# Scalable Cell- and Circuit-Targeted Electrophysiology

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $566,267

## Abstract

The functional activity and dysregulation of neuronal circuits relies critically on the physiology of neuronal
synapses, which are challenging to analyze because they appear in great numbers, and they are difficult to
record in vivo, especially in relation to the dynamic neural codes generated by specific neurons. To make
things even more complex: synapses are incredibly dynamic in fashions that are dependent on recent history,
sensory stimuli, disease state, and other behaviorally relevant contexts. Ideally there would be a technology
that would allow for individual investigators to rapidly analyze synapses between neurons exhibiting neural
codes in a behavioral context, so that it is possible to understand how information is trans formed at synapses.
We here propose to develop a simple, easily deployable toolbox for achieving this, building from several recent
discoveries. First, we have found (manuscript in preparation) that it is possible to automatically perform whole
cell patch clamp neural recording of cells in the living mouse brain that have been identified via two-photon
fluorescence microscopy (e.g., cells of a given type that express a genetically encoded fluorophore). We here
propose to invent a multiple-neuron patching version of this “imagepatching” robot, to enable the simultaneous
characterization of the neural codes in multiple neurons, as well as the synaptic connections between them
(Aim 1). We will also develop miniaturized and optimized hardware capable of performing imagepatching,
neurosurgery, and patch clamp electrode reuse for improved yield and throughput of synaptic assessment.
(Aim 2). Also, we have discovered that it is possible to physically expand preserved neural circuits, by
embedding them in swellable polymers, and then chemically expanding those polymers, a technology we call
expansion microscopy (ExM), which enables nanoscale imaging of 3-D tissues and organisms. We propose to
optimize ExM for the analyses of synapses (Aim 3). We here propose a fast-paced, 4-year grant, to create a
powerful, easy-to-use toolbox that makes the critical task of in vivo synaptic physiology into a routine,
automated procedure. We will distribute all tools and datasets as freely as possible, sharing all algorithms,
circuit designs, and assembly instructions, and hosting visitors to learn these technologies – for which we have
an extensive track record.

## Key facts

- **NIH application ID:** 9893932
- **Project number:** 5R01NS102727-04
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Edward S. Boyden
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $566,267
- **Award type:** 5
- **Project period:** 2017-07-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9893932

## Citation

> US National Institutes of Health, RePORTER application 9893932, Scalable Cell- and Circuit-Targeted Electrophysiology (5R01NS102727-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9893932. Licensed CC0.

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