Biology subjects

Jatsenko, T.

Publications and source records attributed to Jatsenko, T..

5 recordsLinked to original sources

bioRxiv2025

Longitudinal cell-free DNA methylome and fragmentome profiles in health uncover signatures of cell type and demographic origin

Cell-free DNA (cfDNA) is a powerful analyte for liquid biopsy applications. However, the composition and fragmentation of cfDNA in health remains incompletely characterized. Here, we profiled 432 plasma cfDNA samples from healthy individuals using targeted enzymatic methyl-sequencing, allowing cell-type of origin inference and assessment of fragmentation features. Both in a diurnal and a cross-sectional cohort, we observed that cfDNA levels and cellular contributions show lower variability within than between individuals. Hematopoietic lineages are the dominant cfDNA sources, with interindividual variability particularly evident in granulocyte contributions. Demographic factors such as sex, age and body mass index (BMI) contribute to changes in the contribution of various blood cell types, and cfDNA concentrations are 1.6-fold higher in early morning collections (P = 1.8 x 10-5). By integrating cell-type-specific cfDNA methylation, fragment size and dinucleotide end motif information, we demonstrate distinct associations of these characteristics with specific cell types. Granulocyte-derived fragments showed a consistent enrichment in mononucleosomal sizes (P = 3.5 x 10-54) and CC end motifs, while also cfDNA from non-hematopoietic cells exhibited distinct size and end-motif profiles. In addition, we observed hypomethylated DNA to be associated with shorter fragment sizes and altered end-motif frequencies, emphasizing interactions between DNA methylation, nuclease activity and chromatin context in shaping cfDNA features. Together, our results provide a view on processes and cell types involved in cfDNA biogenesis in healthy individuals. They underscore that demographic variables and sampling time should be considered for cfDNA-based assay design, but also highlight novel opportunities to improve the representation of specific cell types in cfDNA, thus providing a foundation for optimizing cfDNA diagnostics by leveraging multiple axes of information.

genomics↗

bioRxiv2024

Long-read whole-genome sequencing-based concurrent haplotyping and aneuploidy profiling of single cells

Long-read whole-genome sequencing (lrWGS) enhances haplotyping by providing more phasing information per read compared to short-read sequencing. However, its use for single-cell haplotype phasing remains underexplored. This proof-of-concept study examines lrWGS data from single cells for small variant (SNV and indel) calling and haplotyping using the Genome in a Bottle (GIAB) Ashkenazi trio. lrWGS was performed on single-cell (1 cell) and multi-cell (10 cells) samples from the offspring. Chromosome-length haplotypes were obtained by leveraging both long reads and pedigree information. These haplotypes were further refined by replacing them with matched parental haplotypes. In single-cell and multi-cell samples, 92% and 98% of heterozygous SNVs, and 74% and 78% of heterozygous indels were accurately haplotyped. Applied to human embryos for preimplantation genetic testing (PGT), lrWGS demonstrated 100% consistency with array-based methods for detecting monogenic disorders, without requiring phasing references. Aneuploidies were accurately detected, with insights into the mechanistic origins of chromosomal abnormalities inferred from the parental unique allele fractions. We show that lrWGS-based concurrent haplotyping and aneuploidy profiling of single cells provides an alternative to current PGT methods, with applications potential in areas such as cell-based prenatal diagnosis and animal and plant breeding.

genomics↗

bioRxiv2024

Mechanistic origin and preimplantation development of uniparental and polyploid blastomeres

Whole-genome (WG) abnormalities, such as uniparental diploidy and triploidy, cause fetal death. Occasionally, they coexist with biparental diploid cells in live births. Understanding the origin and early development of WG abnormal blastomeres is crucial for explaining the formation of androgenotes, gynogenotes, triploidy, chimerism, and mixoploidy. By haplotyping 118 blastomeres from first cleavages, we identified various mechanisms of heterogoneic divisions that lead to WG abnormal blastomeres or their coexistence with normal blastomeres in both multipolar and bipolar cleaving zygotes. After culturing the totipotent blastomeres to three preimplantation stages and performing transcriptome profiling on over 600 cells, we discovered that stress responses contribute to developmental impairment in WG abnormal cells, resulting in either cell arrest or blastocyst formation. Some first-cleavage-derived WG abnormal blastomeres can survive early development and progress to blastocysts. Their potential dominance in preimplantation embryos represents an overlooked cause of abnormal development. Haplotype based screening could further increase pregnancy rates.

genetics↗

bioRxiv2024

Alleviating cell-free DNA sequencing biases with optimal transport

Cell-free DNA (cfDNA) is a rich source of biomarkers for various (patho)physiological conditions. Recent developments have used Machine Learning on large cfDNA data sets to enhance the detection of cancers and immunological diseases. Preanalytical variables, such as the library preparation protocol or sequencing platform, are major confounders that influence such data sets and lead to domain shifts (i.e., shifts in data distribution as those confounders vary across time or space). Here, we present a domain adaptation method that builds on the concept of optimal transport, and explicitly corrects for the effect of such preanalytical variables. Our approach can be used to merge cohorts representative of the same population but separated by technical biases. Moreover, we also demonstrate that it improves cancer detection via Machine Learning by alleviating the sources of variation that are not of biological origin. Our method also improves over the widely used GC-content bias correction, both in terms of bias removal and cancer signal isolation. These results open perspectives for the downstream analysis of larger data sets through the integration of cohorts produced by different sequencing pipelines or collected in different centers. Notably, the approach is rather general with the potential for application to many other genomic data analysis problems.

bioinformatics↗

bioRxiv2018

TAC-seq: targeted DNA and RNA sequencing for precise biomarker molecule counting

Targeted next-generation sequencing based biomarker detection methods have become essential for biomedical diagnostics. In addition to their sensitivity and high-throughput capacity, absolute molecule counting based on unique molecular identifier (UMI) has high potential to increase biomarker detection accuracy even further through the reduction of systematic technical biases. Here, we present TAC-seq, a simple and cost-effective targeted allele counting by sequencing method that uses UMIs to estimate the original molecule counts of different biomarker types like mRNAs, microRNAs and cell-free DNA. We applied TAC-seq in three different applications and compared the results with standard sequencing technologies. RNA samples extracted from human endometrial biopsies were analyzed using previously described 57 mRNA-based receptivity biomarkers and 49 selected microRNAs at different expression levels. Cell-free DNA aneuploidy testing was based on cell line (47,XX,+21) genomic DNA. TAC-seq mRNA biomarker profiling showed identical clustering results to full transcriptome RNA sequencing, and microRNA detection demonstrated significant reduction in amplification bias, allowing to determine minor expression changes between different samples that remained undetermined by standard sequencing. The mimicking experiment for cell-free DNA fetal aneuploidy analysis showed that TAC-seq can be applied to count highly fragmented DNA, detecting significant (p=4.8x10-11) excess of molecules in case of trisomy 21. Based on three proof-of-principle applications we show that TAC-seq is a highly accurate and universal method for targeted nucleic acid biomarker profiling.

genomics↗