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Generation of vascular endothelial and smooth muscle cells from human pluripotent stem cells

Abstract

The use of human pluripotent stem cells for in vitro disease modelling and clinical applications requires protocols that convert these cells into relevant adult cell types. Here, we report the rapid and efficient differentiation of human pluripotent stem cells into vascular endothelial and smooth muscle cells. We found that GSK3 inhibition and BMP4 treatment rapidly committed pluripotent cells to a mesodermal fate and subsequent exposure to VEGF-A or PDGF-BB resulted in the differentiation of either endothelial or vascular smooth muscle cells, respectively. Both protocols produced mature cells with efficiencies exceeding 80% within six days. On purification to 99% via surface markers, endothelial cells maintained their identity, as assessed by marker gene expression, and showed relevant in vitro and in vivo functionality. Global transcriptional and metabolomic analyses confirmed that the cells closely resembled their in vivo counterparts. Our results suggest that these cells could be used to faithfully model human disease.

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Figure 1: Canonical Wnt activation by GSK3β inhibitors and mesoderm induction.
Figure 2: VEGF-A and PDGF-BB-mediated differentiation of hPSCs into vascular endothelial or vascular smooth muscle cells.
Figure 3: Global transcriptome and metabolomic analyses confirm vascular cell identity of differentiated hPSCs.
Figure 4: In vitro characterization of hPSC ECs and hPSC VSMCs.
Figure 5: Co-culture experiments and in vivo characterization of hPSC ECs.

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Acknowledgements

We thank A. Einhaus, S. Zimmermann, C. Stücki and N. Dahm for excellent technical support and all members of the Roche Stem Cell Platform for helpful discussions. We thank C. MacGillivray at the HSCRB Histology Core facility. We also thank S. Aigner and C. Warren for critically reviewing the manuscript and A. Gündner, C. Hudak and K. Song for help with the illustrations. We acknowledge the continued support of H.-L. Roche and thank M. Steger from Roche Pharma Partnering for his input and support of the project. C.P. and E.C.T. were supported by Roche Postdoctoral Fellowships (RPF). A part of the research received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement number 115439, resources of which are composed of financial contribution from the European Union’s Seventh Framework Program (FP7/2007-2013) and EFPIA companies’ in kind contribution. L.C.-M. was supported by a fellowship from the Swiss National Science Foundation. F.G.K. was supported by a fellowship from the German Cancer Aid. This work was supported by HHMI and NIH grants R01 HL04880, 5P30 DK49216, 5R01 DK53298, 5U01 HL10001-05, and R24 DK092760 (to L.I.Z.); HL106018, HL083867, HL60963 (E.L.C.); 2R01DK081572 and an AHA Established Investigator Award (R.E.G.); R01DK097768, U01HL100408, the Harvard Stem Cell Institute and Harvard University (C.A.C.).

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Contributions

C.P., L.C.-M., E.C.T. and E.U. designed and performed experiments, analysed and interpreted data and wrote the manuscript. S.J.G., F.G.K., L.S., K.C., Y.X., M.H.C.F., W.H., W.P., C.R.W., R.J.-R. and I.A. designed and performed experiments and analysed data. M.P. performed high-content imaging analysis. T.H. designed and performed gene expression experiments, performed and interpreted bioinformatic analyses and wrote the manuscript. J.F.O’S. designed and performed metabolomic experiments, interpreted and analysed the data and wrote the manuscript. U.C. and R.J. provided scientific input. P.-O.F., D.K., P.H., L.I.Z., E.L.C. and R.E.G. analysed and interpreted the data and supervised experiments. M.G. and R.I. analysed and interpreted the data and supervised the project. C.A.C. interpreted the data, supervised the project and wrote the manuscript. C.P., L.C.-M., E.C.T., E.U., J.F.O’S., M.P., D.K., T.H. and W.H. contributed to description of online methods.

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Correspondence to Christoph Patsch or Chad A. Cowan.

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Integrated supplementary information

Supplementary Figure 1 Canonical Wnt activation by GSK3β inhibitors and mesoderm induction.

(A) Molecular structure of different GSK3β inhibitors. (B) Schematic illustration of the luciferase reporter assay used in this study to detect the expression of β-catenin. (C) Cell viability assay on treatment with different concentration of GSK3β inhibitors. A 6-point 3-fold serial dilution of each compound was performed (10, 3, 1, 0.3, 0.1, 0.03 μm, last 2 concentration data not shown). Columns show mean +/− s.d. of 5 independent experiments. (D) Binding constant (Kd) determination for the GSK3β inhibitors tested in this study. An 11-point 3-fold serial dilution of each test compound was prepared in 100% DMSO at 100× final test concentration and subsequently diluted to 1x in the assay (final DMSO concentration = 2.5%). Representative curve of four independent experiments is shown. (E) Bright field pictures of hESCs treated with increasing concentration of the GSK3β inhibitor BIO illustrating its cell toxicity. Representative images of one experiment are shown.

Supplementary Figure 2 VEGF and PDGF-BB-mediated differentiation of hPSCs into vascular endothelial or smooth muscle cells.

(A) BMP4-dependent expression of mesoderm markers T, MIXL, and EOMES at day 4 of differentiation. Columns show mean of 3 technical replicates of a single well of a single experiment. (B) Comparison of the ability of different GSK3β inhibitors to induce endothelial cell differentiation as shown by immunostaining for the endothelial marker VE-Cadherin. Representative images of 3 independent experiments. (C) Effect of BMP4 on hPSC-ECs differentiation. 3 wells per conditions of a single experiment. (D) Potency of different GSK3β inhibitors to induce hPSC-ECs differentiation. Mean values +/− s.d. of 3 independent experiments are shown. (E) Differentiation efficiency of hPSC-ECs on day 6 after sorting for CP21 and CHIR when used at their optimal concentration (defined in D). 3 wells per conditions of a single experiment. (F) Representative FACS plots showing the improvement of differentiation efficiency when BMP4 and forskolin are added to the media. This experiment was done once and 3 wells per conditions were analysed. (G) FACS analysis of CD144+ hPSC-ECs on day 10 showing the expression of ECs-specific markers (KDR, CD31, CD34, CD105) and the absence of hematopoietic markers (CD43, CD45). Representative results of 5 independent experiments. (H) The endothelial cell differentiation protocol produces a small amount of alpha smooth actin positive cells as shown by immunostaining on day 6 before MACS sorting. Representative image of 5 independent experiments. (I) Role of ActivinA and PDGF-BB in the differentiation efficiency of hPSC-VSMCs. 3 wells per conditions of a single experiment. (J) Differentiation efficiency of hPSC-VSMCs for the two GSK3β inhibitors CP21 and CHIR when used at their optimal concentration (1 μM and 6 μM, respectively). 3 wells per conditions of a single experiment. (K) Effect of BMP4 on hPSC-VSMCs differentiation, n = 3 wells of a single experiment. (K) Example of the efficiency of MACS sorting and the purity of the resulting hPSC-ECs population, representative result from 2 independent experiments.

Supplementary Figure 3 Global Transcriptome analysis during vascular wall cell differentiation.

(A) Principal component projections of transcriptomes coloured by sample type. The variability of the data set along Principal component 1 is 28.8% and along Principal component 2 is 24.1%. Note the clustering of precursor cells during the early time points and the clustering of differentiated vascular wall cells with their respective primary cells. (B) Dynamic gene expression of representative spatio-temporal regulated genes during the course of endothelial cell differentiation. The detection limit of the microarray platform is indicated by a dotted line; this signal is derived from 5000 random probes (60-mers of random nucleotides), which serve as a metric of non-specific annealing and background fluorescence. (C) Heat map of genes sets of biological processes (GO terms) significantly over- or under-represented in stem cell derived ECs, VSMCs, and primary cells in comparison to ESCs. Rows represent genes, and columns are samples. Row Z-score transformation was performed on log2 expression values for each gene with blue denoting a lower and red a higher expression level according to the average expression level. Hierarchical clustering of genes and samples is based on average linkage and correlation distance.

Supplementary Figure 4 In vitro characterization of hPSC-ECs and hPSC-VSMCs.

(A) Calcium imaging of SC-VSMCs at day 13 of differentiation. Stimulation with vasoconstrictive reagents resulted in increase in intracellular calcium. Time course of calcium flux after treatment. RFU was measured every second and average values of three independent experiments are shown. (B) Example of collagen gel contraction assay after 48 h with or without U46619. Gel surface areas were measured and further analysed using ImageJ. Scale bars: 1 cm. (C) Fibronectin production of hPSC-VSMCs on TGF-β treatment. Immunofluorescence staining of extracellular fibronectin depositions after 24 h of TGF-β treatment in the presence of absence of TFG-β inhibitors. Scale bars: 50 μM. Representative images of 3 independent experiments are shown.

Supplementary Figure 5 Whole mount view of fibrinogen implants.

Representative pictures of whole mount implants. This experiment was conducted once with 5 mice per conditions and 2 implants per mice (=10 implants per conditions) (A) hPSC-ECs only (B) HUVECs + MSCs (C) hPSC-ECs + MSCs and (D) hPSC-ECs + hPSC-VSMCs. Scale bars = 500 μM except A) scale bar = 5 mm.

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Patsch, C., Challet-Meylan, L., Thoma, E. et al. Generation of vascular endothelial and smooth muscle cells from human pluripotent stem cells. Nat Cell Biol 17, 994–1003 (2015). https://doi.org/10.1038/ncb3205

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