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. 2017 Jan 20;12(1):e0170384.
doi: 10.1371/journal.pone.0170384. eCollection 2017.

Investigation of Cross-Contamination and Misidentification of 278 Widely Used Tumor Cell Lines

Yaqing Huang  1 , Yuehong Liu  1 , Congyi Zheng  1   2 , Chao Shen  1   2
Affiliations

Investigation of Cross-Contamination and Misidentification of 278 Widely Used Tumor Cell Lines

Yaqing Huang et al. PLoS One. .

Abstract

In recent years, biological research involving human cell lines has been rapidly developing in China. However, some of the cell lines are not authenticated before use. Therefore, misidentified and/or cross-contaminated cell lines are unfortunately commonplace. In this study, we present a comprehensive investigation of cross-contamination and misidentification for a panel of 278 cell lines from 28 institutes in China by using short tandem repeat profiling method. By comparing the DNA profiles with the cell bank databases of ATCC and DSMZ, a total of 46.0% (128/278) cases with cross-contamination/misidentification were uncovered coming from 22 institutes. Notably, 73.2% (52 out of 71) of the cell lines established by the Chinese researchers were misidentified and accounted for 40.6% of total misidentification (52/128). Further, 67.3% (35/52) of the misidentified cell lines established in laboratories of China were HeLa cells or a possible hybrid of HeLa with another kind of cell line. Furthermore, the bile duct cancer cell line HCCC-9810 and degenerative lung cancer Calu-6 exhibited 88.9% match in the ATCC database (9-loci), indicating that they were from the same origin. However, when we used 21-loci to compare these two cell lines with the same algorithm, the percent match was only 48.2%, indicating that these two cell lines were different. The SNP profiles of HCCC-9810 and Calu-6 also revealed that they were different cell lines. 150 cell lines with unique profiles demonstrated a wide range of in vitro phenotypes. This panel of 150 genomically validated cancer cell lines represents a valuable resource for the cancer research community and will advance our understanding of the disease by providing a standard reference for cell lines that can be used for biological as well as preclinical studies.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Incidence of misidentification among cell lines used in laboratories in China.
278 samples collected from 28 independent sources of China are divided into 2 groups according to their original source: Non-Chinese cell models (n = 193) and Chinese cell models (n = 71).
Fig 2
Fig 2. The numbers of cross-contamination in the non-Chinese model.
Fig 3
Fig 3. The numbers of cross-contamination in the Chinese model.
See this image and copyright information in PMC

References

    1. Masters JR. Cell-line authentication: End the scandal of false cell lines. Nature. 2012;492(7428):186. - PubMed
    1. Nardone RM. Eradication of cross-contaminated cell lines: a call for action. Cell biology and toxicology. 2007;23(6):367–72. 10.1007/s10565-007-9019-9 - DOI - PubMed
    1. Markovic O, Markovic N. Cell cross-contamination in cell cultures: the silent and neglected danger. In vitro cellular & developmental biology Animal. 1998;34(1):1–8. - PubMed
    1. Liang-Chu MM, Yu M, Haverty PM, Koeman J, Ziegle J, Lee M, et al. Human biosample authentication using the high-throughput, cost-effective SNPtrace(TM) system. PloS one. 2015;10(2):e0116218 PubMed Central PMCID: PMC4340925. 10.1371/journal.pone.0116218 - DOI - PMC - PubMed
    1. Valletta E, Kucera L, Prokes L, Amato F, Pivetta T, Hampl A, et al. Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks. PloS one. 2016;11(1):e0147414 PubMed Central PMCID: PMC4731057. 10.1371/journal.pone.0147414 - DOI - PMC - PubMed