Cell line authentication: a necessity for reproducible biomedical research.

Immortalized or continuous cell lines are invaluable tools in basic and preclinical research. However, the widespread use of misidentified cell lines is a serious threat to scientific reproducibility. Based on the experiences of mandatory cell line authentication at the International Journal of Cancer (IJC), we provide an overview of the issues pertinent to misidentified cell lines and discuss available solutions. We also summarize the lessons learned, revealing that at least 5% of the human cell lines used in manuscripts considered for peer review are misidentified. About 4% of the considered manuscripts are rejected for severe cell line problems, and most are subsequently published in other journals. In order to diminish such malpractice and its consequences for the scientific record, we postulate that strict multi-layered quality control is essential. Besides journals and publishers, we encourage scientists, research institutions, and funders to take action on the matter and revise their respective policies. Hence, we provide concrete recommendations on introducing regular authentication schemes and staff training, and discuss future steps for enhancing good cell culture practices.

Identification of human gene research articles with wrongly identified nucleotide sequences.

Nucleotide sequence reagents underpin molecular techniques that have been applied across hundreds of thousands of publications. We have previously reported wrongly identified nucleotide sequence reagents in human research publications and described a semi-automated screening tool Seek & Blastn to fact-check their claimed status. We applied Seek & Blastn to screen >11,700 publications across five literature corpora, including all original publications in Gene from 2007 to 2018 and all original open-access publications in Oncology Reports from 2014 to 2018. After manually checking Seek & Blastn outputs for >3,400 human research articles, we identified 712 articles across 78 journals that described at least one wrongly identified nucleotide sequence. Verifying the claimed identities of >13,700 sequences highlighted 1,535 wrongly identified sequences, most of which were claimed targeting reagents for the analysis of 365 human protein-coding genes and 120 non-coding RNAs. The 712 problematic articles have received >17,000 citations, including citations by human clinical trials. Given our estimate that approximately one-quarter of problematic articles may misinform the future development of human therapies, urgent measures are required to address unreliable gene research articles.

The Extensive and Expensive Impacts of HEp-2 [HeLa], Intestine 407 [HeLa], and Other False Cell Lines in Journal Publications.

Cell lines are essential models for biomedical research. However, they have a common and important problem that needs to be addressed. Cell lines can be misidentified, meaning that they no longer correspond to the donor from whom the cells were first obtained. This problem may arise due to cross-contamination: the accidental introduction of cells from another culture. The contaminant, which is often a rapidly dividing cell line, will overgrow and replace the original culture. The end result is a false cell line, also known as a misidentified or imposter cell line. False cell lines may come from an entirely different species, tissue, or cell type than the original donor. If undetected, false cell lines produce unreliable and irreproducible results that pollute the biomedical literature and threaten the development of reliable drug discovery and meaningful patient treatments.The goal of this study was to ascertain how widespread this problem is and how it affects the literature, as well as to estimate how much funding has been used to produce pools of scientific literature of questionable value. We focus on HEp-2 [HeLa] and Intestine 407 [HeLa], two false cell lines that are widely used in the scientific literature but were shown to be cross-contaminated in 1967. These two cell lines have been used in 8497 and 1397 published articles and extensively described as laryngeal cancer and normal intestine, respectively, rather than their true identity: the cervical cancer cell line HeLa. Discussed are tools, approaches, and resources that can address this issue-both retrospectively and prospectively.

Ancestry matters: Building inclusivity into preclinical study design.

Human cell lines (CLs) are key assets for biomedicine but lack ancestral diversity. Here, we explore why genetic diversity among cell-based models is essential for making preclinical research more equitable and widely translatable. We lay out practical actions that can be taken to improve inclusivity in study design.

Cross contamination meets misclassification: Awakening of CHP-100 from sleeping beauty sleep-A reviewed model for Ewing's sarcoma.

A human cell line of neuroblastic tissue, which was believed to have been lost to science due to its unavailability in public repositories, is revived and reclassified. In the 1970s, a triple set of neuroblastoma (NB) cell lines became available for research as MYCN-amplified vs nonamplified models (CHP-126/-134 and CHP-100, respectively). Confusingly, CHP-100 was used in subsequent years as a model for NB and, since the 1990s, as a model for neuroepithelioma and later as a model for Ewing's sarcoma (ES), which inevitably led to non-reproducible results. A deposit at a bioresource center revealed that globally available stocks of CHP-100 were identical to the prominent NB cell line IMR-32 and CHP-100 was included into the list of misidentified cell lines. Now we report on the rediscovery of an authentic CHP-100 cell line and provide evidence of incorrect classification during establishment. We show that CHP-100 cells carry a t(11;22)(q24;q12) type II EWSR1-FLI1 fusion and identify it as a classic ES. Although the question of whether CHP-100 was a virtual and never existing cell line from the beginning is now clarified, the results of all relevant publications should be considered questionable. Neither the time of the cross-contamination event with IMR-32 is known nor was the final classification as a model for Ewing family of tumors available with an associated short tandem repeat profile. After a long road of errors and confusion, authentic CHP-100 is now characterized as a type II EWSR1-FLI1 fusion model 44 years after its establishment.

CLASTR: The Cellosaurus STR similarity search tool - A precious help for cell line authentication.

Despite an increased awareness of the problematic of cell line cross-contamination and misidentification, it remains nowadays a major source of erroneous experimental results in biomedical research. To prevent it, researchers are expected to frequently test the authenticity of the cell lines they are working on. STR profiling was selected as the international reference method to perform cell line authentication. While the experimental protocols and manipulations for generating a STR profile are well described, the available tools and workflows to analyze such data are lacking. The Cellosaurus knowledge resource aimed to improve the situation by compiling all the publicly available STR profiles from the literature and other databases. As a result, it grew to become the largest database in terms of human STR profiles, with 6,474 distinct cell lines having an associated STR profile (release July 31, 2019). Here we present CLASTR, the Cellosaurus STR similarity search tool enabling users to compare one or more STR profiles with those available in the Cellosaurus cell line knowledge resource. It aims to help researchers in the process of cell line authentication by providing numerous functionalities. The tool is publicly accessible on the SIB ExPASy server (https://web.expasy.org/cellosaurus-str-search) and its source code is available on GitHub under the GPL-3.0 license.

Cell Lines as Biological Models: Practical Steps for More Reliable Research.

Research in toxicology relies on in vitro models such as cell lines. These living models are prone to change and may be described in publications with insufficient information or quality control testing. This article sets out recommendations to improve the reliability of cell-based research.

The Possibility of Systematic Research Fraud Targeting Under-Studied Human Genes: Causes, Consequences, and Potential Solutions.

A major reason for biomarker failure is the selection of candidate biomarkers based on inaccurate or incorrect published results. Incorrect research results leading to the selection of unproductive biomarker candidates are largely considered to stem from unintentional research errors. The additional possibility that biomarker research may be actively misdirected by research fraud has been given comparatively little consideration. This review discusses what we believe to be a new threat to biomarker research, namely, the possible systematic production of fraudulent gene knockdown studies that target under-studied human genes. We describe how fraudulent papers may be produced in series by paper mills using what we have described as a 'theme and variations' model, which could also be considered a form of salami slicing. We describe features of these single-gene knockdown publications that may allow them to evade detection by journal editors, peer reviewers, and readers. We then propose a number of approaches to facilitate their detection, including improved awareness of the features of publications constructed in series, broader requirements to post submitted manuscripts to preprint servers, and the use of semi-automated literature screening tools. These approaches may collectively improve the detection of fraudulent studies that might otherwise impede future biomarker research.

Incidences of problematic cell lines are lower in papers that use RRIDs to identify cell lines.

The use of misidentified and contaminated cell lines continues to be a problem in biomedical research. Research Resource Identifiers (RRIDs) should reduce the prevalence of misidentified and contaminated cell lines in the literature by alerting researchers to cell lines that are on the list of problematic cell lines, which is maintained by the International Cell Line Authentication Committee (ICLAC) and the Cellosaurus database. To test this assertion, we text-mined the methods sections of about two million papers in PubMed Central, identifying 305,161 unique cell-line names in 150,459 articles. We estimate that 8.6% of these cell lines were on the list of problematic cell lines, whereas only 3.3% of the cell lines in the 634 papers that included RRIDs were on the problematic list. This suggests that the use of RRIDs is associated with a lower reported use of problematic cell lines.

A Standard Nomenclature for Referencing and Authentication of Pluripotent Stem Cells.

Unambiguous cell line authentication is essential to avoid loss of association between data and cells. The risk for loss of references increases with the rapidity that new human pluripotent stem cell (hPSC) lines are generated, exchanged, and implemented. Ideally, a single name should be used as a generally applied reference for each cell line to access and unify cell-related information across publications, cell banks, cell registries, and databases and to ensure scientific reproducibility. We discuss the needs and requirements for such a unique identifier and implement a standard nomenclature for hPSCs, which can be automatically generated and registered by the human pluripotent stem cell registry (hPSCreg). To avoid ambiguities in PSC-line referencing, we strongly urge publishers to demand registration and use of the standard name when publishing research based on hPSC lines.

Authentication of M14 melanoma cell line proves misidentification of MDA-MB-435 breast cancer cell line.

A variety of analytical approaches have indicated that melanoma cell line UCLA-SO-M14 (M14) and breast carcinoma cell line MDA-MB-435 originate from a common donor. This indicates that at some point in the past, one of these cell lines became misidentified, meaning that it ceased to correspond to the reported donor and instead became falsely identified (through cross-contamination or other means) as a cell line from a different donor. Initial studies concluded that MDA-MB-435 was the misidentified cell line and M14 was the authentic cell line, although contradictory evidence has been published, resulting in further confusion. To address this question, we obtained early samples of the melanoma cell line (M14), a lymphoblastoid cell line from the same donor (ML14), and donor serum preserved at the originator's institution. M14 samples were cryopreserved in December 1975, before MDA-MB-435 cells were established in culture. Through a series of molecular characterizations, including short tandem repeat (STR) profiling and cytogenetic analysis, we demonstrated that later samples of M14 and MDA-MB-435 correspond to samples of M14 frozen in 1975, to the lymphoblastoid cell line ML14, and to the melanoma donor's STR profile, sex and blood type. This work demonstrates conclusively that M14 is the authentic cell line and MDA-MB-435 is misidentified. With clear provenance information and authentication testing of early samples, it is possible to resolve debates regarding the origins of problematic cell lines that are widely used in cancer research.

Widespread Use of Misidentified Cell Line KB (HeLa): Incorrect Attribution and Its Impact Revealed through Mining the Scientific Literature.

Continuous cell lines are widely used, but can result in invalid, irreproducible research data. Cell line misidentification is a common problem that can be detected by authentication testing; however, misidentified cell lines continue to be used in publications. Here we explore the impact of one misidentified cell line, KB (HeLa), on the scientific literature. We identified 574 articles between 2000 and 2014 that provided an incorrect attribution for KB, in accordance with its false identity as oral epidermoid carcinoma, but only 57 articles that provided a correct attribution for KB, as HeLa or cervical adenocarcinoma. Statistical analysis of 57 correct and 171 incorrect articles showed that the number of citations to these articles increased over time. Content analysis of 200 citing articles showed there was a tendency to describe the cell line in accordance with the description in the cited paper. Analysis of journal impact factor showed no significant difference between correct and incorrect groups. Articles using KB or citing that usage were most frequently published in the subject areas of pharmacology, pharmacy, oncology, and medicinal chemistry. These findings are important for science policy and support the need for journals to require authentication testing as a condition of publication. Cancer Res; 77(11); 2784-8. ©2017 AACR.

The need for a worldwide consensus for cell line authentication: Experience implementing a mandatory requirement at the International Journal of Cancer.

Cell lines are used in life science research worldwide as biological surrogates. All cell lines are subject to major limitations when used as research tools, including (i) cross-contamination with other cells cultured in the same laboratory environment and (ii) evolution in vitro that renders a given cell line inappropriate as a surrogate for a specific biological hypothesis. There is ample evidence that cross-contamination or phenotypic drift of cells in culture can generate irreproducible or misleading data. A small number of scientific journals-the International Journal of Cancer being at the forefront-and funding agencies have recently moved forward to ask for obligatory cell line authentication data. The history of implementing such rules by the International Journal of Cancer exemplifies the difficulties encountered when installing mandatory quality measures in life sciences.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line.

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.

Authentication: A Standard Problem or a Problem of Standards?

Reproducibility and transparency in biomedical sciences have been called into question, and scientists have been found wanting as a result. Putting aside deliberate fraud, there is evidence that a major contributor to lack of reproducibility is insufficient quality assurance of reagents used in preclinical research. Cell lines are widely used in biomedical research to understand fundamental biological processes and disease states, yet most researchers do not perform a simple, affordable test to authenticate these key resources. Here, we provide a synopsis of the problems we face and how standards can contribute to an achievable solution.

Is cell culture a risky business? Risk analysis based on scientist survey data.

Cell culture is a technique that requires vigilance from the researcher. Common cell culture problems, including contamination with microorganisms or cells from other cultures, can place the reliability and reproducibility of cell culture work at risk. Here we use survey data, contributed by research scientists based in Australia and New Zealand, to assess common cell culture risks and how these risks are managed in practice. Respondents show that sharing of cell lines between laboratories continues to be widespread. Arrangements for mycoplasma and authentication testing are increasingly in place, although scientists are often uncertain how to perform authentication testing. Additional risks are identified for preparation of frozen stocks, storage and shipping.

Match criteria for human cell line authentication: where do we draw the line?

Continuous human cell lines have been used extensively as models for biomedical research. In working with these cell lines, researchers are often unaware of the risk of cross-contamination and other causes of misidentification. To reduce this risk, there is a pressing need to authenticate cell lines, comparing the sample handled in the laboratory to a previously tested sample. The American Type Culture Collection Standards Development Organization Workgroup ASN-0002 has developed a Standard for human cell line authentication, recommending short tandem repeat (STR) profiling for authentication of human cell lines. However, there are known limitations to the technique when applied to cultured samples, including possible genetic drift with passage. In our study, a dataset of 2,279 STR profiles from four cell banks was used to assess the effectiveness of the match criteria recommended within the Standard. Of these 2,279 STR profiles, 1,157 were grouped into sets of related cell lines-duplicate holdings, legitimately related samples or misidentified cell lines. Eight core STR loci plus amelogenin were used to unequivocally authenticate 98% of these related sets. Two simple match algorithms each clearly discriminated between related and unrelated samples, with separation between related samples at ≥80% match and unrelated samples at <50% match. A small degree of overlap was noted at 50-79% match, mostly from cell lines known to display variable STR profiles. These match criteria are recommended as a simple and effective way to interpret results from STR profiling of human cell lines.