Researchers Describe New Method for Cell Line Authentication
Misidentification and contamination of cells used in research studies may lead scientists to draw incorrect conclusions from their findings, wasting time and effort. In a new study, NIAID researchers and their colleagues describe a method to assess mouse cell line misidentification and contamination and detect genetic abnormalities in the cells. They report their results in BMC Genomics.
Cell lines-cultures consisting of cells with a uniform genetic makeup-are widely used for a variety of applications, from basic research to drug screening. While cell lines are a valuable research tool, the prevalence of misidentified and contaminated cells can call into question the validity of experimental findings. Recent analyses have suggested that at least 13 percent of human cell lines and 4 percent of mouse cell lines are falsely identified.
The standard method for authentication of cell lines focuses on short tandem repeats (STRs), or short repeating segments of DNA. STR sequences vary in length and number of repeats, and scientists can identify a cell line by its distinct STR profile, or “fingerprint.” However, STR profiling has a limited ability to distinguish closely related cell lines or to detect partial contamination. In addition, STR markers cannot reliably identify chromosomal abnormalities that may arise in cultured cells.
Results of Study
To address these limitations, researchers led by Herbert C. Morse III, M.D., of NIAID’s Laboratory of Immunogenetics and Fernando Pardo-Manuel de Villena, Ph.D., of the University of North Carolina at Chapel Hill investigated an alternative strategy for cell line authentication. Their strategy focuses on single nucleotide polymorphisms (SNPs), or variations at a single DNA site. SNPs are the most common type of genomic variation.
Using a commercially available SNP profiling tool, the researchers compiled a database of SNP profiles for hundreds of commonly used mouse strains and cell lines. They also developed statistical software, called CLASP, to analyze the SNP profiling data. The scientists tested their authentication strategy on 99 mouse cell lines, 15 of which turned out to have different-than-expected genetic backgrounds. CLASP could distinguish between closely related cell lines, detect contaminants present at concentrations as low as 5 percent, and spot chromosomal abnormalities. The scientists have made CLASP and the reference database freely available for use by other researchers.
The findings suggest that SNP profiling is an effective method to detect misidentification, contamination, and chromosomal abnormalities in mouse cell lines. The researchers also note that the method may have applications beyond cell line authentication, such as forensics and monitoring of mouse strains used in biomedical research.
The researchers propose that laboratories test cell lines periodically, reporting on changes that occur, and that journals and funding agencies require cell line authentication. In addition, they suggest that future work focus on the impact of chromosomal abnormalities and other genomic variations that occur in cell lines over time.