Skip to main content
SpringerLink
Log in
Book cover

Pluripotent Stem Cells in Eye Disease Therapy pp 171–193Cite as

3D Engineering of Ocular Tissues for Disease Modeling and Drug Testing

  • Chapter
  • First Online:

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1186))

Abstract

The success rate from investigational new drug filing to drug approval has remained low for decades despite major scientific and technological advances, and a steady increase of funding and investment. The failure to demonstrate drug efficacy has been the major reason that drug development does not progress beyond phase II and III clinical trials. The combination of two-dimensional (2D) cellular in vitro and animal models has been the gold standard for basic science research and preclinical drug development studies. However, most findings from these systems fail to translate into human trials because these models only partly recapitulate human physiology and pathology. The lack of a dynamic three-dimensional microenvironment in 2D cellular models reduces the physiological relevance, and for these reasons, 3D and microfluidic model systems are now being developed as more native-like biological assay platforms. 3D cellular in vitro systems, microfluidics, self-organized organoids, and 3D biofabrication are the most promising technologies to mimic human physiology because they provide mechanical cues and a 3D microenvironment to the multicellular components. With the advent of human-induced pluripotent stem cell (iPSC) technology, the 3D dynamic in vitro systems further enable extensive access to human-like tissue models. As increasingly complex 3D cellular systems are produced, the use of current visualization technologies is limited due to the thickness and opaqueness of 3D tissues. Tissue-clearing techniques improve light penetration deep into tissues by matching refractive indices among the 3D components. 3D segmentation enables quantitative measurements based on 3D tissue images. Using these state-of-the-art technologies, high-throughput screening (HTS) of thousands of drug compounds in 3D tissue models is slowly becoming a reality. In order to screen thousands of compounds, machine learning will need to be applied to help maximize outcomes from the use of cheminformatics and phenotypic approaches to drug screening. In this chapter, we discuss the current 3D ocular models recapitulating physiology and pathology of the back of the eye and further discuss visualization and quantification techniques that can be implemented for drug screening in ocular diseases.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Scannell JW, Blanckley A, Boldon H, Warrington B (2012) Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov 11(3):191–200

    Article  CAS  PubMed  Google Scholar 

  2. Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J (2014) Clinical development success rates for investigational drugs. Nat Biotechnol 32(1):40–51

    Article  CAS  PubMed  Google Scholar 

  3. BIO BaA (2016) Clinical development success rates 2006-2015. BIO, Washington

    Google Scholar 

  4. Harrison RK (2016) Phase II and phase III failures: 2013-2015. Nat Rev Drug Discov 15(12):817–818

    Article  CAS  PubMed  Google Scholar 

  5. Repetto G, del Peso A, Zurita JL (2008) Neutral red uptake assay for the estimation of cell viability/cytotoxicity. Nat Protoc 3(7):1125–1131

    Article  CAS  PubMed  Google Scholar 

  6. Trapnell C et al (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28(5):511–515

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Potter H (2003) Transfection by electroporation. Curr Protoc Mol Biol Chapter 9:Unit 9 3

    PubMed  Google Scholar 

  8. Connor KM et al (2009) Quantification of oxygen-induced retinopathy in the mouse: a model of vessel loss, vessel regrowth and pathological angiogenesis. Nat Protoc 4(11):1565–1573

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. McAvoy JW, Chamberlain CG, de Iongh RU, Hales AM, Lovicu FJ (1999) Lens development. Eye (Lond) 13(Pt 3b):425–437

    Article  Google Scholar 

  10. Ferrara N, Hillan KJ, Gerber HP, Novotny W (2004) Discovery and development of bevacizumab, an anti-VEGF antibody for treating cancer. Nat Rev Drug Discov 3(5):391–400

    Article  CAS  PubMed  Google Scholar 

  11. Rongvaux A et al (2014) Development and function of human innate immune cells in a humanized mouse model. Nat Biotechnol 32(4):364–372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chiu JJ, Chien S (2011) Effects of disturbed flow on vascular endothelium: pathophysiological basis and clinical perspectives. Physiol Rev 91(1):327–387

    Article  PubMed  Google Scholar 

  13. Li YS, Haga JH, Chien S (2005) Molecular basis of the effects of shear stress on vascular endothelial cells. J Biomech 38(10):1949–1971

    Article  PubMed  Google Scholar 

  14. Huh D et al (2010) Reconstituting organ-level lung functions on a chip. Science 328(5986):1662–1668

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Huh D, Torisawa YS, Hamilton GA, Kim HJ, Ingber DE (2012) Microengineered physiological biomimicry: organs-on-chips. Lab Chip 12(12):2156–2164

    Article  CAS  PubMed  Google Scholar 

  16. Newman AC, Nakatsu MN, Chou W, Gershon PD, Hughes CC (2011) The requirement for fibroblasts in angiogenesis: fibroblast-derived matrix proteins are essential for endothelial cell lumen formation. Mol Biol Cell 22(20):3791–3800

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Chen JX, Stinnett A (2008) Disruption of Ang-1/Tie-2 signaling contributes to the impaired myocardial vascular maturation and angiogenesis in type II diabetic mice. Arterioscler Thromb Vasc Biol 28(9):1606–1613

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wakui S et al (2006) Localization of Ang-1, -2, Tie-2, and VEGF expression at endothelial-pericyte interdigitation in rat angiogenesis. Lab Invest 86(11):1172–1184

    Article  CAS  PubMed  Google Scholar 

  19. Moya ML, Alonzo LF, George SC (2014) Microfluidic device to culture 3D in vitro human capillary networks. Methods Mol Biol 1202:21–27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wang X, Phan DTT, George SC, Hughes CCW, Lee AP (2017) 3D anastomosed microvascular network model with living capillary networks and endothelial cell-lined microfluidic channels. Methods Mol Biol 1612:325–344

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Jeon JS et al (2015) Human 3D vascularized organotypic microfluidic assays to study breast cancer cell extravasation. Proc Natl Acad Sci U S A 112(1):214–219

    Article  CAS  PubMed  Google Scholar 

  22. Kim S, Lee H, Chung M, Jeon NL (2013) Engineering of functional, perfusable 3D microvascular networks on a chip. Lab Chip 13(8):1489–1500

    Article  CAS  PubMed  Google Scholar 

  23. Chung M et al (2018) Wet-AMD on a chip: modeling outer blood-retinal barrier in vitro. Adv Healthc Mater 7(2)

    Google Scholar 

  24. Becerra SP et al (2004) Pigment epithelium-derived factor in the monkey retinal pigment epithelium and interphotoreceptor matrix: apical secretion and distribution. Exp Eye Res 78(2):223–234

    Article  CAS  PubMed  Google Scholar 

  25. Saint-Geniez M, Kurihara T, Sekiyama E, Maldonado AE, D’Amore PA (2009) An essential role for RPE-derived soluble VEGF in the maintenance of the choriocapillaris. Proc Natl Acad Sci U S A 106(44):18751–18756

    Article  PubMed  PubMed Central  Google Scholar 

  26. Sonoda S et al (2009) Attainment of polarity promotes growth factor secretion by retinal pigment epithelial cells: relevance to age-related macular degeneration. Aging 2(1):28–42

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bailey TA et al (2004) Oxidative stress affects the junctional integrity of retinal pigment epithelial cells. Invest Ophthalmol Vis Sci 45(2):675–684

    Article  PubMed  Google Scholar 

  28. Hamilton RD, Foss AJ, Leach L (2007) Establishment of a human in vitro model of the outer blood-retinal barrier. J Anat 211(6):707–716

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Song MJ, Quinn R, Dejene R, Bharti K (2017) 3D tissue engineered RPE/“choroid” to identify mechanism of AMD-disease initiation and progression. Assoc Res Vis Ophthalmol 58(8):3760–3760

    Google Scholar 

  30. Song MJ, Bharti K (2016) Looking into the future: using induced pluripotent stem cells to build two and three dimensional ocular tissue for cell therapy and disease modeling. Brain Res 1638(Pt A):2–14

    Article  CAS  PubMed  Google Scholar 

  31. Hampton C et al (2018) Hypoxia of retina pigment epithelium induces type 1 CNV-like morphology within 3D engineered iPSC-RPE/“Choroid” tissues. Invest Ophthalmol Vis Sci 59(9):3272–3272

    Google Scholar 

  32. Hotaling NA et al (2016) Nanofiber scaffold-based tissue-engineered retinal pigment epithelium to treat degenerative eye diseases. J Ocul Pharmacol Ther 32(5):272–285

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ablonczy Z, Crosson CE (2007) VEGF modulation of retinal pigment epithelium resistance. Exp Eye Res 85(6):762–771

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Curcio CA, Johnson M (2012) Structure, function, and pathology of Bruch’s membrane. Elastic:465–481

    Google Scholar 

  35. Baba T et al (2009) Maturation of the fetal human choriocapillaris. Invest Ophthalmol Vis Sci 50(7):3503–3511

    Article  PubMed  Google Scholar 

  36. Takahashi K et al (2007) Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131(5):861–872

    Article  CAS  PubMed  Google Scholar 

  37. Reh TA (2017) The development of the retina. Ryan’s Retina. Elsevier, Amsterdam

    Google Scholar 

  38. Zuber ME, Gestri G, Viczian AS, Barsacchi G, Harris WA (2003) Specification of the vertebrate eye by a network of eye field transcription factors. Development 130(21):5155–5167

    Article  CAS  PubMed  Google Scholar 

  39. Lamba DA, Karl MO, Ware CB, Reh TA (2006) Efficient generation of retinal progenitor cells from human embryonic stem cells. Proc Natl Acad Sci U S A 103(34):12769–12774

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Osakada F et al (2008) Toward the generation of rod and cone photoreceptors from mouse, monkey and human embryonic stem cells. Nat Biotechnol 26(2):215–224

    Article  CAS  PubMed  Google Scholar 

  41. Mellough CB, Sernagor E, Moreno-Gimeno I, Steel DH, Lako M (2012) Efficient stage-specific differentiation of human pluripotent stem cells toward retinal photoreceptor cells. Stem Cells 30(4):673–686

    Article  CAS  PubMed  Google Scholar 

  42. Hunt NC et al (2017) 3D culture of human pluripotent stem cells in RGD-alginate hydrogel improves retinal tissue development. Acta Biomater 49:329–343

    Article  CAS  PubMed  Google Scholar 

  43. Eiraku M et al (2011) Self-organizing optic-cup morphogenesis in three-dimensional culture. Nature 472(7341):51–56

    Article  CAS  PubMed  Google Scholar 

  44. Nakano T et al (2012) Self-formation of optic cups and storable stratified neural retina from human ESCs. Cell Stem Cell 10(6):771–785

    Article  CAS  PubMed  Google Scholar 

  45. Kuwahara A et al (2015) Generation of a ciliary margin-like stem cell niche from self-organizing human retinal tissue. Nat Commun 6:6286

    Article  CAS  PubMed  Google Scholar 

  46. Wahlin KJ et al (2017) Photoreceptor outer segment-like structures in long-term 3D retinas from human pluripotent stem cells. Sci Rep 7(1):766

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Volkner M et al (2016) Retinal organoids from pluripotent stem cells efficiently recapitulate retinogenesis. Stem Cell Rep 6(4):525–538

    Article  CAS  Google Scholar 

  48. Gonzalez-Cordero A et al (2013) Photoreceptor precursors derived from three-dimensional embryonic stem cell cultures integrate and mature within adult degenerate retina. Nat Biotechnol 31(8):741–747

    Article  CAS  PubMed  Google Scholar 

  49. Meyer JS et al (2009) Modeling early retinal development with human embryonic and induced pluripotent stem cells. Proc Natl Acad Sci U S A 106(39):16698–16703

    Article  PubMed  PubMed Central  Google Scholar 

  50. Meyer JS et al (2011) Optic vesicle-like structures derived from human pluripotent stem cells facilitate a customized approach to retinal disease treatment. Stem Cells 29(8):1206–1218

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Zhong X et al (2014) Generation of three-dimensional retinal tissue with functional photoreceptors from human iPSCs. Nat Commun 5:4047

    Article  CAS  PubMed  Google Scholar 

  52. Luo Z et al (2018) An optimized system for effective derivation of three-dimensional retinal tissue via wnt signaling regulation. Stem Cells 36:1709

    Article  CAS  PubMed  Google Scholar 

  53. Zhu Y et al (2013) Three-dimensional neuroepithelial culture from human embryonic stem cells and its use for quantitative conversion to retinal pigment epithelium. PLoS One 8(1):e54552

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lowe A, Harris R, Bhansali P, Cvekl A, Liu W (2016) Intercellular adhesion-dependent cell survival and ROCK-regulated actomyosin-driven forces mediate self-formation of a retinal organoid. Stem Cell Rep 6(5):743–756

    Article  CAS  Google Scholar 

  55. Shirai H et al (2016) Transplantation of human embryonic stem cell-derived retinal tissue in two primate models of retinal degeneration. Proc Natl Acad Sci U S A 113(1):E81–E90

    Article  CAS  PubMed  Google Scholar 

  56. Mandai M et al (2017) Autologous induced stem-cell-derived retinal cells for macular degeneration. N Engl J Med 376(11):1038–1046

    Article  CAS  PubMed  Google Scholar 

  57. Dorrie J, Wellner V, Kampgen E, Schuler G, Schaft N (2006) An improved method for RNA isolation and removal of melanin contamination from melanoma tissue: implications for tumor antigen detection and amplification. J Immunol Methods 313(1-2):119–128

    Article  CAS  PubMed  Google Scholar 

  58. Eckhart L, Bach J, Ban J, Tschachler E (2000) Melanin binds reversibly to thermostable DNA polymerase and inhibits its activity. Biochem Biophys Res Commun 271(3):726–730

    Article  CAS  PubMed  Google Scholar 

  59. Chung JY et al (2016) A melanin-bleaching methodology for molecular and histopathological analysis of formalin-fixed paraffin-embedded tissue. Lab Invest 96(10):1116–1127

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Liu CH et al (2013) Melanin bleaching with dilute hydrogen peroxide: a simple and rapid method. Appl Immunohistochem Mol Morphol 21(3):275–279

    CAS  PubMed  Google Scholar 

  61. Kim SY, Assawachananont J (2016) A new method to visualize the intact subretina from retinal pigment epithelium to retinal tissue in whole mount of pigmented mouse eyes. Transl Vis Sci Technol 5(1):6

    Article  PubMed  PubMed Central  Google Scholar 

  62. Thanos A et al (2012) Evidence for baseline retinal pigment epithelium pathology in the Trp1-Cre mouse. Am J Pathol 180(5):1917–1927

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Tainaka K, Kuno A, Kubota SI, Murakami T, Ueda HR (2016) Chemical principles in tissue clearing and staining protocols for whole-body cell profiling. Annu Rev Cell Dev Biol 32:713–741

    Article  CAS  PubMed  Google Scholar 

  64. Silvestri L, Costantini I, Sacconi L, Pavone FS (2016) Clearing of fixed tissue: a review from a microscopist’s perspective. J Biomed Opt 21(8):081205

    Article  PubMed  Google Scholar 

  65. Jacques SL (2013) Optical properties of biological tissues: a review. Phys Med Biol 58(11):R37–R61

    Article  PubMed  Google Scholar 

  66. Spalteholz W (1914) Über das Durchsichtigmachen von menschlichen und tierischen Präparaten und seine theoretischen Bedingungen, nebst Anhang: Über Knochenfärbung. S. Hirzel, Leipzig

    Google Scholar 

  67. Erturk A et al (2012) Three-dimensional imaging of solvent-cleared organs using 3DISCO. Nat Protoc 7(11):1983–1995

    Article  CAS  PubMed  Google Scholar 

  68. Ke MT, Fujimoto S, Imai T (2013) SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction. Nat Neurosci 16(8):1154–1161

    Article  CAS  PubMed  Google Scholar 

  69. Hama H et al (2011) Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain. Nat Neurosci 14(11):1481–1488

    Article  CAS  PubMed  Google Scholar 

  70. Hama H et al (2015) ScaleS: an optical clearing palette for biological imaging. Nat Neurosci 18(10):1518–1529

    Article  CAS  PubMed  Google Scholar 

  71. Susaki EA, Ueda HR (2016) Whole-body and whole-organ clearing and imaging techniques with single-cell resolution: toward organism-level systems biology in mammals. Cell Chem Biol 23(1):137–157

    Article  CAS  PubMed  Google Scholar 

  72. Chung K et al (2013) Structural and molecular interrogation of intact biological systems. Nature 497(7449):332–337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Yang B et al (2014) Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158(4):945–958

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Murray E et al (2015) Simple, scalable proteomic imaging for high-dimensional profiling of intact systems. Cell 163(6):1500–1514

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Zheng H, Rinaman L (2016) Simplified CLARITY for visualizing immunofluorescence labeling in the developing rat brain. Brain Struct Funct 221(4):2375–2383

    Article  CAS  PubMed  Google Scholar 

  76. Phillips J et al (2016) Development of passive CLARITY and immunofluorescent labelling of multiple proteins in human cerebellum: understanding mechanisms of neurodegeneration in mitochondrial disease. Sci Rep 6:26013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Kuwajima T et al (2013) ClearT: a detergent- and solvent-free clearing method for neuronal and non-neuronal tissue. Development 140(6):1364–1368

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Boutin ME et al (2018) A high-throughput imaging and nuclear segmentation analysis protocol for cleared 3D culture models. Sci Rep 8(1):11135

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Grist SM, Nasseri SS, Poon T, Roskelley C, Cheung KC (2016) On-chip clearing of arrays of 3-D cell cultures and micro-tissues. Biomicrofluidics 10(4):044107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Silva Santisteban T, Rabajania O, Kalinina I, Robinson S, Meier M (2017) Rapid spheroid clearing on a microfluidic chip. Lab Chip 18(1):153–161

    Article  PubMed  Google Scholar 

  81. Rajasekaran B, Uriu K, Valentin G, Tinevez JY, Oates AC (2016) Object segmentation and ground truth in 3D embryonic imaging. PLoS One 11(6):e0150853

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Li L, Zhou Q, Voss TC, Quick KL, LaBarbera DV (2016) High-throughput imaging: focusing in on drug discovery in 3D. Methods 96:97–102

    Article  CAS  PubMed  Google Scholar 

  83. Schmitz A, Fischer SC, Mattheyer C, Pampaloni F, Stelzer EH (2017) Multiscale image analysis reveals structural heterogeneity of the cell microenvironment in homotypic spheroids. Sci Rep 7:43693

    Article  PubMed  PubMed Central  Google Scholar 

  84. Jones TR et al (2009) Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proc Natl Acad Sci U S A 106(6):1826–1831

    Article  PubMed  PubMed Central  Google Scholar 

  85. Inglese J et al (2007) High-throughput screening assays for the identification of chemical probes. Nat Chem Biol 3(8):466–479

    Article  CAS  PubMed  Google Scholar 

  86. Ko HC, Gelb BD (2014) Concise review: drug discovery in the age of the induced pluripotent stem cell. Stem Cells Transl Med 3(4):500–509

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Haasen D et al (2017) How phenotypic screening influenced drug discovery: lessons from five years of practice. Assay Drug Dev Technol 15(6):239–246

    Article  CAS  PubMed  Google Scholar 

  88. Ursu A, Scholer HR, Waldmann H (2017) Small-molecule phenotypic screening with stem cells. Nat Chem Biol 13(6):560–563

    Article  CAS  PubMed  Google Scholar 

  89. Smith K et al (2018) Phenotypic image analysis software tools for exploring and understanding big image data from cell-based assays. Cell Syst 6(6):636–653

    Article  CAS  PubMed  Google Scholar 

  90. Kaewkhaw R et al (2016) Treatment paradigms for retinal and macular diseases using 3-D retina cultures derived from human reporter pluripotent stem cell lines. Invest Ophthalmol Vis Sci 57(5):ORSFl1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Fuller JA et al (2014) A high content screening approach to identify molecules neuroprotective for photoreceptor cells. Adv Exp Med Biol 801:773–781

    Article  PubMed  PubMed Central  Google Scholar 

  92. Chang YC et al (2014) The generation of induced pluripotent stem cells for macular degeneration as a drug screening platform: identification of curcumin as a protective agent for retinal pigment epithelial cells against oxidative stress. Front Aging Neurosci 6:191

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Ito SI, Onishi A, Takahashi M (2017) Chemically-induced photoreceptor degeneration and protection in mouse iPSC-derived three-dimensional retinal organoids. Stem Cell Res 24:94–101

    Article  CAS  PubMed  Google Scholar 

  94. Vergara MN et al (2017) Three-dimensional automated reporter quantification (3D-ARQ) technology enables quantitative screening in retinal organoids. Development 144(20):3698–3705

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Parfitt DA et al (2016) Identification and correction of mechanisms underlying inherited blindness in human iPSC-derived optic cups. Cell Stem Cell 18(6):769–781

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Zhou T et al (2017) High-content screening in hPSC-neural progenitors identifies drug candidates that inhibit zika virus infection in fetal-like organoids and adult brain. Cell Stem Cell 21(2):274–283. e275

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Mathews Griner LA et al (2016) Large-scale pharmacological profiling of 3D tumor models of cancer cells. Cell Death Dis 7(12):e2492

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Hou S et al (2018) Advanced development of primary pancreatic organoid tumor models for high-throughput phenotypic drug screening. SLAS Discov 23(6):574–584

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Carragher N et al (2018) Concerns, challenges and promises of high-content analysis of 3D cellular models. Nat Rev Drug Discov 17:606

    Article  CAS  PubMed  Google Scholar 

  100. Fujitani M et al (2017) Morphology-based non-invasive quantitative prediction of the differentiation status of neural stem cells. J Biosci Bioeng 124(3):351–358

    Article  CAS  PubMed  Google Scholar 

  101. Kobayashi H et al (2017) Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning. Sci Rep 7(1):12454

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Matsuoka F et al (2013) Morphology-based prediction of osteogenic differentiation potential of human mesenchymal stem cells. PLoS One 8(2):e55082

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Sasaki H et al (2014) Label-free morphology-based prediction of multiple differentiation potentials of human mesenchymal stem cells for early evaluation of intact cells. PLoS One 9(4):e93952

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Anderson DM et al (2014) High resolution MALDI imaging mass spectrometry of retinal tissue lipids. J Am Soc Mass Spectrom 25(8):1394–1403

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Deutskens F, Yang J, Caprioli RM (2011) High spatial resolution imaging mass spectrometry and classical histology on a single tissue section. J Mass Spectrom 46(6):568–571

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Seeley EH, Schwamborn K, Caprioli RM (2011) Imaging of intact tissue sections: moving beyond the microscope. J Biol Chem 286(29):25459–25466

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Srinivasan B et al (2015) TEER measurement techniques for in vitro barrier model systems. J Lab Autom 20(2):107–126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Ferrer M et al (2014) A multiplex high-throughput gene expression assay to simultaneously detect disease and functional markers in induced pluripotent stem cell-derived retinal pigment epithelium. Stem Cells Transl Med 3(8):911–922

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Lo YC, Rensi SE, Torng W, Altman RB (2018) Machine learning in chemoinformatics and drug discovery. Drug Discov Today 23:1538

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Horvath P, Wild T, Kutay U, Csucs G (2011) Machine learning improves the precision and robustness of high-content screens: using nonlinear multiparametric methods to analyze screening results. J Biomol Screen 16(9):1059–1067

    Article  CAS  PubMed  Google Scholar 

  111. O’Duibhir E et al (2018) Machine learning enables live label-free phenotypic screening in three dimensions. Assay Drug Dev Technol 16(1):51–63

    Article  CAS  PubMed  Google Scholar 

  112. Piccinini F et al (2017) Advanced cell classifier: user-friendly machine-learning-based software for discovering phenotypes in high-content imaging data. Cell Syst 4(6):651–655. e655

    Article  CAS  PubMed  Google Scholar 

  113. Smith K, Horvath P (2014) Active learning strategies for phenotypic profiling of high-content screens. J Biomol Screen 19(5):685–695

    Article  PubMed  Google Scholar 

  114. Strang BL et al (2018) Identification of lead anti-human cytomegalovirus compounds targeting MAP4K4 via machine learning analysis of kinase inhibitor screening data. PLoS One 13(7):e0201321

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Fuller JA, Berlinicke CA, Inglese J, Zack DJ (2016) Use of a machine learning-based high content analysis approach to identify photoreceptor neurite promoting molecules. Adv Exp Med Biol 854:597–603

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Center for Advancing Translational Sciences (NCATS), Rockville, MD, USA

    M. E. Boutin, M. Ferrer & M. J. Song

  2. National Eye Institute (NEI), Bethesda, MD, USA

    C. Hampton, R. Quinn & M. J. Song

Authors
  1. M. E. Boutin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Hampton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Quinn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Ferrer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. J. Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. J. Song .

Editor information

Editors and Affiliations

  1. National Institutes of Health NEI, Bethesda, MD, USA

    Kapil Bharti

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Boutin, M.E., Hampton, C., Quinn, R., Ferrer, M., Song, M.J. (2019). 3D Engineering of Ocular Tissues for Disease Modeling and Drug Testing. In: Bharti, K. (eds) Pluripotent Stem Cells in Eye Disease Therapy. Advances in Experimental Medicine and Biology, vol 1186. Springer, Cham. https://doi.org/10.1007/978-3-030-28471-8_7

Download citation

Publish with us

Policies and ethics

Search

Navigation