# Integrated experimental and statistical tools for ultra-high-throughput spatial transcriptomics

> **NIH NIH R21** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2023 · $436,150

## Abstract

ABSTRACT
Imaging-based single cell transcriptomics technologies create a single molecule resolution map of near complete
transcriptome in native tissues, unlocking the long-standing dream of comprehending the spatial organization of
molecules and cells in intact tissues. The structural organization of molecules and cells is closely tied to their
functional organization, thus, transcriptome-scale RNA imaging would provide invaluable insights into how
molecules and cells interact and collectively perform systems-level functions in healthy and diseased tissues.
Among different technologies, MERFISH (multiplexed error-robust fluorescence in situ hybridization) occupies a
leading position with its high spatial resolution, high detection efficiency, single molecule sensitivity, and high
multiplexing capability. However, current technologies are not fast enough to process tissue blocks of any
meaningful size, leaving critical questions like 3D tissue profiling, cross-tissue comparisons, and large-scale
atlas efforts out of reach. Here, we propose to close this gap by at least an order of magnitude by combining
custom biotechnology with modern statistics to build a next-generating imaging-based single cell transcriptomics
platform. We will develop experimental techniques and analytical procedures for 1) hyperspectral imaging and
2) computational deconvolution of optically crowded RNA molecules. Few efforts along these directions exist,
and no method has proven to be effective. The biggest hurdle is the absence of real experiment-based reference
datasets with known ground-truth signals, without which no new methods can be properly validated. For each
strategy, we propose to generate a high-quality MERFISH reference dataset as well as develop new statistical
models and inference procedures to recover the true signals. Our proposed methods can be integrated with each
other and with other approaches to increasing throughput. In long term, we aim to create an in situ single-cell
platform that can profile millions of cells in >100mm2 tissue volumes within a day and perform large-scale
comparative studies of thick tissue/organ blocks. This will enable multi-tissue analysis, comparative studies of
relevant tissue volumes, and large-scale atlas establishment, thereby unlocking new dimensions of human
genome research.

## Key facts

- **NIH application ID:** 10727130
- **Project number:** 1R21HG013180-01
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** Hee-Sun Han
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $436,150
- **Award type:** 1
- **Project period:** 2023-08-22 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10727130

## Citation

> US National Institutes of Health, RePORTER application 10727130, Integrated experimental and statistical tools for ultra-high-throughput spatial transcriptomics (1R21HG013180-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10727130. Licensed CC0.

---

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