Dataset Information

High-throughput SNP-based authentication of human cell lines.

ABSTRACT: Use of false cell lines remains a major problem in biological research. Short tandem repeat (STR) profiling represents the gold standard technique for cell line authentication. However, mismatch repair (MMR)-deficient cell lines are characterized by microsatellite instability, which could force allelic drifts in combination with a selective outgrowth of otherwise persisting side lines, and, thus, are likely to be misclassified by STR profiling. On the basis of the high-throughput Luminex platform, we developed a 24-plex single nucleotide polymorphism profiling assay, called multiplex cell authentication (MCA), for determining authentication of human cell lines. MCA was evaluated by analyzing a collection of 436 human cell lines from the German Collection of Microorganisms and Cell Cultures, previously characterized by eight-loci STR profiling. Both assays showed a very high degree of concordance and similar average matching probabilities (~1 × 10(-8) for STR profiling and ~1 × 10(-9) for MCA). MCA enabled the detection of less than 3% of contaminating human cells. By analyzing MMR-deficient cell lines, evidence was obtained for a higher robustness of the MCA compared to STR profiling. In conclusion, MCA could complement routine cell line authentication and replace the standard authentication STR technique in case of MSI cell lines.

SUBMITTER: Castro F

PROVIDER: S-EPMC3492511 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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High-throughput SNP-based authentication of human cell lines.

Castro Felipe F Dirks Wilhelm G WG Fähnrich Silke S Hotz-Wagenblatt Agnes A Pawlita Michael M Schmitt Markus M

International journal of cancer 20120628 2

Use of false cell lines remains a major problem in biological research. Short tandem repeat (STR) profiling represents the gold standard technique for cell line authentication. However, mismatch repair (MMR)-deficient cell lines are characterized by microsatellite instability, which could force allelic drifts in combination with a selective outgrowth of otherwise persisting side lines, and, thus, are likely to be misclassified by STR profiling. On the basis of the high-throughput Luminex platfor ...[more]

PMID: 22700458

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Project description:Although short tandem repeat (STR) analysis is available as a reliable method for the determination of the genetic origin of cell lines, the occurrence of misauthenticated cell lines remains an important issue. Reasons include the cost, effort and time associated with STR analysis. Moreover, there are currently no methods for the discrimination between isogenic cell lines (cell lines of the same genetic origin, e.g. different cell lines derived from the same organism, clonal sublines, sublines adapted to grow under certain conditions). Hence, additional complementary, ideally low-cost and low-effort methods are required that enable (1) the monitoring of cell line identity as part of the daily laboratory routine and 2) the authentication of isogenic cell lines. In this research, we automate the process of cell line identification by image-based analysis using deep convolutional neural networks. Two different convolutional neural networks models (MobileNet and InceptionResNet V2) were trained to automatically identify four parental cancer cell line (COLO 704, EFO-21, EFO-27 and UKF-NB-3) and their sublines adapted to the anti-cancer drugs cisplatin (COLO-704rCDDP1000, EFO-21rCDDP2000, EFO-27rCDDP2000) or oxaliplatin (UKF-NB-3rOXALI2000), hence resulting in an eight-class problem. Our best performing model, InceptionResNet V2, achieved an average of 0.91 F1-score on tenfold cross validation with an average area under the curve (AUC) of 0.95, for the 8-class problem. Our best model also achieved an average F1-score of 0.94 and 0.96 on the authentication through a classification process of the four parental cell lines and the respective drug-adapted cells, respectively, on a four-class problem separately. These findings provide the basis for further development of the application of deep learning for the automation of cell line authentication into a readily available easy-to-use methodology that enables routine monitoring of the identity of cell lines including isogenic cell lines. It should be noted that, this is just a proof of principal that, images can also be used as a method for authentication of cancer cell lines and not a replacement for the STR method.

Project description:BackgroundSingle nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable.ResultsWe developed SNP-PHAGE (SNP discovery Pipeline with additional features for identification of common haplotypes within a sequence tagged site (Haplotype Analysis) and GenBank (-dbSNP) submissions. This tool was applied for analyzing sequence traces from diverse soybean genotypes to discover over 10,000 SNPs. This package was developed on UNIX/Linux platform, written in Perl and uses a MySQL database. Scripts to generate a user-friendly web interface are also provided with common queries for preliminary data analysis. A machine learning tool developed by this group for increasing the efficiency of SNP discovery is integrated as a part of this package as an optional feature. The SNP-PHAGE package is being made available open source at http://bfgl.anri.barc.usda.gov/ML/snp-phage/.ConclusionSNP-PHAGE provides a bioinformatics solution for high throughput SNP discovery, identification of common haplotypes within an amplicon, and GenBank (dbSNP) submissions. SNP selection and visualization are aided through a user-friendly web interface. This tool is useful for analyzing sequence tagged sites (STSs) of genomic sequences, and this software can serve as a starting point for groups interested in developing SNP markers.

Dataset Information

High-throughput SNP-based authentication of human cell lines.

Publications

High-throughput SNP-based authentication of human cell lines.

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