Book contents
- Manual of Embryo Selection in Human Assisted Reproduction
- Manual of Embryo Selection in Human Assisted Reproduction
- Copyright page
- Contents
- Contributors
- Preface
- Chapter 1 Introduction
- Chapter 2 Embryo Developmental Programming
- Chapter 3 Embryo–Maternal Interactions
- Chapter 4 The Sperm’s Role in Embryo Development
- Chapter 5 The Oocyte’s Role in Embryo Development
- Chapter 6 The Laboratory’s Role in Embryo Development
- Chapter 7 Handling of Gametes and Embryos
- Chapter 8 Noninvasive Morphological Selection of Oocytes
- Chapter 9 Prospects for Bioenergetics for Embryo Selection
- Chapter 10 Static Morphological Assessment for Embryo Selection
- Chapter 11 Dynamic Morphological Assessment for Embryo Selection
- Chapter 12 Noninvasive Analysis of Embryo Nutrient Utilization for Embryo Selection
- Chapter 13 Genomics for Embryo Selection
- Chapter 14 Biopsy Techniques from Polar Body to Blastocyst
- Chapter 15 Cell-Free DNA Analysis for PGT-A
- Chapter 16 What Science May Come for Embryo Selection?
- Epilogue
- Index
- References
Chapter 16 - What Science May Come for Embryo Selection?
Published online by Cambridge University Press: 26 April 2023
Book contents
- Manual of Embryo Selection in Human Assisted Reproduction
- Manual of Embryo Selection in Human Assisted Reproduction
- Copyright page
- Contents
- Contributors
- Preface
- Chapter 1 Introduction
- Chapter 2 Embryo Developmental Programming
- Chapter 3 Embryo–Maternal Interactions
- Chapter 4 The Sperm’s Role in Embryo Development
- Chapter 5 The Oocyte’s Role in Embryo Development
- Chapter 6 The Laboratory’s Role in Embryo Development
- Chapter 7 Handling of Gametes and Embryos
- Chapter 8 Noninvasive Morphological Selection of Oocytes
- Chapter 9 Prospects for Bioenergetics for Embryo Selection
- Chapter 10 Static Morphological Assessment for Embryo Selection
- Chapter 11 Dynamic Morphological Assessment for Embryo Selection
- Chapter 12 Noninvasive Analysis of Embryo Nutrient Utilization for Embryo Selection
- Chapter 13 Genomics for Embryo Selection
- Chapter 14 Biopsy Techniques from Polar Body to Blastocyst
- Chapter 15 Cell-Free DNA Analysis for PGT-A
- Chapter 16 What Science May Come for Embryo Selection?
- Epilogue
- Index
- References
Summary
The success of artificial reproductive technologies (ART) requires effective methods for selecting embryos with optimal potential for generating a live birth. A glimpse into the landscape of future of ART reveals a field replete with breakthrough opportunities. In the context of the current pace of scientific discovery and technological development, we can expect the development of different novel technologies for gametes and embryo selection. Among these new technologies we can envision embryo selection technologies based on metabolic markers; the use of artificial intelligence and deep learning; non-invasive, label-free microscopy; and cell-sorting techniques for sperm selection. It is also likely that embryos will be scored based on the relative activation of stress-pathways; embryos showing an intermediate, “Goldilocks” level of stress will be preferentially selected. Overall, it will be important to thoughtfully consider and discuss the ethical implications of new technologies that could be used in ART.
Keywords
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- Chapter
- Information
- Manual of Embryo Selection in Human Assisted Reproduction , pp. 168 - 176Publisher: Cambridge University PressPrint publication year: 2023
References
Densen, P. Challenges and opportunities facing medical education. Trans Am Clin Climatol Assoc. 2011;122:48–58.Google Scholar
Larose, H, Shami, AN, Abbott, H, Manske, G, Lei, L, Hammoud, SS. Gametogenesis: a journey from inception to conception. Curr Top Dev Biol. 2019;132:257–310.CrossRefGoogle ScholarPubMed
Nadal, A, Najmabadi, S, Addis, B, Buster, JE. Novel uterine lavage system for recovery of human embryos fertilized and matured in vivo. Med Devices (Auckl). 2019;12:133–41.Google ScholarPubMed
ASRM. Human somatic cell nuclear transfer and reproductive cloning: an Ethics Committee opinion. Fertil Steril. 2016;105(4):e1–4.Google Scholar
Suchy, F, Nakauchi, H. Lessons from interspecies mammalian chimeras. Annu Rev Cell Dev Biol. 2017;33:203–17.CrossRefGoogle ScholarPubMed
Curchoe, CL, Bormann, CL. Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J Assist Reprod Genet. 2019;36(4):591–600.CrossRefGoogle ScholarPubMed
Khosravi, P, Kazemi, E, Zhan, Q, Malmsten, JE, Toschi, M, Zisimopoulos, P, et al. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digit Med. 2019;2:21.CrossRefGoogle ScholarPubMed
Matsunari, H, Nagashima, H, Watanabe, M, Umeyama, K, Nakano, K, Nagaya, M, et al. Blastocyst complementation generates exogenic pancreas in vivo in apancreatic cloned pigs. Proc Natl Acad Sci U S A. 2013;110(12):4557–62.Google Scholar
Romany, L, Garrido, N, Motato, Y, Aparicio, B, Remohí, J, Meseguer, M. Removal of annexin V-positive sperm cells for intracytoplasmic sperm injection in ovum donation cycles does not improve reproductive outcome: a controlled and randomized trial in unselected males. Fertil Steril. 2014;102(6):1567–75.e1.CrossRefGoogle Scholar
Cakar, Z, Cetinkaya, B, Aras, D, Koca, B, Ozkavukcu, S, Kaplanoglu, I, et al. Does combining magnetic-activated cell sorting with density gradient or swim-up improve sperm selection? J Assist Reprod Genet. 2016;33(8):1059–65.CrossRefGoogle ScholarPubMed
Quinn, MM, Jalalian, L, Ribeiro, S, Ona, K, Demirci, U, Cedars, MI. Microfluidic sorting selects sperm for clinical use with reduced DNA damage compared to density gradient centrifugation with swim-up in split semen samples. Hum Reprod. 2018;33(8):1388–93.CrossRefGoogle ScholarPubMed
Oldenbourg, R, Mei, G. New polarized light microscope with precision universal compensator. J Microsc. 1995;180(Pt 2):140–7.CrossRefGoogle ScholarPubMed
Petersen, CG, Oliveira, JBA, Mauri, AL, Massaro, FC, Baruffi, RLR, Pontes, A, et al. Relationship between visualization of meiotic spindle in human oocytes and ICSI outcomes: a meta-analysis. Reprod Biomed Online. 2009;18(2):235–43.CrossRefGoogle ScholarPubMed
Montag, M, Köster, M, van der Ven, K, van der Ven, H. Gamete competence assessment by polarizing optics in assisted reproduction. Hum Reprod Update. 2011;17(5):654–66.Google Scholar
Haifler, M, Girshovitz, P, Band, G, Dardikman, G, Madjar, I, Shaked, NT. Interferometric phase microscopy for label-free morphological evaluation of sperm cells. Fertil Steril. 2015;104(1):43–7.e2.CrossRefGoogle ScholarPubMed
Mirsky, S, Barnea, I, Shaked, N. Label-free quantitative imaging of sperm for in vitro fertilization using interferometric phase microscopy. J. Fertil. Vitr.-IVF-Worldw. Reprod Med. Genet. Stem Cell Biol. 2016;4(3):1000190.Google Scholar
Heraud, P, Marzec, KM, Zhang, QH, Yuen, WS, Carroll, J, Wood, BR. Label-free in vivo Raman microspectroscopic imaging of the macromolecular architecture of oocytes. Sci Rep. 2017;7(1):8945.CrossRefGoogle ScholarPubMed
Sanchez, T, Venturas, M, Aghvami, SA, Yang, X, Fraden, S, Sakkas, D, Needleman, DJ. Combined noninvasive metabolic and spindle imaging as potential tools for embryo and oocyte assessment. Hum Reprod. 2019;34(12):2349–61.CrossRefGoogle ScholarPubMed
Nguyen, TH, Kandel, MK, Rubessa, M, Wheeler, MB, Popescu, G. Gradient light interference microscopy for 3D imaging of unlabeled specimens. Nat Commun. 2017;8(1):210.Google Scholar
Leese, HJ, Guerif, F, Allgar, V, Brison, DR, Lundin, K, Sturmey, RG. Biological optimization, the Goldilocks principle, and how much is lagom in the preimplantation embryo. Mol Reprod Dev. 2016;83(9):748–54.CrossRefGoogle ScholarPubMed
Barker, DJP, Winter, PD, Osmond, C, Margetts, B, Simmonds, SJ. Weight in infancy and death from ischaemic heart disease. Lancet. 1989;2:577–80.Google Scholar