Stem cell bioengineering: building from stem cell biology

Abstract

New fundamental discoveries in stem cell biology have yielded potentially transformative regenerative therapeutics. However, widespread implementation of stem-cell-derived therapeutics remains sporadic. Barriers that impede the development of these therapeutics can be linked to our incomplete understanding of how the regulatory networks that encode stem cell fate govern the development of the complex tissues and organs that are ultimately required for restorative function. Bioengineering tools, strategies and design principles represent core components of the stem cell bioengineering toolbox. Applied to the different layers of complexity present in stem-cell-derived systems — from gene regulatory networks in single stem cells to the systemic interactions of stem-cell-derived organs and tissues — stem cell bioengineering can address existing challenges and advance regenerative medicine and cellular therapies.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: The stem cell hierarchy.
Fig. 2: Layers of complexity in stem cell systems.
Fig. 3: Stem cell bioengineers apply engineering design principles to overcome barriers in stem cell research.
Fig. 4: Feedback regulation in stem cell systems provides robust control of system dynamics.
Fig. 5: Reverse and forward engineering strategies are used to map and manipulate the stem cell gene regulatory network.
Fig. 6: A combined computational modelling and bioreactor design strategy enables enhanced expansion of haematopoietic stem cells.
Fig. 7: Niche engineering by micropatterning of cells to control colony shape and size.
Fig. 8: Microfluidics technologies enable organ-on-a-chip and human-on-a-chip applications.

References

  1. 1.

    Till, J. E. & McCulloch, E. A. A direct measurement of the radiation sensitivity of normal mouse bone marrow cells. Radiat. Res. 14, 213–222 (1961).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Henig, I. & Zuckerman, T. Hematopoietic stem cell transplantation — 50 years of evolution and future perspectives. Rambam Maimonides Med. J. 5, e0028 (2014).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03167203 (2018).

  4. 4.

    US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03178149 (2018).

  5. 5.

    US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT02302157 (2018).

  6. 6.

    US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03163511?term=ViaCyte&rank=1 (2018).

  7. 7.

    Mavilio, F. et al. Correction of junctional epidermolysis bullosa by transplantation of genetically modified epidermal stem cells. Nat. Med. 12, 1397–1402 (2006).

    CAS  PubMed  Google Scholar 

  8. 8.

    Hirsch, T. et al. Regeneration of the entire human epidermis using transgenic stem cells. Nature 551, 327–332 (2017).

    CAS  Google Scholar 

  9. 9.

    Blanpain, C. & Simons, B. D. Unravelling stem cell dynamics by lineage tracing. Nat. Rev. Mol. Cell Biol. 14, 489–502 (2013).

    CAS  PubMed  Google Scholar 

  10. 10.

    Dekkers, J. F. et al. A functional CFTR assay using primary cystic fibrosis intestinal organoids. Nat. Med. 19, 939–945 (2013).

    CAS  PubMed  Google Scholar 

  11. 11.

    Huch, M. et al. Long-term culture of genome-stable bipotent stem cells from adult human liver. Cell 160, 299–312 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Dekkers, J. F. et al. Characterizing responses to CFTR-modulating drugs using rectal organoids derived from subjects with cystic fibrosis. Sci. Transl Med. 8, 344ra84 (2016).

    PubMed  Google Scholar 

  13. 13.

    Lindemans, C. A. et al. Interleukin 22 promotes intestinal-stem-cell-mediated epithelial regeneration. Nature 528, 560–564 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Wells, J. M. & Watt, F. M. Diverse mechanisms for endogenous regeneration and repair in mammalian organs. Nature 557, 322–328 (2018).

    CAS  PubMed  Google Scholar 

  15. 15.

    Matsumura, H. et al. Hair follicle aging is driven by transepidermal elimination of stem cells via COL17A1 proteolysis. Science 351, aad4395 (2016).

    PubMed  Google Scholar 

  16. 16.

    Watanabe, M. et al. Type XVII collagen coordinates proliferation in the interfollicular epidermis. eLife 6, a015206 (2017).

    Google Scholar 

  17. 17.

    Jaenisch, R. & Bird, A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat. Genet. 33, S245–S254 (2003).

    Google Scholar 

  18. 18.

    Reik, W. Stability and flexibility of epigenetic gene regulation in mammalian development. Nature 447, 425–432 (2007).

    CAS  Google Scholar 

  19. 19.

    Alon, U. An Introduction to Systems Biology: Design Principles of Biological Circuits 60, 63–64 (Chapman and Hall, 2007).

  20. 20.

    Morrison, S. J. & Spradling, A. C. Stem cells and niches: mechanisms that promote stem cell maintenance throughout life. Cell 132, 598–611 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Yin, X. et al. Engineering stem cell organoids. Cell Stem Cell 18, 25–38 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    López-Onieva, L., Fernández-Miñán, A. & González-Reyes, A. Jak/Stat signalling in niche support cells regulates dpp transcription to control germline stem cell maintenance in the Drosophila ovary. Development 135, 533–540 (2008).

    PubMed  Google Scholar 

  23. 23.

    Yamashita, Y. M., Mahowald, A. P., Perlin, J. R. & Fuller, M. T. Asymmetric inheritance of mother versus daughter centrosome in stem cell division. Science 315, 518–521 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Ohlstein, B. & Spradling, A. Multipotent Drosophila intestinal stem cells specify daughter cell fates by differential notch signaling. Science 315, 988–992 (2007).

    CAS  Google Scholar 

  25. 25.

    Kretzschmar, K. & Clevers, H. Organoids: modeling development and the stem cell niche in a dish. Dev. Cell 38, 590–600 (2016).

    CAS  PubMed  Google Scholar 

  26. 26.

    Crane, G. M., Jeffery, E. & Morrison, S. J. Adult haematopoietic stem cell niches. Nat. Rev. Immunol. 17, 573–590 (2017).

    CAS  PubMed  Google Scholar 

  27. 27.

    Wang, B., Zhao, L., Fish, M., Logan, C. Y. & Nusse, R. Self-renewing diploid Axin2+ cells fuel homeostatic renewal of the liver. Nature 524, 180–185 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Nabhan, A., Brownfield, D. G., Harbury, P. B., Krasnow, M. A. & Desai, T. J. Single-cell Wnt signaling niches maintain stemness of alveolar type 2 cells. Science 137, 1–12 (2018).

    Google Scholar 

  29. 29.

    Page, M. E., Lombard, P., Ng, F., Göttgens, B. & Jensen, K. B. The epidermis comprises autonomous compartments maintained by distinct stem cell populations. Cell Stem Cell 13, 471–482 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009).

    CAS  Google Scholar 

  31. 31.

    Pilz, G. A. et al. Live imaging of neurogenesis in the adult mouse hippocampus. Science 359, 658–662 (2018).

    CAS  PubMed  Google Scholar 

  32. 32.

    Raspopovic, J., Marcon, L., Russo, L. & Sharpe, J. Modeling digits. Digit patterning is controlled by a Bmp-Sox9 Wnt Turing network modulated by morphogen gradients. Science 345, 566–570 (2014).

    CAS  PubMed  Google Scholar 

  33. 33.

    Sick, S., Reinker, S., Timmer, J. & Schlake, T. WNT and DKK determine hair follicle spacing through a reaction-diffusion mechanism. Science 314, 1447–1450 (2006).

    CAS  PubMed  Google Scholar 

  34. 34.

    Economou, A. D. et al. Periodic stripe formation by a Turing mechanism operating at growth zones in the mammalian palate. Nat. Genet. 44, 348–U163 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Donati, G. et al. Wounding induces dedifferentiation of epidermal Gata6+ cells and acquisition of stem cell properties. Nat. Cell Biol. 19, 603–613 (2017).

    CAS  PubMed  Google Scholar 

  36. 36.

    Tata, P. R. et al. Dedifferentiation of committed epithelial cells into stem cells in vivo. Nature 503, 218–223 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    van Es, J. H. et al. Dll1+ secretory progenitor cells revert to stem cells upon crypt damage. Nat. Cell Biol. 14, 1099–1104 (2012).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Green, J. B. A. & Sharpe, J. Positional information and reaction-diffusion: two big ideas in developmental biology combine. Development 142, 1203–1211 (2015).

    CAS  PubMed  Google Scholar 

  39. 39.

    Tewary, M. et al. A stepwise model of reaction-diffusion and positional-information governs self-organized human peri-gastrulation-like patterning. Development 144, 4298–4312 (2017). This study employs an in vitro model of a stem-cell-derived, developmentally-relevant, fate-patterning system and demonstrates that reaction diffusion and positional information can work in concert to give rise to emergent complexity in stem cell systems.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Lancaster, M. A. & Knoblich, J. A. Organogenesis in a dish: modeling development and disease using organoid technologies. Science 345, 1247125 (2014).

    PubMed  Google Scholar 

  41. 41.

    Turing, A. M. The chemical basis of morphogenesis. Phil. Trans. R. Soc. Lond. B Biol. Sci. 237, 37–72 (1952).

    Google Scholar 

  42. 42.

    Wolpert, L. Positional information & pattern formation. Phil. Trans. Soc. R. Lond. B Biol. Sci. 295, 441–450 (1981).

    CAS  Google Scholar 

  43. 43.

    Wolpert, L. Positional information and the spatial pattern of cellular differentiation. J. Theor. Biol. 25, 1–47 (1969).

    CAS  Google Scholar 

  44. 44.

    Cooke, J. & Zeeman, E. C. A clock and wavefront model for control of the number of repeated structures during animal morphogenesis. J. Theor. Biol. 58, 455–476 (1976).

    CAS  PubMed  Google Scholar 

  45. 45.

    Thavandiran, N. et al. Design and formulation of functional pluripotent stem cell-derived cardiac microtissues. Proc. Nat. Acad. Sci. USA 110, E4698–E4707 (2013).

    CAS  PubMed  Google Scholar 

  46. 46.

    Droujinine, I. A. & Perrimon, N. Interorgan communication pathways in physiology: focus on Drosophila. Annu. Rev. Genet. 50, 539–570 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Evans, A. N. & Rooney, B. F. Personnel Psychological Vol. 62 (eds Craig, S. B. & Clark, A. P.) 633–636 (Wiley, 2009).

  48. 48.

    Way, J. C., Collins, J. J., Keasling, J. D. & Silver, P. A. Integrating biological redesign: where synthetic biology came from and where it needs to go. Cell 157, 151–161 (2014).

    CAS  PubMed  Google Scholar 

  49. 49.

    Antebi, Y. E. et al. Combinatorial signal perception in the BMP pathway. Cell 170, 1184–1196 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Mirams, G. R. et al. Chaste: an open source C +  + library for computational physiology and biology. PLOS Comput. Biol. 9, e1002970 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Nerurkar, N. L., Mahadevan, L. & Tabin, C. J. BMP signaling controls buckling forces to modulate looping morphogenesis of the gut. Proc. Nat. Acad. Sci. USA 114, 2277–2282 (2017).

    CAS  PubMed  Google Scholar 

  52. 52.

    Cosentino, C. & Bates, D. Feedback Control in Systems Biology (CRC Press, 2011).

  53. 53.

    Bleris, L. et al. Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template. Molecular Systems Biology 7, 519 (2011) .

    Google Scholar 

  54. 54.

    Freeman, M. Feedback control of intercellular signalling in development. Nature 408, 313–319 (2000). This review explores the role of positive and negative feedback loops in the dynamic regulation of developmental signalling.

    CAS  PubMed  Google Scholar 

  55. 55.

    Doyle, J. & Csete, M. Motifs, control, and stability. PLOS Biol. 3, e392 (2005).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Hussein, S. M. I. et al. Genome-wide characterization of the routes to pluripotency. Nature 516, 198–206 (2014).

    CAS  PubMed  Google Scholar 

  57. 57.

    Guo, F. et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967–988 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Yan, L. et al. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat. Struct. Mol. Biol. 20, 1131–1139 (2013).

    CAS  PubMed  Google Scholar 

  61. 61.

    Kumar, P., Tan, Y. & Cahan, P. Understanding development and stem cells using single cell-based analyses of gene expression. Development 144, 17–32 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Zhou, F. et al. Tracing haematopoietic stem cell formation at single-cell resolution. Nature 533, 487–492 (2016).

    CAS  PubMed  Google Scholar 

  63. 63.

    Shakiba, N. et al. CD24 tracks divergent pluripotent states in mouse and human cells. Nat. Commun. 6, 7329 (2015). This study employs mass spectrometry analysis of the surface proteome of reprogramming cells to identify a key marker that can distinguish different reprogramming and PSC states in both murine and human systems.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    O’Malley, J. et al. High-resolution analysis with novel cell-surface markers identifies routes to iPS cells. Nature 499, 88–91 (2013).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Ng, S. W. K. et al. A 17 gene stemness score for rapid determination of risk in acute leukaemia. Nature 540, 433–437 (2016). This study utilizes computational and statistical analysis to identify a 17-gene signature that is predictive of clinical survival following leukaemia.

    CAS  Google Scholar 

  66. 66.

    Barker, N. et al. Identification of stem cells in small intestine and colon by marker gene Lgr5. Nature 449, 1003–1007 (2007).

    CAS  PubMed  Google Scholar 

  67. 67.

    Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Yordanov, B. et al. A method to identify and analyze biological programs through automated reasoning. NPJ Syst. Biol. Appl. 2, 16010 (2016).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Chai, L. E. et al. A review on the computational approaches for gene regulatory network construction. Comput. Biol. Med. 48, 55–65 (2014).

    CAS  PubMed  Google Scholar 

  70. 70.

    Qiao, W. et al. Intercellular network structure and regulatory motifs in the human hematopoietic system. Mol. Syst. Biol. 10, 741–741 (2014). This study combines genomic and phenotypic data with high-content experiments to build a directional cell–cell communication network between 12 cell types in human umbilical cord blood.

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Koike-Yusa, H., Li, Y., Tan, E. P., Velasco-Herrera, M. D. C. & Yusa, K. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32, 267–273 (2014).

    CAS  PubMed  Google Scholar 

  72. 72.

    Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

    CAS  Google Scholar 

  73. 73.

    Golipour, A. et al. A late transition in somatic cell reprogramming requires regulators distinct from the pluripotency network. Cell Stem Cell 11, 769–782 (2012).

    CAS  PubMed  Google Scholar 

  74. 74.

    Nazareth, E. J. P., Rahman, N., Yin, T. & Zandstra, P. W. A. Multi-lineage screen reveals mTORC1 inhibition enhances human pluripotent stem cell mesendoderm and blood progenitor production. Stem Cell Rep. 6, 679–691 (2016).

    CAS  Google Scholar 

  75. 75.

    Dunn, S. J., Martello, G., Yordanov, B., Emmott, S. & Smith, A. G. Defining an essential transcription factor program for naïve pluripotency. Science 344, 1156–1160 (2014). This paper reports the development of a data-constrained, computational approach to derive a simple GRN that functionally captures the mouse PSC state.

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Moignard, V. et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33, 269–276 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Kinoshita, A. Y. et al. Modeling signaling-dependent pluripotency with Boolean logic to predict cell fate transitions. Mol. Syst. Biol. 14, e7952 (2018).

    Google Scholar 

  78. 78.

    Cahan, P. et al. CellNet: network biology applied to stem cell engineering. Cell 158, 903–915 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Livet, J. et al. Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature 450, 56–62 (2007).

    CAS  Google Scholar 

  80. 80.

    Gerrits, A. et al. Cellular barcoding tool for clonal analysis in the hematopoietic system. Blood 115, 2610–2618 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).

    PubMed  PubMed Central  Google Scholar 

  82. 82.

    Pei, W. et al. Polylox barcoding reveals haematopoietic stem cell fates realized in vivo. Nature 548, 456–460 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2016). This paper describes a synthetic system to record lineage information and event histories in the genome of ESCs in a manner that is read out at the single level in situ.

    PubMed  Google Scholar 

  84. 84.

    Schroeder, T. Long-term single-cell imaging of mammalian stem cells. Nat. Methods 8, S30–S35 (2011). This perspective piece provides an overview of the utility of continuous long-term live imaging for providing key insights on stem cell function.

    CAS  PubMed  Google Scholar 

  85. 85.

    Prochazka, L., Benenson, Y. & Zandstra, P. W. Synthetic gene circuits and cellular decision-making in human pluripotent stem cells. Curr. Opin. Syst. Biol. 5, 93–103 (2017). This review explores the intersection of synthetic biology and stem cell engineering towards the development of decision-making gene circuits to control cell fate.

    Google Scholar 

  86. 86.

    Teague, B. P., Guye, P. & Weiss, R. Synthetic morphogenesis. Cold Spring Harb. Perspect. Biol. 8, a023929 (2016).

    PubMed  PubMed Central  Google Scholar 

  87. 87.

    Kitada, T., DiAndreth, B., Teague, B. & Weiss, R. Programming gene and engineered-cell therapies with synthetic biology. Science 359, eaad1067 (2018).

    PubMed  Google Scholar 

  88. 88.

    Mathur, M., Xiang, J. S. & Smolke, C. D. Mammalian synthetic biology for studying the cell. J. Cell Biol. 216, 73–82 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Cameron, D. E., Bashor, C. J. & Collins, J. J. A brief history of synthetic biology. Nat. Rev. Microbiol. 12, 381–390 (2014).

    CAS  PubMed  Google Scholar 

  90. 90.

    Lienert, F., Lohmueller, J. J., Garg, A. & Silver, P. A. Synthetic biology in mammalian cells: next generation research tools and therapeutics. Nat. Rev. Mol. Cell Biol. 15, 95–107 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Khalil, A. S. & Collins, J. J. Synthetic biology: applications come of age. Nat. Rev. Genet. 11, 367–379 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Xie, Z., Wroblewska, L., Prochazka, L., Weiss, R. & Benenson, Y. Multi-input RNAi-based logic circuit for identification of specific cancer cells. Science 333, 1307–1311 (2011).

    CAS  PubMed  Google Scholar 

  93. 93.

    Kiani, S. et al. CRISPR transcriptional repression devices and layered circuits in mammalian cells. Nat. Methods 11, 723–726 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Prochazka, L., Angelici, B., Haefliger, B. & Benenson, Y. Highly modular bow-tie gene circuits with programmable dynamic behaviour. Nat. Commun. 5, 4729 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Weinberg, B. H. et al. Large-scale design of robust genetic circuits with multiple inputs and outputs for mammalian cells. Nat. Biotechnol. 35, 453–462 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Morsut, L. et al. Engineering customized cell sensing and response behaviors using synthetic notch receptors. Cell 164, 780–791 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Roybal, K. T. et al. Engineering T cells with customized therapeutic response programs using synthetic notch receptors. Cell 167, 419–432 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Saxena, P. et al. A programmable synthetic lineage-control network that differentiates human IPSCs into glucose-sensitive insulin-secreting beta-like cells. Nat. Commun. 7, 11247 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Del Vecchio, D., Dy, A. J. & Qian, Y. Control theory meets synthetic biology. J. R. Soc. Interface 13, 20160380 (2016).

    PubMed  PubMed Central  Google Scholar 

  100. 100.

    Del Vecchio, D., Abdallah, H., Qian, Y. & Collins, J. J. A. Blueprint for a synthetic genetic feedback controller to reprogram cell fate. Cell Syst. 4, 109–120 (2017).

    PubMed  PubMed Central  Google Scholar 

  101. 101.

    Davies, J. Using synthetic biology to explore principles of development. Development 144, 1146–1158 (2017).

    CAS  PubMed  Google Scholar 

  102. 102.

    Shakiba, N. & Zandstra, P. W. Engineering cell fitness: lessons for regenerative medicine. Curr. Opin. Biotechnol. 47, 7–15 (2017).

    CAS  PubMed  Google Scholar 

  103. 103.

    Jackson, H. J., Rafiq, S. & Brentjens, R. J. Driving CAR T cells forward. Nat. Rev. Clin. Oncol. 13, 370–383 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Li, P. et al. Morphogen gradient reconstitution reveals Hedgehog pathway design principles. Science 360, 543–548 (2018).

    CAS  PubMed  Google Scholar 

  105. 105.

    Sun, C. & Bernards, R. Feedback and redundancy in receptor tyrosine kinase signaling: relevance to cancer therapies. Trends Biochem. Sci. 39, 465–474 (2014).

    CAS  PubMed  Google Scholar 

  106. 106.

    Dublanche, Y., Michalodimitrakis, K., Kümmerer, N., Foglierini, M. & Serrano, L. Noise in transcription negative feedback loops: simulation and experimental analysis. Mol. Syst. Biol. 2, 41 (2006).

    PubMed  PubMed Central  Google Scholar 

  107. 107.

    Austin, D. W. et al. Gene network shaping of inherent noise spectra. Nature 439, 608–611 (2006).

    CAS  PubMed  Google Scholar 

  108. 108.

    Becskei, A. & Serrano, L. Engineering stability in gene networks by autoregulation. Nature 405, 590–593 (2000).

    CAS  PubMed  Google Scholar 

  109. 109.

    Briat, C., Zechner, C. & Khammash, M. Design of a synthetic integral feedback circuit: dynamic analysis and DNA implementation. ACS Synth. Biol. 5, 1108–1116 (2016).

    CAS  PubMed  Google Scholar 

  110. 110.

    Briat, C., Gupta, A. & Khammash, M. Antithetic integral feedback ensures robust perfect adaptation in noisy biomolecular networks. Cell Syst 2, 15–26 (2016).

    CAS  PubMed  Google Scholar 

  111. 111.

    Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Maamar, H. & Dubnau, D. Bistability in the Bacillus subtilis K state (competence) system requires a positive feedback loop. Mol. Microbiol. 56, 615–624 (2005).

    CAS  PubMed  Google Scholar 

  113. 113.

    Xiong, W. & Ferrell, J. E. A positive-feedback-based bistable ‘memory module’ that governs a cell fate decision. Nature 426, 460–465 (2003).

    CAS  Google Scholar 

  114. 114.

    Isaacs, F. J., Hasty, J., Cantor, C. R. & Collins, J. J. Prediction and measurement of an autoregulatory genetic module. Proc. Natl Acad. Sci. USA 100, 7714–7719 (2003).

    CAS  PubMed  Google Scholar 

  115. 115.

    Süel, G. M., Garcia-Ojalvo, J., Liberman, L. M. & Elowitz, M. B. An excitable gene regulatory circuit induces transient cellular differentiation. Nature 440, 545–550 (2006).

    PubMed  Google Scholar 

  116. 116.

    Kramer, B. P. & Fussenegger, M. Hysteresis in a synthetic mammalian gene network. Proc. Natl Acad. Sci. USA 102, 9517–9522 (2005).

    CAS  PubMed  Google Scholar 

  117. 117.

    Losick, R. & Desplan, C. Stochasticity and cell fate. Science 320, 65–68 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Wernet, M. F. et al. Stochastic spineless expression creates the retinal mosaic for colour vision. Nature 440, 174–180 (2006).

    CAS  PubMed  Google Scholar 

  119. 119.

    Johnston, R. J. & Desplan, C. Stochastic neuronal cell fate choices. Curr. Opin. Neurobiol. 18, 20–27 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120.

    Abranches, E. et al. Stochastic NANOG fluctuations allow mouse embryonic stem cells to explore pluripotency. Development 141, 2770–2779 (2014).

    CAS  PubMed  Google Scholar 

  121. 121.

    Guye, P. et al. Genetically engineering self-organization of human pluripotent stem cells into a liver bud-like tissue using Gata6. Nat. Commun. 7, 10243–10212 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Watt, F. M., Jordan, P. W. & O’Neill, C. H. Cell shape controls terminal differentiation of human epidermal keratinocytes. Proc. Natl Acad. Sci. USA 85, 5576–5580 (1988).

    CAS  PubMed  Google Scholar 

  123. 123.

    McBeath, R., Pirone, D. M., Nelson, C. M., Bhadriraju, K. & Chen, C. S. Cell shape, cytoskeletal tension, and RhoA regulate stem cell lineage commitment. Dev. Cell 6, 483–495 (2004).

    CAS  PubMed  Google Scholar 

  124. 124.

    Dupont, S. et al. Role of YAP/TAZ in mechanotransduction. Nature 474, 179–U212 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. 125.

    Chen, S. et al. Interrogating cellular fate decisions with high-throughput arrays of multiplexed cellular communities. Nat. Commun. 7, 10309 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. 126.

    Peerani, R., Onishi, K., Mahdavi, A., Kumacheva, E. & Zandstra, P. W. Manipulation of signaling thresholds in ‘engineered stem cell niches’ identifies design criteria for pluripotent stem cell screens. PLOS ONE 4, e6438 (2009).

    PubMed  PubMed Central  Google Scholar 

  127. 127.

    Onishi, K., Tonge, P. D., Nagy, A. & Zandstra, P. W. Microenvironment-mediated reversion of epiblast stem cells by reactivation of repressed JAK-STAT signaling. Integr. Biol. 4, 1367–1376 (2012).

    CAS  Google Scholar 

  128. 128.

    Onishi, K., Tonge, P. D., Nagy, A. & Zandstra, P. W. Local BMP-SMAD1 signaling increases LIF receptor-dependent STAT3 responsiveness and primed-to-naive mouse pluripotent stem cell conversion frequency. Stem Cell Rep. 3, 156–168 (2014).

    CAS  Google Scholar 

  129. 129.

    Peerani, R. et al. Niche-mediated control of human embryonic stem cell self-renewal and differentiation. EMBO J. 26, 4744–4755 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. 130.

    Bauwens, C. L. et al. Control of human embryonic stem cell colony and aggregate size heterogeneity influences differentiation trajectories. Stem Cells 26, 2300–2310 (2008).

    PubMed  Google Scholar 

  131. 131.

    Lee, L. H. et al. Micropatterning of human embryonic stem cells dissects the mesoderm and endoderm lineages. Stem Cell Res. 2, 155–162 (2009).

    CAS  PubMed  Google Scholar 

  132. 132.

    Nazareth, E. J. P. et al. High-throughput fingerprinting of human pluripotent stem cell fate responses and lineage bias. Nat. Methods 10, 1225–1231 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. 133.

    Nemashkalo, A., Ruzo, A., Heemskerk, I. & Warmflash, A. Morphogen and community effects determine cell fates in response to BMP4 signaling in human embryonic stem cells. Development 144, 3042–3053 (2017).

    CAS  PubMed  Google Scholar 

  134. 134.

    Flaim, C. J., Chien, S. & Bhatia, S. N. An extracellular matrix microarray for probing cellular differentiation. Nat. Methods 2, 119–125 (2005).

    CAS  PubMed  Google Scholar 

  135. 135.

    Shukla, S. et al. Progenitor T-cell differentiation from hematopoietic stem cells using Delta-like-4 and VCAM-1. Nat. Methods 14, 531–538 (2017).

    CAS  PubMed  Google Scholar 

  136. 136.

    Gilbert, P. M. et al. Substrate elasticity regulates skeletal muscle stem cell self-renewal in culture. Science 329, 1078–1081 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. 137.

    Trappmann, B. et al. Extracellular-matrix tethering regulates stem-cell fate. Nat. Mater. 11, 642–649 (2012).

    CAS  PubMed  Google Scholar 

  138. 138.

    Chaudhuri, O. et al. Hydrogels with tunable stress relaxation regulate stem cell fate and activity. Nat. Mater. 15, 326–334 (2016).

    CAS  PubMed  Google Scholar 

  139. 139.

    Madl, C. M. et al. Maintenance of neural progenitor cell stemness in 3D hydrogels requires matrix remodelling. Nat. Mater. 16, 1233–1242 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. 140.

    Kirouac, D. et al. Cell-cell interaction networks regulate blood stem and progenitor cell fate. Mol. Syst. Biol. 5, 293 (2009).

    PubMed  PubMed Central  Google Scholar 

  141. 141.

    Kirouac, D. C. et al. Dynamic interaction networks in a hierarchically organized tissue. Mol. Syst. Biol. 6, 417 (2010).

    PubMed  PubMed Central  Google Scholar 

  142. 142.

    Csaszar, E. et al. Rapid expansion of human hematopoietic stem cells by automated control of inhibitory feedback signaling. Cell Stem Cell 10, 218–229 (2012). This paper reports an integrated computational and experimental strategy that enables tunable reduction in the global levels of paracrine signalling factors in an automated closed-system for the improved expansion of HSCs.

    CAS  PubMed  Google Scholar 

  143. 143.

    Lipsitz, Y. Y., Timmins, N. E. & Zandstra, P. W. Quality cell therapy manufacturing by design. Nat. Biotechnol. 34, 393–400 (2016).

    CAS  PubMed  Google Scholar 

  144. 144.

    Goyal, S., Kim, S., Chen, I. S. Y. & Chou, T. Mechanisms of blood homeostasis: lineage tracking and a neutral model of cell populations in rhesus macaques. BMC Biol. 13, 85 (2015).

    PubMed  PubMed Central  Google Scholar 

  145. 145.

    Lipsitz, Y. Y., Bedford, P., Davies, A. H., Timmins, N. E. & Zandstra, P. W. Achieving efficient manufacturing and quality assurance through synthetic cell therapy design. Cell Stem Cell 20, 13–17 (2017).

    CAS  PubMed  Google Scholar 

  146. 146.

    Todhunter, M. E. et al. Programmed synthesis of three-dimensional tissues. Nat. Methods 12, 975–981 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  147. 147.

    Leng, L., McAllister, A., Zhang, B., Radisic, M. & Günther, A. Mosaic hydrogels: one-step formation of multiscale soft materials. Adv. Mater. Weinheim 24, 3650–3658 (2012).

    CAS  PubMed  Google Scholar 

  148. 148.

    Kang, H. W. et al. A 3D bioprinting system to produce human-scale tissue constructs with structural integrity. Nat. Biotechnol. 34, 312–319 (2016).

    CAS  PubMed  Google Scholar 

  149. 149.

    Young, M. et al. A TRACER 3D co-culture tumour model for head and neck cancer. Biomaterials 164, 54–69 (2018).

    CAS  Google Scholar 

  150. 150.

    Rodenhizer, D. et al. A three-dimensional engineered tumour for spatial snapshot analysis of cell metabolism and phenotype in hypoxic gradients. Nat. Mater. 15, 227–234 (2016).

    CAS  Google Scholar 

  151. 151.

    Eiraku, M. et al. Self-organizing optic-cup morphogenesis in three-dimensional culture. Nature 472, 51–56 (2011).

    CAS  PubMed  Google Scholar 

  152. 152.

    Lancaster, M. A. et al. Cerebral organoids model human brain development and microcephaly. Nature 501, 373–379 (2013).

    CAS  Google Scholar 

  153. 153.

    Rivron, N. C. et al. Blastocyst-like structures generated solely from stem cells. Nature 557, 106–111 (2018).

    CAS  PubMed  Google Scholar 

  154. 154.

    Lancaster, M. A. et al. Guided self-organization and cortical plate formation in human brain organoids. Nat. Biotechnol. 35, 659–666 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. 155.

    Shao, Y. et al. A pluripotent stem cell-based model for post-implantation human amniotic sac development. Nat. Commun. 8, 208 (2017).

    PubMed  PubMed Central  Google Scholar 

  156. 156.

    Shao, Y. et al. Self-organized amniogenesis by human pluripotent stem cells in a biomimetic implantation-like niche. Nat. Mater. 16, 419–425 (2017).

    CAS  PubMed  Google Scholar 

  157. 157.

    Cerchiari, A. E. et al. A strategy for tissue self-organization that is robust to cellular heterogeneity and plasticity. Proc. Nat. Acad. Sci. USA 112, 2287–2292 (2015).

    CAS  PubMed  Google Scholar 

  158. 158.

    Hughes, A. J. et al. Engineered tissue folding by mechanical compaction of the mesenchyme. Dev. Cell 44, 165–178 (2017).

    PubMed  Google Scholar 

  159. 159.

    Gjorevski, N. et al. Designer matrices for intestinal stem cell and organoid culture. Nature 539, 560–564 (2016).

    CAS  Google Scholar 

  160. 160.

    Arora, N. et al. A process engineering approach to increase organoid yield. Development 144, 1128–1136 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  161. 161.

    Czerniecki, S. M. et al. High-throughput screening enhances kidney organoid differentiation from human pluripotent stem cells and enables automated multidimensional phenotyping. Cell Stem Cell 22, 929–940 (2018).

    CAS  PubMed  Google Scholar 

  162. 162.

    Warmflash, A., Sorre, B., Etoc, F., Siggia, E. D. & Brivanlou, A. H. A method to recapitulate early embryonic spatial patterning in human embryonic stem cells. Nat. Methods 11, 847–854 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  163. 163.

    Morgani, S. M., Metzger, J. J., Nichols, J., Siggia, E. D. & Hadjantonakis, A. K. Micropattern differentiation of mouse pluripotent stem cells recapitulates embryo regionalized cell fate patterning. eLife 7, 1040 (2018).

    Google Scholar 

  164. 164.

    Blin, G., Picart, C., Thery, M. & Puceat, M. Geometrical confinement guides Brachyury self-patterning in embryonic stem cells. bioRxiv https://doi.org/10.1101/138354 (2017).

  165. 165.

    Thery, M. Micropatterning as a tool to decipher cell morphogenesis and functions. J. Cell Sci. 123, 4201–4213 (2010).

    CAS  PubMed  Google Scholar 

  166. 166.

    Azioune, A., Storch, M., Bornens, M., Thery, M. & Piel, M. Simple and rapid process for single cell micro-patterning. Lab. Chip 9, 1640–1642 (2009).

    CAS  PubMed  Google Scholar 

  167. 167.

    Etoc, F. et al. A balance between secreted inhibitors and edge sensing controls gastruloid self-organization. Dev. Cell 39, 302–315 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  168. 168.

    Kunche, S., Yan, H., Calof, A. L., Lowengrub, J. S. & Lander, A. D. Feedback, lineages and self-organizing morphogenesis. PLOS Comput. Biol. 12, e1004814 (2016).

    PubMed  PubMed Central  Google Scholar 

  169. 169.

    Rubenstein, M., Sai, Y., Chuong, C. M. & Shen, W. M. Regenerative patterning in swarm robots: mutual benefits of research in robotics & stem cell biology. Int. Dev, J. Biol. 53, 869–881 (2009).

    Google Scholar 

  170. 170.

    Rubenstein, M., Cornejo, A. & Nagpal, R. Robotics. Programmable self-assembly in a thousand-robot swarm. Science 345, 795–799 (2014).

    CAS  PubMed  Google Scholar 

  171. 171.

    Finerty, J. C. Parabiosis in physiological studies. Physiol. Rev. 32, 277–302 (1952).

    CAS  PubMed  Google Scholar 

  172. 172.

    Conboy, I. M. et al. Rejuvenation of aged progenitor cells by exposure to a young systemic environment. Nature 433, 760–764 (2005).

    CAS  PubMed  Google Scholar 

  173. 173.

    Loffredo, F. S. et al. Growth differentiation factor 11 is a circulating factor that reverses age-related cardiac hypertrophy. Cell 153, 828–839 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  174. 174.

    Katsimpardi, L. et al. Vascular and neurogenic rejuvenation of the aging mouse brain by young systemic factors. Science 344, 630–634 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  175. 175.

    Seok, J. et al. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc. Nat. Acad. Sci. USA 110, 3507–3512 (2013).

    CAS  PubMed  Google Scholar 

  176. 176.

    Benam, K. H. et al. Engineered in vitro -disease models. Annu. Rev. Pathol. 10, 195–262 (2015).

    CAS  PubMed  Google Scholar 

  177. 177.

    Pammolli, F., Magazzini, L. & Riccaboni, M. The productivity crisis in pharmaceutical R&D. Nat. Rev. Drug Discov. 10, 428–438 (2011).

    CAS  PubMed  Google Scholar 

  178. 178.

    Prantil-Baun, R. et al. Physiologically based pharmacokinetic and pharmacodynamic analysis enabled by microfluidically linked organs-on-chips. Annu. Rev. Pharmacol. Toxicol. 58, 37–64 (2018).

    CAS  PubMed  Google Scholar 

  179. 179.

    Skardal, A. et al. Multi-tissue interactions in an integrated three-tissue organ-on a-chip platform. Sci. Rep. 7, 8837 (2017).

    PubMed  PubMed Central  Google Scholar 

  180. 180.

    Huh, D. et al. Microfabrication of human organs-on-chips. Nat. Protoc. 8, 2135–2157 (2013).

    CAS  PubMed  Google Scholar 

  181. 181.

    Danhof, M. et al. Mechanism-based pharmacokinetic-pharmacodynamic modeling: biophase distribution, receptor theory, and dynamical systems analysis. Annu. Rev. Pharmacol. Toxicol. 47, 357–400 (2007).

    CAS  PubMed  Google Scholar 

  182. 182.

    Edington, C. D. et al. Interconnected microphysiological systems for quantitative biology and pharmacology studies. Sci. Rep. 8, 4530 (2018).

    PubMed  PubMed Central  Google Scholar 

  183. 183.

    Kasendra, M. et al. Development of a primary human small intestine-on a-chip using biopsy-derived organoids. Sci. Rep. 8, 2 (2018).

    Google Scholar 

  184. 184.

    Tsamandouras, N. et al. Integrated gut and liver microphysiological systems for quantitative in vitro pharmacokinetic studies. AAPS J. 19, 1499–1512 (2017).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank C. Bauwens for her insightful feedback on this review. The authors especially thank D. Lauffenburger (Massachusetts Institute of Technology), a strong proponent of the cue–signal–response paradigm and with whom P.W.Z. has had many helpful discussions on the engineering approach to biology over the years. The authors apologize to their colleagues whose important work could not be included because of space constraints. The authors are funded by the Canadian Institutes for Health Research and Medicine by Design, a Canada First Research Excellence Programme at the University of Toronto. P.W.Z. is the Canada Research Chair in Stem Cell Engineering.

Reviewer information

Nature Reviews Genetics thanks D. V. Schaffer and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Affiliations

Authors

Contributions

The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to Peter W. Zandstra.

Ethics declarations

Cometing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Glossary

Cell therapies

Clinical treatments that introduce living cellular material into a patient. They may engraft in the body, leading to long-term replacement of damaged or missing tissue, or stimulate endogenous repair and promote endogenous viability.

Embryonic stem cell

(ESC). A type of pluripotent stem cell, derived from the inner cell mass of the developing embryo, that is responsible for giving rise to all of the cells in the developing fetus but not the extra-embryonic tissues.

Organoid

A minimal and miniaturized organ that is developed from a suspension of stem cells in vitro. These stem cells undergo division and self-organization to give rise to a 3D structure that mimics the anatomy of organs in the body. Thus, organoids can serve as models for understanding organ development and for modelling disease states.

Cell fate

A cell’s identity based on its expression of genetic, proteomic and epigenetic markers but also in terms of its functional abilities. Cell fate determines a cell’s self-renewal ability, proliferative ability, differentiation potential, survival and motility.

Autocrine

A form of cellular signalling in which secreted chemicals bind to receptors on the same cell. By contrast, juxtacrine and paracrine signalling induce responses in neighbouring cells, either through direct contact (juxtacrine) or secreted chemicals (paracrine).

Extracellular matrix

(ECM). A collection of extracellular molecules, including proteins, proteoglycans and polysaccharides, that supports the growth of nearby cells by providing biomechanical and biochemical cues. It enables cell adhesion and cell–cell communication.

Gene regulatory networks

(GRNs). A set of genes and their direct and indirect regulatory interactions with one another. GRNs are akin to decision-making computational circuits that serve to process input signals and generate robust outputs in cell behaviour.

Network motifs

Interaction patterns that recur more frequently than in randomized networks — for example, negative autoregulation (or ‘autorepression’) and the feedforward loop.

Niches

The in vivo microenvironments in which stem cells reside that regulate their homeostasis and fate choices.

Morphogenesis

The process by which developing organisms acquire their structure and shape.

Bayesian networks

Probabilistic models that relate the dependencies of the expression of a set of genes on one another through a directed graph.

Boolean networks

Models of gene regulatory networks that can predict gene expression outcomes given the initial state of genes in the network as well as the derivation of steady-state gene expression status.

Artificial neural networks

Networks composed of nodes, which can be genes, that process and transmit information. The output of each node is a nonlinear function of a sum of its regulatory inputs.

Ordinary differential equations

A mathematical framework capturing gene expression dynamics as a function of the presence of regulators and the rate of change of mRNA and/or protein concentration due to production and degradation.

Reverse engineering

The process of analysing a system to uncover underlying design rules to create representations of the system at higher levels of abstraction (inverse of forward engineering).

Forward engineering

The iterative process by which a system is designed, prototyped, tested and further optimized from a model (the classical engineering design process).

Micropatterning

Technology that enables transfer of miniature ‘islands’ of extracellular matrix proteins to enforce control of the shape and size of adherent cells either as single cells or cell colonies.

Stemness

The characteristic of a cell that makes it a stem cell. That is, the ability to self-renew and differentiate to specify to different cell types.

Bioreactors

Vessels in which biological species, such as stem cells and their progeny, are grown, maintained and manipulated in a controlled environment (pH, oxygen and media change) for cell manufacturing pipelines.

Bioprinting

Utilization of printing techniques ranging from inkjet printers to 3D printers to combine cells, biomaterials, extracellular matrix, growth factors, etc. to fabricate complex tissue surrogates in vitro.

Fate patterning

A process during embryogenesis in which cell fates are allocated or ‘patterned’ as a function of space and time.

Morphogens

Signalling molecules, typically soluble chemicals, for which the asymmetric distribution in a developing tissue gives rise to fate patterning and morphogenesis.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tewary, M., Shakiba, N. & Zandstra, P.W. Stem cell bioengineering: building from stem cell biology. Nat Rev Genet 19, 595–614 (2018). https://doi.org/10.1038/s41576-018-0040-z

Download citation

Further reading