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Cell-type-specific patterns and consequences of somatic mutation in development and aging brain.

Andrea J Kriz1,2, Shulin Mao1,3,4,5, Diane D Shao1,2,6

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Duplex-Multiome accurately identifies somatic mutations in individual cells, revealing cell-type-specific aging patterns and gene expression changes in brain tissue. This method advances understanding of somatic mosaicism and its impact on health and disease.

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Area of Science:

  • Genomics and Molecular Biology
  • Neuroscience
  • Cancer Research

Background:

  • Accurate characterization of somatic mosaicism across cell types is crucial for understanding cancer, aging, and healthy tissue function.
  • Existing low-throughput technologies limit the study of cell-type-specific somatic mutations and their functional impact.
  • Developing high-throughput methods is essential to assay somatic mutations and their phenotypic consequences.

Purpose of the Study:

  • To develop a high-throughput method, Duplex-Multiome, for simultaneous identification of somatic single-nucleotide variants (sSNVs) and multi-omic profiling (snATAC-seq, snRNA-seq) from individual nuclei.
  • To accurately quantify sSNVs in rare cell populations and characterize their mutational spectra.
  • To investigate cell-type-specific somatic mutation rates and patterns in postmortem brain tissue and their correlation with gene expression and disease states.

Main Methods:

  • Duplex consensus sequencing was integrated with single-nucleus multi-omic assays (snATAC-seq, snRNA-seq) to enhance accuracy and reduce sequencing errors.
  • Strand-tagging was introduced into snATAC-seq library construction to eliminate artifactual mutational signatures.
  • The Duplex-Multiome method was validated using mixed cell lines and applied to over 51,400 nuclei from postmortem human brain tissue.

Main Results:

  • Duplex-Multiome achieved high precision (>92%) in identifying sSNVs in low-frequency cell populations (2%) and accurately captured known mutational spectra.
  • The method revealed distinct age-related mutation rates and patterns across diverse neuronal and glial cell types in the brain.
  • Clonal sSNVs were identified in glia of aged brains and correlated with altered gene expression in both neurotypical and autism spectrum disorder (ASD) individuals.

Conclusions:

  • Duplex-Multiome significantly enhances the accuracy and throughput of somatic mutation detection across multiple cell types and omics layers.
  • The findings demonstrate that distinct brain cell types exhibit unique age-related somatic mutation profiles, offering new insights into brain aging.
  • Somatic mutagenesis directly influences gene expression phenotypes, implicating it in both normal aging and neurodevelopmental disorders like ASD.