Biomedical subjects

Judith B Zaugg

Publications and source records attributed to Judith B Zaugg.

2 recordsLinked to original sources

PubMed2025

FootprintCharter: unsupervised detection and quantification of footprints in single molecule footprinting data.

SUMMARY: Single molecule footprinting profiles the heterogeneity of TF occupancy at cis-regulatory elements across cell populations at unprecedented resolution. The single molecule nature of the data in principle allows for observing the footprint of individual transcription factors and nucleosomes. However, we currently lack algorithms to quantify these occupancy patterns of chromatin binding factors in an automated way and without prior assumptions on their genomic location. Here we present FootprintCharter, an unsupervised tool to detect and quantify footprints for transcription factors (TFs) and nucleosomes from single molecule footprinting data. After detection, TF footprints can be labeled with orthogonal motif annotations provided by the user. FootprintCharter allows for the quantification of complex molecular states such as positioning of unphased nucleosomes and combinatorial co-binding of multiple TFs. AVAILABILITY AND IMPLEMENTATION: FootprintCharter is freely available on Bioconductor with version 2.2.0 of https://bioconductor.org/packages/SingleMoleculeFootprinting through the functions FootprintCharter, PlotFootprints, and Plot_FootprintCharter_SM.

Transcription Factors↗

PubMed2025

Functional phenotyping of genomic variants using joint multiomic single-cell DNA-RNA sequencing.

Genetic variants (both coding and noncoding) can impact gene function and expression, driving disease mechanisms such as cancer progression. The systematic study of endogenous genetic variants is hindered by inefficient precision editing tools, combined with technical limitations in confidently linking genotypes to gene expression at single-cell resolution. We developed single-cell DNA-RNA sequencing (SDR-seq) to simultaneously profile up to 480 genomic DNA loci and genes in thousands of single cells, enabling accurate determination of coding and noncoding variant zygosity alongside associated gene expression changes. Using SDR-seq, we associate coding and noncoding variants with distinct gene expression in human induced pluripotent stem cells. Furthermore, we demonstrate that in primary B cell lymphoma samples, cells with a higher mutational burden exhibit elevated B cell receptor signaling and tumorigenic gene expression. SDR-seq provides a powerful platform to dissect regulatory mechanisms encoded by genetic variants, advancing our understanding of gene expression regulation and its implications for disease.

Humans↗