# Rapid, Robust, and Routine:  Multiplexed Microscopy for Spatially Resolved Whole-Transcriptomic Single-Cell Profiling and the Construction of Cell Atlases of all Tissues and in all Organisms

> **NIH NIH R01** · BOSTON CHILDREN'S HOSPITAL · 2021 · $429,240

## Abstract

Image-based approaches to single-cell transcriptomics represent one of the most exciting emerging biomedical
research tools. These technologies leverage massively multiplexed single-molecule RNA imaging to provide a
direct measure of not just the expression profile of every cell within intact samples but also the location of every
RNA molecule within those cells. As such, these techniques combine the ability of single-cell RNA sequencing
to generate whole-transcriptome expression measurements and discover and catalog cell types, states, and
lineage with the ability of high-resolution, fluorescence microscopy to interrogate the molecular organization of
cells, define their morphology, and reveal their interactions and organization. Thus, in situ transcriptome-scale
molecular imaging promises advances in a vast array of topics, from the role of intracellular RNA organization in
synaptic remodeling, to the spatial organization of commensal microbial communities and its effect on host gene
expression, to the modulatory role of the microenvironment in tumorigenesis, to name only a few examples.
 One image-based single-cell transcriptomics technique—MERFISH (multiplexed error robust fluorescence
in situ hybridization)—has emerged as a leading technology given its high resolution, high capture efficiency,
single-molecule sensitivity, and unparalleled throughput combined with its proven ability to map the intracellular
organization of large fractions of the transcriptome and discover, functionally annotate, and map cell types within
intact tissues. However, MERFISH remains a nascent technology, and to fully unlock the transformative potential
of both MERFISH and spatially resolved single-cell transcriptomics in general, this technology must be matured.
 First, MERFISH must be made whole-transcriptome. Multiplexing is not the barrier, rather several RNA
categories—highly expressed RNAs, short RNAs, and highly homologous RNAs—remain challenging for this
technique. Through a combination of new experimental and computational advances, we will extend MERFISH
to these categories, creating whole-transcriptome MERFISH and allowing hypothesis-free discovery.
 Second, the biological demands for single-cell throughput are staggering, as even small tissues often contain
tens of millions of cells. By combining new sample preparation techniques, an emerging approach to ultra-high-
throughput microscopy, and advanced image storage and analysis tools, we will increase the throughput of
MERFISH by orders of magnitude, allowing characterization of large tissue areas and tens of millions of cells.
 Finally, the transformative potential for whole-transcriptome imaging could be very broad, yet MERFISH has
been validated in only a few tissues. Thus, we will provide a robust suite of sample preparation protocols and
quality metrics to make routine the application of MERFISH to all tissues and organisms.
 Here we will unlock the potential of this emerging technique by deliverin...

## Key facts

- **NIH application ID:** 10278148
- **Project number:** 1R01GM143277-01
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Jeffrey Moffitt
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $429,240
- **Award type:** 1
- **Project period:** 2021-09-24 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10278148

## Citation

> US National Institutes of Health, RePORTER application 10278148, Rapid, Robust, and Routine:  Multiplexed Microscopy for Spatially Resolved Whole-Transcriptomic Single-Cell Profiling and the Construction of Cell Atlases of all Tissues and in all Organisms (1R01GM143277-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10278148. Licensed CC0.

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