Non-invasive testing of human preimplantation embryos in vitro as a way to predict the outcomes of in vitro fertilization programs
- Авторлар: Valiakhmetova E.Z1, Kulakova E.V1, Skibina Y.S.2, Gryaznov A.Y.2, Sysoeva A.P1, Makarova N.P1, Kalinina E.A1
-
Мекемелер:
- Academician V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of the Russian Federation
- Research and Production Enterprise "Nanostructured Glass Technology" and International Research and Education Center "Structure-Mediated al Nanobiophotonics"
- Шығарылым: № 5 (2021)
- Беттер: 5-16
- Бөлім: Articles
- URL: https://journals.eco-vector.com/0300-9092/article/view/249104
- DOI: https://doi.org/10.18565/aig.2021.5.5-16
- ID: 249104
Дәйексөз келтіру
Аннотация
Толық мәтін
Авторлар туралы
E. Valiakhmetova
Academician V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of the Russian Federation
Email: ibraeva1988@list.ru
postgraduate student at the Department of Assisted Reproductive Technologies in the Treatment of Infertility 117997, Russia, Moscow, Ac. Oparina str., 4
E. Kulakova
Academician V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of the Russian Federation
Email: e_kulakova@oparina4.ru
Ph.D. in physics and mathematics, Director 117997, Russia, Moscow, Ac. Oparina str., 4
Yu. Skibina
Research and Production Enterprise "Nanostructured Glass Technology" and International Research and Education Center "Structure-Mediated al Nanobiophotonics"
Email: director@nano-glass.ru
Ph.D. in physics and mathematics, Director 410033, Russia, Saratov, pr. 50 let Octyabrya, 101
A. Gryaznov
Research and Production Enterprise "Nanostructured Glass Technology" and International Research and Education Center "Structure-Mediated al Nanobiophotonics"
Email: director@nano-glass.ru
Researcher 410033, Russia, Saratov, pr. 50 let Octyabrya, 101
A. Sysoeva
Academician V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of the Russian Federation
Email: a_sysoeva@oparina4.ru
embryologist at the Department of Assisted Reproductive Technologies in the Treatment of Infertility 117997, Russia, Moscow, Ac. Oparina str., 4
N. Makarova
Academician V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of the Russian Federation
Email: np_makarova@oparina4.ru
Dr. Bio. Sci., Leading Researcher at the Department of Assisted Reproductive Technologies in the Treatment of Infertility 117997, Russia, Moscow, Ac. Oparina str., 4
E. Kalinina
Academician V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of the Russian Federation
Email: e_kalinina@oparina4.ru
Dr. Med. Sci., Professor, Head of the Department of Assisted Reproductive Technologies in the Treatment of Infertility 117997, Russia, Moscow, Ac. Oparina str., 4
Әдебиет тізімі
- Armstrong S., Bhide P, Jordan V., Pacey A., Farquhar C. Time-lapse systems for embryo incubation and assessment in assisted reproduction. Cochrane Database Syst. Rev. 2019; (5): CD011320. https://dx.doi.org/10.1002/ 14651858.CD011320.pub4.
- Сысоева А.П,Макарова Н.П.,Калинина Е.А., Скибина Ю.С.,Занишевская А.А., Янчук Н.О., Грязнов А.Ю. Повышение эффективности вспомогательных репродуктивных технологий с помощью искусственного интеллекта и машинного обучения на эмбриологическом этапе. Акушерство и гинекология. 2020; 7: 28-36. https://dx.doi.org/10.18565/aig.2020.7.28-36.
- Зорина И.М., Смольникова В.Ю., Эльдаров Ч.М., Ярыгина С. А., Горшинова В.К., Макарова Н.П., Калинина Е.А., Бобров М.Ю. Анализ потребления глюкозы и глутамата в питательных средах как метод оценки качества эмбрионов человека пятых суток развития. Акушерство и гинекология. 2018; 5: 64-9
- Драпкина Ю.С., Тимофеева А.В., Чаговец В.В., Макарова Н.П., Калинина Е.А. Прогнозирование результативности программ ВРТ по профилю экспрессии малых некодирующих РНКвкультуральной среде эмбриона. Акушерство и гинекология. 2020; 4 (Приложение): 82-3
- Hong B., Hao Y. The outcome of human mosaic aneuploid blastocysts after intrauterine transfer: A retrospective study. Medicine (Baltimore). 2020; 99(9): e18768. https://dx.doi.org/10.1097/MD.0000000000018768.
- Gleicher N., Orvieto R.J. Is the hypothesis of preimplantation genetic screening (PGS) still supportable? A review. J. Ovarian Res. 2017; 10(1): 21. https:// dx.doi.org/10.1186/s13048-017-0318-3.
- Rubio C., Rienzi L., Navarro-Sanchez L., Cimadomo D., Garcia-Pascual C.M., Albricci L. et al. Embryonic cell-free DNA versus trophectoderm biopsy for aneuploidy testing: concordance rate and clinical implications. Fertil. Steril. 2019; 112(3): 510-9. https://dx.doi.org/10.1016/j.fertnstert.2019.04.038.
- Yeung Q.S.Y., Zhang Y.X., Chung J.P.W., Lui W.T., Kwok Y.K.Y., Gui B. et al. A prospective study of non-invasive preimplantation genetic testing for aneuploidies (NiPGT-A) using next-generation sequencing (NGS) on spent culture media (SCM). Assist. Reprod. Genet. 2019; 36(8): 1609-21. https://dx.doi.org/10.1007/ s10815-019-01517-7.
- Jiao J., Shi B., Sagnelli M., Yang D., Yao Y., Li W. et al. Minimally invasive preimplantation genetic testing using blastocyst culture medium. Hum. Reprod. 2019; 34(7): 1369-79. https://dx.doi.ois/10.1093/humrep/dez075.
- Vagnini L.D., Petersen C.G., Renzi A., Dieamant F., Oliveira J.B.A., Oliani A.H. et al. Relationship between age and blastocyst chromosomal ploidy analyzed by noninvasive preimplantation genetic testing for aneuploidies (niPGT-A). JBRA Assist. Reprod. 2020; 24(4): 395-9. https://dx.doi.org/10.5935/ 1518-0557.20200061.
- Capalbo A., Ubaldi F.M., Cimadomo D., Noli L., Khalaf Y., Farcomeni A. et al. MicroRNAs in spent blastocyst culture medium are derived from trophectoderm cells and can be explored for human embryo reproductive competence assessment. Fertil. Steril. 2016; 105(1): 225-35. e1-3. https:// dx.doi.org/10.1016/j.fertnstert.2015.09.014.
- Cecchino G.N., Garcia-Velasco J.A. Mitochondrial DNA copy number as a predictor of embryo viability. Fertil. Steril. 2019; 111(2): 205-11. https:// dx.doi.org/10.1016/j.fertnstert.2018.11.021.
- Sanchez T., Zhang M., Needleman D., Seli E. Metabolic imaging via fluorescence lifetime imaging microscopy for egg and embryo assessment. Fertil. Steril. 2019; 111(2): 212-8. https://dx.doi.org/10.1016/j.fertnstert.2018.12.014.
- Тимофеева А.В., Калинина Е.А., Драпкина Ю.С., Чаговец В.В., Макарова Н.П., Сухих Г.Т. Оценка качества эмбриона по профилю экспрессии малых некодирующих РНК в культуральной среде эмбриона в программах ВРТ. Акушерство и гинекология. 2019; 6: 79-86.
- Ni M., Xue Y., Ding J., Yang S., Zheng A., Pu Y. et al. Correlation between differential expression of microRNA and quality of embryos. Zhonghua Yi Xue Yi Chuan Xue Za Zhi. 2020; 37(9): 938-41. https://dx.doi.org/10.3760/ cma.j.cn511374-20190516-00244.
- Sdnchez-Ribas I., Diaz-Gimeno P., Quinonero A., Ojeda M., Larreategui Z., Ballesteros A., Dominguez F. NGS analysis of human embryo culture media reveals miRNAs of extra embryonic origin. Reprod. Sci. 2019 ; 26(2): 214-22. https:// dx.doi.org/10.1177/1933719118766252.
- Abu-Halima M, Khaizaran Z.A., Ayesh B.M., Fischer U., Khaizaran S.A., Al-Battah F. et al. MicroRNAs in combined spent culture media and sperm are associated with embryo quality and pregnancy outcome. Fertil. Steril. 2020; 113(5): 970-80. e2. https://dx.doi.org/10.1016/j.fertnstert.2019.12.028.
- Zhou W., Dimitriadis E. Secreted micro RNA to predict embryo implantation outcome: from research to clinical diagnostic application. Front. Cell Dev. Biol. 2020; 8: 586510. https://dx.doi.org/10.3389/fcell.2020.586510.
- Драпкина Ю.С., Тимофеева А.В., Чаговец В.В., Кононихин А. С., Франкевич В.Е., Калинина Е.А. Применение омиксных технологий в решении проблем репродуктивной медицины. Акушерство и гинекология. 2018; 9: 24-32.
- Rinschen M.M., Ivanisevic J., Giera M., Siuzdak G. Identification of bioactive metabolites using activity metabolomics: A review. Nat. Rev. Mol. Cell Biol. 2019; 20(6): 353-67. https://dx.doi.org/10.1038/s41580-019-0108-4.
- Зорина И.М., Эльдаров Ч.М., Ярыгина С.А., Макарова Н.П., Трофимов Д.Ю., Смольникова В.Ю., Калинина Е.А., Бобров М.Ю. Профилирование метаболитов в питательных средах пятидневных эмбрионов человека. Биомедицинская химия. 2017; 63(5): 385-91.
- Ding J., Xu T., Tan X., Jin H., Shao J., Li H. Raman spectrum: A potential biomarker for embryo assessment during in vitro fertilization. Exp. Ther. Med. 2017; 13(5): 1789-92. https://dx.doi.org/10.3892/ etm.2017.4160.
- Seli E., Botros L., Sakkas D., Burns D.H. Noninvasive metabolomic profiling of embryo culture media using proton nuclear magnetic resonance correlates with reproductive potential of embryos in women undergoing in vitro fertilization. Fertil. Steril. 2008; 90(6): 2183-9. https://dx.doi.org/10.1016/ j.fertnstert.2008.07.1739.
- Katz-Jaffe M.G., Gardner D.K., Schoolcraft W.B. Proteomic analysis of individual human embryos to identify novel biomarkers of development and viability. Fertil. Steril. 2006; 85(1): 101-7. https://dx.doi.org/10.1016/j.fertnstert.2005.09.011.
- Abreu C.M., Thomas V., Knaggs P., Bunkheila A., Cruz A., Teixeira S.R. et al. Noninvasive molecular assessment of human embryo development and implantation potential. Biosens. Bioelectron. 2020; 157: 112144. https:// dx.doi.org/10.1016/j.bios.2020.112144.
- Huang G., Zhou C., Wei C.J., Zhao S., Sun F., Zhou H. et al. Evaluation of in vitro fertilization outcomes using interleukin-8 in culturemedium of human preimplantation embryos. Fertil. Steril. 2017; 107(3): 649-56. https:// dx.doi.org/10.1016/j.fertnstert.2016.11.031.
- Ferreira L.M.R., Meissner T.B., Tilburgs T., Strominger J.L. HLA-G: At the interface of maternal-fetal tolerance. Trends Immunol. 2017; 38(4): 272-86. https://dx.doi.org/10.1016/j.it.2017.01.009.
- Diaz R.R., Blanes Z.R., Sanchez V., Gonzalez Perez J., Bethencourt J.C.A. Embryo sHLA-G secretion is related to pregnancy rate. Zygote. 2019; 27(2): 78-81. https://dx.doi.org/1017/S0967199419000054.
- Niu Z., Wang L., Pang R.T.K., Guo Y., Yeung W.S.B., Yao Y. A meta-analysis of the impact of human leukocyte antigen-G on the outcomes of IVF/ICSI. Reprod. Biomed. Online. 2017; 34(6): 611-8. https://dx.doi.org/10.1016/ j.rbmo.2017.03.002.
- Lindgren K.E., Gulen Yaldir F., Hreinsson J., Holte J., Karehed K., Sundstrom-Poromaa I. et al. Differences in secretome in culture media when comparing blastocysts and arrested embryos using multiplex proximity assay. Med. Sci. 2018; 123(3): 143-52. https://dx.doi.org/10.1080/03009734.2018.1490830.
- Kaihola H., Yaldir F.G., Bohlin T., Samir R., Hreinsson J., Akerud H. Levels of caspase-3 and histidine-rich glycoprotein in the embryo secretome as biomarkers of good-quality day-2 embryos and high-quality blastocysts. PLoS One. 2019; 14(12): e0226419. https://dx.doi.org/10.1371/journal.pone.0226419.
- Castillo J., Jodar M., Oliva R. The contribution of human sperm proteins to the development and epigenome of the preimplantation embryo. Hum. Reprod. Update. 2018; 24(5): 535-55. https://dx.doi.org/10.1093/humupd/ dmy017.
- Rinschen M.M., Ivanisevic J., Giera M., Siuzdak G. Identification of bioactive metabolites using activity metabolomics. Nat. Rev. Mol. Cell Biol. 2019; 20(6): 353-67. https://dx.doi.org/10.1038/s41580-019-0108-4.
- Burmistrova N.A., Pidenko P.S., Pidenko S.A., Skibina Yu.S., Monakhova Yu.B. Simultaneous determination of proteins in microstructured optical fibers supported by chemometric tools. Anal. Bioanal. Chem. 2019; 411(27): 7055-9. https://dx.doi.org/10.1007/s00216-019-02085-6.
- Cordero E., Latka I., Matthaus C., Schie I., Popp J.J. Invivo Raman spectroscopy: from basics to applications. Biomed. Opt. 2018; 23(7): 1-23. https:// dx.doi.org/10.1117/1.JBO.23.7.071210.
- Liang B., Gao Y., Xu J., Song Y., Xuan L., Shi T. et al. Raman profiling of embryo culture medium to identify aneuploid and euploid embryos. Fertil. Steril. 2019; 111(4): 753-62. e1. https://dx.doi.org/10.1016/j.fertnstert.2018.11.036.
- Ba§tu E., Parlatan U., Ba§ar G., Yumru H., Bavili N., Sag F. et al. Spectroscopic analysis of embryo culture media for predicting reproductive potential in patients undergoing in vitro fertilization. Turk. J. Obstet. Gynecol. 2017; 14(3): 145-50. https://dx.doi.org/10.4274/tjod.92604.
- Bingol K., Brtischweiler R. Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods. Curr. Opin. Biotechnol. 2017; 43: 17-24. https://dx.doi.org/10.1016/j.copbio.2016.07.006.
- Gulin A., Nadtochenko V., Solodina A., Pogorelova M., Panait A., Pogorelov A. A novel approach for 3D reconstruction of mice full-grown oocytes by time-of-flight secondary ion mass spectrometry. Anal. Bioanal. Chem. 2020; 412(2): 311-9. https://dx.doi.org/10.1007/s00216-019-02237-8.
- Gulin A., Nadtochenko V., Astafiev A., Pogorelova V., Rtimi S., Pogorelov A. Correlating microscopy techniques and ToF-SIMS analysis of fully grown mammalian oocytes. Analyst. 2016; 141(13): 4121-9. https:// dx.doi.org/10.1039/c6an00665e.
- Iles R.K., Sharara F.I., Zmuidinaite R., Abdo G., Keshavarz.S, Butler S.A. Secretome profile selection of optimal IVF embryos by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. J. Assist. Reprod. Genet. 2019; 36(6): 1153-60. https://dx.doi.org/10.1007/s10815-019-01444-7.
- Curchoe C.L., Bormann C.L. 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. https://dx.doi.org/10.1007/ s10815-019-01408-x.
- Kaufmann S.J., Eastaugh J.L., Snowden S., Smye S.W., Sharma V. The application of neural networks in predicting the outcome of in-vitro fertilization. Hum. Reprod. 1997; 12(7): 1454-7. https://dx.doi.org/10.1093/humrep/12.7.1454.
- Miyagi Y., Habara T., Hirata R., Hayashi N. Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image. Reprod. Med. Biol. 2019; 18(2): 204-11. https://dx.doi.org/10.1002/rmb2.12267.
- Spicer R., Salek R.M., Moreno P., Canueto D., Steinbeck C. Navigating freely-available software tools for metabolomics analysis. Metabolomics. 2017; 13(9): 106. https://dx.doi.org/10.1007/s11306-017-1242-7.
- Gessulat S., Schmidt T., Zolg D.P., Samaras P., Schnatbaum K., Zerweck J., Knaute T. et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat. Methods. 2019; 16(6): 509-18. https://dx.doi. org/10.1038/s41592-019-0426-7.