Extraordinary BioMed Seminar: Single-cell and single-molecule computational epigenomics
Wednesday 10 January 2024, 12:00h
Maria Colomé-Tatché, PhD., Biomedical Center, Faculty of Medicine, Ludwig Maximilian University Munich and Institute of Computational Biology, Helmholtz Center Munich, Germany
Title: Single-cell and single-molecule computational epigenomics
Host: Dr. Núria López Bigas, Group Leader, Biomedical Genomics Laboratory in IRB Barcelona’s Cancer Science Programme.
Recent breakthroughs in high-throughput sequencing of single cells are revolutionizing the biological and biomedical sector. Among the different -omics layers that can be measured at the single-cell level, single-cell epigenomic measurements present a rich layer of regulatory information that stands between the genome and the transcriptome. These measurements can be obtained for large heterogeneous samples of single cells to profile tissues, organs and whole organisms, and to study dynamic processes like cellular differentiation, reprogramming or cancer evolution. These data types provide an unprecedented level of measurement resolution.
In this talk I will discuss how single-cell ATAC-seq and single-cell DNA methylation data can be used to study cell identity [1,2]. I will introduce and compare multiple feature space constructions for epigenetic data analysis and show the feasibility of common clustering, dimension reduction, batch integration and trajectory learning techniques for both single-cell DNA methylation data and scATAC-seq data.
Studying single-cell DNA methylation heterogeneity using single-cell DNA methylation measurements is however complicated, as experimental protocols are costly and difficult to implement. I will present an alternative strategy, which involves minION sequencing combined with deconvolution of single-molecule methylation signals to reconstruct cell-type methylation profiles. I will show how, using this method, it is possible to deconvolve the methylomes of different cell types from an in-silico mix of cells.
Another level of genomic information that can be extracted from single-cell data are single-cell copy number variations (CNVs). I will present a novel algorithm, epiAneufinder , which exploits the read count information from scATAC-seq data to extract genome-wide CNVs for individual single-cells, and I will show how the obtained CNVs are comparable to the ones obtained from single-cell whole genome sequencing data. Thanks to epiAneufinder it is therefore possible to add a relevant extra layer of genomic information, namely single-cell copy number variation, to every scATAC-seq dataset without the need of additional experiments.
IMPORTANT: Attendees outside the PCB community must register at least 24h before the seminar.