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Biology subjects

Starkenburg, S.

Publications and source records attributed to Starkenburg, S..

4 recordsLinked to original sources

Resolving in vitro heterogeneity of host functional responses to HCoV-229E via single-cell analyses

Understanding host-pathogen interactions at the molecular level is essential to elucidating the mechanisms that govern immunity and pathogenesis. While diverse responses to viral infection have been documented, it remains unclear if this variability arises from the complex host milieu or represents an intrinsic property of the infection itself. To investigate this, we performed single-cell (sc) RNA sequencing analysis of infected MRC-5 fibroblast cells with human coronavirus 229E (HCoV-229E), which revealed pronounced variation in viral load across time points and biological replicates. While viral sequences were higher at 24 hours post-infection (hpi), more host genes were differentially expressed at 48 hpi, indicating dynamic responses during infection progression. Cells with higher viral load upregulated expression of unfolded protein response genes and NF-{kappa}B regulators, and downregulated expression of fibroblast identity and cell cycle genes, reflecting a shift toward a stress-adaptive, antiviral state. Network analyses identified NF-{kappa}B signaling as a central regulatory axis, with negative regulators overexpressed in high-viral-load clusters. Additionally, ATAC-seq analysis showed that regions of increased chromatin accessibility were enriched for NF-{kappa}B subunits, further supporting its role in transcriptional control. These findings demonstrate that even within ostensibly uniform cell cultures, viral infection induces distinct heterogeneous transcriptional and regulatory responses.

microbiology↗

AI-Assisted Cryo-ET Workflow for 3D Visualization of Chromatin during Cellular Differentiation

Understanding how chromatin architecture changes during cellular differentiation requires structural methods that can resolve native genomic organization at high resolution. Here, we present an AI-assisted cryo-electron tomography (cryo-ET) and segmentation workflow to quantify chromatin compaction across various stages of motor neuron differentiation from induced pluripotent stem cells (iPSC). By directly imaging extracted and vitrified chromatin, we preserve native structure and avoid artifacts from heavy metal staining and resin embedding. Using three-dimensional (3D) density analysis, we measure chromatin density and capture the progressive increase in chromatin compaction with lineage commitment. This is then correlated with population-averaged Hi-C experiments, observing consistency between the microscale higher order structure of chromatin and global contact patterns. Our approach enables direct visualization of chromatin organization under near-physiological conditions, bridging the gap between structural imaging and genome-wide contact mapping. This platform therefore establishes an AI-assisted experimental framework for linking chromatin architecture to regulatory mechanisms during differentiation.

genomics↗

From 2D to 4D: a Containerized Workflow and Browser to Explore Dynamic Chromatin Architecture

BackgroundCharacterizing the physical organization of the genome is essential for understanding long-range gene reg- ulation, chromatin compartmentalization, and epigenetic accessibility. Hi-C experiments generate two- dimensional (2D) genome-wide contact maps of chromatin interactions by capturing the spatial proximity between genomic loci, which reveal interaction frequencies but lack the spatial resolution needed to interpret the three-dimensional (3D) genome structure(s). Emerging evidence suggests that epigenetic regulation is closely linked to 3D genome architecture, and that structural changes over time (4D) drive key biological pro- cesses in development, disease, and environmental response. Thus, integrating 3D structure with functional data is critical for a more complete understanding of genome regulation. Previous work, most notably the 4DHiC chromosome modeling framework, has shown that physical multi-dimensional modeling approaches rooted in polymer physics and molecular dynamics can resolve these structures at biologically meaningful resolutions by integrating temporal Hi-C data with physical constraints to uncover dynamic chromosome reorganization. Thus, molecular dynamics simulations, constrained by Hi-C contact matrices, can resolve fine-scale structural changes and reveal functionally significant transitions in chromatin conformation. ResultsHerein, we present the 4D Genome Browser Workflow (4DGBWorkflow) and the 4D Genome Browser (4DGB). The algorithm is based on the 4DHiC method and the containerized tool is an end-to-end workflow that can transform, filter, and view 4D epigenomics and chromatin datasets, allowing non-specialists to apply three-dimensional modeling principles to diverse datasets and experimental conditions. The software executes on a laptop running macOS, Linux or Windows. From input Hi-C files (.hic), the 4DGBWorkflow produces 3D reconstructions of chromosomes, integrates the reconstruction with track data (e.g., epigenetic marks, transcriptome profiles), and provides comparative visualization of the results in a single workflow. ConclusionsThe 4DGBWorkflow and 4D Genome Browser are open-source tools for comparative analysis and visual- ization of 4D chromosome datasets, including chromatin architecture and epigenomic signals. Automatic integration of Hi-C data with molecular dynamics democratizes the construction of time resolved 3D genome structures, simplifying complex simulations and data integration schemes.

bioinformatics↗

Improved Quality Metrics for Association and Reproducibility in Chromatin Accessibility Data Using Mutual Information

BackgroundCorrelation metrics are widely utilized in genomics analysis and often implemented with little regard to assumptions of normality, homoscedasticity, and independence of values. This is especially true when comparing values between replicated sequencing experiments that probe chromatin accessibility, such as assays for transposase-accessible chromatin via sequencing (ATAC-seq). Such data can possess several regions across the human genome with little to no sequencing depth and are thus non-normal with a large portion of zero values. Despite distributed use in the epigenomics field, few studies have evaluated and benchmarked how correlation and association statistics behave across ATAC-seq experiments with known differences or the effects of removing specific outliers from the data. Here, we developed a computational simulation of ATAC-seq data to elucidate the behavior of correlation statistics and to compare their accuracy under set conditions of reproducibility. ResultsUsing these simulations, we monitored the behavior of several correlation statistics, including the Pearsons R and Spearmans{rho} coefficients as well as Kendalls{tau} and Top-Down correlation. We also test the behavior of association measures, including the coefficient of determination R2, Kendalls W, and normalized mutual information. Our experiments reveal an insensitivity of most statistics, including Spearmans{rho} , Kendalls{tau} , and Kendalls W, to increasing differences between simulated ATAC-seq replicates. The removal of co-zeros (regions lacking mapped sequenced reads) between simulated experiments greatly improves the estimates of correlation and association. After removing co-zeros, the R2 coefficient and normalized mutual information display the best performance, having a closer one-to-one relationship with the known portion of shared, enhanced loci between simulated replicates. When comparing values between experimental ATAC-seq data using a random forest model, mutual information best predicts ATAC-seq replicate relationships. ConclusionsCollectively, this study demonstrates how measures of correlation and association can behave in epigenomics experiments. We provide improved strategies for quantifying relationships in these increasingly prevalent and important chromatin accessibility assays.

bioinformatics↗