Biology subjects

Grellscheid, S. N.

Publications and source records attributed to Grellscheid, S. N..

13 recordsLinked to original sources

bioRxiv2026

The scaffold nucleoporin Nup188 undergoes liquid-liquid phase separation

Components of nuclear pore complexes are broadly classified into two categories: intrinsically disordered FG-repeat nucleoporins such as Nup98, which can undergo liquid-liquid phase separation, and scaffold nucleoporins, which form the NPC's structural framework. The basis for crosstalk of these nucleoporins within NPC and beyond is poorly understood. Here we show that human Nup188, a helical inner-ring scaffold protein, forms condensates both in purified form as well as at endogenous level. This condensate exhibits fusion, dissolved in 1,6-hexanediol and recovered after photobleaching, with dynamics and material properties comparable to those of hNup98 condensates. Moreover, these condensates are not colocalized to annulate lamellae. Our analysis also revealed that hNup188CTD is sufficient to form condensates and MD simulations revealed the key roles of IDR1-3 for condensation. Notably, hNup93 expression along with hNup188 suppressed the condensate formation. Together, these findings indicate that a structured scaffold nucleoporin can adopt a condensate-forming state via its CTD and Nup93 binding can suppress this phenomenon.

biochemistry↗

bioRxiv2026

Shapes of condensate droplets containing filaments

The interactions of droplets and filaments can lead to mutual deformations and complex combined behavior. Such interactions also occur within the cell, where biomolecular condensates, distinct liquid phases often composed of proteins, have been observed to structure and affect the organization of the cytoskeleton. In particular, biomolecular condensates have been shown to undergo characteristic deformations when cytoskeletal filaments are fully embedded within them. However, a full understanding of the underlying physical mechanisms is still missing. Here, we combine experiments with coarse-grained molecular dynamics simulations and analytical models to uncover the physical mechanisms that define emerging shapes of droplets containing filaments. We find that the surface tension of the liquid phase and the bending energy of the filament(s) suffice to accurately capture emerging shapes if the length of the filament is small compared to the liquid volume. As the volume fraction of filament(s) increases, wetting effects become increasingly important, setting physical constraints within which surface and bending energies compete to define the droplet shapes. We find that mutual deformations of condensate and filament extend accessible shapes beyond classical stability considerations, leading to structuring and entrapment of contained filaments. Shape deformations may further affect ripening dynamics that favor certain geometries. Our findings provide a physical framework for a better understanding of the possible roles of biomolecular condensates in cytoskeletal organization.

biophysics↗

bioRxiv2026

A relic at risk: Genomic evidence for an early-diverging domesticated lineage in Norwegian farmhouse yeast

The use of Saccharomyces cerevisiae to ferment alcoholic beverages is an ancient tradition, with genetic evidence indicating origins in Neolithic Asia, although the domestication process of the species is not fully understood. Kveik is a group of traditional yeasts used in farmhouse brewing in western Norway preserved through generations of rural brewing practice. While recent studies have highlighted the distinctiveness of kveik, its precise phylogenetic position, genetic diversity, and domestication history remain unclear. We performed whole-genome sequencing on 64 isolates from 25 traditional brewing cultures from Western Norway selected using cultural heritage criteria, and generated telomere-to-telomere (T2T) assemblies for selected strains. Phylogenomic and population genetic analyses reveal that kveik strains form a paraphyletic and early diverging group with respect to other domesticated S. cerevisiae strains. Most strains exhibit low diversity among isolates from the same brewing culture, strong geographic clustering, and little evidence of gene-flow or admixture. Mitochondrial genomes and Ty1 retrotransposon profiles corroborate this distinct lineage history. We further show that previously reported signals of gene flow between kveik and Asian fermentation strains are likely artifacts caused by population structure and selection. Divergence time estimates suggest that the common ancestor of beer, kveik, and other liquid-phase fermenting strains originated from ancestral populations 4,000 to 8,000 years ago. Our genomic resource sheds light on yeast evolution and domestication. Kveik likely comprises some of the oldest domesticated lineages in continuous use until today, connecting intangible cultural heritage to an early genetic origin. Significance statementYeast has been used to brew beer and ferment food for thousands of years, but we still do not fully understand how and where the different domesticated strains we are using today arose. We sequenced the largest collection of Norwegian farmhouse yeasts (kveik) to date, from cultures used by rural traditional brewers for generations. Our genetic analyses show that kveik forms a distinct and early branch among European brewing yeasts. This shows that historical traditions have created unique local types of yeast and helps us better understand and preserve the diversity of domesticated yeasts.

genomics↗

bioRxiv2025

DynaMiCs - Dynamic cell-type deconvolution ensembles for Mapping in mixed Conditions

Single-cell techniques facilitate the molecular analysis of individual cells, providing insights into cellular diversity, function, and the complexity of biological systems. However, their application is typically limited to small-scale studies involving individual or a few dozen samples, as a consequence of costs and experimental requirement. This complicates the inference of robust conclusions about populations. Bulk transcriptomics offers cost-efficient measurements with low experimental requirements. However, the cellular resolution is lost and only a complex linear combination of signals from multiple cells is observed. Thus, gene expression changes cannot be attributed to individual cells or cell populations. Cell-type deconvolution methods infer cellular compositions from bulk transcriptomics data. State-of-the-art approaches use single-cell data to build molecular reference profiles and identify powerful cell-type markers for improved deconvolution. In this context, we propose Dynamic cell-type deconvolution ensembles for Mapping in mixed Conditions (DynaMiCs) for the integration of single-cell and bulk transcriptomics data. Specifically, DynaMiCs dynamically extracts information from single-cell experiments to (1) provide more accurate estimates of cellular compositions, and (2) establish a mapping between bulk and single-cell data. Consequently, DynaMiCs enables the investigation of how cell populations change in both quantity and molecular characteristics between different phenotypes, informed by single-cell experiments.

bioinformatics↗

bioRxiv2025

FlickerPrint: An Analysis Package for Measuring Interfacial Tension and Bending Rigidity of Biomolecular Condensates and Vesicles at Scale

1 In BriefWilliamson, Law et. al. present FlickerPrint, a computational analysis tool which can be used for measuring the interfacial tension and bending rigidity of soft fluctuating bodies, including biomolecular condensates, droplets or vesicles, from confocal microscopy images using flicker spectroscopy. This method is highly scalable so can be used to analyse the properties of whole populations of thousands of such soft bodies. 2 MotivationBiomolecular condensates play fundamental roles in sub-cellular organisation and it is well-known that the composition of condensates can affect their function. Measuring the conden-sates mechanical properties (for example, interfacial tension and bending rigidity) can aid the understanding of their biomolecular composition and cellular functions. However, measuring the properties of individual condensates under physiological conditions is very challenging and cum-bersome to scale to the population level using traditional methods. To overcome these issues, we have developed a software package to run flicker spectroscopy analysis of condensates at scale, to determine their interfacial tension and bending rigidity. At the same time, FlickerPrint can be harnessed to analyse other soft, fluctuating bodies such as lipid vesicles. 3 SummaryAccurate measurement of the mechanical properties of biomolecular condensates is an essential step in understanding their behaviour within cells. We present FlickerPrint, an open-source Python package to determine the interfacial tension and bending rigidity of thousands of biomolecular condensates using flicker spectroscopy by analysing their shape fluctuations in confocal microscopy images. We detail the workflow used by FlickerPrint to scale up these individual measurements to the population level and the computational requirements to run Flicker-Print. We provide examples of experiments in live cells and in vitro which are suitable for analysis with FlickerPrint as well as scenarios where the package cannot be used. Using these examples, we show that the results obtained from the analysis are robust to changes in the imaging setup, including frame rate. This implementation enables a step-change in the capability to measure two key properties of biomolecular condensates, the interfacial tension and bending rigidity. Moreover, the tools in FlickerPrint are also applicable for analysing other soft, fluctuating bodies, which we demonstrate here using vesicles.

cell biology↗

bioRxiv2025

HIDE: Hierarchical cell-type Deconvolution

MotivationCell-type deconvolution is a computational approach to infer cellular distributions from bulk transcriptomics data. Several methods have been proposed, each with its own advantages and disadvantages. Reference based approaches make use of archetypic transcriptomic profiles representing individual cell types. Those reference profiles are ideally chosen such that the observed bulks can be reconstructed as a linear combination thereof. This strategy, however, ignores the fact that cellular populations arise through the process of cellular differentiation, which entails the gradual emergence of cell groups with diverse morphological and functional characteristics. ResultsHere, we propose Hierarchical cell-type Deconvolution (HIDE), a cell-type deconvolution approach which incorporates a cell hierarchy for improved performance and interpretability. This is achieved by a hierarchical procedure that preserves estimates of major cell populations while inferring their respective subpopulations. We show in simulation studies that this procedure produces more reliable and more consistent results than other state-of-the-art approaches. Finally, we provide an example application of HIDE to explore breast cancer specimens from TCGA. AvailabilityA python implementation of HIDE is available at zenodo (doi:10.5281/zenodo.14724906). Supplementary informationSupplementary material is available at Bioinformatics online.

bioinformatics↗

bioRxiv2024

Deconvolution of omics data in Python with Deconomix -- cellular compositions, cell-type specific gene regulation, and background contributions

BackgroundGene expression profiles derived from heterogeneous bulk samples contain signals from various cell populations. Cell-type deconvolution approaches are computational tools to reverse engineer the composition of bulks in term of cell populations. Accurate estimates of cell compositions are crucial for identifying cell populations relevant for disease. Moreover, analyses, such as the identification of differentially expressed genes, can be confounded by cellular composition, as differences in gene expression may arise from both variations in cellular composition and gene regulation. ResultsWe present Deconvolution of omics data (Deconomix) - a comprehensive toolbox for the cell-type deconvolution of bulk transcriptomics data, available as a Python package and standalone graphical user interface. Deconomix stands apart from competing solutions with rich functionality and highly efficient implementations. It facilitates (A) the inference of cellular compositions from bulk transcriptomics data, (B) the machine learning-based optimization of gene weights to resolve small cell populations and to disentangle phenotypically related cells, (C) the inference of background contributions which otherwise would deteriorate cell-type deconvolution, and (D) population estimates of cell-type specific gene regulation. To showcase the application of Deconomix, we present a case study on breast cancer data from TCGA, highlighting subtype-specific cellular compositions and cell-type-specific gene-regulatory programs. ConclusionWe present Deconomix, a comprehensive Python package including a graphical user interface for the inference of cellular compositions, cell-type-specific gene regulation, and background contributions from bulk transcriptomics data. O_TEXTBOXKey PointsO_LIDeconomix optimizes gene weights to disentangle small cell populations and phenotypically related cells. C_LIO_LIDeconomix estimates cell compositions, unknown background contributions and cell-type specific gene regulation from bulk transcriptomics data. C_LIO_LIA Python package is available as code repository and installable via PyPI [1]. C_LIO_LIA standalone graphical user interface is available as code repository [2]. C_LIO_LIAn exemplary analysis of a breast cancer case study is provided as tutorial [3]. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2023

An integrated RNA-proteomic landscape of drug induced senescence in a cancer cell line

Senescent cells are characterized by an arrest in proliferation. In addition to replicative senescence resulting from telomere exhaustion, sub-lethal genotoxic stress resulting from DNA damage, oncogene activation, mitochondrial dysfunction or reactive metabolites also elicits a senescence phenotype. Senescence is a controlled programme affecting a wide variety of biological processes with some core hallmarks of senescence as well as tissue specific changes. This study presents an integrative multi-omic analysis of proteomic and RNA-seq from proliferating and senescent osteosarcoma cells. This study demonstrates senescence induction in a widely used cell line which can be used as a model system for characterising cancer cell responses to sub-lethal doses of chemotherapeutic agents, and makes available both RNA-seq and proteomic data from proliferating and senescent cells in open access repositories to aid reuse by the community.

cell biology↗

bioRxiv2023

Adaptive Digital Tissue Deconvolution

MotivationThe inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systematically improve estimates. Such approaches allow to infer additional, less abundant cell types. However, they rely on training data which do not capture the full biological diversity encountered in transcriptomics analyses; data can contain cellular contributions not seen in the training data and as such, analyses can be biased or blurred. Thus, computational approaches have to deal with unknown, hidden contributions. Moreover, most methods are based on cellular archetypes which serve as a reference; e.g., a generic T-cell profile is used to infer the proportion of T-cells. It is well known that cells adapt their molecular phenotype to the environment and that pre-specified cell archetypes can distort the inference of cellular compositions. ResultsWe propose Adaptive Digital Tissue Deconvolution (ADTD) to estimate cellular proportions of pre-selected cell types together with possibly unknown and hidden background contributions. Moreover, ADTD adapts prototypic reference profiles to the molecular environment of the cells, which further resolves cell-type specific gene regulation from bulk transcriptomics data. We verify this in simulation studies and demonstrate that ADTD improves existing approaches in estimating cellular compositions. In an application to bulk transcriptomics data from breast cancer patients, we demonstrate that ADTD provides insights into cell-type specific molecular differences between breast cancer subtypes. Availability and implementationA python implementation of ADTD and a tutorial are available at Gitlab and zenodo (doi:10.5281/zenodo.7548362). Contactmichael.altenbuchinger@bioinf.med.uni-goettingen.de and sushma.grellscheid@uib.no Supplementary informationSupplementary material is available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

Using Shape Fluctuations to Probe the Mechanics of Stress Granules

Surface tension plays a significant role in many functions of biomolecular condensates, from governing the dynamics of droplet coalescence to determining how condensates interact with and deform lipid membranes and biological filaments. To date, however, there is a lack of accurate methods to measure the surface tension of condensates in living cells. Here, we present a high-throughput flicker spectroscopy technique that is able to analyse the thermal fluctuations of the surfaces of tens of thousands of condensates to extract the distribution of surface tensions. Demonstrating this approach on stress granules, we show for the first time that the measured fluctuation spectra cannot be explained by surface tension alone. It is necessary to include an additional energy contribution, which we attribute to an elastic bending rigidity and suggests the presence of structure at the granule-cytoplasm interface. Our data also show that stress granules do not have a spherical base-shape, but fluctuate around a more irregular geometry. Taken together, these results demonstrate quantitatively that the mechanics of stress granules clearly deviate from those expected for simple liquid droplets.

cell biology↗

bioRxiv2020

Drug and siRNA screens identify ROCK2 as a therapeutic target for ciliopathies.

Primary cilia are microtubule-based organelles that act as cellular antennae to mediate vertebrate development and growth factor signalling. Defects in primary cilia result in a group of inherited developmental conditions known as ciliopathies. Ciliopathies often present with cystic kidney disease, a major cause of early renal failure that requires renal replacement therapies. Currently, only one drug, Tolvaptan, is licensed to slow the decline of renal function for the ciliopathy polycystic kidney disease. Novel therapeutic interventions for these conditions remain a pressing clinical need. We screened clinical development compounds for positive effects on cilia formation and function and identified fasudil hydrochloride as the top hit. Fasudil is a generic, off-patent drug that is a potent but broadly selective Rho-associated coiled-coil-containing protein kinase (ROCK) inhibitor. In a parallel whole genome siRNA-based reverse genetics phenotypic screen of positive modulators of cilia formation, we identified ROCK2 as the target molecule. We demonstrate that ROCK2 is a key mediator of cilium formation and function through effects on actin cytoskeleton remodelling. Our results indicate that specific ROCK2 inhibitors such as belumosudil (KD-025) could be repurposed for pharmacological intervention in cystic kidney disease. We propose that ROCK2 inhibition represents a novel, disease-modifying therapeutic approach for heterogeneous ciliopathies.

cell biology↗

bioRxiv2020

Deep conservation of ribosome stall sites across RNA processing genes

The rate of translation can vary considerably depending on the mRNA template. During the elongation phase the ribosome can transiently pause or permanently stall. A pause can provide the nascent protein with the required time to fold or be transported, while stalling can serve as quality control and trigger degradation of aberrant mRNA and peptide. Ribosome profiling has allowed for the genome-wide detection of such pause and stall sites, but due to library-specific biases, these predictions are often unreliable. Here, we address this by taking advantage of the deep conservation of the protein synthesis machinery, hypothesizing that similar conservation could exist for functionally important positions of ribosome slowdown - here collectively called stall sites. We analyze multiple ribosome profiling datasets from a phylogenetically diverse group of eukaryotes: yeast, fruit fly, zebrafish, mouse, and human and identify conserved stall sites. We find thousands of stall sites across multiple species, with proline, glycine, and negatively charged amino acids being the main facilitators of stalling. Many of the sites are found in RNA processing genes, suggesting that stalling might have a conserved regulatory effect on RNA metabolism. In summary, our results provide a rich resource for the study of conserved stalling and indicate possible roles of stalling in gene regulation.

genomics↗

bioRxiv2019

Unrestrained ESCRT-III drives chromosome fragmentation and micronuclear catastrophe

The ESCRT-III membrane fission machinery1,2 restores nuclear envelope integrity during mitotic exit3,4 and interphase5,6. Whereas primary nuclei resealing takes minutes, micronuclear envelope ruptures appear irreversible and result in catastrophic collapse associated with chromosome fragmentation and rearrangements (chromothripsis), thought to be a major driving force in cancer development7-10. Despite its importance11-13, the mechanistic underpinnings of nuclear envelope sealing in primary nuclei and the defects observed in micronuclei remain largely unknown. Here we show that CHMP7, the nucleator of ESCRT-III filaments at the nuclear envelope3,14, and the inner nuclear membrane protein LEMD215 act as a compartmentalization sensor detecting the loss of nuclear integrity. In cells with intact nuclear envelope, CHMP7 is actively excluded from the nucleus to preclude its binding to LEMD2. Nuclear influx of CHMP7 results in stable association with LEMD2 at the inner nuclear membrane that licenses local polymerization of ESCRT-III. Tight control of nuclear CHMP7 levels is critical, as induction of nuclear CHMP7 mutants is sufficient to induce unrestrained growth of ESCRT-III foci at the nuclear envelope, causing dramatic membrane deformation, local DNA torsional stress, single-stranded DNA formation and fragmentation of the underlying chromosomes. At micronuclei, membrane rupture is not associated with repair despite timely recruitment of ESCRT-III. Instead, micronuclei inherently lack the capacity to restrict accumulation of CHMP7 and LEMD2. This drives unrestrained ESCRT-III recruitment, membrane deformation and DNA defects that strikingly resemble those at primary nuclei upon induction of nuclear CHMP7 mutants. Preventing ESCRT-III recruitment suppresses membrane deformation and DNA damage, without restoring nucleocytoplasmic compartmentalization. We propose that the ESCRT-III nuclear integrity surveillance machinery is a double-edged sword, as its exquisite sensitivity ensures rapid repair at primary nuclei while causing unrestrained polymerization at micronuclei, with catastrophic consequences for genome stability16-18.

cell biology↗