# PIXEL-seq-based spatial, multi-omic profiling for senescent cell mapping with single-cell resolution

> **NIH NIH UG3** · UNIVERSITY OF WASHINGTON · 2022 · $542,500

## Abstract

ABSTRACT
Comprehensive identification and characterization of senescent cells in morphologically intact human tissues is
important for understanding senescence in vivo and the targeted removal of these cells to improve healthspan
and lifespan. This task has been challenging due to the lack of universal and unequivocal markers characterizing
the senescence state, which reflects the complexity of the senescence phenotype and the existence of highly
heterogeneous senescence programs. A preferred avenue for discovering senescence markers is to spatially
map ‘omics’ states of cell types in different tissues and life stages at single cell resolution. The overall goal of
this project is to (i) develop a spatial, single-cell-resolution, multimodal method that simultaneously analyze
transcriptome, open chromatin, and proteome (or secretome), and (ii) optimize and scale it for mapping
senescent cells in human tissues. The PI’s laboratory has recently developed a novel technique PIXEL-seq
(polony-indexed library-sequencing) and applied it to spatially profile transcriptome with 1-µm resolution and high
RNA capture efficiency. To realize its potential for studying in vivo senescence mechanism and production-scale
data generation, three specific aims will be pursued: 1) In UG3 Year 1, demonstrate PIXEL-seq-based spatial
transcriptome, proteome, and ATAC-seq assays with single-cell resolution; 2) In UH3 Year 2, optimize and
combine these assays for human tissue mapping; and 3) In UH3 Years 3-4, scale up application to human heart,
liver, and lung tissue mapping. Under the first aim, PIXEL-seq will be developed to achieve single-cell resolution
by image-guided cell segmentation (Aim 1A) and expanded to spatial proteome (Aim 1B) and open chromatin
accessibility assays (Aim 1C) by rendering DNA-tagged antibodies and Tn5-treated chromosomal DNAs,
respectively, to capture by polony gels. For the second aim, the proteome assay will be optimized and scaled to
200-plex using polyclonal mini-binders, allowing the cross-validation of senescence markers and associated
isoforms and post-translational modifications (Aim 2A). These assays will be integrated for multimodal data
capture and validated using human tissues (Aim 2B). In the third aim, the application will be scaled up by
increasing throughput of polony gel fabrication (Aim 3A) and to deliver to the CODCC for public release of high-
quality data on several sites of multiple organs from several individual tissue donors (Aim 3B and 3C). The
investigators will also participate in the Consortium common project and other collaborations yet to be formed.
The proposed project is innovative in that this method will for the first time generate the spatial multimodal human
tissue data at unprecedented depth and resolution. It is significant because the assays do not require specialized
equipment and can be widely implemented in the SenNet and other single cell consortia.

## Key facts

- **NIH application ID:** 10494128
- **Project number:** 5UG3CA268096-02
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Liangcai Gu
- **Activity code:** UG3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $542,500
- **Award type:** 5
- **Project period:** 2021-09-24 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10494128

## Citation

> US National Institutes of Health, RePORTER application 10494128, PIXEL-seq-based spatial, multi-omic profiling for senescent cell mapping with single-cell resolution (5UG3CA268096-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10494128. Licensed CC0.

---

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