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

NIH RePORTER · NIH · R43 · $246,812 · view on reporter.nih.gov ↗

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
WELLSIM BIOMEDICAL TECHNOLOGIES, INC.
Principal Investigator
Yuchao Chen
Activity code
R43
Funding institute
NIH
Fiscal year
2022
Award amount
$246,812
Award type
1
Project period
2022-08-15 → 2024-02-14