# A versatile approach for highly multiplexed, high-resolution imaging of endogenous molecules

> **NIH NIH RF1** · UNIVERSITY OF CONNECTICUT STORRS · 2022 · $2,242,506

## Abstract

Project Summary
 The quest to understand the brain’s complex structure has become more challenging as the high
degree of molecular heterogeneity among brain cells has become evident in recent years. Mapping the brain in
detail will require incorporating large amounts of molecular information into high-resolution imaging. Current
imaging methods are limited by the number of distinguishable detection channels, so greater degrees of
multiplexing entail repeated cycles of stripping and reapplying probes. These methods degrade tissue integrity
and impair sensitivity, and do not address the other major challenge of multiplexing- incompatibility between
protein and RNA labeling methods and the need to compromise both for simultaneous detection. We propose
a novel imaging approach, Serial-section parallel immuno/ Fluorescence In Situ Hybridization (SpiFISH),
whose core strategy is to physically subdivide specimens into sections two orders of magnitude smaller than a
neuronal cell body. Each section is treated as a separate sample for labeling and imaging, so hundreds of
discrete labeling experiments can be performed in parallel on a given neuron. The method is based on ultrathin
sectioning, but unlike existing ultrathin sectioning methods such as electron microscopy (EM) and array
tomography, SpiFISH does not use EM embedding resins. Without resin interfering, sensitive immunolabeling
and RNA detection are possible. Each section is labeled and imaged separately, so that any given cell can be
labeled with many different antibodies and RNA probes under conditions optimized for each. Sections are
shelf-stable, so large datasets can be built up across time and even across laboratories. The method allows
multiplexing of techniques as well as labels, so the same sample can be used with multiple imaging and
staining platforms. The goal of this project is to develop robust, reproducible protocols and workflows from
sample preparation through data analysis across scales. This will include small samples through whole rodent
brains and streamlined methods for fully manual through fully automated data collection and analysis.

## Key facts

- **NIH application ID:** 10505946
- **Project number:** 1RF1MH130472-01
- **Recipient organization:** UNIVERSITY OF CONNECTICUT STORRS
- **Principal Investigator:** LINNAEA E OSTROFF
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,242,506
- **Award type:** 1
- **Project period:** 2022-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10505946

## Citation

> US National Institutes of Health, RePORTER application 10505946, A versatile approach for highly multiplexed, high-resolution imaging of endogenous molecules (1RF1MH130472-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10505946. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*