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. 2018 Feb 2;359(6375):550-555.
doi: 10.1126/science.aan8690. Epub 2017 Dec 7.

Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis

Taejeong Bae  1 , Livia Tomasini  2 , Jessica Mariani  2 , Bo Zhou  3 , Tanmoy Roychowdhury  1 , Daniel Franjic  4 , Mihovil Pletikos  4 , Reenal Pattni  3 , Bo-Juen Chen  5 , Elisa Venturini  5 , Bridget Riley-Gillis  5 , Nenad Sestan  4   6 , Alexander E Urban  3 , Alexej Abyzov  7 , Flora M Vaccarino  8   4   6
Affiliations

Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis

Taejeong Bae et al. Science. .

Abstract

Somatic mosaicism in the human brain may alter function of individual neurons. We analyzed genomes of single cells from the forebrains of three human fetuses (15 to 21 weeks postconception) using clonal cell populations. We detected 200 to 400 single-nucleotide variations (SNVs) per cell. SNV patterns resembled those found in cancer cell genomes, indicating a role of background mutagenesis in cancer. SNVs with a frequency of >2% in brain were also present in the spleen, revealing a pregastrulation origin. We reconstructed cell lineages for the first five postzygotic cleavages and calculated a mutation rate of ~1.3 mutations per division per cell. Later in development, during neurogenesis, the mutation spectrum shifted toward oxidative damage, and the mutation rate increased. Both neurogenesis and early embryogenesis exhibit substantially more mutagenesis than adulthood.

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Figures

Figure 1.
Figure 1.
SNV discovery in brains. a) Three approaches of discovering mosaic SNVs were contrasted: comparing clones to the VZ/SVZ tissue of origin, comparing clones to the spleen, and comparing clones with each other (see Fig. S3). The three approaches give largely concordant calls. The comparison is for calls from all three brains. b) Calls unique to clone-to-original tissue (in blue) and clone-to-spleen (in red) discovery approaches are dramatically enriched for bases with less confident calling (as defined by the mask of the 1000 Genomes Project). These residual calls were not included in the final call set. c) VAF of genotyped SNVs from deep re-sequencing in all three brains. The clone-to-clone discovery approach allows finding high frequency mosaic SNVs in brain tissue (green line) that are missed from clone-to-tissue/spleen comparisons. d) Counts of mosaic SNVs per clone increase linearly with fetal age. e) Contribution of each substitution type to the mutation spectrum is not different between different fetuses and brain regions.
Figure 2.
Figure 2.
Genotyping of SNV in original tissues. a) Several dozens of mosaic SNVs with VAF of 0.3% to 30% in tissues from various brain regions and from spleen are genotyped by the capture-resequencing approach (green line). For hundreds more SNVs, the evidence for presence in tissue is indistinguishable from background noise (blue line). b) Venn diagram of genotyped mosaic SNVs across brain regions and spleen for subject 316. Almost 60% of mosaic SNVs could be genotyped in one or more brain regions and spleen and 44% could be genotyped in all brain regions and spleen. c,d) Comparative VAFs for mosaic SNVs across different brain regions and spleen for the same subject. Many SNVs are shared by multiple brain regions and by brain and spleen with similar VAFs (shared across two tissues are in green, red and blue, while shared across three tissues are in magenta).
Figure 3.
Figure 3.
Reconstruction of mosaic SNV mutations during early development of subject 316. a) Hierarchical clustering of SNVs genotyped in the different brain regions and spleen by their VAFs revealed grouping consistent with SNVs sharing between clones (white squares represent zero VAF). Black and gray squares denote, respectively, SNVs discovered in clones and SNVs missed during discovery but genotyped afterwards. For completeness five SNVs (marked with *) were included in the analysis if present in multiple clones but the corresponding VAF estimation from capture-seq was not available. Their VAFs were estimated from whole genome tissue sequencing. Based on the corresponding average VAF (shown underneath each cluster), each cluster was assigned to consecutive post-zygotic divisions: D1 (no SNVs observed), D2, D3, D4, and D5. b) The reconstructed cell progeny tree during those divisions had only two conflicts of SNVs assignment, denoted by “?”, between clusters and clones. “Expected VAF” denotes VAF of mutations arising at each stage assuming equal contribution of all progenies to tissues. c) Mutational spectra of likely early mosaic SNVs (solid colors), and presumably later arising SNVs (shaded colors), are different. The difference in the spectra is due to the shift in frequency of C:G>T:A transitions, particularly in in CpG motifs, and in C:G>A:T transversions. The spectrum of early SNVs is much closer to the spectrum for de novo SNVs in the human population (triangle with correlation R-values). Random distribution represents correlation coefficients when randomly but proportionally subsampling early and late mutations.
Figure 4.
Figure 4.
Properties of mosaic SNVs in brain. a) Depletion of mosaic SNVs in DNase hypersensitive sites, possibly indicating a better efficiency of DNA repair pathways in those regions (22, 23). b) Density of mosaic SNVs correlates negatively with histone marks in embryonic stem cells and fetal brain, revealing similarity to somatic SNVs in cancers. c) Mutational signatures 8 and 18 found in brain cancers have highest correlations with the mutation spectrum of mosaic SNVs. d) Exhaustive combinations of pairs of signatures consistently shows that signatures 1B and 5 also contribute to description of the mutation spectrum in combination with signature 18. Thus, signature 18 is the best descriptor of mosaic SNVs in developing brain.
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Comment in

  • Tracing single-cell histories.
    Lee JH. Lee JH. Science. 2018 Feb 2;359(6375):521-522. doi: 10.1126/science.aar6335. Epub 2018 Feb 1. Science. 2018. PMID: 29420280 Free PMC article. No abstract available.

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