Characterizing Mutagenesis Across Developmental Time with single-cell indexing (sci) ATAC-seq

Abstract

Mutations in DNA are caused by replication errors or exposure to mutagen that damage the DNA repair mechanisms. Germline variants are mutations that occur in germ cells and can be passed onto offspring, ultimately becoming polymorphic sites; while somatic variants are mutations that arise spontaneously in the soma cells during growth and aging. Somatic mutations have been traditionally studied in cancer due to their natural clonal expansion. However, recent work has described an association of the accumulation of somatic mutations in healthy tissues with aging and age-related diseases. The current methodologies for obtaining clonal sequences from healthy and developing tissues are either costly or laborious, limiting scalability. Therefore, in this thesis, we explored the feasibility of using single-cell combinatorial indexing (sci) ATAC-seq data toidentify somatic and germline mutations and to study mutational processes across tissues during embryogenesis. Leveraging available sci-ATAC-seq datasets from fruit fly embryo and human fetal samples, we were able to identify mutations and differentiate them into germline polymorphisms and somatic mutations. In the fruit fly embryo dataset, we detected population structure based on the extracted germline polymorphisms, while in human fetal samples, we observed an increase in somatic mutation burden over developmental time. We also performed mutational signature extraction, finding that the activities of clock-like signatures, such as SBS1 and SBS5, positively correlated with developmental time. This indicates a potential association between somatic mutation accumulation and aging during embryogenesis. We also observed variations in the mutational processes across tissues. Finally, we reconstructed the main tissue layers of early development from the detected somatic mutations, suggesting these datasets may help validate transcriptionally based lineage predictions. Our analysis showed that the reanalysis of available sci-ATAC-seq datasets can be an alternative solution to study somatic mutagenesis at a more affordable cost and enhanced scalability. These findings also have provided insights into developmental processes and cell lineage tracing during embryonic development.