Biology subjects

Skupin, A.

Publications and source records attributed to Skupin, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Circulating immune profiling reveals impaired monocyte states and trajectories driving immunosuppression in glioblastoma

Glioblastoma (GBM) is an aggressive and lethal brain tumor marked by profound local and systemic immune dysfunction. Yet, the diagnostic and therapeutic relevance of peripheral impairments remains undefined. To clinically dissect their underlying mechanisms and pathological implications, we combined mass and flow cytometry with single-cell RNA-sequencing of peripheral blood mononuclear cells from GBM patients and healthy donors. GBM blood profiles were characterized by heterogeneous changes in classical monocytes, encompassing expanded, reduced and unchanged subsets, presenting distinct functional states, including antigen-presenting, interferon and metabolic subsets. Additional adaptations included myeloid-derived suppressor cell (MDSC) expansion and loss of non-classical monocytes. Trajectory analyses positioned MDSCs as an intermediate state, in continuum with the metabolic subset. Single-cell RNA-sequencing further showed antigen-presenting monocyte propensity to differentiate into tumor-associated macrophages. Circulating monocytes shared a "GBM-classical monocytic signature" exhibiting low MHC class II expression, altered cell-cell communication and increased anti-inflammatory mediators, such as IL1R2 and CD163. Lastly, lymphocyte alterations included decreased proportions of CD4+ T, natural killer (NK) and CD56+ T cells, retaining relatively conserved activation profiles, exemplified by up-regulation of alarmins S100A8/S100A9. These findings map systemic immune reprogramming in GBM, suggesting new avenues for non-invasive biomarker discovery and therapeutic strategies to restore anti-tumor immunity.

cancer biology↗

bioRxiv2025

ZFHX4 is necessary for dopaminergic neuron differentiation and controls cell cycle by regulating LIN28A

The selective degeneration of midbrain dopaminergic neurons (mDANs) is the main pathological hallmark of Parkinsons disease (PD). Although many transcription factors (TFs) guiding mDAN development have been identified, the details of the underlying regulatory networks remain elusive. We have previously generated time-series transcriptomic and epigenomic profiles of human induced pluripotent stem cell (hiPSC)-derived mDANs. Integrative analysis of the data identified ZFHX4 as a prominent super-enhancer-controlled TF induced in mDAN differentiation. ZFHX4 has been associated with neurodevelopmental processes in several species and shows reduced expression in midbrain of PD patients. Using in vitro knockdown (KD) and overexpression experiments, we show that ZFHX4 is necessary but not sufficient for mDAN differentiation. ZFHX4 binds preferentially at active promoter regions and transcriptomic analysis upon ZFHX4 depletion during mDAN differentiation revealed putative primary target genes to be enriched for targets of cell-cycle-related TFs and pathways. Consistently, ZFHX4-depleted cells accumulated in G2-phase of the cell cycle, preventing normal cell cycle progression and exit. The RNA-binding protein LIN28A, involved in stem-cell maintenance and microRNA (miRNA) maturation, emerged as one of the most upregulated genes upon ZFHX4-KD, in parallel with downregulation of neurogenic miRNA miR-9. Moreover, the LIN28A locus was enriched for ZFHX4 binding in CUT&Tag analysis. Taken together, our analysis indicates a pivotal role for ZFHX4 in regulating the cell cycle, specifically in silencing multipotency and proliferative programs, while maintaining mDANs in a post-mitotic state by controlling LIN28A-miR-9 axis.

molecular biology↗

bioRxiv2024

Machine Learning-based Predictions of Spatial Metabolic Profiles Demonstrate the Impact of Morphology on Astrocytic Energy Metabolism

This work introduces a machine learning framework that allows the investigation of the influence of reaction centers on the metabolic state of astrocyte cells. The proposed ML framework takes advantage of spatial astrocyte metabolic data stemming from numerical simulations for different reaction center configurations and allows for the following: (i) Discovery of cell groups of similar metabolic states and investigation of the reaction center configuration within each group. This approach allows for an analysis of the importance of the specific location of the reaction centers for a potentially critical metabolic state of the cell. (ii) Qualitative prediction of the energetic state of the cell (based on [ATP]: [ADP]) and quantitative prediction of the metabolic state of the cell by predicting the spatial average concentration of the metabolites or the complete spatial metabolic profile within the cell. (iii) Finally, the framework allows for the post hoc analysis of the developed quantitative predictive models using a SHAP approach to investigate the influence of the reaction center positions for further support of the insights drawn in steps (i)-(iii). Following the implementation of the framework, we observe that a uniform mitochondrial distribution within the cell results in the most robust energetic cell state. On the contrary, realizations of polarized mitochondrial distributions exhibit the worst overall cell health. Furthermore, we can make accurate qualitative predictions regarding cell health (accuracy = 0.9515, recall = 0.9753) and satisfactory predictions for the spatial average concentration and spatial concentration profiles of most of the metabolites involved. The techniques proposed in this study are not restricted to the dataset used. They can be easily used in other datasets that include findings from various metabolic computational models.

systems biology↗

bioRxiv2024

MONFIT: Multi-omics factorization-based integration of time-series data sheds light on Parkinson's disease

Parkinsons disease (PD) is a severe and complex multifactorial neurodegenerative disease with still elusive pathophysiology preventing the development of curative treatments. Molecular deep phenotyping by longitudinal multi-omics is a promising approach to identify mechanisms of PD aetiology and its progression. However, the heterogeneous data require new analysis frameworks to understand disease progression across biological entities and processes. Here, we present MONFIT, a holistic analysis pipeline that integrates and mines time-series single-cell RNA-sequencing data with bulk proteomics and metabolomics data by non-negative matrix tri-factorization, enabling prior knowledge incorporation from molecular networks. First, MONIFT integrates time-point-specific data and then holistically mines the integrated data across time points. By applying MONFIT to longitudinal multi-omics data of differentiation of PD and control patient-derived induced pluripotent stem cells into dopaminergic neurons, we identify novel PD-associated genes, emphasize molecular pathways that play important roles in PD pathology, and suggest new intervention opportunities using drug-repurposing. MONFIT is fully adaptable to other multi-omics data sets.

bioinformatics↗

bioRxiv2023

Multi-omics integration of scRNA-seq time series data predicts new intervention points for Parkinson's disease

Parkinsons disease (PD) is a complex neurodegenerative disorder without a cure. The onset of PD symptoms corresponds to 50% loss of midbrain dopaminergic (mDA) neurons, limiting early-stage understanding of PD. To shed light on early PD development, we study time series scRNA-seq datasets of mDA neurons obtained from patient-derived induced pluripotent stem cell differentiation. We develop a new data integration method based on Non-negative Matrix Tri-Factorization that integrates these datasets with molecular interaction networks, producing condition-specific "gene embeddings". By mining these embeddings, we predict 193 PD-related genes that are largely supported (49.7%) in the literature and are specific to the investigated PINK1 mutation. Enrichment analysis in Kyoto Encyclopedia of Genes and Genomes pathways highlights 10 PD-related molecular mechanisms perturbed during early PD development. Finally, investigating the top 20 prioritized genes reveals 12 previously unrecognized genes associated with PD that represent interesting drug targets.

bioinformatics↗

bioRxiv2023

Mechanism-based classification of SARS-CoV-2 Variants by Molecular Dynamics Resembles Phylogenetic Tree

The COVID-19 pandemics has demonstrated the vulnerability of our societies to viral infectious disease. The mitigation of COVID-19 was complicated by the emergence of Variants of Concern (VOCs) with varying properties including increased transmissibility and immune evasion. Traditional population sequencing proved to be slow and not conducive for timely action. To tackle this challenge, we introduce the Persistence Score (PS) that assesses the pandemic potential of VOCs based on molecular dynamics of the interactions between the SARS-CoV-2 Receptor Binding Domain (RBD) and the ACE2 residues. Our mechanism-based classification approach successfully grouped VOCs into clinically relevant subgroups with higher sensitivity than classical affinity estimations and allows for risk assessment of hypothetical new VOCs. The PS-based interaction analysis across VOCs resembled the phylogenetic tree of SARS-Cov-2 demonstrating its predictive relevance for pandemic preparedness. Thus, PS allows for early detection of a variants pandemic potential, and an early risk evaluation for data-driven policymaking.

bioinformatics↗

bioRxiv2023

Glioblastoma-instructed microglia transit to heterogeneous phenotypic states with phagocytic and dendritic cell-like features in patient tumors and patient-derived orthotopic xenografts

BackgroundA major contributing factor to glioblastoma (GBM) development and progression is its ability to evade the immune system by creating an immune-suppressive environment, where GBM-associated myeloid cells, including resident microglia and peripheral monocyte-derived macrophages, play critical pro-tumoral roles. However, it is unclear whether recruited myeloid cells are phenotypically and functionally identical in GBM patients and whether this heterogeneity is recapitulated in patient-derived orthotopic xenografts (PDOXs). A thorough understanding of the GBM ecosystem and its recapitulation in preclinical models is currently missing, leading to inaccurate results and failures of clinical trials. MethodsHere, we report systematic characterization of the tumor microenvironment (TME) in GBM PDOXs and patient tumors at the single-cell and spatial levels. We applied single-cell RNA-sequencing, spatial transcriptomics, multicolor flow cytometry, immunohistochemistry and functional studies to examine the heterogeneous TME instructed by GBM cells. GBM PDOXs representing different tumor phenotypes were compared to glioma mouse GL261 syngeneic model and patient tumors. ResultsWe show that GBM tumor cells reciprocally interact with host cells to create a GBM patient-specific TME in PDOXs. We detected the most prominent transcriptomic adaptations in myeloid cells, with brain-resident microglia representing the main population in the cellular tumor, while peripheral-derived myeloid cells infiltrated the brain at sites of blood-brain barrier disruption. More specifically, we show that GBM-educated microglia undergo transition to diverse phenotypic states across distinct GBM landscapes and tumor niches. GBM-educated microglia subsets display phagocytic and dendritic cell-like gene expression programs. Additionally, we found novel microglial states expressing cell cycle programs, astrocytic or endothelial markers. Lastly, we show that temozolomide treatment leads to transcriptomic plasticity and altered crosstalk between GBM tumor cells and adjacent TME components. ConclusionOur data provide novel insights into the phenotypic adaptation of the heterogeneous TME instructed by GBM tumors. We show the key role of microglial phenotypic states in supporting GBM tumor growth and response to treatment. Our data place PDOXs as relevant models to assess the functionality of the TME and changes in the GBM ecosystem upon treatment. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/531162v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1549a3corg.highwire.dtl.DTLVardef@159f16aorg.highwire.dtl.DTLVardef@1f89500org.highwire.dtl.DTLVardef@fe6b67_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

Formate Promotes Invasion and Metastasis by Activating Fatty Acid Synthesis and Matrix Metalloproteinases

Metabolic rewiring is essential to enable cancer onset and progression. One important metabolic pathway that is often hijacked by cancer cells is the one-carbon cycle, in which the third carbon of serine is oxidized to formate. We have previously shown that formate production in cancer cells often exceeds the anabolic demand, resulting in formate overflow. Furthermore, we observed that high extracellular formate promotes the in vitro invasiveness of glioblastoma (GBM) cells. However, additional data supporting this in vitro observation and mechanistic details remained elusive so far. In the present study, we now demonstrate that inhibition of formate overflow results in a decreased invasiveness of GBM cells ex vivo and in vivo. Additionally, we observed that exposure to exogeneous formate can induce a transiently stable pro-invasive phenotype that results in increased metastasis formation in vivo. All in all, these results suggest that a local formate increase within the tumor microenvironment may be one factor that can promote cancer cell motility and dissemination. Mechanistically, we uncover a previously undescribed interplay where formate acts as a trigger to alter fatty acid metabolism and matrix metalloproteinase (MMP) activity which in turn impacts cancer cell invasiveness. We thus highlight the role of formate as a pro-invasive metabolite. Gaining a deeper understanding of formate overflow and how it promotes invasion in cancer, may open new therapeutic opportunities to prevent cancer cell dissmination.

cancer biology↗

bioRxiv2022

Systematic analysis and optimization of early warning signals for critical transitions

Abrupt shifts between alternative regimes occur in complex systems, from cell regulation to brain functions to ecosystems. Several model-free Early Warning Signals (EWS) have been proposed to detect impending transitions, but failure or poor performance in some systems have called for better investigation of their generic applicability. In particular, there are still ongoing debates whether such signals can be successfully extracted from data. In this work, we systematically investigate properties and performance of dynamical EWS in different deteriorating conditions, and we propose an optimised combination to trigger warnings as early as possible, eventually verified on experimental data. Our results explain discrepancies observed in the literature between warning signs extracted from simulated models and from real data, provide guidance for EWS selection based on desired systems and suggest an optimised composite indicator to alert for impending critical transitions. HighlightsO_LIHow to extract early warning signals (EWS) against critical transitions from data is still poorly understood C_LIO_LIA mathematical framework assesses and explains the performance of EWS in noisy deteriorating conditions C_LIO_LIComposite indicators are optimised to alert for impending shifts C_LIO_LIThe results are applicable to wide classes of systems, as shown with models and on empirical data. C_LI

systems biology↗

bioRxiv2022

Mechanistic multiscale modelling of energy metabolism in human astrocytes indicates morphological effects in Alzheimer's Disease

Astrocytes with their specialized morphology are essential for brain homeostasis as metabolic mediators between blood vessels and neurons. In neurodegenerative diseases such as Alzheimers disease (AD), astrocytes adopt reactive profiles with molecular and morphological changes that could lead to the impairment of their metabolic support and impact disease progres-sion. However, the underlying mechanisms how metabolic function of human astrocytes is impaired by their morphological changes in AD is still elusive. To address this challenge, we developed and applied a metabolic multiscale modelling approach integrating the dynamics of metabolic energy pathways and physiological astrocyte morphologies acquired in human AD and age-matched control brain samples. The results demonstrate that the complex cell shape and intracellular organization of energetic pathways determine the metabolic profile and support capacity of astrocytes in health and AD conditions. Thus, our mechanistic approach indicates the importance of spatial orchestration in metabolism and allows for the identification of protective mechanisms against disease-associated metabolic impairments.

systems biology↗

bioRxiv2022

Concomitant AD and DLB pathologies shape subfield microglia responses in the hippocampus

Hippocampal alteration is at the centre of memory decline in the most common age-related neurodegenerative diseases: Alzheimers disease (AD) and Dementia with Lewy Bodies (DLB). However, the subregional deterioration of the hippocampus differs between both diseases with more severe atrophy in the CA1 subfield of the AD patients. How AD and DLB-typical pathologies compose the various local microenvironment of the hippocampus across AD and DLB needs to be further explored to understand this process. Additionally, microglia responses could further impact the atrophy rate. Some studies suggest that microglia react differently according to the underlying neurodegenerative disorder. How microglia are transformed across hippocampal subfields in AD and DLB, and how their changes are associated with disease-typical pathologies remains to be determined. To these purposes, we performed a volumetric analysis of phospho-Tau (P-Tau), Amyloid-{beta} (A{beta}), and phospho--Synuclein (P-Syn) loads, quantified and classified microglia according to distinct morphological phenotypes using high-resolution confocal 3D microscopy of hippocampal CA1, CA3 and DG/CA4 subfields of late-onset AD (n=10) and DLB (n=8) as well as age-matched control samples (n=11). We found that each of the Tau, A{beta} and Synuclein pathologies followed a specific subregional distribution, relatively preserved across AD and DLB. P-Tau, A{beta} and P-Syn burdens were significantly exacerbated in AD, with Tau pathology being particularly severe in the AD CA1. P-Tau and P-Syn burdens were highly correlated across subfields and conditions (R2Spear = 0.79; P < 0.001) and result from a local co-distribution of P-Tau and P-Syn inclusions in neighbouring neurons, with only a low proportion of double-positive cells. In parallel, we assessed the changes of the microglia responses by measuring 16 morphological features of more than 35,000 individual microglial cells and classifying them into seven-distinct morphological clusters. We found microglia features- and clusters-variations subfield- and condition-dependent. Two of the seven morphological clusters, with more amoeboid and less branched forms, were identified as disease-enriched and found to be further increased in AD. Interestingly, some microglial features or clusters were associated with one but more often with a combination of two pathologies in a subfield-dependent manner. In conclusion, our study shows a multimodal association of the hippocampal microglia responses with the co-occurrence, distribution and severity of AD and DLB pathologies. In DLB hippocampi, pathological imprint and microglia responses follow AD trends but with lesser severity. Our study suggests that the increased pathological burdens of P-Tau and P-Syn and associated microglia alterations are involved in a more severe deterioration of the CA1 in AD as compared to DLB.

neuroscience↗

bioRxiv2021

Neuronal hyperactivity in a LRRK2-G2019S cellular model of Parkinson's Disease

Monogenic Parkinsons Disease can be caused by a mutation in the leucine-rich repeat kinase 2 (LRRK2) gene, causing a late-onset autosomal dominant inherited form of Parkinsons Disease. The function of the LRRK2 gene is incompletely understood, but several in vitro studies have reported that LRRK2-G2019S mutations affect neurite branching, calcium homeostasis and mitochondrial function, but thus far, there have been no reports of effects on electrophysiological activity. We assessed the neuronal activity of induced pluripotent stem cell derived neurons from Parkinsons Disease patients with LRRK2-G2019S mutations and isogenic controls. Neuronal activity of spontaneously firing neuronal populations was recorded with a fluorescent calcium-sensitive dye (Fluo-4) and analysed with a novel image analysis pipeline that combined semi-automated neuronal segmentation and quantification of calcium transient properties. Compared with controls, LRRK2-G2019S mutants have shortened inter-spike intervals and an increased rate of spontaneous calcium transient induction.

neuroscience↗

bioRxiv2021

Morphological principles of neuronal mitochondria

In the highly dynamic metabolic landscape of a neuron, mitochondrial membrane architectures can provide critical insight into the unique energy balance of the cell. Current theoretical calculations of functional outputs like ATP and heat often represent mitochondria as idealized geometries and therefore can miscalculate the metabolic fluxes. To analyze mitochondrial morphology in neurons of mouse cerebellum neuropil, 3D tracings of complete synaptic and axonal mitochondria were constructed using a database of serial TEM tomography images and converted to watertight meshes with minimal distortion of the original microscopy volumes with a granularity of 1.6 nanometer isotropic voxels. The resulting in silico representations were subsequently quantified by differential geometry methods in terms of the mean and Gaussian curvatures, surface areas, volumes, and membrane motifs, all of which can alter the metabolic output of the organelle. Finally, we identify structural motifs that are present across this population of mitochondria; observations which may contribute to future modeling studies of mitochondrial physiology and metabolism in neurons.

neuroscience↗

bioRxiv2020

Single nuclei chromatin profiles of ventral midbrain reveal cell identity transcription factors and cell type-specific gene regulatory variation

Cell types in ventral midbrain are involved in diseases with variable genetic susceptibility such as Parkinsons disease and schizophrenia. Many genetic variants affect regulatory regions and alter gene expression. We report 20 658 single nuclei chromatin accessibility profiles of ventral midbrain from two genetically and phenotypically distinct mouse strains. We distinguish ten cell types based on chromatin profiles and analysis of accessible regions controlling cell identity genes highlights cell type-specific key transcription factors. Regulatory variation segregating the mouse strains manifests more on transcriptome than chromatin level. However, cell type-level data reveals changes not captured at tissue level. To discover the scope and cell-type specificity of cis-acting variation in midbrain gene expression, we identify putative regulatory variants and show them to be enriched at differentially expressed loci. Finally, we find TCF7L2 to mediate trans-acting variation selectively in midbrain neurons. Our dataset provides an extensive resource to study gene regulation in mesencephalon.

genomics↗

bioRxiv2020

Primary and recurrent glioma patient-derived orthotopic xenografts (PDOX) represent relevant patient avatars for precision medicine

Patient-derived cancer models are essential tools for studying tumor biology and preclinical interventions. Here, we show that glioma patient-derived orthotopic xenografts (PDOXs) enable long-term propagation of patient tumors and represent clinically relevant patient avatars. We created a large collection of PDOXs from primary and recurrent gliomas with and without mutations in IDH1, which retained histopathological, genetic, epigenetic and transcriptomic features of patient tumors with no mouse-specific clonal evolution. Longitudinal PDOX models recapitulate the limited genetic evolution of gliomas observed in patient tumors following treatment. PDOX-derived standardized tumor organoid cultures enabled assessment of drug responses, which were validated in mice. PDOXs showed clinically relevant responses to Temozolomide and to targeted treatments such as EGFR and CDK4/6 inhibitors in (epi)genetically defined groups, according to MGMT promoter and EGFR/CDK status respectively. Dianhydrogalactitol, a bifunctional alkylating agent, showed promising potential against glioblastoma. Our study underlines the clinical relevance of glioma PDOX models for translational research and personalized treatment studies.

cancer biology↗

bioRxiv2019

Design principles of ROS dynamic networks relevant to precision therapies for age-related diseases

The eminently complex regulatory network protecting the cell against oxidative stress, surfaces in several disease maps, including that of Parkinsons disease (PD). How this molecular networking achieves its various functionalities and how processes operating at the seconds-minutes time scale cause a disease at a time scale of multiple decennia is enigmatic.\n\nBy computational analysis, we here disentangle the reactive oxygen species (ROS) regulatory network into a hierarchy of subnetworks that each correspond to a different functionality. The detailed dynamic model of ROS management obtained integrates these functionalities and fits in vitro data sets from two different laboratories.\n\nThe model shows effective ROS-management for a century, followed by a sudden systems collapse due to the loss of p62 protein. PD related conditions such as lack of DJ-1 protein or increased -synuclein accelerated the systems collapse. Various in-silico interventions (e.g. addition of antioxidants or caffeine) slowed down the collapse of the system in silico, suggesting the model may help discover new medicinal and nutritional therapies.

systems biology↗

bioRxiv2019

Mitochondrial morphology provides a mechanism for energy buffering at synapses

Mitochondria as the main energy suppliers of eukaryotic cells are highly dynamic organelles that fuse, divide and are transported along the cytoskeleton to ensure cellular energy homeostasis. While these processes are well established, substantial evidence indicates that the internal structure is also highly variable in dependence on metabolic conditions. However, a quantitative mechanistic understanding of how mitochondrial morphology affects energetic states is still elusive. To address this question, we here present an agent-based dynamic model using three-dimensional morphologies from electron microscopy tomography which considers the molecular dynamics of the main ATP production components. We apply our modeling approach to mitochondria at the synapse which is the largest energy consumer within the brain. Interestingly, comparing the spatiotemporal simulations with a corresponding space-independent approach, we find minor space dependence when the system relaxes toward equilibrium but a qualitative difference in fluctuating environments. These results suggest that internal mitochondrial morphology is not only optimized for ATP production but also provides a mechanism for energy buffering and may represent a mechanism for cellular robustness.

biophysics↗

bioRxiv2019

Single-cell transcriptomics reveals multiple neuronal cell types in human midbrain-specific organoids

Human stem cell-derived organoids have great potential for modelling physiological and pathological processes. They recapitulate in vitro the organisation and function of a respective organ or part of an organ. Human midbrain organoids (hMOs) have been described to contain midbrain-specific dopaminergic neurons that release the neurotransmitter dopamine. However, the human midbrain contains also additional neuronal cell types, which are functionally interacting with each other. Here, we analysed hMOs at high-resolution by means of single-cell RNA-sequencing (scRNA-seq), imaging and electrophysiology to unravel cell heterogeneity. Our findings demonstrate that hMOs show essential neuronal functional properties as spontaneous electrophysiological activity of different neuronal subtypes, including dopaminergic, GABAergic, and glutamatergic neurons. Recapitulating these in vivo features makes hMOs an excellent tool for in vitro disease phenotyping and drug discovery.

neuroscience↗