Tracing cell lineages is fundamental for understanding the rules governing development in multicellular organisms and delineating complex biological processes involving the differentiation of multiple cell types with distinct lineage hierarchies. In humans, experimental lineage tracing is unethical, and one has to rely on natural-mutation markers that are created within cells as they proliferate and age. We have demonstrated that it is now possible to trace lineages in normal, noncancerous cells with a variety of data types using natural variations in the nuclear and mitochondrial DNA. In our lab we are studying lineage ancestry in the earliest stages of human development. It is also apparent that the scientific community is on the verge of being able to make a comprehensive and detailed cell lineage map of human embryonic and fetal development. We intend to contribute to that knowledge.
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.
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. Dropping price of sequencing and 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. Our group is part of the Somatic Mosaicism Across Human Tissues (SMaHT) project.
Little is known about the origin of germ cells in humans. We previously leveraged post-zygotic mutations to reconstruct zygote-rooted cell lineage ancestry trees in a phenotypically normal woman, termed NC0. Here, by sequencing the genome of her children and their father, we analyze the transmission of early pre-gastrulation lineages and corresponding mutations across human generations. We find that the germline in NC0 is polyclonal and is founded by at least two cells likely descending from the two blastomeres arising from the first zygotic cleavage. Analyzes of public data from several multi-children families and from 1934 familial quads confirm this finding in larger cohorts, revealing that known imbalances of up to 90:10 in early lineages allocation in somatic tissues are not reflected in mutation transmission to offspring, establishing a fundamental difference in lineage allocation between the soma and the germline. Analyzes of all the data consistently suggest that the germline has a balanced 50:50 lineage allocation from the first two blastomeres.
More: https://www.nature.com/articles/s41467-024-53485-x
Copy number variation (CNV) and alteration (CNA) analysis is a crucial component in many genomic studies and its applications span from basic research to clinic diagnostics and personalized medicine. CNVpytor is a tool featuring a read depth-based caller and combined read depth and B-allele frequency (BAF) based 2D caller to find CNVs and CNAs. The tool stores processed intermediate data and CNV/CNA calls in a compact HDF5 file—pytor file. Here, we describe a new track in igv.js that utilizes pytor and whole genome variant files as input for on-the-fly read depth and BAF visualization, CNV/CNA calling and analysis. Embedding into HTML pages and Jupiter Notebooks enables convenient remote data access and visualization simplifying interpretation and analysis of omics data.
More: https://academic.oup.com/bioinformatics/article/40/8/btae453/7715874
Regulation of gene expression through enhancers is one of the major processes shaping the structure and function of the human brain during development. High-throughput assays have predicted thousands of enhancers involved in neurodevelopment, and confirming their activity through orthogonal functional assays is crucial. Here, we utilized Massively Parallel Reporter Assays (MPRAs) in stem cells and forebrain organoids to evaluate the activity of ~ 7000 gene-linked enhancers previously identified in human fetal tissues and brain organoids. We used a Gaussian mixture model to evaluate the contribution of background noise in the measured activity signal to confirm the activity of ~ 35% of the tested enhancers, with most showing temporal-specific activity, suggesting their evolving role in neurodevelopment. The temporal specificity was further supported by the correlation of activity with gene expression. Our findings provide a valuable gene regulatory resource to the scientific community.
More: https://www.nature.com/articles/s41598-024-54302-7
Somatic mosaicism is defined as an occurrence of two or more populations of cells having genomic sequences differing at given loci in an individual who is derived from a single zygote. It is a characteristic of multicellular organisms that plays a crucial role in normal development and disease. To study the nature and extent of somatic mosaicism in autism spectrum disorder, bipolar disorder, focal cortical dysplasia, schizophrenia, and Tourette syndrome, a multi-institutional consortium called the Brain Somatic Mosaicism Network (BSMN) was formed through the National Institute of Mental Health (NIMH). In addition to genomic data of affected and neurotypical brains, the BSMN also developed and validated a best practices somatic single nucleotide variant calling workflow through the analysis of reference brain tissue. These resources, which include >400 terabytes of data from 1087 subjects, are now available to the research community via the NIMH Data Archive (NDA) and are described here.
More: https://www.nature.com/articles/s41597-023-02645-7
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) 288-7890
Email: Benike.Rubie@mayo.edu
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