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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 739 records · Page 41Linked to original sources

bioRxiv2025

Proximity-based super-resolution imaging enabled by DNA base-stacking interactions

Super-resolution imaging has gained significant traction in recent years due to its unprecedented ability to visualize target biomolecules at nanometer resolution. Here, we demonstrate the capability to detect target pairs that are in close proximity by exploiting base-stacking interactions between two DNA strands, each labelling one target. Our DNA probes hybridize transiently with the each other only when both target molecules are proximal, thus creating a hybridization site for fluorophore-conjugated DNA strand, called imager. In this design, hybridization and co-axial base-stacking act synergistically to enable imager binding, with stacking interactions providing essential stabilization that allows for transient hybridization. This synergy generates the stochastic binding events required for DNA-PAINT imaging, which we call Stack-Proximity-PAINT (Stack-pPAINT). To gain mechanistic insights into hybridization of our probe, we performed atomistic equilibrium and steered molecular dynamics (MD) simulations. The simulations reveal that DNA base-stacking and fluorophore stacking together stabilize the imager. We utilized programmable DNA nanostructures to benchmark the applicability of Stack-pPAINT. As a cellular proof of concept, we visualized microtubular structures using Stack-pPAINT with antibodies targeting both alpha- and beta-tubulin molecules. This probe technology offers promising applications in cell biology research aimed at elucidating spatial interactomes at high resolution within cells.

biophysics↗

bioRxiv2026

Expression and role of the RNA-binding protein, ZFP36L1, in mouse T follicular helper cell differentiation and function

T follicular helper (Tfh) cells are critical for germinal centers (GC), the specialised microenvironment where long-lived humoral immunity is generated in response to vaccination or infection. Within the GC, B cells engage with Tfh cells and elicit their help in the form of cytokines and cell-surface co-stimulator molecules. Tfh helper activity must be rapidly available in response to B cell engagement, yet the mechanisms controlling this helper activity remain poorly characterized. Post-transcriptional regulation of mRNA decay and translation offer one way to rapidly and temporally tune Tfh cell activity. ZFP36L1, a member of the ZFP family of RNA-binding proteins, is a candidate modulator of Tfh cell helper activity as it controls cytokine production and responses in other T cell lineages, modulating their differentiation and function. We sought to determine if ZFP36L1 is also important for Tfh cell biology. In this study, we show expression of ZFP36L1 by Tfh cells. We selectively delete ZFP36L1 from Tfh cells and analyze the effect this has on the GCs. Surprisingly, we find the GC response and affinity maturation is resilient to deletion of ZFP36L1 from Tfh cells.

immunology↗

bioRxiv2026

Dynamics of single cell-cell junctions as an indicator of cell state switch

Cell-cell junctions (CCJs) are dynamic biopolymeric systems essential for adherence of biological cells organizing into a tissue or organ. In vertebrate organisms, CCJs present in stable epithelial tissues are maintained primarily by cell-to-cell protein bridges made of an adhesion receptor E-cadherin. CCJs are destabilized during Epithelial (E) to mesenchymal (M) transformation (EMT), an essential step in cancer metastasis, where cells switch states and acquire migratory features. An essential trigger for EMT is a cadherin switch process from E-cadherins with N-cadherin, another cadherin isoform. EMT proceeds through several intermediate states referred to as hybrid E/M cells. These states are characterized by mixed levels of E- and N-cadherins at the junctions and exhibit versatile cancerous traits that are more aggressive than cancerous fully mesenchymal cells. As a result, many such states have emerged as key targets in cancer therapy. However, development of a therapeutic design to counter the hybrid E/M cells has been limited by the absence of a comprehensive understanding of the mechanics and dynamics of hybrid E/M states. Here, we develop a physical model of CCJs as a non-equilibrium system composed of a variable ratio of E- and N-cadherins, considered as coarse-grained molecules driven by ATP-powered machinery observed at CCJs. Our model predicts a robust measure of strength of junctions that captures previous experimental observations, and reveals a minimal mechanochemical landscape of hybrid E/M states. We show the emergence of several groups of CCJ states in this landscape with variable adhesion strengths, many of which resemble different hybrid E/M-characteristics observed experimentally. Finally, we identify that a difference in mechanosenstivity of the two cadherin isoforms towards cytoskeletal forces could be why the hybrid E/M states come into existence.

biophysics↗

bioRxiv2026

Human neuromodulatory assembloids to study serotonin signaling and disease

Neuromodulators influence critical functions of the developing human brain and regulate behavioral states. Dysfunction of neuromodulatory systems is often involved in neuropsychiatric disease and many drugs for these conditions act on these signaling pathways. Recent advances in stem cell biology have made it possible to derive a wide range of cells across the developing human nervous system in regionalized organoids and to functionally integrate them into assembloids, however they currently do not systematically incorporate neuromodulation. Here, we generated human midbrain-hindbrain organoids (hMHO) from human induced pluripotent stem (hiPS) cells and fused them with human cortical organoids (hCO) to form neuromodulatory assembloids (hNMA). We focus on serotonin (5-hydroxytryptamine, 5-HT) as one key neuromodulator and found characteristic gene expression patterns and electrophysiological properties of serotonergic neurons (5-HT neurons) in the hMHO. In hNMA, 5-HT neurons projected into hCO, released 5-HT and modulated cortical network activity. To explore the applicability of this system in human disease, we studied 22q11.2 deletion syndrome (22q11.2DS), a common microdeletion associated with high risk for neuropsychiatric disease and defects in 5-HT signaling. We found aberrant 5-HT dynamics in hNMA from patient hiPS cell lines that were rescued by administration of a selective serotonin reuptake inhibitor (SSRI). Taken together, hNMA can be used to study human 5-HT dynamics and uncover disease phenotypes which could facilitate therapeutic development.

neuroscience↗

bioRxiv2024

Generalizable Morphological Profiling of Cells by Interpretable Unsupervised Learning

The intersection of advanced microscopy and machine learning is revolutionizing cell biology into a quantitative, data-driven science. While traditional morphological profiling of cells relies on labor-intensive manual feature extraction susceptible to biases, deep learning offers promising alternatives but struggles with the interpretability of its black-box operation and dependency on extensive labeled data. We introduce MorphoGenie, an unsupervised deep-learning framework designed to address these challenges in single-cell morphological profiling. Enabling disentangled representation learning integrated with high-fidelity image reconstructions, MorphoGenie possesses a critical attribute to learn a compact, generalizable and interpretable latent space. This facilitates the extraction of biologically meaningful features without human annotation, additionally overcoming the "curse of dimensionality" inherent in manual methods. Unlike prior models, MorphoGenie introduces a systematic approach to mapping disentangled latent representations to fundamental hierarchical morphological attributes, ensuring both semantic and biological interpretability. Moreover, it adheres to the concept of combinatorial generalization--a core principle of human intelligence-- which greatly enhances the models capacity to generalize across a broad spectrum of imaging modalities (e.g., quantitative phase imaging and fluorescence imaging) and experimental conditions (ranging from discrete cell type/state classification to continuous trajectory inference). The framework offers a new, generalized strategy for unbiased and comprehensive morphological profiling, potentially revealing insights into cellular behavior in health and disease that might be overlooked by expert visual examination.

bioinformatics↗

bioRxiv2025

Gadd45 regulates fate decisions of myeloid-type blood progenitor cells in Drosophila

Gadd45 is a highly conserved stress response protein that interacts with multiple cellular proteins, playing a crucial role in regulating the cell cycle and responding to various forms of stress. Altered expression of Gadd45 is linked to several solid tumors and hematopoietic malignancies. It is also identified as a gene crucial for cytokine-induced myeloid differentiation, although the precise molecular mechanisms underlying this process remain unclear. We utilized the Drosophila hematopoietic system to investigate the role of Gadd45 in the development of myeloid-type blood cells, employing in vivo genetics and cell biological methods. We show that the absence of Gadd45 reduces the number of crystal cells but does not significantly impact macrophage-like cell plasmatocyte differentiation. However, Gadd45 overexpression caused intermediate progenitors to differentiate into lamellocytes and plasmatocytes to transdifferentiate into lamellocytes, a response typically observed during larval stages in response to immune challenges and injury. Interestingly, Gadd45 overexpression initiated the lamellocyte differentiation program by activating a combination of Toll, JNK, and JAK/STAT signaling pathways. This closely resembles the process by which specialized macrophages are derived in mammals. Moreover, Gadd45 overexpression does not induce cell death but alters the cell cycle status of hematopoietic progenitor cells. This study enhances our understanding of how Gadd45 functions during myeloid cell development. Significance StatementO_LILoss of Gadd45 causes a reduction in crystal cells but does not significantly impact macrophage-like plasmatocyte differentiation. C_LIO_LIGadd45 overexpression causes myeloid-type progenitor cells to differentiate into lamellocyte, as well as plasmatocyte transdifferentiate to lamellocyte. Gadd45 triggers differentiation by activating a combination of Toll, JNK, and JAK/STAT signaling pathways. C_LIO_LIOverexpressing Gadd45 does not induce apoptosis but alters the cell cycle behavior of hematopoietic progenitor cells. C_LI

developmental biology↗

bioRxiv2026

Simulation-free approximate Bayesian computation for stochastic reaction networks

Uncovering stochastic reaction networks from data is central to single-cell biology. Simulation-based inference has evolved into a flexible paradigm for this purpose, circumventing the computational intractability of the likelihood. Notably, approximate Bayesian computation performs this inference by carrying out extensive Gillespie simulations, usually at a high computational cost. Here, we propose a simulation-free approximate Bayesian computation (SFABC) framework that avoids both simulation and explicit likelihood solutions of the network dynamics. Instead, SFABC exploits the Chemical Master Equation to formulate a tractable convex optimization with ensuing constraints, which are then used to accept sampled parameters consistent with the observed data. The resulting posterior approximation comes with theoretical guarantees: it can be tightened systematically, and it provably converges to the exact Bayesian posterior distributions. Using synthetic benchmarks, we show that SFABC matches the accuracy of standard simulation-based algorithms at a substantially reduced computational cost. We then showcase the applicability of SFABC to experimental systems biology by analyzing gene transcription bursting kinetics from snapshot, time-resolved, and cell-cycle-resolved single-cell data.

systems biology↗

bioRxiv2024

Actin turnover and myosin contractility determine emergent Thickness Robustness of Cell-Mimicking Cortex

Cell shape deformation in eukaryotes, is primarily determined by the actin cortex, a thin network of actin filaments and myosin motors. This network is attached to the plasma membrane endowing cells with their mechanical stability and structural integrity. Since inside the cells hundreds of proteins are associated with the actin cortex, identifying the roles of the individual components and the mechanisms maintaining a functional cortex is highly challenging. Here, using a minimal set of components, we succeeded in recreating long-lasting dynamic treadmilling in an artificial actomyosin cortex on a bilayer membrane with cell-mimicking characteristics, both with and without myosin-II motors, replicating features observed in cells, such as myosin-induced increased cortical thickness and stress-dependent dynamics. We mechanistically reveal how treadmilling is regulated by actin network disassembly factors and myosin contractility, and the emergence of robustness of the cortical thickness to concentration fluctuations in cytoskeletal components. The robustness and responsiveness of the actin cortex which we found has a fundamental significance for the ability of Eukaryotic cells to maintain the mechanical integrity of their membrane, despite concentration fluctuation of the cytoskeleton components. Unlike complex biological cells, this system enables high-resolution and systematic studies of cortical dynamics under controlled conditions. It thus provides a cell-mimicking artificial system for designing interventions to modify the cellular cortex to improve our understanding of the basic mechanisms driving fundamental biological processes as well as for potential medical applications. SignificanceWe report a breakthrough in understanding the actin cortex, a key structure governing cell shape, stability, and motility. By reconstructing in-vitro a treadmilling actomyosin cortex on a lipid bilayer using minimal components, we replicate essential cellular features. This system reveals how cortical thickness is maintained despite concentration fluctuations, highlighting an actin turnover mechanism that buffers variability. The inclusion of myosin motors is found to modify the cortical dynamics, mimicking cellular properties such as the increased thickness and stress-dependent dynamics. Unlike complex living cells, this artificial system enables systematic studies of cortical behavior, with fine control over the components and high-resolution imaging. Our artificial system provides a platform for designing interventions to modify cortical dynamics, with broad implications for biology and medicine. One-Sentence SummaryIn-vitro recreation of a cell-like, membrane-bound cortical actin skeleton composed of a minimal set of building-blocks with continuous turnover and emergent robustness.

biophysics↗

bioRxiv2024

Implementation of pupylation-based proximity labelling in plant biology reveals regulatory factors in cellulose biosynthesis

Knowledge about how and where proteins interact provides a pillar for cell biology. Biotin-related proximity labelling approaches are efficiently monitoring protein interactions but may have labelling-related drawbacks. Here, we introduce pupylation-based proximity labelling (PUP-IT) as a tool for protein interaction detection in plant biology. We show that PUP-IT readily confirmed and extended protein interactions for several known protein complexes, across different types of plant systems. To further demonstrate the power of PUP-IT, we used the system to identify protein interactions of the protein complex that underpin cellulose synthesis in plants. Apart from known complex components, we identified the ARF-GEF BEN1 (BFA-VISUALIZED ENDOCYTIC TRAFFICKING DEFECTIVE1). We show that BEN1 contributes to cellulose synthesis by regulating the trafficking of the cellulose synthesis protein complex between the trans-Golgi network and the plasma membrane. Our results, therefore, introduce PUP-IT as a new and powerful proximity labelling system to identify protein interactions in plant cells.

plant biology↗

bioRxiv2022

CHCHD2 mediates glioblastoma cell proliferation, mitochondrial metabolism, hypoxia-induced invasion, and therapeutic resistance

Glioblastoma (GBM) is the most common and malignant primary brain tumor in adults and remains incurable. The mitochondrial coiled-coil-helix-coiled-coil-helix domain-containing protein 2 (CHCHD2) is demonstrated to mediate mitochondrial respiration, nuclear gene expression, and cell migration, but evidence of this in GBM is lacking. We hypothesized that CHCHD2 would serve a functional role in U87 GBM cells expressing the constitutively active epidermal growth factor receptor variant III (EGFRvIII). Amplification of the CHCHD2 gene was found to be associated with decreased patient overall survival and progression-free survival. CHCHD2 mRNA levels were increased in high-versus low-grade glioma, IDH-wt GBMs, and in tumor versus non-tumor tissue. Additionally, CHCHD2 protein expression was greatest in invasive, EGFRvIII-expressing patient-derived samples. CRISPR-Cas9-mediated knockout of CHCHD2 in EGFRvIII-expressing U87 cells resulted in altered mitochondrial respiration and glutathione status, decreased cell growth and invasion in both normoxia and hypoxia, and increased sensitivity to cytotoxic agents. CHCHD2 was distributed in both mitochondria and nuclei of U87 and U87vIII cells, and U87vIII displayed greater nuclear CHCHD2 compared to isogenic U87 cells. Incubation in hypoxia, serum starvation, and reductive unfolding of CHCHD2 induced nuclear accumulation of CHCHD2 in both cell lines. Collectively, these data indicate that CHCHD2 mediates a variety of GBM cell hallmark characteristics and highlights mitonuclear retrograde signaling as a pathway of interest in GBM cell biology. ImplicationsThese data demonstrate CHCHD2 as a mediator of a number of GBM cell functions representing disease hallmarks, as well as highlight its subcellular distribution in response to metabolic stressors. These results may inspire therapeutic strategies undermining mitochondrial biology to potentially improve GBM tumor management.

cancer biology↗

bioRxiv2025

Role of genetic frustrations in cell reprogramming

The cell fate transition is a fundamental characteristic of living organisms. By introducing external perturbations, it is possible to artificially intervene in cell fate and trigger cell reprogramming. Revealing the general principle underlying the induced phenotypic reshaping of cell populations remains a central focus in the field of cell biology. In this study, we investigate the energetic and dynamic features of induced cell phenotypic transition from differentiated somatic state to pluripotent state by constructing a Boolean genetic network model. The simulation and experimental results highlight the critical role of genetic frustration in initiating cell fate transitions, although the two ending phenotypic states are typically featured by minimal frustration. In addition, the altered gene expression profiles exhibit a scale-free distribution, suggesting that there exist a small number of critical genes responsible for the cell fate transition. This study provides important insights into the dynamic principles governing effective cell reprogramming caused by artificial or exogenous interventions.

systems biology↗

bioRxiv2021

Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics

Given the growing number of clinically integrated cancer single-cell transcriptomic studies, robust differential enrichment methods for gene signatures to dissect tumor cellular states for discovery and translation are critical. Current analysis strategies neither adequately represent the hierarchical structure of clinical single-cell transcriptomic datasets nor account for the variability in the number of recovered cells per sample, leading to results potentially confounded by sample-driven biology with high false positives instead of accurately representing true differential enrichment of group-level biology (e.g., treatment responders vs. non-responders). This problem is especially prominent for single-cell analyses of the tumor compartment, because high intra-patient similarity (as opposed to inter-patient similarity) results in stricter hierarchical structured data that confounds enrichment analysis. Furthermore, to identify signatures which are truly representative of the entire group, there is a need to quantify the robustness of otherwise statistically significant signatures to sample exclusion. Here, we present a new nonparametric statistical method, BEANIE, to account for these issues, and demonstrate its utility in two cancer cohorts stratified by clinical groups to reduce biological hypotheses and guide translational investigations. Using BEANIE, we show how the consideration of sample-specific versus group biology greatly decreases the false positive rate and guides identification of robust signatures that can also be corroborated across different cell type compartments.

bioinformatics↗

bioRxiv2020

Specific splice junction detection in single cells with SICILIAN

Precise splice junction calls are currently unavailable in scRNA-seq pipelines such as the 10x Chromium platform but are critical for understanding single-cell biology. Here, we introduce SICILIAN, a new method that assigns statistical confidence to splice junctions from a spliced aligner to improve precision. SICILIANs precise splice detection achieves high accuracy on simulated data, improves concordance between matched single-cell and bulk datasets, increases agreement between biological replicates, and reliably detects un-annotated splicing in single cells, enabling the discovery of novel splicing regulation.

bioinformatics↗

bioRxiv2024

Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution

The ability to accurately quantify biological age could help monitor and control healthy aging. Epigenetic clocks have emerged as promising tools for estimating biological age, yet they have been developed from heterogeneous bulk tissues, and are thus composites of two aging processes, one reflecting the change of cell-type composition with age and another reflecting the aging of individual cell-types. There is thus a need to dissect and quantify these two components of epigenetic clocks, and to develop epigenetic clocks that can yield biological age estimates at cell-type resolution. Here we demonstrate that in blood and brain, approximately 39% and 12% of an epigenetic clocks accuracy is driven by underlying shifts in lymphocyte and neuronal subsets, respectively. Using brain and liver tissue as prototypes, we build and validate neuron and hepatocyte specific DNA methylation clocks, and demonstrate that these cell-type specific clocks yield improved estimates of chronological age in the corresponding cell and tissue-types. We find that neuron and glia specific clocks display biological age acceleration in Alzheimers Disease with the effect being strongest for glia in the temporal lobe. Moreover, CpGs from these clocks display a small but significant overlap with the causal DamAge-clock, mapping to key genes implicated in neurodegeneration. The hepatocyte clock is found accelerated in liver under various pathological conditions. In contrast, non-cell-type specific clocks do not display biological age-acceleration, or only do so marginally. In summary, this work highlights the importance of dissecting epigenetic clocks and quantifying biological age at cell-type resolution.

bioinformatics↗

bioRxiv2019

Stochastic modeling of aging cells reveals how damage accumulation, repair, and cell-division asymmetry affect clonal senescence and population fitness

BackgroundAsymmetry during cellular division, both in the uneven partitioning of damaged cellular components and of cell volume, is a cell biological phenomenon experienced by many unicellular organisms. Previous work based on a deterministic model claimed that such asymmetry in the partitioning of cell volume and of aging-associated damage confers a fitness benefit in avoiding clonal senescence, primarily by diversifying the cellular population. However, clonal populations of unicellular organisms are already naturally diversified due to the inherent stochasticity of biological processes.\n\nResultsApplying a model of aging cells that accounts for natural cell-to-cell variations across a broad range of parameter values, here we show that the parameters directly controlling the accumulation and repair of damage are the most important factors affecting fitness and clonal senescence, while the effects of both segregation of damaged components and division asymmetry are frequently minimal and generally context-dependent.\n\nConclusionsWe conclude that damage segregation and division asymmetry, perhaps counterintuitively, are not necessarily beneficial from an evolutionary perspective.

systems biology↗

bioRxiv2023

Targeted volume Correlative Light and Electron Microscopy of an environmental marine microorganism

Photosynthetic microalgae are responsible for an important fraction of CO2 fixation and O2 production on Earth. Three-dimensional ultrastructural characterization of these organisms in their natural environment can contribute to a deeper understanding of their cell biology. However, the low throughput of volume electron microscopy (vEM) methods, along with the complexity and heterogeneity of environmental samples, pose great technical challenges. In the present study, we used a workflow based on a specific EM sample preparation, compatible with both light and vEM imaging in order to target one cell among a complex natural community. This method revealed the 3D subcellular landscape of a photosynthetic dinoflagellate with quantitative characterization of multiple organelles. We could show that this cell contains a single convoluted chloroplast and the arrangement of the flagellar apparatus with its associated photosensitive elements. Moreover, we observed chromatin features that could shed light on how transcriptional activity takes place in organisms where chromosomes are permanently condensed. Together with providing insights in dinoflagellates biology, this proof-of-principle study illustrates an efficient tool for the targeted ultrastructural analysis of environmental microorganisms in heterogeneous mixes.

cell biology↗

bioRxiv2024

Dissecting the role of CAR signaling architectures on T cell activation and persistence using pooled screening and single-cell sequencing

Chimeric antigen receptor (CAR) T cells represent a promising approach for cancer treatment, yet challenges remain such as limited efficacy due to a lack of T cell persistence. Given its critical role in promoting and modulating T cell responses, it is crucial to understand how alterations in the CAR signaling architecture influence T cell function. Here, we designed a combinatorial CAR signaling domain library and performed repeated antigen stimulation assays, pooled screening and single-cell sequencing to investigate T-cell responses triggered by different CAR architectures. Parallel comparisons of CAR variants, at early, middle and late timepoints during chronic antigen stimulation systematically assessed the impact of modifying signaling domains on T cell activation and persistence. Our data reveal the predominant influence of membrane-proximal domains in driving T cell phenotype. Additionally, we highlight the critical role of CD40 costimulation in promoting potent and persistent T cell responses, followed by CTLA4, which induces a long-term cytotoxic phenotype. This work deepens the understanding of CAR T cell biology and may be used to guide the future engineering of CAR T cell therapies.

synthetic biology↗

bioRxiv2023

Modelling count data with partial differential equation models in biology

Partial differential equation (PDE) models are often used to study biological phenomena involving movement-birth-death processes, including ecological population dynamics and the invasion of populations of biological cells. Count data, by definition, is non-negative, and count data relating to biological populations is often bounded above by some carrying capacity that arises through biological competition for space or nutrients. Parameter estimation, parameter identifiability, and making model predictions usually involves working with a measurement error model that explicitly relating experimental measurements with the solution of a mathematical model. In many biological applications, a typical approach is to assume the data are normally distributed about the solution of the mathematical model. Despite the widespread use of the standard additive Gaussian measurement error model, the assumptions inherent in this approach are rarely explicitly considered or compared with other options. Here, we interpret scratch assay data, involving migration, proliferation and delays in a population of cancer cells using a reaction-diffusion PDE model. We consider relating experimental measurements to the PDE solution using a standard additive Gaussian measurement error model alongside a comparison to a more biologically realistic binomial measurement error model. While estimates of model parameters are relatively insensitive to the choice of measurement error model, model predictions for data realisations are very sensitive. The standard additive Gaussian measurement error model leads to biologically inconsistent predictions, such as negative counts and counts that exceed the carrying capacity across a relatively large spatial region within the experiment. Furthermore, the standard additive Gaussian measurement error model requires estimating an additional parameter compared to the binomial measurement error model. In contrast, the binomial measurement error model leads to biologically plausible predictions and is simpler to implement. We provide open source Julia software on GitHub to replicate all calculations in this work, and we explain how to generalise our approach to deal with coupled PDE models with several dependent variables through a multinomial measurement error model, as well as pointing out other potential generalisations by linking our work with established practices in the field of generalised linear models.

systems biology↗