Biology subjects

Scialdone, A.

Publications and source records attributed to Scialdone, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Aquarius RNA helicase Protects Pluripotent Stem Cell Identity

Pluripotent stem cells must reconcile rapid replication with a highly dynamic transcriptional program, creating an inherent susceptibility to transcription-replication conflicts (TRCs). We demonstrate that embryonic stem cells (ESCs) operate in a "resilient" replication mode, tolerating high genomic traffic through the constitutive upregulation of R-loop and TRC resolution pathways. Through a targeted functional screen, we identify the RNA helicase Aquarius (AQR) as an essential safeguard of this state. AQR depletion downregulates these resolution factors, collapsing this stress-resistant program and driving ESCs into unstable, heterogenous states, marked by increased transcriptional entropy and cell-to-cell noise. Mechanistically, we show that key identity-defining genes are preferentially located within R-loop and TRC prone regions, making them uniquely vulnerable to AQR depletion. Our findings establish AQR as a critical governor of transcriptional fidelity, demonstrating that genomic resilience is fundamental to maintaining pluripotent cell identity.

cell biology↗

bioRxiv2026

Retrograde signalling mediates cellular adaptation to mitochondrial DNA copy number alterations

Eukaryotic cells maintain multiple copies of the mitochondrial genome, which is essential for cellular metabolism. Accordingly, alterations in the mitochondrial DNA (mtDNA) copy number are associated with severe human diseases and ageing. However, the mechanisms through which cells regulate mtDNA copy number and the cellular consequences of altered copy number remain poorly understood. Here, using budding yeast as a model, we show that mtDNA copy number is determined by the amount of three limiting factors, Mip1, Abf2, and Rim1. By synthetically tuning the concentrations of only these three proteins, we can modulate mtDNA dosage inside the cell. This revealed that cells are surprisingly robust to mtDNA copy number alterations, with increased copy numbers even accelerating cell growth. Our findings suggest that this robustness is due to protein dosage compensation and independent regulation of mitochondrial morphology. Mechanistically, we identified a critical role of the retrograde signalling pathway for this adaptation. We show that signalling from mitochondria to the nucleus is upregulated in cells with higher mtDNA copy number, and disruption of this regulation diminishes their faster growth. Taken together, our work reveals regulatory principles that enable cells to adapt to mtDNA copy number alterations.

cell biology↗

bioRxiv2026

Benchmarking computational tools for locus-specific analysis of transposable elements in single-cell RNA-seq datasets

BackgroundTransposable elements (TEs) are increasingly recognized as regulators of gene expression and cellular identity in development and disease. Single-cell RNA-sequencing (scRNA-seq) enables the analysis of their transcription at cellular resolution, but the repetitive nature of TEs and their frequent overlap with genes create substantial mapping ambiguity. Although several tools quantify TE expression, few support locus-specific analysis, and their performance in single-cell data has not been systematically evaluated. ResultsWe present a comprehensive benchmarking framework for locus-level TE quantification in short-read scRNA-seq, combining real datasets with simulations that provide read-level ground truth. TE-derived reads constitute a considerable fraction of the transcriptome and capture meaningful biological structure. Our simulations reveal that older, sequence-diverged insertions can be quantified with relatively high accuracy, whereas young TEs remain intrinsically difficult to resolve due to unreliable assignment of multi-mapping reads. We observe pronounced family-specific biases and identify gene-TE disambiguation as a major unresolved challenge. Among evaluated methods, SoloTE (unique-mapper mode) and Stellarscope (with an expectation-maximization-based reallocation of multi-mappers) showed comparable performance, while including multi-mappers generally increased false positives without substantially improving locus-level accuracy. ConclusionsOur benchmark delineates the fundamental limits imposed by short-read scRNA-seq on locus-specific TE quantification, providing practical guidance for prospective users. Suggested best practices include focusing locus-level analyses on older insertions, applying unique-mapper strategies to improve precision, aggregating counts at the subfamily level for young TEs, and explicitly checking for gene-TE overlaps. Our workflow is fully reproducible and extensible, providing a foundation for evaluating emerging methods aimed at resolving TE transcription at single-locus resolution.

bioinformatics↗

bioRxiv2025

Methionine synthase reductase regulates heterochromatin independently of methionine synthesis through mitochondrial homeostasis

Metabolic enzymes can influence chromatin organization by modulating the availability of key metabolites, yet how specific metabolic reactions affect chromatin function remains poorly understood. Here, we show that in Caenorhabditis elegans, the methionine-cycle enzyme methionine synthase reductase (MTRR-l/MSR) regulates heterochromatin independently of methionine synthesis. Loss of MTRR-1, but not of the methionine synthase METR-l/MS, specifically reduces heterochromatic histone methylation, derepresses repetitive elements, and causes developmental delay. Multi-omics profiling revealed that mtrr-1 mutants activate transcriptional programs associated with mitochondrial stress and accumulate long-chain acylcamitines, indicating disrupted mitochondrial homeostasis. Functional assays confirmed altered mitochondrial respiration in mtrr-1 mutants, while genetic suppression of the PMK-3/MAPK mitochondrial retrograde signaling pathway partially restored repeat silencing. Consistently, direct perturbation of mitochondrial function was sufficient to induce heterochromatin derepression in wild type animals. Together, our results reveal a previously unrecognized mitochondria-to-chromatin axis controlled by the methionine-cycle enzyme MTRR-l/MSR, demonstrating that mitochondrial homeostasis is required for heterochromatin maintenance independently of canonical methionine metabolism.

cell biology↗

bioRxiv2025

Hidden sampling biases inflate performance in gene regulatory network inference

Accurate reconstruction of gene regulatory networks (GRNs) from single-cell transcriptomic data remains a major methodological challenge. Recent machine learning approaches, particularly graph neural networks and graph autoencoders, have reported improved performance, yet these gains do not consistently translate to realistic biological settings. Here, we show that a key reason for that is the way negative regulatory interactions are sampled for supervised training and evaluation. We find that widely used sampling strategies introduce node-degree biases that allow models to exploit trivial graph-structural cues rather than biological signals. Across multiple benchmarks, simple degree-based heuristics match or exceed state-of-the-art graph neural network models under these biased evaluation protocols. We further introduce a degree-aware sampling approach that eliminates these artifacts and provides more reliable assessments of GRN inference methods. Our results call for standardized, bias-aware benchmarking practices to ensure meaningful progress in supervised GRN inference from single-cell RNA-seq data.

bioinformatics↗

bioRxiv2025

Intrinsic dimensionality of single-cell transcriptomic data reveals potency landscapes during cell reprogramming

Cell potency, the ability of a cell to generate other cell types, is a fundamental property that drives development, regeneration, and reprogramming. Recent computational advances have enabled estimation of potency directly from single-cell RNA sequencing (scRNA-seq) data, with intrinsic dimensionality (ID) emerging as a promising, data-driven measure of transcriptional complexity linked to developmental potential. While ID offers an unbiased and interpretable framework, existing implementations have two main limitations: they have been applied in only a narrow range of biological contexts, and rely on clustering, which restricts resolution at the single-cell level. Here, we introduce IDEAS (Intrinsic Dimensionality Estimation Analysis of single-cell RNA sequencing data), a Python-based toolkit that computes both global and single-cell ID from scRNA-seq data, enabling potency scoring without requiring predefined clusters. IDEAS extends ID-based potency estimation to single cells, showing its usefulness in a new biological setting. It offers a clear and reliable way to study cell plasticity, simplifies ID score calculation, and makes the method easier to use across different biological systems, speeding up research on this approach to measuring cell potency. We apply IDEAS to multiple datasets of cellular reprogramming, a dynamic and heterogeneous process in which cells transition between identities. Our analysis reveals that ID scores effectively capture changes in potency, identify partially reprogrammed intermediates, and delineate alternative reprogramming trajectories.

systems biology↗

bioRxiv2025

A sensory map of the gastrointestinal tract

The gut, like the skin, exhibits a spatially organized sensory system with distinct regions specialized for detecting and responding to diverse signals including immune, microbial, and nutrient stimuli. This study employed spatial transcriptomics to characterize the molecular landscape of gut sensory cells along the gastrointestinal tract, revealing regional differences in gene expression profiles associated with various sensory modalities. We found that genes related to hormone and neurotransmission expression varied along the gut length at different resolutions, suggesting a division of labor where hormones mediate broader effects and neurotransmission enables rabid, localized signaling. Immune-related genes also exhibit spatial patterns, with increased expression towards the colon, consistent with the higher microbial density in this region. Furthermore, nutrient sensing is spatially regulated, with differential expression of receptors for fats, sugars, proteins, vitamins, and minerals along the gut. This spatial map of gut sensory cell gene expression provides a foundation for deciphering the complex code underlying gut-brain communication.

neuroscience↗

bioRxiv2025

geneRNIB: a living benchmark for gene regulatory network inference

Gene regulatory networks (GRNs) underpin cellular identity and function, playing a key role in health and disease. GRN inference has received substantial attention, motivating systematic benchmarking. Despite various benchmarking efforts, existing studies remain limited in the number of methods, datasets, and metrics, fail to capture the context-specific nature of regulatory interactions across biological conditions, and are constrained by the absence of a reliable ground truth. Here, we introduce geneRNIB, a comprehensive GRN inference benchmarking framework built on three key principles: continuous integration, context-specific evaluation, and holistic assessment in the absence of a true reference network. geneRNIB enables the seamless incorporation of new algorithms, datasets, and evaluation metrics to reflect ongoing developments. In the current version, we systematically integrated and assessed 12 GRN inference methods, spanning single- and multiomics approaches across 11 datasets including thousands of perturbation scenarios. We introduced complementary metrics specifically designed to assess context-specific inference. Our findings indicate that simple models with fewer assumptions often outperform more complex pipelines across several perturbation-informed and predictive metrics. Notably, gene expression-based algorithms yielded better results than more advanced multimodal approaches. In addition, we identify several potential factors that influence the performance of GRN inference and offer actionable guidelines for the future development of the method. By addressing these critical limitations in existing benchmarks, geneRNIB advances GRN inference research and fosters progress toward personalized medicine.

bioinformatics↗

bioRxiv2025

H3K4 methylation-promoted transcriptional memory ensures faithful zygotic genome activation and embryonic development

In the life of a vertebrate embryo, gene expression is initiated for the first time at zygotic genome activation (ZGA). Maternally expressed transcription factors present in the embryo are essential for this process. However, it is unknown if active chromatin modifications established in the gamete are propagated in the embryo as an epigenetic memory to support ZGA and embryonic development. Here, we provide evidence that in Xenopus laevis, H3K4 methylation provides an epigenetic memory of active chromatin states. We show that this is required for faithful zygotic genome activation and successful embryonic development. Chromatin configurations of promoters displaying high H3K4me3 intensity and breadth, alongside DNA hypomethylation and increased GC content, are propagated from the gametes to the embryo across multiple cell divisions and a transcriptionally quiescent phase in early development. We show that this transmission of H3K4 methylation is essential for precise zygotic genome activation and expression of key pioneer ZGA transcription factors Pou5f3.2 and Sox3. Finally, we demonstrate that the H3K4 methyltransferases Kmt2b and Cxxc1 ensure transcription-independent propagation of H3K4me3 and proper zygotic gene expression. In summary, our study establishes the role of H3K4 methylation in maintaining memory of active chromatin states in Xenopus embryos and reveals its importance for successful embryonic development.

developmental biology↗

bioRxiv2024

Semaphorin 4A maintains functional diversity of the hematopoietic stem cell pool

Somatic stem cell pools are comprised of diverse, highly specialized subsets whose individual contribution is critical for the overall regenerative function. In the bone marrow, myeloid-biased HSC (myHSC) are indispensable for replenishment of myeloid cells and platelets during inflammatory response but at the same time, become irreversibly damaged during inflammation and aging. Here, we identify an extrinsic factor, Semaphorin 4A (Sema4A), which non cell-autonomously confers myHSC resilience to inflammatory stress. We show that the absence of Sema4A, myHSC inflammatory hyper-responsiveness in young mice drives excessive myHSC expansion, myeloid bias and profound loss of regenerative function with age. Mechanistically, Sema4A is mainly produced by neutrophils, signals via a cell surface receptor Plexin D1 and safeguards myHSC epigenetic state. Our study shows that by selectively protecting a distinct stem cell subset, an extrinsic factor preserves functional diversity of somatic stem cell pool throughout organismal lifespan.

cell biology↗

bioRxiv2024

Somatic Cell Identity Affects Specific Cell Differentiation Programs Upon Reprogramming In Vivo

Nuclear reprogramming can change cellular fates. Yet, reprogramming efficiency is low and the resulting cell types are often not functional. Here, we used nuclear transfer to eggs to follow single cells during reprogramming in vivo. We show that the differentiation success of reprogrammed cells varies across cell types and depends on the expression of genes specific to the previous cellular identity. We find subsets of reprogramming resistant cells that fail to form functional cell types, undergo cell death, or disrupt normal body patterning. Reducing expression levels of genes specific to the cell-type of origin leads to better reprogramming and improved differentiation trajectories. Thus, our work demonstrates that failing to reprogram in vivo is cell-type specific and emphasises the necessity of minimising aberrant transcripts of the previous somatic identity for improving reprogramming.

developmental biology↗

bioRxiv2024

Community assesses differential cell communication using large multi-sample case-control scRNAseq datasets.

Cell-cell communication is essential for physiological tissue function. In disease, this communication often gets disbalances by changes in the tissue cell type composition, fraction of cell engaged in communication and the rising or dropping expression levels of ligands, receptors and adhesion molecules. The changes in all these components of communication can be studied using single cell RNA-sequencing (scRNAseq) methods. With dropping sequencing costs, it is now possible to perform scRNAseq studies in larger cohorts of case and control samples to better address the heterogeneity of diseases. Here we present community, an R-based tool that is designed to perform differential communication analysis using scRNAseq between large cohorts of cases and controls. Community is able to reconstruct communication between different cell types both in the case and the control cohort of a dataset, and subsequently analyze which communication channels are affected in disease. Community is the first tool that integrates cell type abundance into the calculation of an interaction strength. Community is also able to disentangle the mechanisms underlying these changes, as well as detect interactions that are kept compensated by a sender or a receiver despite the disbalanced signaling from the counterpart. We tested community on two disease entities, ulcerative colitis and acute myeloid leukemia, using published scRNAseq datasets. We compared the performance of our tool to other differential communication pipelines, which community outperformed in speed and robustness. Overall, community is a fast, well-scalable, user-friendly R tool to assess differential cell-cell communication using large case-control scRNAseq datasets disentangling the driving mechanisms of communication shifts in disease.

bioinformatics↗

bioRxiv2023

Single molecule MATAC-seq reveals key determinants of DNA replication origin efficiency

SummaryStochastic origin activation gives rise to significant cell-to-cell variability in the pattern of genome replication. The molecular basis for heterogeneity in efficiency and timing of individual origins is a long-standing question. Here, we developed Methylation Accessibility of TArgeted Chromatin domain Sequencing (MATAC-Seq) to determine single-molecule chromatin accessibility of specific genomic loci after targeted purification in their native chromatin context. Applying MATAC-Seq to selected early-efficient (EE) and late-inefficient (LI) budding yeast replication origins revealed large heterogeneity of chromatin states. Disruption of INO80 or ISW2 chromatin remodeling complexes leads to changes at individual nucleosomal positions that correlate with changes in their replication efficiency. We found a chromatin state with an optimal 100-115bp nucleosome-free region in combination with surrounding well-positioned nucleosomes and open +2 linker region is a strong predictor for efficient origin activation. Thus, MATAC-Seq identifies the large spectrum of alternative chromatin states that co-exist on a given locus previously masked in population-based experiments and provides a mechanistic basis for origin activation heterogeneity during DNA replication of eukaryotic cells. Consequently, our single-molecule assay for chromatin accessibility will be ideal to define single-molecule heterogeneity across many fundamental biological processes such as transcription, replication, or DNA repair in vitro and ex vivo.

biochemistry↗

bioRxiv2022

Molecular characterization of Left-Right symmetry breaking in the mouse embryo

The asymmetric morphology of the mammalian heart is essential to its function as the organ of pulmonary and systemic double circulation. Left-right asymmetry is established by a leftward flow in the node that results in the asymmetric expression of Nodal. This triggers a cascade of asymmetric expression of downstream genes, such as Pitx2c, in the lateral plate mesoderm that gives rise to the first morphologically recognizable primordial heart structure, the cardiac crescent. Relatively little is known about gene expression asymmetries in the cardiac crescent that might underpin asymmetric cardiac morphogenesis. To systematically identify asymmetrically expressed genes, we performed a single-cell transcriptional analysis of manually dissected left and right halves of the cardiac crescent at stages spanning symmetry breaking. This revealed both left and right-sided genes that have not previously been implicated in left-right symmetry breaking. Some of these were expressed in multiple cell types but showed asymmetric expression in only a sub-set of cell types. We validated these findings using multiplexed in situ Hybridization Chain Reaction (HCR) and high-resolution volume imaging to characterize the expression patterns of select genes. Using Dnahiv/iv mutant embryos that show randomized situs, we established that all the genes tested tracked the asymmetric expression of Pitx2c, indicating their asymmetric expression also arose from the early asymmetries at the node. This study provides a high-fidelity molecular characterization of left-right symmetry breaking during cardiac crescent formation, providing a basis for future mechanistic studies on asymmetric cardiac morphogenesis.

developmental biology↗

bioRxiv2022

STREAMLINE: Structural and TopologicalPerformance Analysis of Algorithms for the Inference of Gene Regulatory Networks from Single-Cell Transcriptomic Data

In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms ability to capture structural properties of networks, which are fundamental, for example, for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs. To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four algorithms and provide guidance on which algorithm should be used depending on the global network property of interest.

bioinformatics↗

bioRxiv2022

CIARA: a cluster-independent algorithm for the identification of markers of rare cell types from single-cell RNA seq data

A powerful feature of single-cell RNA-sequencing data analysis is the possibility to identify novel rare cell types. However, rare cell types are often missed by standard clustering approaches. We have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a computational tool available in R and Python that outperforms existing methods for rare cell type detection. With CIARA, we found a small group of precursor cells among mouse embryonic stem cells and previously uncharacterized rare populations of cells in a human gastrula.

bioinformatics↗

bioRxiv2022

Myeloid-biased HSC require Semaphorin 4A from the bone marrow niche for self-renewal under stress and life-long persistence

Tissue stem cells are hierarchically organized. Those that are most primitive serve as key drivers of regenerative response but the signals that selectively preserve their functional integrity are largely unknown. Here, we identify a secreted factor, Semaphorin 4A (Sema4A), as a specific regulator of myeloid-biased hematopoietic stem cells (myHSC), which are positioned at the top of the HSC hierarchy. Lack of Sema4A leads to exaggerated myHSC (but not downstream "balanced" HSC) proliferation after acute inflammatory stress, indicating that Sema4A enforces myHSC quiescence. Strikingly, aged Sema4A knock-out myHSC expand but almost completely lose reconstitution capacity. The effect of Sema4A is non cell-autonomous, since upon transplantation into Sema4A-deficient environment, wild-type myHSC excessively proliferate but fail to engraft long-term. Sema4A constrains inflammatory signaling in myHSC and acts via a surface receptor Plexin-D1. Our data support a model whereby the most primitive tissue stem cells critically rely on a dedicated signal from the niche for self-renewal and life-long persistence.

cell biology↗

bioRxiv2021

The EBNA2-EBF1 complex promotes oncogenic MYC expression levels and metabolic processes required for cell cycle progression of Epstein-Barr virus-infected B cells

Epstein-Barr virus (EBV) is a human tumor virus, which preferentially infects resting human B cells. Upon infection in vitro, EBV activates and immortalizes these cells. The viral latent protein EBV nuclear antigen (EBNA) 2 is essential for B cell activation and immortalization; it targets and binds the cellular and ubiquitously expressed DNA binding protein CBF1, thereby transactivating a plethora of viral and cellular genes. In addition, EBNA2 uses its N-terminal dimerization (END) domain to bind early B cell factor (EBF) 1, a pioneer transcription factor specifying the B cell lineage. We found that EBNA2 exploits EBF1 to support key metabolic processes and to foster cell cycle progression of infected B cells in their first cell cycles upon activation. An 1-helix within the END domain was found to promote EBF1 binding. EBV mutants lacking the 1-helix in EBNA2 can infect and activate B cells efficiently, but the activated cells fail to complete the early S phase of their initial cell cycle. Expression of MYC, target genes of MYC and E2F as well as multiple metabolic processes linked to cell cycle progression are impaired in EBV{Delta}1 infected B cells. Our findings indicate that EBF1 controls B cell activation via EBNA2 and, thus, has a critical role in regulating the cell cycle of EBV infected B cells. This is a function of EBF1 going beyond its well-known contribution to B cell lineage specification. Significance statementEpstein-Barr virus (EBV) infects primary B cells and establishes life-long latent infection in these cells. EBV nuclear antigen (EBNA) 2 drives early processes of B cell activation and cell cycle entry. The surface of the N-terminal dimerization domain of EBNA2 exposes a five amino acid -helix (1) that recruits EBF1 to activate MYC and downstream targets of both MYC and E2F to support critical metabolic processes in infected B cells and to drive them through S phase in the first cell cycle post-infection. Our study demonstrates how EBNA2 exploits EBF1, a key factor of B cell lineage specification to initiate proliferation and high-lights the 1-helix as a potential Achilles heel of the virus at the stage when latent infection is established.

molecular biology↗