Mutations in DNA that accumulate during the lifespan of each individual result in mosaic bodies, in which each cell has unique variants in the genome. That phenomenon is called somatic mosaicism. Despite the prevalence of somatic mosaicism, studying it has been limited by the lack of means to detect such variants at the level of single cells. Recent advances in single-cell genomics, however, make such research possible. Our group develops computational methods for precisely detecting somatic mosaic variants by harnessing new experimental approaches, including clonal expansion and whole genome amplification. By applying those methods to human samples, we aim to answer questions about the origin, spread, and consequence of mosaic mutations, which involves determining mutation rates, differences in the number and pattern of mutations between tissues and ages, relevance of the mutation to diseases and aging. Additionally, we are developing scalable approaches for tracing cell lineages using mutations as lineage markers.
Single-cell sequencing is the ultimate way to study somatic mosaicism in healthy tissues and in cancer. However, due to the scarcity of DNA in a single cell, an amplification process is required. Such amplifications can be achieved via clonal expansion, in which a single cell is cultured to produce a colony, and via in vitro whole genome amplification (WGA), in which DNA is amplified by using polymerases. We are currently formulating strategies for the quality control of WGA and to distinguish signal from noise that may be introduced during cell culture or DNA amplification, as well as developing approaches to estimate the contributions of signal and noise when they cannot be distinguished unambiguously.
During the past decade, high-throughput next-generation technologies coupled with computational algorithms have enabled us to better understand the biology of cancer as well as the molecular underpinnings of its development and progression. Numerous functionally significant point mutations as well as structural alterations have been identified in several types and subtypes of cancers that illustrate the diverse landscape of the cancer genome. In our laboratory, we focus on the discovery and analysis of somatic point mutations and structural alterations, including deletions, duplications, and copy number changes, in colon cancer and glioma. We are especially interested in understanding the relationship between patterns of genetic alterations and modes of evolution of cancer, as well as molecular differences between cancer-free and cancer-adjacent polyps.
Copy number variation (CNV) in the genome is a complex phenomenon that remains incompletely understood. Frequent in cancers, somatic copy number alterations (CNA) have been related to cancer susceptibility, cancer progression and invasiveness, individual response to the treatment, and patients’ quality of life after treatment. The detection of CNVs and CNAs is important to address a wide spectrum of clinical and scientific questions. Research in our laboratory is focused on the discovery and analysis of CNVs and CNAs along with their relevance to diseases. We have developed and continually improved a method, CNVnator/CNVpytor, for CNV discovery and genotyping from a read-depth analysis of personal genome or cancer sequencing that currently ranks among the best, most widely used methods for CNV analysis.
Simultaneous advances in genomics (i.e., in variant discovery), epigenomics, and functional genomics (i.e., emergence of ChiP-seq, ATAC-seq, Hi-C, and RNA-seq techniques) provide opportunities to study both the origins and consequences of genomic variants. We are interested in understanding various epigenomic properties that predispose mutational processes generating single nucleotide variation (SNV) and structural variation (SV). Inversely, germline and somatic variants affect genome function. However, because many of those variants occur in non-coding regions of the genome, their effects remain poorly understood. In response, our laboratory is actively working to elucidate such effects with a particular focus on variants contributing to neuro-developmental disorders such as autism spectrum disorders and Tourette syndrome.
Various barcoding and labelling strategies have been developed for cell-lineage tracing ...
More: https://www.nature.com/articles/s41576-021-00358-4
Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells. Here, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from 0.005 to 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. This study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.
More: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02285-3
Mosaic mutations can be used to track cell lineages in humans. We used cell cloning to analyze embryonic cell lineages in two living individuals and a postmortem human specimen. Of 10 reconstructed postzygotic divisions, none resulted in balanced contributions of daughter lineages to tissues. In both living individuals, one of two lineages from the first cleavage was dominant across tissues, with 90% frequency in blood. We propose that the efficiency of DNA repair contributes to lineage imbalance. Allocation of lineages in postmortem brain correlated with anterior-posterior axis, associating lineage history with cell fate choices in embryos. We establish a minimally invasive framework for defining cell lineages in any living individual, which paves the way for studying their relevance in health and disease.
More: https://science.sciencemag.org/content/371/6535/1245
Somatic mosaicism, manifesting as single nucleotide variants (SNVs), mobile element insertions, and structural changes in the DNA, is a common phenomenon in human brain cells, with potential functional consequences. Using a clonal approach, we previously detected 200-400 mosaic SNVs per cell in three human fetal brains (15-21 wk postconception). However, structural variation in the human fetal brain has not yet been investigated. Here, we discover and validate four mosaic structural variants (SVs) in the same brains and resolve their precise breakpoints. The SVs were of kilobase scale and complex, consisting of deletion(s) and rearranged genomic fragments, which sometimes originated from different chromosomes. Sequences at the breakpoints of these rearrangements had microhomologies, suggesting their origin from replication errors. One SV was found in two clones, and we timed its origin to ~14 wk postconception. No large scale mosaic copy number variants (CNVs) were detectable in normal fetal human brains, suggesting that previously reported megabase-scale CNVs in neurons arise at later stages of development. By reanalysis of public single nuclei data from adult brain neurons, we detected an extrachromosomal circular DNA event. Our study reveals the existence of mosaic SVs in the developing human brain, likely arising from cell proliferation during mid-neurogenesis. Although relatively rare compared to SNVs and present in ~10% of neurons, SVs in developing human brain affect a comparable number of bases in the genome (~6200 vs. ~4000 bp), implying that they may have similar functional consequences.
More: https://genome.cshlp.org/content/30/12/1695
ALUMNI:
10. | An AP endonuclease 1-DNA polymerase beta complex: theoretical prediction of interacting surfaces. , Uzun A, Strauss PR, Ilyin VA PLoS Comput Biol 2008; 4(4):e1000066 ![]() ![]() |
1. | Efficiency Profile Method To Study The Hit Efficiency Of Drift Chambers. , Bel'kov A, Lanyov A, Spiridonov A, Walter M, Hulsbergen W Particles and Nuclei, Letters 2002; 5 (114); :40-52 |
application accepted year-round
Applicants are invited to apply for a post-doctoral (i.e., postdoc) position in Abyzov lab at Mayo Clinic. The choice of project will depend on the applicant's interests and skills, however, the research must be purely computational and focus on one of the following main fields of computational biology: population/personal human omics, cancer omics, single cell and somatic omics, and the analysis of next-generation sequencing data. Specific sub-areas of interest are discovery, annotation, and the functional annotation of human genomic variants, cancer genomics, cancer evolution, somatic mosaicism in normal human cells.
The ideal applicant will have a Ph.D. in computational biology or bioinformatics, experience in one of the aforementioned research areas, demonstrate a record of peer-reviewed publications, and possess motivation for independent research. He or she should have a very strong understanding of biology and be skilled in programming and using computers to solve problems (e.g., experience with C/C++, Java, Python/Perl, R/ROOT, etc.). Oral and written proficiency in English is also a big plus.
To apply, please email your CV, including a list of publications and details for three references, to abyzov dot alexej at mayo dot edu. Please include the phrase “PostDoc application” and your full name in the subject of the email.
application accepted year-round
Applications are invited for an internship at the Mayo Clinic. Anticipated projects will be related to the analysis of whole genome sequencing data, with the aims of studying germline and somatic variants (SNPs, CNVs, etc.). The analysis will involve applications of commonly used, and in-house developed, software tools, and making biological hypothesis from statistical data analysis. Intern applicants with strong programming skills will have opportunities to participate in developing new tools and improving our existing software.
To apply, please email your CV, including a list of publications to abyzov dot alexej at mayo dot edu. Please include the phrase “Internship application” and your full name in the subject of the email.
Graduate students (M.S or Ph.D.) wishing to conduct research in the Abyzov lab at Mayo Clinic are invited to contact Dr. Abyzov (abyzov dot alexej at mayo dot edu). The choice of the project will depend on the applicant's interests and skills. However, the research must be purely computational and focus on one of the following main fields of computational biology: population/personal human omics, cancer omics, single cell and somatic omics, and the analysis of next-generation sequencing data. Specific sub-areas of interest are discovery, annotation, and the functional annotation of human genomic variants such as SNPs, SNVs, indels, structural variations, retrotransposition, etc.
We are looking for candidates that possess motivation for independent research, have experience in computational biology or bioinformatics, and are familiar with one of the aforementioned research areas. They should have a very strong understanding of biology and be skilled in programming and using computers to solve problems (e.g., experience with C/C++ Java, Python/Perl, R/ROOT, etc.). Record of peer-reviewed publications and oral and written proficiency in English is also a big plus.
Dr. Abyzov is affiliated with Mayo Clinic College of Medicine and with the program in Biomedical Informatics and Computational Biology at University of Minnesota Rochester.
Please express your interest by emailing your CV, including a list of publications to abyzov dot alexej at mayo dot edu. Please include the phrase “PhD/MS interest” and your full name in the subject of the email.
Email: abyzov.alexej@mayo.edu
Phone: (507) 284-5569
Email: wagner.leighann@mayo.edu
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