Microfluidics and Microanalytics to Facilitate Quantitative Assessment of Human Embryo Physiology

  • David K. GardnerEmail author
  • Philipp Reineck
  • Brant C. Gibson
  • Jeremy G. Thompson


Morphological assessment of the human preimplantation embryo has been used for 40 years to try to quantitate developmental potential. Although certain traits of embryo development appear linked to viability, the assessment of morphology alone remains subjective and hard to quantitate. Metabolic activity of embryos has been linked to viability post transfer. Glucose uptake on days 4 and 5 of human development is positively correlated with pregnancy outcome. However, nutrient analysis of small volumes of culture media remains technically demanding and limited to a few laboratories worldwide. With the advent of microfluidics, it has been possible to develop laboratory-on-a-chip devices which can accurately quantitate metabolite levels in culture media. Further, new microscopies, such as hyperspectral and FLIM, can quantitate the endogenous fluorescence of embryos, which in turn is related to their metabolic functions. The future of human IVF could therefore include the use of one or more of these technologies, which can readily be used in parallel with novel time-lapse analysis systems to create a wealth of information from each embryo.


Analysis Blastocyst Culture Metabolism Metabolomics Hyperspectral FLIM Viability 


  1. 1.
    Adashi EY, Barri PN, Berkowitz R, Braude P, Bryan E, Carr J, et al. Infertility therapy-associated multiple pregnancies (births): an ongoing epidemic. Reprod Biomed Online. 2003;7:515–42.PubMedGoogle Scholar
  2. 2.
    Gardner DK, Lane M. Culture and selection of viable blastocysts: a feasible proposition for human IVF? Hum Reprod Update. 1997;3:367–82.PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Gardner DK, Meseguer M, Rubio C, Treff NR. Diagnosis of human preimplantation embryo viability. Hum Reprod Update. 2015;21:727–47.PubMedCrossRefGoogle Scholar
  4. 4.
    Gardner DK, Balaban B. Assessment of human embryo development using morphological criteria in an era of time-lapse, algorithms and ‘OMICS’: is looking good still important? Mol Hum Reprod. 2016;22:704–18.PubMedCrossRefGoogle Scholar
  5. 5.
    Wong CC, Loewke KE, Bossert NL, Behr B, De Jonge CJ, Baer TM, et al. Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nat Biotechnol. 2010;28:1115–21.CrossRefGoogle Scholar
  6. 6.
    Meseguer M, Herrero J, Tejera A, Hilligsoe KM, Ramsing NB, Remohi J. The use of morphokinetics as a predictor of embryo implantation. Hum Reprod. 2011;26:2658–71.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Liu Y, Chapple V, Feenan K, Roberts P, Matson P. Time-lapse deselection model for human day 3 in vitro fertilization embryos: the combination of qualitative and quantitative measures of embryo growth. Fertil Steril. 2016;105:656–62 e1.CrossRefGoogle Scholar
  8. 8.
    Petersen B, Boel M, Montag M, Gardner DK. Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3. Hum Reprod. 2016;31:2231–44.PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Gardner DK, Wale PL. Analysis of metabolism to select viable human embryos for transfer. Fertil Steril. 2013;99:1062–72.PubMedCrossRefGoogle Scholar
  10. 10.
    Leese HJ. Metabolism of the preimplantation embryo: 40 years on. Reproduction. 2012;143:417–27.PubMedCrossRefGoogle Scholar
  11. 11.
    Gardner DK, Harvey AJ. Blastocyst metabolism. Reprod Fertil Dev. 2015;27:638–54.PubMedCrossRefGoogle Scholar
  12. 12.
    Gardner DK. Lactate production by the mammalian blastocyst: manipulating the microenvironment for uterine implantation and invasion? BioEssays. 2015;37:364–71.PubMedPubMedCentralGoogle Scholar
  13. 13.
    Leese HJ. History of oocyte and embryo metabolism. Reprod Fertil Dev. 2015;27:567–71.PubMedCrossRefGoogle Scholar
  14. 14.
    Renard JP, Philippon A, Menezo Y. In-vitro uptake of glucose by bovine blastocysts. J Reprod Fertil. 1980;58:161–4.PubMedCrossRefGoogle Scholar
  15. 15.
    Gardner DK, Leese HJ. Assessment of embryo viability prior to transfer by the noninvasive measurement of glucose uptake. J Exp Zool. 1987;242:103–5.PubMedCrossRefGoogle Scholar
  16. 16.
    Lane M, Gardner DK. Selection of viable mouse blastocysts prior to transfer using a metabolic criterion. Hum Reprod. 1996;11:1975–8.PubMedCrossRefGoogle Scholar
  17. 17.
    Gardner DK, Wale PL, Collins R, Lane M. Glucose consumption of single post-compaction human embryos is predictive of embryo sex and live birth outcome. Hum Reprod. 2011;26:1981–6.PubMedCrossRefGoogle Scholar
  18. 18.
    Leese HJ, Biggers JD, Mroz EA, Lechene C. Nucleotides in a single mammalian ovum or preimplantation embryo. Anal Biochem. 1984;140:443–8.PubMedCrossRefGoogle Scholar
  19. 19.
    Botros L, Sakkas D, Seli E. Metabolomics and its application for non-invasive embryo assessment in IVF. Mol Hum Reprod. 2008;14:679–90.PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Seli E, Sakkas D, Scott R, Kwok SC, Rosendahl SM, Burns DH. Noninvasive metabolomic profiling of embryo culture media using Raman and near-infrared spectroscopy correlates with reproductive potential of embryos in women undergoing in vitro fertilization. Fertil Steril. 2007;88:1350–7.PubMedCrossRefGoogle Scholar
  21. 21.
    Seli E, Bruce C, Botros L, Henson M, Roos P, Judge K, et al. Receiver operating characteristic (ROC) analysis of day 5 morphology grading and metabolomic Viability Score on predicting implantation outcome. J Assist Reprod Genet. 2011;28:137–44.PubMedCrossRefGoogle Scholar
  22. 22.
    Hardarson T, Ahlstrom A, Rogberg L, Botros L, Hillensjo T, Westlander G, et al. Non-invasive metabolomic profiling of Day 2 and 5 embryo culture medium: a prospective randomized trial. Hum Reprod. 2012;27:89–96.PubMedCrossRefGoogle Scholar
  23. 23.
    Krisher RL, Heuberger AL, Paczkowski M, Stevens J, Pospisil C, Prather RS, et al. Applying metabolomic analyses to the practice of embryology: physiology, development and assisted reproductive technology. Reprod Fertil Dev. 2015;27:602–20.PubMedCrossRefGoogle Scholar
  24. 24.
    Oh SJ, Gong SP, Lee ST, Lee EJ, Lim JM. Light intensity and wavelength during embryo manipulation are important factors for maintaining viability of preimplantation embryos in vitro. Fertil Steril. 2007;88:1150–7.PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Squirrell JM, Wokosin DL, White JG, Bavister BD. Long-term two-photon fluorescence imaging of mammalian embryos without compromising viability. Nat Biotechnol. 1999;17:763–7.PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Dumollard R, Hammar K, Porterfield M, Smith PJ, Cibert C, Rouviere C, et al. Mitochondrial respiration and Ca2+ waves are linked during fertilization and meiosis completion. Development. 2003;130:683–92.PubMedCrossRefGoogle Scholar
  27. 27.
    Sutton-McDowall ML, Purdey M, Brown HM, Abell AD, Mottershead DG, Cetica PD, et al. Redox and anti-oxidant state within cattle oocytes following in vitro maturation with bone morphogenetic protein 15 and follicle stimulating hormone. Mol Reprod Dev. 2015;82:281–94.PubMedCrossRefGoogle Scholar
  28. 28.
    Cinco R, Digman MA, Gratton E, Luderer U. Spatial characterization of bioenergetics and metabolism of primordial to preovulatory follicles in whole ex vivo murine ovary. Biol Reprod. 2016;95:129.PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Gosnell ME, Anwer AG, Mahbub SB, Menon Perinchery S, Inglis DW, Adhikary PP, et al. Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features. Sci Rep. 2016;6:23453.PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Sutton-McDowall ML, Gosnell M, Anwer AG, White M, Purdey M, Abell AD, et al. Hyperspectral microscopy can detect metabolic heterogeneity within bovine post-compaction embryos incubated under two oxygen concentrations (7% versus 20%). Hum Reprod. 2017;32:2016–25.PubMedCrossRefGoogle Scholar
  31. 31.
    Gardner DK. Mammalian embryo culture in the absence of serum or somatic cell support. Cell Biol Int. 1994;18:1163–79.PubMedCrossRefGoogle Scholar
  32. 32.
    Urbanski JP, Johnson MT, Craig DD, Potter DL, Gardner DK, Thorsen T. Noninvasive metabolic profiling using microfluidics for analysis of single preimplantation embryos. Anal Chem. 2008;80:6500–7.PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Heo YS, Cabrera LM, Bormann CL, Smith GD, Takayama S. Real time culture and analysis of embryo metabolism using a microfluidic device with deformation based actuation. Lab Chip. 2012;12:2240–6.PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Roman GT, Kennedy RT. Fully integrated microfluidic separations systems for biochemical analysis. J Chromatogr A. 2007;1168:170–88.PubMedCrossRefGoogle Scholar
  35. 35.
    Liu P, Mathies RA. Integrated microfluidic systems for high-performance genetic analysis. Trends Biotechnol. 2009;27:572–81.PubMedCrossRefGoogle Scholar
  36. 36.
    West J, Becker M, Tombrink S, Manz A. Micro total analysis systems: latest achievements. Anal Chem. 2008;80:4403–19.PubMedCrossRefGoogle Scholar
  37. 37.
    Kraly JR, Holcomb RE, Guan Q, Henry CS. Review: microfluidic applications in metabolomics and metabolic profiling. Anal Chim Acta. 2009;653:23–35.PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Cui XQ, Lee LM, Heng X, Zhong WW, Sternberg PW, Psaltis D, et al. Lensless high-resolution on-chip optofluidic microscopes for Caenorhabditis elegans and cell imaging. Proc Natl Acad Sci U S A. 2008;105:10670–5.PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Szekely L, Guttman A. New advances in microchip fabrication for electrochromatography. Electrophoresis. 2005;26:4590–604.PubMedCrossRefGoogle Scholar
  40. 40.
    Weibel DB, Diluzio WR, Whitesides GM. Microfabrication meets microbiology. Nat Rev Microbiol. 2007;5:209–18.PubMedCrossRefGoogle Scholar
  41. 41.
    Wang JD, Douville NJ, Takayama S, Elsayed M. Quantitative analysis of molecular absorption into PDMS microfluidic channels. Ann Biomed Eng. 2012;40:1862–73.PubMedCrossRefGoogle Scholar
  42. 42.
    Regehr KJ, Domenech M, Koepsel JT, Carver KC, Ellison-Zelski SJ, Murphy WL, et al. Biological implications of polydimethylsiloxane-based microfluidic cell culture. Lab Chip. 2009;9:2132–9.PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Wilson GS, Gifford R. Biosensors for real-time in vivo measurements. Biosens Bioelectron. 2005;20:2388–403.PubMedCrossRefGoogle Scholar
  44. 44.
    Rocha JC, Passalia FJ, Matos FD, Takahashi MB, Ciniciato DS, Maserati MP, et al. A method based on artificial intelligence to fully automatize the evaluation of bovine blastocyst images. Sci Rep. 2017;7:7659.PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Tan TC, Ritter LJ, Whitty A, Fernandez RC, Moran LJ, Robertson SA, et al. Gray level Co-occurrence Matrices (GLCM) to assess microstructural and textural changes in pre-implantation embryos. Mol Reprod Dev. 2016;83:701–13.PubMedCrossRefGoogle Scholar
  46. 46.
    Milewski R, Kuczynska A, Stankiewicz B, Kuczynski W. How much information about embryo implantation potential is included in morphokinetic data? A prediction model based on artificial neural networks and principal component analysis. Adv Med Sci. 2017;62:202–6.PubMedCrossRefGoogle Scholar
  47. 47.
    Reineck P, Gibson BC. Near-Infrared fluorescent nanomaterials for bioimaging and sensing. Adv Optical Mater. 2017;5:1600446.CrossRefGoogle Scholar
  48. 48.
    Purdey MS, Thompson JG, Monro TM, Abell AD, Schartner EP. A dual sensor for pH and hydrogen peroxide using polymer-coated optical fibre tips. Sensors. 2015;15:31904–13.PubMedCrossRefGoogle Scholar
  49. 49.
    Shang L, Nienhaus K, Nienhaus GU. Engineered nanoparticles interacting with cells: size matters. J Nanobiotechnol. 2014;12:5.CrossRefGoogle Scholar
  50. 50.
    Anselmo AC, Mitragotri S. Nanoparticles in the clinic. Bioeng Transl Med. 2016;1:10–29.PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    Feugang JM, Youngblood RC, Greene JM, Willard ST, Ryan PL. Self-illuminating quantum dots for non-invasive bioluminescence imaging of mammalian gametes. J Nanobiotechnol. 2015;13:38.CrossRefGoogle Scholar
  52. 52.
    Taylor U, Garrels W, Barchanski A, Peterson S, Sajti L, Lucas-Hahn A, et al. Injection of ligand-free gold and silver nanoparticles into murine embryos does not impact pre-implantation development. Beilstein J Nanotechnol. 2014;5:677–88.PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Smith GD, Takayama S, Swain JE. Rethinking in vitro embryo culture: new developments in culture platforms and potential to improve assisted reproductive technologies. Biol Reprod. 2012;86:62.PubMedGoogle Scholar
  54. 54.
    Thompson JG, Brown HM, Sutton-McDowall ML. Measuring embryo metabolism to predict embryo quality. Reprod Fertil Dev. 2016;28:41–50.PubMedCrossRefGoogle Scholar
  55. 55.
    Ramanujam N. Fluorescence spectroscopy of neoplastic and non-neoplastic tissues. Neoplasia. 2000;2:89–117.PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Ferrick L, Lee YSL, Gardner DK. Reducing time to pregnancy and facilitating the birth of healthy children through functional analysis of embryo physiology. Biol Reprod. 2019; PMID: 30649216.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • David K. Gardner
    • 1
    Email author
  • Philipp Reineck
    • 2
  • Brant C. Gibson
    • 2
  • Jeremy G. Thompson
    • 3
  1. 1.School of BioSciencesUniversity of MelbourneParkvilleAustralia
  2. 2.ARC Centre of Excellence for Nanoscale BioPhotonics (CNBP), School of ScienceRMIT UniversityMelbourneAustralia
  3. 3.ARC Centre of Excellence for Nanoscale BioPhotonics (CNBP), The Robinson Research Institute, Adelaide Medical SchoolThe University of AdelaideAdelaideAustralia

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