The ghosts of HeLa: How cell line misidentification contaminates the scientific literature

Horbach, Serge P. J. M.; Halffman, Willem

doi:10.1371/journal.pone.0186281

Cited by 121 publications

(102 citation statements)

References 46 publications

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“…Parallelized NGS analysis of tissues from many different patients is also commonplace in clinical genomics pipelines. In these settings, sample or data mislabeling, where datasets are incorrectly associated with a donor, can lead to erroneous conclusions, misdirect future research, and affect treatment decisions [1][2][3] (Fig 1a). Verifying the relatedness of samples that nominally share a donor is therefore a crucial quality-control step in any NGS pipeline.…”

Section: Mainmentioning

confidence: 99%

Detecting sample swaps in diverse NGS data types using linkage disequilibrium

Javed

Farjoun

Fennell

et al. 2020

Preprint

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As the number of genomics datasets grows rapidly, sample mislabeling has become a high stakes issue. We present CrosscheckFingerprints (Crosscheck), a tool for quantifying sample-relatedness and detecting incorrectly paired sequencing datasets from different donors. Crosscheck outperforms similar methods and is effective even when data are sparse or from different assays. Application of Crosscheck to 8851 ENCODE ChIP-, RNA-, and DNase-seq datasets enabled us to identify and correct dozens of mislabeled samples and ambiguous metadata annotations, representing ~1% of ENCODE datasets.

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Section: Mainmentioning

confidence: 99%

Detecting sample swaps in diverse NGS data types using linkage disequilibrium

Javed

Farjoun

Fennell

et al. 2020

Preprint

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“…However, recent findings on their identities and lack of authentication have been alarming, thus challenging the framework in which we can trust these large‐scale data sets including metabolomics data. A meta‐analysis of 32 755 articles reporting on research with misidentified cells found that most reportedly used JCA‐1 as “prostate cancer cell lines” after it became known that JCA‐1 actually originated from bladder carcinoma . Thus, misidentifying the origins of cell lines helps promote and accumulate meaningless and misleading data that plagues the research and eventually, the public domain.…”

Section: Cell Tissue and Organ Culture Practices Need Further Methomentioning

confidence: 99%

“…A meta-analysis of 32 755 articles reporting on research with misidentified cells found that most reportedly used JCA-1 as "prostate cancer cell lines" after it became known that JCA-1 actually originated from bladder carcinoma. [31] Thus, misidentifying the origins of cell lines [32] helps promote and accumulate meaningless and misleading data that plagues the research and eventually, the public domain. Another challenge is strain variability in cell lines and, more recently, identified as a problem in rodent models as well.…”

Section: Cell Tissue and Organ Culture Practices Need Further Methomentioning

confidence: 99%

Challenges and Opportunities in Cancer Metabolomics

Kumar

Misra

2019

Proteomics

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Challenges in metabolomics for a given spectrum of disease are more or less comparable, ranging from the accurate measurement of metabolite abundance, compound annotation, identification of unknown constituents, and interpretation of untargeted and analysis of high throughput targeted metabolomics data leading to the identification of biomarkers. However, metabolomics approaches in cancer studies specifically suffer from several additional challenges and require robust ways to sample the cells and tissues in order to tackle the constantly evolving cancer landscape. These constraints include, but are not limited to, discriminating the signals from given cell types and those that are cancer specific, discerning signals that are systemic and confounded, cell culture‐based challenges associated with cell line identities and media standardizations, the need to look beyond Warburg effects, citrate cycle, lactate metabolism, and identifying and developing technologies to precisely and effectively sample and profile the heterogeneous tumor environment. This review article discusses some of the current and pertinent hurdles in cancer metabolomics studies. In addition, it addresses some of the most recent and exciting developments in metabolomics that may address some of these issues. The aim of this article is to update the oncometabolomics research community about the challenges and potential solutions to these issues.

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“…Examples of this include the Chang liver cell line (Hall, 2017) and the bladder cancerderived cell line, KU-7 (Jager et al, 2013), both of which were unknowingly contaminated with the immortalized cervical adenocarcinoma cell line, HeLa, a common occurrence (Masters, 2010;Horbach & Halffman, 2017), and the RGC-5 retinal ganglion cell line (Krishnamoorthy, Clark, Daudt, Vishwanatha, & Yorio, 2013), which was claimed to have "never existed outside of the originating laboratory". Such contaminated tissues are termed imposter cell lines.…”

Section: Of 20mentioning

confidence: 99%

Reagent Validation to Facilitate Experimental Reproducibility

Williams

2018

CP Pharmacology

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Many results reported in the biomedical research literature cannot be independently reproduced, undermining the basic foundations of science. This overview is intended for researchers who are committed to improving the quality and integrity of biomedical science by raising awareness of both the sources of irreproducibility, and activities specifically targeted to address the issue. The irreproducibility of biomedical research is due to a variety of factors, known and unknown, that markedly influence experimental outcomes. Among the known factors are inadequate training or mentoring, or experimenter incompetence. These may result in flawed experimental design and execution that reflect a lack of planning, leading to an underpowering of a study, an absence of appropriate controls, selective data analysis, inappropriate statistical analysis, and/or investigator bias or fraud. Another frequently overlooked source of irreproducibility is failure to ensure that the equipment used is performing up to manufacturer specifications. Failure to validate/authenticate the experimental reagents is another source of irreproducibility. These include compounds, cell lines, antibodies, and the animal models used in a study. This overview discusses how a lack of validation of research reagents negatively impact experimental reproducibility, and provides guidelines to avoid these pitfalls. © 2018 by John Wiley & Sons, Inc.

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The ghosts of HeLa: How cell line misidentification contaminates the scientific literature

Cited by 121 publications

References 46 publications

Detecting sample swaps in diverse NGS data types using linkage disequilibrium

Detecting sample swaps in diverse NGS data types using linkage disequilibrium

Challenges and Opportunities in Cancer Metabolomics

Reagent Validation to Facilitate Experimental Reproducibility

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