also precisely identify the location of synapses on each fiber. Here we
propose to optimize the combination of these two approaches, which will enable a platform for generating a
brainwide microconnectome with single-synapse level resolution. Success in this effort has clear implications
for the future of neuroscience research, including the potential to transform our understanding of both normal
brain circuitry and the specific disruptions that occur within the context of neuropsychiatric disorders.

## Key facts

- **NIH application ID:** 10025780
- **Project number:** 1RF1MH123403-01
- **Recipient organization:** COLD SPRING HARBOR LABORATORY
- **Principal Investigator:** Edward S. Boyden
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $3,325,699
- **Award type:** 1
- **Project period:** 2020-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10025780

## Citation

> US National Institutes of Health, RePORTER application 10025780, High-throughput approaches to local and long-range synaptic connectivity (1RF1MH123403-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10025780. Licensed CC0.

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