Biology subjects

tegner, j.

Publications and source records attributed to tegner, j..

7 recordsLinked to original sources

bioRxiv2026

Single-cell analysis suggests coordinated immune-cell redistribution between blood and cerebrospinal fluid in early multiple sclerosis

Despite immune-cell infiltration being a hallmark of multiple sclerosis (MS), the interplay between the periphery and central nervous system immune responses is still incompletely characterized. We performed single-cell transcriptomic and V(D)J sequencing of paired blood and cerebrospinal fluid (CSF) immune cells from treatment-naive relapsing-remitting women with MS and compared them to age- and sex-matched healthy controls. Across major immune lineages, we identified coordinated, compartment-specific immune alterations, with enrichment of activated and memory lymphocyte populations in the CSF and concomitant depletion of related populations in peripheral blood, suggesting their recruitment from blood to CSF. Clonally expanded CD4 memory T-cells, together with activated, expanded IgM-positive B-cells, accumulated predominantly in the CSF of people with MS compared to healthy subjects. Interestingly, tissue-primed cytotoxic populations and CXCR3-associated memory populations were depleted from the CSF of MS, implying their recruitment to the target tissue in early disease. These findings reveal coordinated, compartment-specific immune changes in early MS and provide a systems-level view of immune-cell trafficking between peripheral and central nervous system compartments.

immunology↗

bioRxiv2026

A Fractal-Dimension Framework for Quantifying Self-Similarity in Chromatin Folding

The three-dimensional folding of DNA is essential for genome function, but its organization remains difficult to summarize quantitatively across genomic scales. Here, we study DNA folding from Hi-C contact data using a network-based notion of fractal dimension. In this representation, genomic loci are treated as nodes, and observed Hi-C contacts define weighted edges, so that frequently interacting loci are closer in the resulting network. We then estimate fractal dimension using two complementary graph-based methods: the correlation dimension and the sandbox dimension. Validation on synthetic networks shows that the proposed estimators detect clear scaling behavior in hierarchical fractal-like networks, while distinguishing them from networks with local clustering but no stable multiscale self-similarity. Applied to intrachromosomal Hi-C data from the IMR90 human cell line, the method reveals approximate linear scaling regimes on log-log plots, suggesting fractal-like organization in chromatin contact networks. At the chromosome level, estimated fractal dimension tends to increase with chromosome size: larger chromosomes often have dimensions closer to 3, consistent with more compact and space-filling organization, whereas shorter chromosomes tend to have lower dimensions, closer to 1, consistent with simpler and more open folding patterns. A sliding-window analysis at 5 kb resolution further shows that fractal organization varies substantially along chromosomes rather than remaining uniform across genomic position. These results suggest that graph-based fractal dimension provides an interpretable summary of DNA folding complexity at both global and local scales. More broadly, the proposed framework offers a quantitative way to study multiscale genome organization from Hi-C data using tools from network geometry.

bioinformatics↗

bioRxiv2025

Thermo-Dynamic Flux Balance Analysis, a novel approach to simulate circadian metabolism

Circadian metabolism arises from complex, time-dependent interactions across organs, yet experimental characterization in humans remains limited. Current whole-body metabolic models are either too large for dynamic simulation or insufficiently detailed to capture temporal physiology. We developed a multi-tissue human metabolic reconstruction, HGEM1.19+, an extensively curated model for dynamic simulation. We introduced Thermo-Dynamic Flux Balance Analysis (tdFBA), a novel formulation that integrates thermodynamic constraints, organ-specific enzyme capacities, solubility limits, osmotic balance and pH buffering. Murine circadian transcriptomics and metabolomics were integrated with human datasets to parameterize temporal dynamics. A genetic algorithm optimized internal parameters under CHOW diet conditions and model performance was evaluated using circadian metabolomics and gene-sage patterns. The framework recapitulated key metabolic oscillations under CHOW diet and partially reconstructed perturbed states including HFD, BMAL1 knockout, and tissue-specific re-entrainment. tdFBA enables physiologically grounded, multi-organ dynamic simulations and provides a foundation for exploring human circadian metabolism and perturbation responses.

systems biology↗

bioRxiv2025

Fundamental Limits of Inferring Dynamical Gene Regulatory Models from Single-Cell Data

A long-standing goal in systems biology is to construct computational models that capture gene regulatory dynamics. On the one hand, graph-based regulatory network models rely on prior knowledge but are constrained by unknown kinetic parameters. On the other hand, recent advancements in single-cell sequencing technologies, particularly RNA velocity and pseudotime analysis, offer data-driven approaches to infer kinetic parameters. However, accurate modeling of biological dynamics requires the estimation of the Jacobian matrix, which captures signal propagation within a regulatory system. Several computational methods have recently emerged to infer Jacobians from single-cell data, including SpliceJAC, scMomentum, and Dynamo. Here, we evaluate these methods by analyzing Jacobian eigenvalues, assessing their ability to capture stability and oscillatory properties. Using simulated ground truth data, we find that methods such as Dynamo and scMomentum generate models that capture the behavioral properties of the cells, however, they do not reliably capture the structural properties. Our results highlight the limitations of dynamic GRN inference, namely, the inability of kinetics to alleviate the need for further structural constrains for the models to adequately reflect the structural properties of the GRN. Developing more robust approaches for Jacobian estimation from single-cell data will be essential for building accurate, large-scale dynamical models of gene regulation. Graphical AbstractOverview of Benchmarking Jacobian-based inference in single-cell RNA Data. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/674509v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@13496e2org.highwire.dtl.DTLVardef@19e0250org.highwire.dtl.DTLVardef@a9db39org.highwire.dtl.DTLVardef@17789e2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Spatial Transcriptomics Reveals a Myeloma Cell Architecture with Dysfunctional T-Cell Distribution, Neutrophil Traps, and Inflammatory Signaling

The bone marrow (BM) is a complex tissue where spatial relationships influence cell behavior, signaling, and function. Consequently, understanding the whole dynamics of cellular interactions requires complementary spatial techniques that preserve and map the architecture of cell populations in situ. We successfully conducted spatial transcriptional profiling using Visium Spatial Gene Expression in formalin-fixed paraffin-embedded (FFPE) BM samples obtained from healthy and Multiple Myeloma (MM) mouse models and patients, addressing the technical challenges of applying spatial technology to long bone samples. A custom data-analysis framework that combines spatial with single-cell transcriptomic profiles identified both the BM cellular composition and the existing cell relations. This allowed us to visualize the spatial distribution of transcriptionally heterogeneous MM plasma cells (MM-PC). We spatially delineated transcriptional programs associated with MM, including NETosis and IL-17-driven inflammatory signaling, which were inversely correlated to malignant PC-enriched regions. Furthermore, a gradient of MM-PC density spatially correlated with a shift from effector-to-exhausted T cell phenotypes. The translational relevance of our findings was confirmed using FFPE BM biopsies from MM patients with varying levels of malignant PC infiltration. In summary, we provide the first spatial transcriptomics analysis applied to a mouse and human mineralized bone tissue and illustrate the BM cellular architecture of MM, revealing deregulated mechanisms underlying MM intercellular communication.

cancer biology↗

bioRxiv2022

Genetic architecture of behavioural resilience to ocean acidification

Genetic variation is essential for adaptation to rapid environmental changes. Identifying genetic variation associated with climate-change related phenotypes is therefore the necessary first step towards predictive models of genomic vulnerability. Here we used a whole-genome scan to identify candidate genetic variants associated with differences in behavioural resilience to ocean acidification in a coral reef fish. We identified three genomic regions that differ between individuals that are behaviourally tolerant compared with behaviourally sensitive to elevated CO2. These include a dopamine receptor (drd4rs), cadherin related family member 5-like (cdhr5l), Synapse-associated protein 1 (syap1), and GRB2 Associated Regulator of MAPK1 Subtype 2 (garem2), which have previously been found to modify behaviour related to boldness, novelty seeking, and learning in other species, and differ between behaviourally tolerant and sensitive individuals. Consequently, the identified genes are promising candidates in the search of the genetic underpinnings and adaptive potential of behavioural resilience to ocean acidification in fishes.

ecology↗

bioRxiv2020

STATegra: Multi-omics data integration - A conceptual scheme with a bioinformatics pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.

bioinformatics↗