# High-throughput Single Cell Co-assay of Histone Modifications and Transcriptome

> **NIH NIH R41** · EPIGENOME TECHNOLOGIES, INC. · 2021 · $350,000

## Abstract

Abstract
 Epigenome analysis can help dissect the transcriptional regulatory sequences that control spatiotemporal
patterns of gene expression during anima development and disease pathogenesis, but a major challenge to
epigenome analysis is the heterogeneity of primary tissues. Conventional epigenome assays that take bulk
tissues as input only produce population average signals. To address this major bottleneck, we propose to
develop an ultra-high throughput single-cell multi-omics method, Paired-Tag, for joint profiling of histone
modifications and transcriptome. In preliminary experiments, we have demonstrated the feasibility and utility of
this method through analysis of the nuclear transcriptome and multiple histone modifications at single cell
resolution in the adult mouse frontal cortex and hippocampus. In the proposed study, we will further optimize
the current Paired-Tag protocol and demonstrate its utility in cancer epigenome analysis. If successful, the
research would add a major toolkit for the production of cell-type-resolved maps of chromatin state and
transcriptome in complex tissues and enable next generation epigenome analysis of tumor samples.

## Key facts

- **NIH application ID:** 10324108
- **Project number:** 1R41GM146330-01
- **Recipient organization:** EPIGENOME TECHNOLOGIES, INC.
- **Principal Investigator:** Bing Ren
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $350,000
- **Award type:** 1
- **Project period:** 2021-09-15 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10324108

## Citation

> US National Institutes of Health, RePORTER application 10324108, High-throughput Single Cell Co-assay of Histone Modifications and Transcriptome (1R41GM146330-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10324108. Licensed CC0.

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