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Automated, high-throughput derivation, characterization and differentiation of induced pluripotent stem cells

Nature Methods volume 12, pages 885–892 (2015)Cite this article

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Abstract

Induced pluripotent stem cells (iPSCs) are an essential tool for modeling how causal genetic variants impact cellular function in disease, as well as an emerging source of tissue for regenerative medicine. The preparation of somatic cells, their reprogramming and the subsequent verification of iPSC pluripotency are laborious, manual processes limiting the scale and reproducibility of this technology. Here we describe a modular, robotic platform for iPSC reprogramming enabling automated, high-throughput conversion of skin biopsies into iPSCs and differentiated cells with minimal manual intervention. We demonstrate that automated reprogramming and the pooled selection of polyclonal pluripotent cells results in high-quality, stable iPSCs. These lines display less line-to-line variation than either manually produced lines or lines produced through automation followed by single-colony subcloning. The robotic platform we describe will enable the application of iPSCs to population-scale biomedical problems including the study of complex genetic diseases and the development of personalized medicines.

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Figure 1: Automated fibroblast and iPSC production.
Figure 2: Automated reprogramming.
Figure 3: Automated iPSC purification and arraying.
Figure 4: Automated embryoid body assay.
Figure 5: Reduced variation in robotically derived iPSCs.
Figure 6: Differentiation of iPSCs derived via automation and demonstration of automated differentiation.

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References

  1. Colman, A. & Dreesen, O. Pluripotent stem cells and disease modeling. Cell Stem Cell 5, 244–247 (2009).

    Article  CAS  Google Scholar 

  2. Takahashi, K. et al. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131, 861–872 (2007).

    Article  CAS  Google Scholar 

  3. Robinton, D.A. & Daley, G.Q. The promise of induced pluripotent stem cells in research and therapy. Nature 481, 295–305 (2012).

    Article  CAS  Google Scholar 

  4. Morris, A.P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Santostefano, K.E. et al. A practical guide to induced pluripotent stem cell research using patient samples. Lab. Invest. 95, 4–13 (2015).

    Article  CAS  Google Scholar 

  6. Cahan, P. & Daley, G.Q. Origins and implications of pluripotent stem cell variability and heterogeneity. Nat. Rev. Mol. Cell Biol. 14, 357–368 (2013).

    Article  CAS  Google Scholar 

  7. Carey, B.W. et al. Reprogramming factor stoichiometry influences the epigenetic state and biological properties of induced pluripotent stem cells. Cell Stem Cell 9, 588–598 (2011).

    Article  CAS  Google Scholar 

  8. Liang, G. & Zhang, Y. Genetic and epigenetic variations in iPSCs: potential causes and implications for application. Cell Stem Cell 13, 149–159 (2013).

    Article  CAS  Google Scholar 

  9. Chen, K.G., Mallon, B.S., McKay, R.D. & Robey, P.G. Human pluripotent stem cell culture: considerations for maintenance, expansion, and therapeutics. Cell Stem Cell 14, 13–26 (2014).

    Article  CAS  Google Scholar 

  10. Thomas, R. et al. Automated, scalable culture of human embryonic stem cells in feeder-free conditions. Biotechnol. Bioeng. 102, 1636–1644 (2009).

    Article  CAS  Google Scholar 

  11. Terstegge, S. et al. Automated maintenance of embryonic stem cell cultures. Biotechnol. Bioeng. 96, 195–201 (2007).

    Article  CAS  Google Scholar 

  12. Conway, M.K. et al. Scalable 96-well plate based iPSC culture and production using a robotic liquid handling system. J. Vis. Exp. 99, e52755 (2015).

    Google Scholar 

  13. Valamehr, B. et al. A novel platform to enable the high-throughput derivation and characterization of feeder-free human iPSCs. Sci. Rep. 2, 213 (2012).

    Article  Google Scholar 

  14. Utikal, J. et al. Immortalization eliminates a roadblock during cellular reprogramming into iPS cells. Nature 460, 1145–1148 (2009).

    Article  CAS  Google Scholar 

  15. Hanna, J. et al. Direct cell reprogramming is a stochastic process amenable to acceleration. Nature 462, 595–601 (2009).

    Article  CAS  Google Scholar 

  16. Fusaki, N., Ban, H., Nishiyama, A., Saeki, K. & Hasegawa, M. Efficient induction of transgene-free human pluripotent stem cells using a vector based on Sendai virus, an RNA virus that does not integrate into the host genome. Proc. Jpn. Acad., Ser. B, Phys. Biol. Sci. 85, 348–362 (2009).

    Article  CAS  Google Scholar 

  17. Warren, L. et al. Highly efficient reprogramming to pluripotency and directed differentiation of human cells with synthetic modified mRNA. Cell Stem Cell 7, 618–630 (2010).

    Article  CAS  Google Scholar 

  18. Warren, L., Ni, Y., Wang, J. & Guo, X. Feeder-free derivation of human induced pluripotent stem cells with messenger RNA. Sci. Rep. 2, 657 (2012).

    Article  Google Scholar 

  19. Kahler, D.J. et al. Improved methods for reprogramming human dermal fibroblasts using fluorescence activated cell sorting. PLoS ONE 8, e59867 (2013).

    Article  CAS  Google Scholar 

  20. Bock, C. et al. Reference maps of human ES and iPS cell variation enable high-throughput characterization of pluripotent cell lines. Cell 144, 439–452 (2011).

    Article  CAS  Google Scholar 

  21. Mayshar, Y. et al. Identification and classification of chromosomal aberrations in human induced pluripotent stem cells. Cell Stem Cell 7, 521–531 (2010).

    Article  CAS  Google Scholar 

  22. Abyzov, A. et al. Somatic copy number mosaicism in human skin revealed by induced pluripotent stem cells. Nature 492, 438–442 (2012).

    Article  CAS  Google Scholar 

  23. Cheng, L. et al. Low incidence of DNA sequence variation in human induced pluripotent stem cells generated by nonintegrating plasmid expression. Stem Cell 10, 337–344 (2012).

    CAS  Google Scholar 

  24. Lian, X. et al. Directed cardiomyocyte differentiation from human pluripotent stem cells by modulating Wnt/β-catenin signaling under fully defined conditions. Nat. Protoc. 8, 162–175 (2013).

    Article  CAS  Google Scholar 

  25. Woodard, C.M. et al. iPSC-derived dopamine neurons reveal differences between monozygotic twins discordant for Parkinson's disease. Cell Reports 9, 1173–1182 (2014).

    Article  CAS  Google Scholar 

  26. Hannan, N.R.F., Segeritz, C.-P., Touboul, T. & Vallier, L. Production of hepatocyte-like cells from human pluripotent stem cells. Nat. Protoc. 8, 430–437 (2013).

    Article  CAS  Google Scholar 

  27. Taguchi, A. et al. Redefining the in vivo origin of metanephric nephron progenitors enables generation of complex kidney structures from pluripotent stem cells. Cell Stem Cell 14, 53–67 (2014).

    Article  CAS  Google Scholar 

  28. Douvaras, P. et al. Efficient generation of myelinating oligodendrocytes from primary progressive multiple sclerosis patients by induced pluripotent stem cells. Stem Cell Reports 3, 250–259 (2014).

    Article  CAS  Google Scholar 

  29. Beers, J. et al. A cost-effective and efficient reprogramming platform for large-scale production of integration-free human induced pluripotent stem cells in chemically defined culture. Sci. Rep. 5, 11319 (2015).

    Article  CAS  Google Scholar 

  30. Zhou, H. et al. Rapid and efficient generation of transgene-free iPSC from a small volume of cryopreserved blood. Stem Cell Rev. 11, 652–665 (2015).

    Article  Google Scholar 

  31. McKernan, R. & Watt, F.M. What is the point of large-scale collections of human induced pluripotent stem cells? Nat. Biotechnol. 31, 875–877 (2013).

    Article  CAS  Google Scholar 

  32. Rohani, L., Johnson, A.A., Arnold, A. & Stolzing, A. The aging signature: a hallmark of induced pluripotent stem cells? Aging Cell 13, 2–7 (2014).

    Article  CAS  Google Scholar 

  33. Kajiwara, M. et al. Donor-dependent variations in hepatic differentiation from human-induced pluripotent stem cells. Proc. Natl. Acad. Sci. USA 109, 12538–12543 (2012).

    Article  CAS  Google Scholar 

  34. Watanabe, K. et al. A ROCK inhibitor permits survival of dissociated human embryonic stem cells. Nat. Biotechnol. 25, 681–686 (2007).

    Article  CAS  Google Scholar 

  35. Li, C. et al. Genetic heterogeneity of induced pluripotent stem cells: results from 24 clones derived from a single C57BL/6 mouse. PLoS ONE 10, e0120585 (2015).

    Article  Google Scholar 

  36. Martincorena, I. et al. Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin. Science (New York, N.Y.) 348, 880–886 (2015).

    Article  CAS  Google Scholar 

  37. Mekhoubad, S. et al. Erosion of dosage compensation impacts human iPSC disease modeling. Cell Stem Cell 10, 595–609 (2012).

    Article  CAS  Google Scholar 

  38. Vallot, C. et al. Erosion of X chromosome inactivation in human pluripotent cells initiates with XACT coating and depends on a specific heterochromatin landscape. Cell Stem Cell 16, 533–546 (2015).

    Article  CAS  Google Scholar 

  39. Harris, P.A. et al. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 42, 377–381 (2009).

    Article  Google Scholar 

  40. Tyson, C. et al. Expansion of a 12-kb VNTR containing the REXO1L1 gene cluster underlies the microscopically visible euchromatic variant of 8q21.2. Eur. J. Hum. Genet. 22, 458–463 (2014).

    Article  CAS  Google Scholar 

  41. R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2012).

Download references

Acknowledgements

We thank L. Rubin, Z. Hall and S. Lipnick for critical reading of the manuscript. This work would not have been possible without S. Solomon's leadership, vision, continual encouragement and unstinting support. The authors also thank The Genomics Core, National Human Genome Research Institute, for performing the SNP arrays and the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health, Bethesda, USA for their contributions. A.M. receives support as a New York Stem Cell Foundation Robertson Investigator, with additional funding through US National Institutes of Health grant P01GM099117.

Author information

Author notes

  1. Keren A Weiss & David J Kahler

    Present address: Present addresses: New York University School of Medicine, RNAi High Throughput Screening Core, New York, New York, USA (D.J.K.); Department of Cell & Molecular Therapies, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia (K.A.W.).,

  2. Daniel Paull, Ana Sevilla, Hongyan Zhou, Aana Kim Hahn, Hesed Kim and Christopher Napolitano: These authors contributed equally to this work.

Authors and Affiliations

  1. The New York Stem Cell Foundation Research Institute, New York, New York, USA

    Daniel Paull, Ana Sevilla, Hongyan Zhou, Aana Kim Hahn, Hesed Kim, Christopher Napolitano, Linshan Shang, Katie Krumholz, Premlatha Jagadeesan, Chris M Woodard, Bruce Sun, Matthew Zimmer, Eliana Forero, Dorota N Moroziewicz, Hector Martinez, Keren A Weiss, Lauren B Vensand, Carmen R Dusenberry, Hannah Polus, Karla Therese L Sy, David J Kahler, Susan L Solomon, Stephen Chang & Scott A Noggle

  2. The Broad Institute, Cambridge, Massachusetts, USA

    Alexander Tsankov, Alexander Meissner & Kevin Eggan

  3. The Harvard Stem Cell Institute, Harvard University, Cambridge, Massachusetts, USA

    Alexander Tsankov, Alexander Meissner & Kevin Eggan

  4. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA

    Alexander Tsankov, Alexander Meissner & Kevin Eggan

  5. Section on Human Biochemical Genetics, Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA

    Thierry Vilboux, May Christine V Malicdan & William A Gahl

  6. Division of Medical Genomics, Inova Translational Medicine Institute, Inova Health System, Falls Church, Virginia, USA

    Thierry Vilboux

  7. NIH Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institute of Health and National Human Genome Research Institute, National Institute of Health, Bethesda, Maryland, USA

    William A Gahl

  8. The Howard Hughes Medical Institute, Cambridge, Massachusetts, USA

    Kevin Eggan

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  1. Daniel Paull
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Contributions

D.P. designed and performed iPSC reprogramming, expansion and QC assays. A.S. designed and performed iPSC expansion and RNA QC assays. H.Z. designed and performed iPSC reprogramming, selection and passaging biology. A.K.H. engineered methods for iPSC expansion and EB and fibroblast QC methods. H.K. engineered methods for fibroblast derivation, iPSC reprogramming, selection and passaging. C.N. designed the integration of the robotic platform and sample tracking systems, and contributed to engineering methods. A.T. performed statistical analysis. K.K. and P.J. performed fibroblast derivation. D.P., A.S., L.S., B.S., C.M.W., D.N.M., H.M., M.Z., K.A.W and S.A.N., performed iPSC reprogramming, expansion, QC and differentiation experiments. E.F., H.P., K.T.L.S., C.R.D. and L.B.V. were involved in the collection of fibroblast samples. T.V., M.C.V.M. and W.A.G. performed SNP genotyping and analysis. K.K., D.J.K. and S.A.N. were involved in system protocol development. S.L.S., S.C., K.E. and S.A.N. designed and supervised the project. A.M. provided statistical tools and supervised statistical analysis. D.P., K.E. and S.A.N. wrote the manuscript with contributions from other authors.

Corresponding authors

Correspondence to Daniel Paull or Scott A Noggle.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 A high level swim lane diagram for the expansion, reprogramming and characterization of iPSC cell production.

Each vertical lane highlights the main robotic platform performing the particular steps in the overall process. An oval shape identifies starting and stopping steps, diamond shapes indicate decision points while the irregular quadrilateral shape indicates manual processes. Whilst this represents one workflow through the system, many tasks such as feeding, scanning and passaging can be performed on any system within one cluster.

Supplementary Figure 2 Schematic of overall workflow and additional fibroblast derivation data

(A) Schematic of workflow through automation system from donor biopsy collection through to iPSC expansion and freezing. (B) Image of system for automated fibroblast production consisting of a liquid handling device, imager, centrifuge and capper-decapper contained in a biosafety cabinet, connected to an automated incubator and managed by system control software. (C) Automated imaging highlighting confluent areas during fibroblast expansion. (D) Phase contrast image of representative fibroblast outgrowth from a biopsy. (E) Mycoplasma detection of samples from in-house automated luminescence assay. Marginal values were confirmed negative with PCR validation. No mycoplasma positive samples have been generated in house during biopsy collection and outgrowth expansion on the automated systems.

Supplementary Figure 3 Fibroblast karyotyping data

Representative traces of fibroblasts karyotyped using the NanoString Karyotype assay with representative traces of normal diploid fibroblasts (i) 46, XX, (ii) 46, XY and an aneuploid fibroblast showing a loss of one X chromosome (iii) 45, X

Supplementary Figure 4 Supplementary automated Sendai virus reprogramming data

(A) Well image of Sendai reprogramming after 20 days showing colonies and TRA-1-60 live stain of the same well in the bottom panel. Only a subset of colonies stain positive for the pluripotency marker. (B) Clonal lines established through manual picking following automated reprogramming were picked into single wells of 24 well plates before being returned to automation. Pluripotency marker staining of established cell lines from automated reprogramming by Sendai virus. Scale bars = 50 μm. (C) Flow cytometry analysis of reprogrammed cultures from automated Sendai transfection demonstrating a high proportion of cells staining TRA-1-60+/SSEA4+ also retained the fibroblast surface marker CD13 (58.2% of cells from n=12 independent reprogrammings, 18 days post infection).

Supplementary Figure 5 Comparisons of automated reprogramming by mRNA and Sendai virus.

(A) Cluster diagram of mRNA vs. Sendai gene expression in automated embryoid body assays. Six independent mRNA-reprogrammed wells are represented with 2-6 biological replicates each. Four Sendai reprogrammed picked colonies are represented with 1-11 biological replicates each. (B) Box plot showing the variation of gene expression of the germ layer and pluripotency markers shown in (A) for BJ fibroblasts reprogrammed on the automated system by mRNA transfection or Sendai infection and isolated by manual colony picking. Distribution medians are not significantly different (p = 0273, Wilcoxon signed rank test). (F) NanoString analysis of Sendai reprogrammed iPSCs showing the elevated expression of MYC-Sendai virus transgene. Incubation at 38.5 °C of samples expressing the virus was performed and RNA collected (indicated by IN) before being assayed again 2-3 passages later. Despite an initial reduction in expression, elevated levels returned (samples S110C5 and S111A6). We concluded that while Sendai viral reprogramming is compatible with automation, the variance introduced by incomplete viral eviction, likely requiring further subcloning for complete elimination, warranted the investigation of an additional reprogramming method Number below HuES samples indicate number of HuES lines analyzed whilst numbers below each other sample indicate technical replicates. Each individually shown iPSC sample is an independent clone picked from a single well of a 24 well plate from two independent automated reprogramming runs of the same parental cell (run 1 = 110 and run 2 = 111). Error bars show standard deviation. HuES samples have never been exposed to Sendai Virus and represent background counts from NanoString analysis.

Supplementary Figure 6 Supplementary automated mRNA reprogramming data

(A) Image of robotic system for automated fibroblast thawing and mRNA transfections. (B) Pluripotency staining of mRNA derived lines and (C) example of a phase contrast image of iPSC produced by mRNA transfection. Scale bars are 100 μm. (D) Automated image-based identification and counting of TRA-1-60 positive colonies to determine reprogramming efficiency. Scale bar = 1 mm.

Supplementary Figure 7 Automated iPSC enrichment supplementary data

(A) Schematic illustration of bulk method of iPSC purification through the depletion of non reprogrammed fibroblast cells following reprogramming in 24-well plates prior to cherry-picking led sample consolidation and freezing. (B) Image of an integrated magnetic bead (MACS) selection robot. (C-D) iPSC-fibroblast mixing experiments to demonstrate the performance of iPSC purification. Representative plots show a mixing experiment whereby iPSCs had been mixed with fibroblasts at a ratio of 1:20.

Supplementary Figure 8 Supplementary data for automated iPSC growth following automated enrichment

(A) Example of a 96 well plate containing sorted iPSCs derived from mRNA mediated reprogramming. Three samples are represented on the plate in a four-point, three-fold serial dilution. Confluent areas are highlighted in green. (B) Representative image for retrospective identification of clonal lines derived from sorted cells from a single cell shown 10 days post sorting (10dps) expressing TRA-1-60. (C) Histogram of doubling time for iPSCs during growth after sorting before freezing. Frequency bins are 6hrs. The mean doubling times ranged from 24-48 hours as calculated from replicate pools produced post-enrichment from a total of 142 individual reprogramming events, representing 65 unique samples (of these, 49 unique samples were from fibroblasts derived under automation). 320 iPSCs, derived from a total of 53 unique fibroblast samples, had a doubling time greater than 48 hours. Median doubling time was 42hr, n=826 samples. (D) Immunofluorescence result for NANOG expression in consolidated cells in whole well view of a 96-well plate.

Supplementary Figure 9 Analysis of iPSCs produced under automation following iPSC enrichment

(A) Differentiation scores averaged for lines using Pluri25 custom codeset (see Extended Methods). (B) Pluri25 custom codeset pluripotency scores, differentiation scores (see Extended Methods), pluripotency markers’ mean gene expression, and differentiation markers’ mean gene expression for all cell lines. (C) Example of overgrown well demonstrating spontaneous differentiation (as marked by SOX1 and SOX17 expression) that was also detectable by the Pluri25 scorecard assay. Scale bar = 500 µm.

Supplementary Figure 10 Supplementary automated iPSC expansion data

(A) Schematic of thawing and passaging and plate replication in 96-well plate format. (B) Image of robotic instrument with 96-well head used for feeding, passaging and freezing. (C) Bright field images of iPSCs in the same well in a 96-well plate recovering after automated thawing method. Confluence was monitored over 5 days. Scale bar = 200 µm. (D) Correlation of confluence data from the automated imaging system prior to cryovial freeze and post-thaw. Both the freezing and thawing of cryovials were performed on the integrated automated system. n = 107 paired samples. (E) Flow cytometry analysis of iPSCs before freezing and post-thaw using automated methods. (F) Percent coefficient of variation was calculated for 3 plates from the replication passages of a representative cell line using data from confluence scans from 3 different time points. (G) Immunostaining of iPSC lines derived on the robotic platform as well as control hESC lines showing the presence of POU5F1 and TRA-1-81; SSEA4 and NANOG, and SOX2 and TRA-1-81. Scale bar = 200 µm.

Supplementary Figure 11 iPSC karyotype data using NanoString assays

(A) The detection of an aneuploid line in 4 of 38 independent iPSCs tested using the NanoString karyotyping assay. (B) NanoString identity test data comparing fibroblast lines to iPSCs derived using the automated process.

Supplementary Figure 12 Supplementary iPSC and ESC marker and scorecard data

(A) Pluripotency marker analysis of reference lines used for lineage scorecard analysis after adaptation to serum-free media used to grow iPSCs on the system. EBs were generated from these lines by the automated system under identical conditions to those used to generate EBs from iPSCs generated on the system. Scale bar = 200 µm. (B) Scorecard differentiation propensities of EBs generated from reference HUES lines under automated conditions. (C-E) Correlation of differentiation propensity for all samples generated using the reference lines in this study and also using previously published Scorecard reference data; differences that arose are likely due to tissue culture techniques.

Supplementary Figure 13 Supplementary scorecard data

(A) Scorecard analysis of EBs generated under manual vs. automated conditions. The differentiation propensity of the reference hESCs is shown by the boxes plotted in black whilst EBs generated from either manually or robotically derived lines are shown in color. (B) Box plot comparisons of standard deviation of gene expression values for cell lines derived using different techniques, considering only a single replicate per sample. For all graphs EC = Ectoderm, ME = Mesoderm and EN = Endoderm.

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Paull, D., Sevilla, A., Zhou, H. et al. Automated, high-throughput derivation, characterization and differentiation of induced pluripotent stem cells. Nat Methods 12, 885–892 (2015). https://doi.org/10.1038/nmeth.3507

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