# Rapid, Simple and Cost-Effective Detection of Human Cell Line Contamination

> **NIH NIH R44** · NUPROBE USA, INC. · 2020 · $254,960

## Abstract

Cell line contamination and misidentification have dramatic repercussions in the field biomedical research,
largely contributing to the growing concerns about irreproducible results and posing a significant burden on the
global biomedical research budget. While the general awareness about these issues has raised over the last
years, current authentication technologies such as short tandem repeat (STR) profiling are often labor intensive,
slow, costly and not suited for cancer cell lines, which make it very difficult to reliably and continuously monitor
human cell cultures. Profiling single nucleotide polymorphisms (SNPs) would provide a good alternative to STR
for monitoring cancer cell cultures, SNPs being less affected by genomic instabilities than STR markers.
NuProbe’s blocker displacement amplification (BDA) technology uniquely enables multiplexed rare allele
enrichment by PCR. Building upon NuProbe’s BDA technology, we propose to develop a series of highly
multiplexed and highly sensitive qPCR-based SNP profiling assays to detect intra-species cross-contaminants
present in human cell cultures at low levels (down to 1%). We anticipate these assays to translate into rapid,
cost effective, reliable, and easy-to-use cell line contamination detection kits compatible with commonly available
qPCR instruments.

## Key facts

- **NIH application ID:** 10004552
- **Project number:** 5R44AG061957-03
- **Recipient organization:** NUPROBE USA, INC.
- **Principal Investigator:** Yan Helen Yan
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $254,960
- **Award type:** 5
- **Project period:** 2018-09-30 → 2021-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10004552

## Citation

> US National Institutes of Health, RePORTER application 10004552, Rapid, Simple and Cost-Effective Detection of Human Cell Line Contamination (5R44AG061957-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10004552. Licensed CC0.

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