307
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Novel bioinformatic analyses of somatic cell contamination in sperm samples

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 174-182 | Received 17 Jul 2023, Accepted 11 Jun 2024, Published online: 22 Jun 2024

References

  • Abraham A, Pedregosa F, Eickenberg M, Gervais P, Mueller A, Kossaifi J, Gramfort A, Thirion B, Varoquaux G. 2014. Machine learning for neuroimaging with scikit-learn. Front Neuroinform. 8:14. doi: 10.3389/fninf.2014.00014.
  • Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, Irizarry RA. 2014. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 30(10):1363–1369. doi: 10.1093/bioinformatics/btu049.
  • Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, Delano D, Zhang L, Schroth GP, Gunderson KL, et al. 2011. High density DNA methylation array with single CpG site resolution. Genomics. 98(4):288–295. doi: 10.1016/j.ygeno.2011.07.007.
  • Fedder J. 1996. Nonsperm cells in human semen: with special reference to seminal leukocytes and their possible influence on fertility. Arch Androl. 36(1):41–65. doi: 10.3109/01485019608987883.
  • Gannon JR, Emery BR, Jenkins TG, Carrell DT. 2014. The sperm epigenome: implications for the embryo. Adv Exp Med Biol. 791:53–66. doi: 10.1007/978-1-4614-7783-9_4.
  • Hicks SA, Andersen JM, Witczak O, Thambawita V, Halvorsen P, Hammer HL, Haugen TB, Riegler MA. 2019. Machine learning-based analysis of sperm videos and participant data for male fertility prediction. Sci Rep. 9(1):16770. doi: 10.1038/s41598-019-53217-y.
  • Jenkins TG, Aston KI, Hotaling JM, Shamsi MB, Simon L, Carrell DT. 2016. Teratozoospermia and asthenozoospermia are associated with specific epigenetic signatures. Andrology. 4(5):843–849. doi: 10.1111/andr.12231.
  • Jenkins TG, Liu L, Aston KI, Carrell DT. 2018. Pre-screening method for somatic cell contamination in human sperm epigenetic studies. Syst Biol Reprod Med. 64(2):146–155. doi: 10.1080/19396368.2018.1434838.
  • Kobayashi H, Sato A, Otsu E, Hiura H, Tomatsu C, Utsunomiya T, Sasaki H, Yaegashi N, Arima T. 2007. Aberrant DNA methylation of imprinted loci in sperm from oligospermic patients. Hum Mol Genet. 16(21):2542–2551. doi: 10.1093/hmg/ddm187.
  • Kopp A. 2012. Dmrt genes in the development and evolution of sexual dimorphism. Trends Genet. 28(4):175–184. doi: 10.1016/j.tig.2012.02.002.
  • Lesani A, Kazemnejad S, Moghimi Zand M, Azadi M, Jafari H, Mofrad MRK, Nosrati R. 2020. Quantification of human sperm concentration using machine learning-based spectrophotometry. Comput Biol Med. 127:104061. doi: 10.1016/j.compbiomed.2020.104061.
  • Marques CJ, Carvalho F, Sousa M, Barros A. 2004. Genomic imprinting in disruptive spermatogenesis. Lancet. 363(9422):1700–1702. doi: 10.1016/S0140-6736(04)16256-9.
  • Miller RH, DeVilbiss EA, Brogaard KR, Norton CR, Pollard CA, Emery BR, Aston KI, Hotaling JM, Jenkins TG. 2023. Epigenetic determinants of reproductive potential augment the predictive ability of the semen analysis. F S Sci. 4(4):279–285. doi: 10.1016/j.xfss.2023.09.001.
  • Nix DA, Courdy SJ, Boucher KM. 2008. Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks. BMC Bioinformatics. 9(1):523. doi: 10.1186/1471-2105-9-523.
  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. 2011. Scikit-Learn: machine learning in Python. J Mach Learn Res. 12:2825–2830.
  • Qi L, Teschendorff AE. 2022. Cell-type heterogeneity: why we should adjust for it in epigenome and biomarker studies. Clin Epigenet. 14(1):31. doi: 10.1186/s13148-022-01253-3.
  • Ricci G, Presani G, Guaschino S, Simeone R, Perticarari S. 2000. Leukocyte detection in human semen using flow cytometry. Hum Reprod. 15(6):1329–1337. doi: 10.1093/humrep/15.6.1329.
  • Schisterman EF, Sjaarda LA, Clemons T, Carrell DT, Perkins NJ, Johnstone E, Lamb D, Chaney K, Van Voorhis BJ, Ryan G, et al. 2020. Effect of folic acid and zinc supplementation in men on semen quality and live birth among couples undergoing infertility treatment: a randomized clinical trial. JAMA. 323(1):35–48. doi: 10.1001/jama.2019.18714.
  • Song B, Chen Y, Wang C, Li G, Wei Z, He X, Cao Y. 2022. Poor semen parameters are associated with abnormal methylation of imprinted genes in sperm DNA. Reprod Biol Endocrinol. 20(1):155. doi: 10.1186/s12958-022-01028-8.
  • Tang Q, Pan F, Yang J, Fu Z, Lu Y, Wu X, Han X, Chen M, Lu C, Xia Y, et al. 2018. Idiopathic male infertility is strongly associated with aberrant DNA methylation of imprinted loci in sperm: a case-control study. Clin Epigenet. 10(1):134. doi: 10.1186/s13148-018-0568-y.
  • You JB, McCallum C, Wang Y, Riordon J, Nosrati R, Sinton D. 2021. Machine learning for sperm selection. Nat Rev Urol. 18(7):387–403. doi: 10.1038/s41585-021-00465-1.