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Multi-omic measurements of heterogeneity in HeLa cells across laboratories

Nature Biotechnology volume 37, pages 314–322 (2019)Cite this article

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Abstract

Reproducibility in research can be compromised by both biological and technical variation, but most of the focus is on removing the latter. Here we investigate the effects of biological variation in HeLa cell lines using a systems-wide approach. We determine the degree of molecular and phenotypic variability across 14 stock HeLa samples from 13 international laboratories. We cultured cells in uniform conditions and profiled genome-wide copy numbers, mRNAs, proteins and protein turnover rates in each cell line. We discovered substantial heterogeneity between HeLa variants, especially between lines of the CCL2 and Kyoto varieties, and observed progressive divergence within a specific cell line over 50 successive passages. Genomic variability has a complex, nonlinear effect on transcriptome, proteome and protein turnover profiles, and proteotype patterns explain the varying phenotypic response of different cell lines to Salmonella infection. These findings have implications for the interpretation and reproducibility of research results obtained from human cultured cells.

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Fig. 1: HeLa cell lines from different laboratories showed varied and evolving genotypes.
Fig. 2: Heterogeneous transcriptome, proteome and protein turnover profiles between HeLa cell lines across laboratories.
Fig. 3: Gene expression comparison between HeLa 12 and 14 representing 3-month passaging.
Fig. 4: Global processes affecting HeLa proteotypes.
Fig. 5: Proteotypes of HeLa cells tightly link to phenotypes.

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Data availability

RNA-seq data are available on GEO (GSE111485). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE70 partner repository with the dataset identifier PXD009273. The full dataset is available at https://HelaProt.shinyapps.io/Crosslab/.

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Acknowledgements

We thank G. Rosenberger, A. Beyer, B. Collins and S. Nikolaev for discussions. We thank L. Reiter, R. Bruderer and O. Rinner from Biognosys AG for sharing their thoughts about cell line proteome analysis from a commercial perspective. We thank H. Zhang and J. Chen from Johns Hopkins University, D. Pflieger and O. Filhol-Cochet from CEA Grenoble, M. Riwanto from University Hospital Zurich, U. Greber and M. Suomalainen from the University of Zurich, C. Arrieumerlou from the University of Basel (through InfectX), M. Beck and M.-T. Mackmull from the European Molecular Biology Laboratory, C. Jorgensen and J. Worboys from the Cancer Research UK Manchester Institute, M. Peter and C. Barnes from ETH Zurich, and A. Venkitaraman and C. Williams from the University of Cambridge for providing us their HeLa cells.

The work was supported by the SystemsX.ch project PhosphoNetX PPM (to R.A.), TargetInfectX (to C.D.), the Swiss National Science Foundation (grant 3100A0-688 107679 to R.A.), the European Research Council (ERC-20140AdG 670821 to R.A.), the JRC for Computational Biomedicine (which was partially funded by Bayer AG, to J.S.-R.), the Swiss National Science Foundation (grant 163180 to S.E.A.), the European Research Council (grants AdG 249968 to S.E.A. and 616441-DISEASEAVATARS to G.T.), the Umberto Veronesi Foundation (fellowship to P.-L.G.), the ERA-NET Neuron Program (P.-L.G.), Regione Lombardia (Ricerca Indipendente 2012 to G.T.) and the Italian Ministry of Health (Ricerca Corrente to G.T.) E.G.W. was supported by an NIH F32 Ruth Kirchstein Fellowship (F32GM119190).

Author information

Author notes

  1. These authors contributed equally: Yang Mi, Torsten Mueller, Saskia Kreibich.

Authors and Affiliations

  1. Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA

    Yansheng Liu

  2. Yale Cancer Biology Institute, Yale University, West Haven, CT, USA

    Yansheng Liu

  3. Heidelberg University, Faculty of Biosciences, Heidelberg, Germany

    Yang Mi

  4. Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, Aachen, Germany

    Yang Mi & Julio Saez-Rodriguez

  5. Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland

    Torsten Mueller, Evan G. Williams, Audrey Van Drogen, Max Frank, Isabell Bludau, Martin Mehnert & Ruedi Aebersold

  6. Institute of Microbiology, ETH Zurich, Zurich, Switzerland

    Saskia Kreibich & Wolf-Dietrich Hardt

  7. Department of Genetic Medicine and Development, University of Geneva Medical School, and University Hospitals of Geneva, Geneva, Switzerland

    Christelle Borel, Fedor Bezrukov & Stylianos E. Antonarakis

  8. IEO, European Institute of Oncology IRCCS, Milan, Italy

    Pierre-Luc Germain & Giuseppe Testa

  9. Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany

    Michael Seifert

  10. National Center for Tumor Diseases, Dresden, Germany

    Michael Seifert

  11. Biozentrum, University of Basel, Basel, Switzerland

    Mario Emmenlauer, Isabel Sorg & Christoph Dehio

  12. Service of Genetic Medicine, University Hospitals of Geneva, Geneva, Switzerland

    Frederique Sloan Bena & Stylianos E. Antonarakis

  13. Department of Analytical Chemistry and CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

    Hu Zhou

  14. Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy

    Giuseppe Testa

  15. Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Bioquant Heidelberg, Germany

    Julio Saez-Rodriguez

  16. iGE3 Institute of Genetics and Genomics of Geneva, Geneva, Switzerland

    Stylianos E. Antonarakis

  17. Faculty of Science, University of Zurich, Zurich, Switzerland

    Ruedi Aebersold

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  1. Yansheng Liu
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Contributions

Y.L. and R.A. designed and supervised the whole project. Y.L., Y.M., E.G.W., P.-L.G., M.F., I.B., M.S., M.E. and F.B. analyzed the data and performed the bioinformatics analysis. Y.M. developed the HeLa Proteome website. T.M. performed the pSILAC experiment. S.K. and Y.L. performed the Let7 experiment. S.K. performed the S.Tm infection experiment. A.V.D., C.B, I.S., C.D. and H.Z. established and cultured the cell lines. Y.L. and M.M. performed the mass spectrometry experiments. I.B. performed pyProphet analysis. F.S.B. generated CNV data. M.S. processed the CNV data. C.B. generated RNA-seq data. M.F. performed sequence variation analysis. F.B. and P.-L.G. analyzed RNA-seq data. M.E. analyzed the microscopy phenotypic data. G.T. and J.S.-R. supervised data interpretation. S.E.A. supervised the genomics data generation. W.-D.H. supervised all the microbiology experiments and provided critical inputs. Y.L., E.G.W. and R.A. wrote the paper.

Corresponding authors

Correspondence to Yansheng Liu or Ruedi Aebersold.

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R.A. holds shares of Biognosys AG, which operates in the field covered by the article.

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Supplementary Figures 1–19, Supplementary Table 1 and Supplementary Notes 1–6

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Liu, Y., Mi, Y., Mueller, T. et al. Multi-omic measurements of heterogeneity in HeLa cells across laboratories. Nat Biotechnol 37, 314–322 (2019). https://doi.org/10.1038/s41587-019-0037-y

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