# Link phenotypic characteristics with gene expression profile of single cells at high throughput for drug discovery and cell therapy development

> **NIH NIH R43** · WELLSIM BIOMEDICAL TECHNOLOGIES, INC. · 2022 · $246,812

## Abstract

PROJECT SUMMARY / ABSTRACT
Researchers have shown substantial interest in the integration of phenomics and transcriptomics data for novel
biological insights since phenotypic characteristics are highly correlated with gene expression patterns. However,
there is a lack of an efficient approach that could link gene expression profiles to cellular phenotypes (e.g.,
protein abundance and localization, cellular morphology, enzymatic and metabolic activity) at single-cell level in
a high-throughput manner. To bridge this gap, WellSIM proposes to develop a platform which can extract
phenotypic characteristics from thousands of individual cells via high-content imaging and link these features
with their transcriptomic profiles. This technology will not only achieve multi-dimensional single cell studies, but
also improve the performance of single-cell RNA sequencing through automatic sample preparation on a
microfluidic device without using barcoded beads.

## Key facts

- **NIH application ID:** 10482180
- **Project number:** 1R43HG012528-01
- **Recipient organization:** WELLSIM BIOMEDICAL TECHNOLOGIES, INC.
- **Principal Investigator:** Yuchao Chen
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $246,812
- **Award type:** 1
- **Project period:** 2022-08-15 → 2024-02-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10482180

## Citation

> US National Institutes of Health, RePORTER application 10482180, Link phenotypic characteristics with gene expression profile of single cells at high throughput for drug discovery and cell therapy development (1R43HG012528-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10482180. Licensed CC0.

---

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