Biology subjects

Kiessling, P.

Publications and source records attributed to Kiessling, P..

6 recordsLinked to original sources

bioRxiv2026

Reversing PROTAC-induced ASH2L degradation reactivates proliferation in senescent cells

Nucleosomes control access to gene promoters. Histone H3 lysine 4 tri-methylation, catalyzed by 6 KMT2 complexes, correlates with accessible promoters and gene expression. The catalytic activity of KMT2 enzymes depends on an obligatory core complex with ASH2L being an essential subunit. We find that PROTAC induced depletion of ASH2L reduces H3K4me3, deregulates gene expression and prevents proliferation. Upon prolonged ASH2L loss, cells develop a senescent phenotype, a process linked to aging and disease. Competing the PROTAC reactivates ASH2L, reestablishes H3K4me3 at promoters and reverts gene expression changes. Cells reenter the cell cycle and resume proliferation, thereby reverting senescence. Structure-function studies demonstrate that these molecular and cellular consequences are primarily due to the loss of ASH2L functions associated with KMT2 complexes. Together, these findings indicate that stress inflicted by the loss of KMT2 catalytic activities promotes a reversible senescence phenotype, suggesting that the functions of KMT2 complexes are implicated in aging. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/722411v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@10ace2forg.highwire.dtl.DTLVardef@667c81org.highwire.dtl.DTLVardef@78039dorg.highwire.dtl.DTLVardef@13563be_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2026

Atlas-scale spatially aware clustering with support for 3D and multimodal data using SpatialLeiden

Here we extend SpatialLeiden, our spatial clustering algorithm, to enable generalised atlas-scale multi-sample, 3D serial-section, and multimodal spatial omics via flexible neighbour-graph multiplexing on batch-corrected latent spaces. It delivers coherent domains aligning with brain atlases across >100 samples, stable 3D reconstruction of cancer tissue structures, and integrated multimodal features, outperforming specialized tools in modularity and scalability on standard hardware. SpatialLeiden is compatible with scverse for broad and intuitive adoption.

bioinformatics↗

bioRxiv2026

Polyploid cardiomyocytes define disease-specific transcriptional states in the mammalian heart

The adult mammalian heart has a limited regenerative capacity. Following injury, cardiomyocytes undergo a hypertrophic response accompanied by polyploidization, which has been described as a barrier to proliferation and regeneration of the heart1,2. However, the unique molecular programs of polyploidy, or genome multiplied cardiomyocytes, and their influence on the disease-related myocardial remodelling process remains unclear. Here, we integrate single-nuclei and high-resolution spatial multi-omics across human, rat, and mouse hearts to define novel cardiac cell states and their tissue niches in ischemic and non-ischemic heart disease. Computational analysis across scales allowed us to generate detailed networks of the cardiac tissue remodelling process as well as tissue and sub-cellular environments uniquely enriched in polyploid cardiomyocytes or their diploid origins. We identify a conserved, dichotomous transcriptional program distinguishing diploid from polyploid cardiomyocytes. Polyploid cardiomyocytes demonstrated rewired metabolic and chromatin-remodeling transcriptional programs and recapitulate the gene signature of immature human fetal cardiomyocytes. Notably, we observe that polyploid cardiomyocytes--rather than the general myocyte population--are the primary sites of enrichment for major heart-failure drug targets, including the mineralocorticoid, {beta}1-adrenergic, and glucagon-like peptide-1 receptors. Based on our cross-species dataset we further identified TNIK, a Wnt-pathway regulator expressed in polyploid cardiomyocytes across species, as a potential therapeutic target and demonstrate that pharmacological TNIK inhibition improves cardiac function after myocardial infarction in rats. Together, this species-spanning, disease-resolved study redefines cardiomyocyte heterogeneity in heart disease and suggests a therapeutic path to heart failure treatment by targeting polyploid cardiomyocytes.

genomics↗

bioRxiv2025

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects tissue and organ function. Since 2020, more than 50 spatially aware clustering (SAC) methods have been developed for this purpose. However, the reliability of current benchmarks is undermined by their narrow focus on Visium and brain tissue datasets, as well as incorrect interpretation of manual annotation as ground truth. Here, we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration, and metric evaluation, and is designed to rapidly incorporate new methods and datasets. SACCELERATOR currently includes 22 SAC methods applied to 15 datasets spanning 9 technologies and diverse tissue types. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods across tissues and platforms. We also demonstrate that anatomical labels commonly used as ground truths are often biased, potentially error-prone, and, in some cases, unsuitable for benchmarking efforts. Rather than scoring and comparing methods, we propose a consensus-guided workflow that aggregates clustering results to generate consensus representations. Descriptive spatial metrics highlight areas of high entropy where method disagreement is highest, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual methods and manual annotations. Our results underscore the need for iterative, expert-in-the-loop analysis and reveal that traditional evaluation metrics do not always capture the subjective qualities of results. By improving tissue annotation and addressing key benchmarking limitations, SACCELERATOR provides a robust foundation for advancing spatial omics research.

genomics↗

bioRxiv2025

2D, or not 2D? Investigating Vertical Signal Integrity of Tissue Slices

Imaging-based spatially resolved transcriptomics can localize transcripts within tissue sections in 3D. Cell segmentation assigns transcripts to cells and precedes annotation of cell function. However, cell segmentation is usually performed in 2D, thus unable to deal with spatial doublets arising from overlapping cells, resulting in segmented cells containing transcripts originating from multiple cell types. Here we present a computational tool called ovrlpy that identifies overlapping cells, tissue folds, and inaccurate cell-segmentation by analyzing transcript localization in 3D.

bioinformatics↗

bioRxiv2024

PHLOWER - Single cell trajectory analysis using Decomposition of the Hodge Laplacian

Multi-modal single-cell sequencing, which captures changes in chromatin and gene expression in the same cells, is a game changer in the study of gene regulation in cellular differentiation processes. Computational trajectory analysis is a key computational task for inferring differentiation trees from this single-cell data, though current methods struggle with complex, multi-branching trees and multi-modal data. To address this, PHLOWER (decomPosition of the Hodge Laplacian for inferring trajectOries from floWs of cEll diffeRentiation) leverages the harmonic component of the Hodge decomposition on simplicial complexes to infer trajectory embeddings. These natural representations of cell differentiation facilitate the estimation of their underlying differentiation trees. We evaluate PHLOWER through benchmarking with multi-branching differentiation trees and using novel kidney organoid multi-modal and spatial single-cell data. These demonstrate the power of PHLOWER in both the inference of complex trees and the identification of transcription factors regulating off-target cells in kidney organoids.

bioinformatics↗