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A resource for cell line authentication, annotation and quality control


Cell line misidentification, contamination and poor annotation affect scientific reproducibility. Here we outline simple measures to detect or avoid cross-contamination, present a framework for cell line annotation linked to short tandem repeat and single nucleotide polymorphism profiles, and provide a catalogue of synonymous cell lines. This resource will enable our community to eradicate the use of misidentified lines and generate credible cell-based data.

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Figure 1: Inconsistencies in cell line nomenclature.
Figure 2: Analysis of STR and SNP fingerprinting of cell lines.
Figure 3

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We thank S. Ghosh for bioinformatics support, E. Hall and Y. Reid (ATCC) for their intellectual input and expertise in genetic testing. M. Kline for supplying STR profiles. J. Settleman and D. Stokoe for discussions.

Author information

Authors and Affiliations



This collection of authenticated cell line data will be made available through NCBI’s BioProject and BioSample databases, accessible through accession number PRJNA271020, for continued community development and refinement. R.M.N. conceived and supervised the study; M.Y., S.K.S., M.M.Y.L.-C. and G.L. were responsible for cell line banking, experimentation and data collection; S.A., M.B., J.Y., C.K., R.B. and J.S.K. performed data curation and wrote the code for SNP and STR analyses; R.M.N., M.Y., S.K.S., M.M.Y.L.-C., M.B. and F.P. performed manual curation of cell line nomenclature and associated data. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Richard M. Neve.

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Competing interests

The majority of authors are employees of Genentech Inc. and/or hold stock in Roche.

Extended data figures and tables

Extended Data Figure 1 Comparison of STR and SNP genotyping assays.

a, Comparison of STR and SNP frequency distributions of pairwise identity alignment scores for 836 lines. Identity scores are computed using the Tanabe algorithm for both 8-locus STR and 48-locus SNP genotype results (compare with Fig. 2a). Total number of comparisons was 349,030 (348,953 non-synonymous and 77 synonymous pairs of cell lines). For plotting purposes, a random subset of 25,000 non-synonymous pairs is displayed. As a consequence of using fewer STR loci, non-synonymous STR standard deviation increased from 0.083 to 0.113, and more truly synonymous pairs now fall below the mean-plus-4-s.d. cutoff. b, Univariate distribution of SNP Tanabe identity scores for data shown in Fig. 2. Results for 2,862 replicate pairs are shown as black dots. (Synonymous pairs are included in density computation, but are so rare compared to non-synonymous pairs that they make no visible change in plotted curve.) Vertical scale is such that total area under curve is 1 unit. Reference lines were computed using non-synonymous pairs only. c, As for b, but showing 16-locus STR identity scores. True replicate pairs are shown in black; pairwise identity scores for a set of seven HeLa-derived lines—which are closely related genetically, but do not constitute true replicates—are shown in red. A mean ± 4s.d. reference line corresponding to a P value of 3.2 × 10−5, is shown for both graphs. Note that reference line is better separated from true replicate results for STR data than for SNP data.

Extended Data Figure 2 Impact of changing the confidence threshold on detecting cell line contamination by SNP profiling.

a, SNP detection using the Fluidigm system was performed on DNA extracted from differing ratios of AU565:Panc 08.13 cells. The raw data was analysed using confidence thresholds of 65 (Th65), 85 (Th85), 90 (Th90) and 95 (Th95). Examples of data are shown for Th65 and Th95. For each SNP XX, XY and YY allele calls are represented by green, blue and red, respectively, and no calls are in grey. b, Table showing percent identity when SNP calls were compared with the database of SNPs. As the confidence threshold increased, a lower level of contamination could be detected as evidenced by decreased correlation values. Ratios depict the relative abundance of AU565:Panc 08.13 cells (for example, 99:2 = 99% AU565 mixed with 2% Panc 08.13). Data are representative of at least two independent experiments.

Extended Data Figure 3 Electropherograms and table of results for STR profiling of DNA extracted from differing ratios of AU565:Panc 08.13 cells.

STRs were determined (see Methods) for DNA extracted from differing ratios of AU565:Panc 08.13 cells. a, Example electropherograms for five (D3S1358, THO1, D21S11, D18S51 and Penta E) of the 16 STR markers are shown. Ratios depict the relative abundance of AU565:Panc 08.13 cells (for example, 99:2 = 99% AU565 mixed with 2% Panc 08.13). Data are representative of at least two independent experiments. b, Table showing STR calls for all STR loci and the top matches when compared to the database of STR calls (Supplementary Table 3).

Extended Data Figure 4 Detection of cross-species contamination.

a, Images of early (p4) and later (p8) passage CoCM-1 cells in culture showing a subpopulation of small, round, loosely attached cells overwhelming the culture over time. b, c, PCR-based detection of human (left panel) and mouse (right panel) cytochrome b oxidase I (COX1) in cell lines (b) and in titrated mixtures of human (MOLT4) and mouse (STV2) cell lines (c) to determine limit of detection. 18S, PCR loading control. d, Flow cytometric analysis of mouse and human CD29 staining in contaminated CoCM-1 cell line. Data are representative of at least two independent experiments.

Supplementary information

Supplementary Information

This file contains the legends for Supplementary Tables 1-14. (PDF 93 kb)

Supplementary Tables

The 2 files in this zipped file contain Supplementary Tables 1-14 (see Supplementary Information file for details). (ZIP 30178 kb)

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Yu, M., Selvaraj, S., Liang-Chu, M. et al. A resource for cell line authentication, annotation and quality control. Nature 520, 307–311 (2015).

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