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Single-cell analysis of human embryos reveals diverse patterns of aneuploidy and mosaicism

Starostik, Margaret R., Sosina, Olukayode A., McCoy, Rajiv C.·2020·Genome Research

Published version of a preprint

Abstract

Less than half of human zygotes survive to birth, primarily due to aneuploidies of meiotic or mitotic origin. Mitotic errors generate chromosomal mosaicism, defined by multiple cell lineages with distinct chromosome complements. The incidence and impacts of mosaicism in human embryos remain controversial, with most previous studies based on bulk DNA assays or comparisons of multiple biopsies of few embryonic cells. Single-cell genomic data provide an opportunity to quantify mosaicism on an embryo-wide scale. To this end, we extended an approach to infer aneuploidies based on dosage-associated changes in gene expression by integrating signatures of allelic imbalance. We applied this method to published single-cell RNA sequencing data from 74 human embryos, spanning the morula to blastocyst stages. Our analysis revealed widespread mosaic aneuploidies, with 59 of 74 (80%) embryos harboring at least one putative aneuploid cell (1% FDR). By clustering copy number calls, we reconstructed histories of chromosome segregation, inferring that 55 (74%) embryos possessed mitotic aneuploidies and 23 (31%) embryos possessed meiotic aneuploidies. We found no significant enrichment of aneuploid cells in the trophectoderm compared to the inner cell mass, although we do detect such enrichment in data from later postimplantation stages. Finally, we observed that aneuploid cells up-regulate immune response genes and down-regulate genes involved in proliferation, metabolism, and protein processing, consistent with stress responses documented in other stages and systems. Together, our work provides a high-resolution view of aneuploidy in preimplantation embryos, and supports the conclusion that low-level mosaicism is a common feature of early human development.

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Figure 1. Approach for detecting aneuploidy in single-cell RNA-seq data based on complementary signatures of chromosome-wide gene expression alteration as well as allelic imbalance.
Figure 1. Approach for detecting aneuploidy in single-cell RNA-seq data based on complementary signatures of chromosome-wide gene expression alteration as well as allelic imbalance.CC BY · PubMed Central
Figure 2. Aneuploidies discovered in scRNA-seq data from human preimplantation embryos (Petropoulos et al. 2016). (A) Proportions of aneuploid chromosomes, cells, and embryos detected at varying false discovery rates (FDR). Error rates were controlled while accounting for the hierarchical dependency structure of the data (chromosomes within cells within embryos) using TreeBH (Bogomolov et al. 2017). (B) Distribution of proportions of aneuploid cells per embryo at a 1% FDR.
Figure 2. Aneuploidies discovered in scRNA-seq data from human preimplantation embryos (Petropoulos et al. 2016). (A) Proportions of aneuploid chromosomes, cells, and embryos detected at varying false discovery rates (FDR). Error rates were controlled while accounting for the hierarchical dependency structure of the data (chromosomes within cells within embryos) using TreeBH (Bogomolov et al. 2017). (B) Distribution of proportions of aneuploid cells per embryo at a 1% FDR.CC BY · PubMed Central
Figure 3. Examples of chromosome abnormalities detected based on scRNA-seq data from human embryos. Each heat map represents data from an individual embryo. Rows of the heat maps represent single cells, whereas columns represent chromosomes (autosomes only). Dendrograms depict hierarchical clustering of aneuploidy signatures, roughly reflecting common ancestry among aneuploid cells. (A) Embryo E7.3 was called euploid with negligible deviations from the null observed for all chromosomes within all cells. (B) Embryo E5.13 exhibits a putative meiotic-origin trisomy of Chromosome 21. (C) Embryo E7.17 exhibits putative meiotic-origin monosomies of Chromosomes 4 and 13, mosaic monosomy of Chromosome 8, and sporadic low-level aneuploidies of other chromosomes. (D) Embryo E7.5 was inferred as mosaic near-haploid, with haploid or near-haploid signatures in eight of nine cells, but near-diploidy in one cell.
Figure 3. Examples of chromosome abnormalities detected based on scRNA-seq data from human embryos. Each heat map represents data from an individual embryo. Rows of the heat maps represent single cells, whereas columns represent chromosomes (autosomes only). Dendrograms depict hierarchical clustering of aneuploidy signatures, roughly reflecting common ancestry among aneuploid cells. (A) Embryo E7.3 was called euploid with negligible deviations from the null observed for all chromosomes within all cells. (B) Embryo E5.13 exhibits a putative meiotic-origin trisomy of Chromosome 21. (C) Embryo E7.17 exhibits putative meiotic-origin monosomies of Chromosomes 4 and 13, mosaic monosomy of Chromosome 8, and sporadic low-level aneuploidies of other chromosomes. (D) Embryo E7.5 was inferred as mosaic near-haploid, with haploid or near-haploid signatures in eight of nine cells, but near-diploidy in one cell.CC BY · PubMed Central
Figure 4. Comparisons of aneuploidy across cell types. (A) Individual cells plotted on the first and second UMAP dimensions, colored by cell type annotations from Stirparo et al. (2018). (B) Same as panel A, but for the second and third UMAP dimensions. (C) Cells plotted on the first and second UMAP dimensions, colored by aneuploidy status. (D) Same as panel C, but for the second and third UMAP dimensions. (E) Proportions of aneuploid cells, stratified by cell type. (F) Average marginal effects (AME) of cell types on aneuploidy rates relative to aneuploidy rates of trophectoderm cells—the source for PGT-A biopsies. Confidence intervals of all estimates overlap zero, indicating no significant difference for any cell type.
Figure 4. Comparisons of aneuploidy across cell types. (A) Individual cells plotted on the first and second UMAP dimensions, colored by cell type annotations from Stirparo et al. (2018). (B) Same as panel A, but for the second and third UMAP dimensions. (C) Cells plotted on the first and second UMAP dimensions, colored by aneuploidy status. (D) Same as panel C, but for the second and third UMAP dimensions. (E) Proportions of aneuploid cells, stratified by cell type. (F) Average marginal effects (AME) of cell types on aneuploidy rates relative to aneuploidy rates of trophectoderm cells—the source for PGT-A biopsies. Confidence intervals of all estimates overlap zero, indicating no significant difference for any cell type.CC BY · PubMed Central
Figure 5. Transcriptional responses to aneuploidy in human embryos. (A) Volcano plot depicting differential expression between euploid and aneuploid cells. Positive values indicate increased expression in aneuploid cells, whereas negative values indicate reduced expression. (B) Hallmark gene sets from the Molecular Signatures Database (MSigDB) that are significantly enriched for genes that are up- or down-regulated in aneuploid cells based on gene set enrichment analysis (GSEA; 5% FDR). (C) Gene set enrichment plot demonstrating that genes regulated by NF-kB in response to tumor necrosis factor are significantly up-regulated in aneuploid cells. (D) Same as panel C, but demonstrating that MYC targets exhibit reduced expression in aneuploid cells. (E) Same as panel C, but demonstrating that genes involved in oxidative phosphorylation are down-regulated in aneuploid cells.
Figure 5. Transcriptional responses to aneuploidy in human embryos. (A) Volcano plot depicting differential expression between euploid and aneuploid cells. Positive values indicate increased expression in aneuploid cells, whereas negative values indicate reduced expression. (B) Hallmark gene sets from the Molecular Signatures Database (MSigDB) that are significantly enriched for genes that are up- or down-regulated in aneuploid cells based on gene set enrichment analysis (GSEA; 5% FDR). (C) Gene set enrichment plot demonstrating that genes regulated by NF-kB in response to tumor necrosis factor are significantly up-regulated in aneuploid cells. (D) Same as panel C, but demonstrating that MYC targets exhibit reduced expression in aneuploid cells. (E) Same as panel C, but demonstrating that genes involved in oxidative phosphorylation are down-regulated in aneuploid cells.CC BY · PubMed Central

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Research works printed but not linked: 54

  1. 10.1111/j.2517-6161.1995.tb02031.x · DOI · Paper page
  2. Bogomolov M , Peterson CB , Benjamini Y , Sabatti C . 2017. Testing hypotheses on a tree: new error rates and controlling strategies. arXiv :1705.07529v2 [stat.ME]. · arXiv
  3. 10.1038/ncomms11165 · DOI
  4. 10.1093/humrep/dew250 · DOI
  5. 10.1371/journal.pgen.1008414 · DOI
  6. 10.1016/j.rbmo.2019.06.012 · DOI
  7. 10.1007/s00441-015-2305-6 · DOI
  8. 10.1101/gr.239830.118 · DOI
  9. 10.1101/gr.200527.115 · DOI
  10. 10.1093/bioinformatics/bts635 · DOI · Paper page
  11. 10.1186/1471-2164-15-139 · DOI
  12. 10.1101/gr.228080.117 · DOI
  13. 10.1016/j.tig.2005.02.012 · DOI
  14. 10.1186/s12958-017-0251-8 · DOI
  15. 10.1093/humrep/deg060 · DOI
  16. 10.1056/NEJMc1500421 · DOI
  17. 10.1186/s12864-017-4253-x · DOI
  18. 10.1101/gr.252981.119 · DOI · Paper page
  19. 10.1038/35066065 · DOI
  20. 10.1242/dev.145318 · DOI
  21. 10.1038/s41598-018-20614-8 · DOI
  22. 10.1101/060012 · DOI
  23. 10.1007/s10815-017-1060-x · DOI
  24. 10.1093/bioinformatics/btp352 · DOI · Paper page
  25. 10.1038/s41598-018-33279-0 · DOI
  26. 10.1038/sigtrans.2017.23 · DOI · Paper page
  27. 10.1038/s41586-018-0321-x · DOI · Paper page
  28. 10.1038/nmeth.3370 · DOI · Paper page
  29. 10.1016/j.tig.2017.04.001 · DOI · Paper page
  30. 10.1126/science.aaa3337 · DOI
  31. 10.1371/journal.pgen.1005601 · DOI
  32. 10.1101/gr.107524.110 · DOI · Paper page
  33. 10.1093/humrep/dez242 · DOI
  34. 10.1016/j.ajhg.2018.10.007 · DOI
  35. 10.1038/ng.3306 · DOI
  36. 10.1038/s41598-017-09693-1 · DOI
  37. 10.1016/j.cell.2016.03.023 · DOI · Paper page
  38. 10.1016/j.fertnstert.2018.06.002 · DOI
  39. 10.1016/j.fertnstert.2019.01.023 · DOI
  40. 10.1016/j.devcel.2017.05.022 · DOI
  41. 10.1073/pnas.1209227109 · DOI
  42. 10.1002/stem.1509 · DOI
  43. 10.1038/nmeth.4612 · DOI · Paper page
  44. 10.1242/dev.169672 · DOI
  45. 10.1371/journal.pone.0195905 · DOI
  46. 10.1038/s41586-019-1187-2 · DOI
  47. 10.1093/humrep/dex286 · DOI
  48. 10.1038/nm.1924 · DOI · Paper page
  49. 10.1530/REP-17-0569 · DOI
  50. 10.1038/ncomms8601 · DOI
  51. 10.1038/s41598-019-39111-7 · DOI
  52. 10.1038/s41591-019-0620-2 · DOI
  53. 10.1038/s41586-019-1500-0 · DOI
  54. 10.1038/s41588-017-0007-6 · DOI

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Metadata

Authors
Starostik, M. R., Sosina, O. A., & McCoy, R. C.
Venue
Genome Research
Published
2020-06
Volume
30
Issue
6
Pages
814-825
ISSN
  • 1088-9051
  • 1549-5469
Cited by
155
References
8
Language
en
Funders
National Institutes of Health (R35GM133747)

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