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Results for “cell biology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 973 records · Page 54Linked to original sources

bioRxiv2021

Molecular basis for PICS-mediated piRNA biogenesis and cell division

By incorporating two mutually exclusive factors, PID-1 and TOST-1, C. elegans PICS complex plays important roles in piRNA biogenesis, chromosome segregation and cell division, respectively. We firstly mapped the interaction network between PICS subunits. By solving the several complex structures, including those of TOFU-6/PICS-1, ERH-2/PICS-1, and ERH-2/TOST-1, we uncover the mechanisms underlying the interactions between PICS subunits. Our biochemical experiment demonstrates that PICS exists as an octamer consisting of two copies of each subunits. Combining structural analyses with mutagenesis experiments, we identified residues of PICS subunits that are critical for maintaining intact PICS complex in vitro. Furthermore, using genetics, cell biology and imaging experiments, we found that those mutants impairing the in vitro interaction network within PICS, also lead to abnormal dysfunction PICS in vivo, including mislocalization of PICS, and reduced levels of piRNAs or abnormal chromosome segregation and cell division. Therefore, our work provides structural insights into understanding the PICS-mediated piRNA biogenesis and cell division.

molecular biology↗

bioRxiv2018

Wnt signalling drives context-dependent differentiation or proliferation in neuroblastoma

Neuroblastoma is one of the commonest and deadliest solid tumours of childhood, and is thought to result from disrupted differentiation of the developing sympathoadrenergic lineage of the neural crest. Neuroblastoma exhibits intra-and intertumoural heterogeneity, with high risk tumours characterised by poor differentiation, which can be attributable to MYCN-mediated repression of genes involved in neuronal differentiation. MYCN is known to co-operate with oncogenic signalling pathways such as Alk, Akt and MEK/ERK signalling, and, together with c-MYC has been shown to be activated by Wnt signalling in various tissues. However, our previous work demonstrated that Wnt3a/Rspo2 treatment of some neuroblastoma cell lines can, paradoxically, decrease c-MYC and MYCN proteins. This prompted us to define the neuroblastoma-specific Wnt3a/Rspo2-driven transcriptome using RNA sequencing, and characterise the accompanying changes in cell biology.\n\nHere we report the identification of ninety Wnt target genes, and show that Wnt signalling is upstream of numerous transcription factors and signalling pathways in neuroblastoma. Using live-cell imaging, we show that Wnt signalling can drive differentiation of SK-N-BE(2)-C and SH-SY5Y cell-lines, but, conversely, proliferation of SK-N-AS cells. We show that cell-lines that differentiate show induction of pro-differentiation BMP4 and EPAS1 proteins, which is not apparent in the SK-N-AS cells. In contrast, SK-N-AS cells show increased CCND1, phosphorylated RB and E2F1 in response to Wnt3a/Rspo2, consistent with their proliferative response, and these proteins are not increased in differentiating lines. By meta-analysis of the expression of our 90 genes in primary tumour gene expression databases, we demonstrate discrete expression patterns of our Wnt genes in patient cohorts with different prognosis. Furthermore our analysis reveals interconnectivity within subsets of our Wnt genes, with one subset comprised of novel putative drivers of neuronal differentiation repressed by MYCN. Assessment of {beta}-catenin immunohistochemistry shows high levels of {beta}-catenin in tumours with better differentiation, further supporting a role for canonical Wnt signalling in neuroblastoma differentiation.

cancer biology↗

bioRxiv2023

BioemuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic SpikingNeural Network

Characterization and modeling of biological neural networks has emerged as a field driving significant advancements in our understanding of brain function and related pathologies. As of today, pharmacological treatments for neurological disorders remain limited, pushing the exploration of promising alternative approaches such as electroceutics. Recent research in bioelectronics and neuromorphic engineering have led to the design of the new generation of neuroprostheses for brain repair. However, its complete development requires deeper understanding and expertise in biohybrid interaction. Here, we show a novel real-time, biomimetic, cost-effective and user-friendly neural network for bio-hybrid experiments and real-time emulation. Our system allows investigation and reproduction of biophysically detailed neural network dynamics while promoting cost-efficiency, flexibility and ease of use. We showcase the feasibility of conducting biohybrid experiments using standard biophysical interfaces and various biological cells as well as real-time emulation of complex models. We anticipate our system to be a step towards developing neuromorphicbased neuroprostheses for bioelectrical therapeutics by enabling communication with biological networks on a similar time scale, facilitated by an easy-to-use and accessible embedded real-time system. Our real-time device further enhances its potential for practical applications in biohybrid experiments.

neuroscience↗

bioRxiv2024

CELL-Diff: Unified diffusion modeling for protein sequences and microscopy images

Fluorescence microscopy is ubiquitously used in cell biology research to characterize the cellular role of a protein. To help elucidate the relationship between the amino acid sequence of a protein and its cellular function, we introduce CELL-Diff, a unified diffusion model facilitating bidirectional transformations between protein sequences and their corresponding microscopy images. Utilizing reference cell morphology images and a protein sequence, CELL-Diff efficiently generates corresponding protein images. Conversely, given a protein image, the model out-puts protein sequences. CELL-Diff integrates continuous and diffusion models within a unified framework and is implemented using a transformer-based network. We train CELL-Diff on the Human Protein Atlas (HPA) dataset and finetune it on the OpenCell dataset. Experimental results demonstrate that CELL-Diff outperforms existing methods in generating high-fidelity protein images, making it a practical tool for investigating subcellular protein localization and interactions.

bioinformatics↗

bioRxiv2025

3D Epigenome Evolution Underlies Divergent Gene Regulatory Programs in Primate Neural Development

The expansion of the neocortex is a hallmark of human evolution and is closely linked to neural stem cell biology. Yet, the epigenetic mechanisms driving divergent gene regulation during primate neurogenesis remain elusive. Here, we comprehensively mapped 3D genome organization, chromatin accessibility and gene expression in induced pluripotent stem cells and derived neural stem cells from human, chimpanzee, gorilla and macaque. We identified human-specific epigenetic signatures including cis-regulatory regions and enhancer-promoter interactions and linked them to gene regulatory dynamics. Deep learning models revealed that complex regulatory grammar at cis-regulatory regions, including transcription factor binding sites, local context and higher-order chromatin organization, underlies species and cell type-specific differences. High-resolution Hi-C uncovered unexpected global shifts in 3D genome architecture in chimpanzee and gorilla neural stem cells while topologically associating domains remain remarkably conserved. Notably, species-specific genes interacted with multiple differentially accessible regions, suggesting that synergistic enhancer activation is a key mechanism driving epigenome evolution. These findings provide new insights into the epigenetic basis of primate brain evolution.

genomics↗

bioRxiv2025

Cut-Detector: A Tool for Automated Temporal Analysis of Late Cytokinetic Events

Cytokinesis is the final step of cell division, resulting in the physical separation of the two daughter cells. Despite its fundamental importance in cell biology, biologists currently lack automatic tools for large-scale profiling of cytokinesis dynamics. In particular, the timing of the first microtubule cut in the intercellular bridge connecting the daughter cells is crucial, as it marks a critical step preceding abscission. Here, we introduce Cut-Detector, an open-source tool for the automatic analysis of late cytokinesis timing from time-lapse microscopy movies. Cut-Detector employs an AI approach to carry out the multiple tasks required to monitor cytokinesis: cell segmentation and tracking, detection of cell division events, localization of the intercellular bridge, and detection of the microtubule cuts. Cut-Detector will facilitate large-scale analyses to uncover new cytokinetic genes, a task that would be impractical without automation.

bioinformatics↗

bioRxiv2018

Autoencoder and Optimal Transport to Infer Single-Cell Trajectories of Biological Processes

Although we can increasingly image and measure biological processes at single-cell resolution, most assays can only take snapshots from a population of cells in time. Here we describe ImageAEOT, which combines an AutoEncoder, to map single-cell Images from different cell populations to a common latent space, with the framework of Optimal Transport to infer cellular trajectories. As a proof-of-concept, we apply ImageAEOT to nuclear and chromatin images during the activation of fibroblasts by tumor cells in engineered 3D tissues. We further validate ImageAEOT on chromatin images of various breast cancer cell lines and human tissue samples, thereby linking alterations in chromatin condensation patterns to different stages of tumor progression. Importantly, ImageAEOT can infer the trajectory of a particular cell from one snapshot in time and identify the changing features to provide early biomarkers for developmental and disease progression.

bioinformatics↗

bioRxiv2022

Reliable and efficient parameter estimation using approximate continuum limit descriptions of stochastic models

Stochastic individual-based mathematical models are attractive for modelling biological phenomena because they naturally capture the stochasticity and variability that is often evident in biological data. Such models also allow us to track the motion of individuals within the population of interest. Unfortunately, capturing this microscopic detail means that simulation and parameter inference can become computationally expensive. One approach for overcoming this computational limitation is to coarse-grain the stochastic model to provide an approximate continuum model that can be solved using far less computational effort. However, coarse-grained continuum models can be biased or inaccurate, particularly for certain parameter regimes. In this work, we combine stochastic and continuum mathematical models in the context of lattice-based models of two-dimensional cell biology experiments by demonstrating how to simulate two commonly used experiments: cell proliferation assays and barrier assays. Our approach involves building a simple statistical model of the discrepancy between the expensive stochastic model and the associated computationally inexpensive coarse-grained continuum model. We form this statistical model based on a limited number of expensive stochastic model evaluations at design points sampled from a user-chosen distribution, corresponding to a computer experiment design problem. With straightforward design point selection schemes, we show that using the statistical model of the discrepancy in tandem with the computationally inexpensive continuum model allows us to carry out prediction and inference while correcting for biases and inaccuracies due to the continuum approximation. We demonstrate this approach by simulating a proliferation assay, where the continuum limit model is the well-known logistic ordinary differential equation, as well as a barrier assay where the continuum limit model is closely related to the well-known Fisher-KPP partial differential equation. We construct an approximate likelihood function for parameter inference, both with and without discrepancy correction terms. Using maximum likelihood estimation, we provide point estimates of the unknown parameters, and use the profile likelihood to characterise the uncertainty in these estimates and form approximate confidence intervals. For the range of inference problems considered, working with the continuum limit model alone leads to biased parameter estimation and confidence intervals with poor coverage. In contrast, incorporating correction terms arising from the statistical model of the model discrepancy allows us to recover the parameters accurately with minimal computational overhead. The main tradeoff is that the associated confidence intervals are typically broader, reflecting the additional uncertainty introduced by the approximation process. All algorithms required to replicate the results in this work are written in the open source Julia language and are available at GitHub.

biophysics↗

bioRxiv2014

Musashi proteins are post-transcriptional regulators of the epithelial-luminal cell state

SummaryThe conserved Musashi (Msi) family of RNA binding proteins are expressed in stem/progenitor and cancer cells, but mostly absent from differentiated cells, consistent with a role in cell state regulation. We found that Msi genes are rarely mutated but frequently overexpressed in human cancers, and associated with an epithelial-luminal cell state. Using ribosome footprint profiling and RNA-seq analysis of genetic mouse models in neuronal and mammary cell types, we found that Msis regulate translation of genes implicated in epithelial cell biology and epithelial-to-mesenchymal transition (EMT) and promote an epithelial splicing pattern. Overexpression of Msi proteins inhibited translation of genes required for EMT, including Jagged1, and repressed EMT in cell culture and in mammary gland in vivo, while knockdown in epithelial cancer cells led to loss of epithelial identity. Our results show that mammalian Msi proteins contribute to an epithelial gene expression program and promote an epithelial-luminal state in both neural and breast cell types.\n\nHighlightsO_LIMsi proteins bind UAG motifs in vitro and in 3 UTRs of mRNAs\nC_LIO_LIMsi proteins are markers of epithelial state in brain and breast tumors, and cell lines\nC_LIO_LIThe Notch regulator Jag1 mRNA is bound and translationally repressed by Msi\nC_LIO_LIMsi overexpression represses EMT in the mammary gland in vivo\nC_LI

Genomics↗

bioRxiv2021

A genome-edited β-cell model of Prader-Willi syndrome reveals chronic deficits in endoplasmic reticulum chaperones and insulin secretion

Prader-Willi syndrome (PWS) is a multisystem disorder with neurobehavioral, metabolic, and hormonal phenotypes, caused by loss of expression of a paternally-expressed imprinted gene cluster. Prior evidence from a PWS mouse model identified abnormal pancreatic islet development with retention of aged insulin and deficient insulin secretion. To determine the collective roles of PWS genes in {beta}-cell biology, we used genome-editing to generate isogenic, clonal INS-1 insulinoma lines having 3.16 Mb deletions of the silent, maternal (control) or active, paternal PWS-alleles. PWS {beta}-cells demonstrated a significant cell-autonomous reduction in basal and glucose-stimulated insulin secretion. Further, proteomic analyses revealed reduced levels of cellular and secreted hormones, including all insulin peptides and amylin, concomitant with reduction of at least ten endoplasmic reticulum (ER) chaperones, including GRP78 and GRP94. Critically, transcriptomic studies demonstrated that the broad reduction of ER chaperones originated from transcriptional downregulation without corresponding changes for Ins1 and Ins2. In contrast to the dosage compensation previously seen for ER chaperones in Grp78 or Grp94 gene knockouts or knockdown, compensation is precluded by the stress-independent deficiency of ER chaperones in PWS {beta}-cells. Consistent with reduced ER chaperones levels, PWS INS-1 {beta}-cells are more sensitive to ER stress, leading to earlier activation of all three arms of the unfolded protein response. These findings suggest that a chronic shortage of ER chaperones in PWS {beta}-cells leads to a deficiency of protein folding and/or delay in ER transit of insulin and other cargo. In summary, our results illuminate the pathophysiological basis of pancreatic {beta}-cell hormone deficits in PWS, with evolutionary implications for the multigenic PWS-domain, and indicate that PWS-imprinted genes coordinate concerted regulation of ER chaperone biosynthesis and {beta}-cell secretory pathway function.

genetics↗

bioRxiv2022

A comparison of marker gene selection methods for single-cell RNA sequencing data

The development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalogue and probe the transcriptional heterogeneity of individual cells in unprecedented detail. A common step in the analysis of scRNA-seq data is the selection of so-called marker genes, most commonly to enable annotation of the biological cell types present in the sample. In this paper we benchmarked 56 computational methods for selecting marker genes in scRNA-seq data. The performance of the methods was compared using 10 real scRNA-seq datasets and over 170 additional simulated datasets. Methods were compared on their ability to recover simulated and expert-annotated marker genes, the predictive performance and characteristics of the gene sets they select, their memory usage and speed and their implementation quality. In addition, various case studies were used to scrutinise the most commonly used methods, highlighting issues and inconsistencies. Overall, we present a comprehensive evaluation of methods for selecting marker genes in scRNA-seq data. Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Students t-test and logistic regression. All code used in the evaluation, including an extensible Snakemake pipeline, is available at: https://gitlab.svi.edu.au/biocellgen-public/mage_2020_marker-gene-benchmarking.

bioinformatics↗

bioRxiv2024

CountASAP: A Lightweight, Easy to Use Python Package for Processing ASAPseq Data

Declining sequencing costs coupled with the increasing availability of easy-to-use kits for the isolation of DNA and RNA transcripts from single cells have driven a rapid proliferation of studies centered around genomic and transcriptomic data. Simultaneously, a wealth of new techniques have been developed that utilize single cell technologies to interrogate a broad range of cell-biological processes. One recently developed technique, transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq), provides a combination of chromatin accessibility assessments with measurements of cell-surface marker expression levels. While software exists for the characterization of these datasets, there currently exists no tool explicitly designed to reformat ASAP surface marker FASTQ data into a count matrix which can then be used for these downstream analyses. To address this, we created CountASAP, an easy-to-use Python package purposefully designed to transform FASTQ files from ASAP experiments into count matrices compatible with commonly-used downstream bioinformatic analysis packages. CountASAP takes advantage of the independence of the relevant data structures to perform fully parallelized matches of each sequenced read to user-supplied input ASAP oligos and unique cell-identifier sequences.

bioinformatics↗

bioRxiv2018

The effect of cortical elasticity and active tension on cell adhesion mechanics

We consider a cell as a filled, elastic shell with an active surface tension and non-specific adhesion. We perform numerical simulations of this model in order to study the mechanics of cell-cell separation. By variation of parameters, we are able to recover well-known limits of JKR, DMT, adhesive vesicles with surface tension (BWdG) and thin elastic shells. We further locate biological cells on this parameter space by comparing to existing experiments on S180 cells. Using this model, we show that mechanical parameters can be obtained that are consistent with both Dual Pipette Aspiration (DPA) and Micropipette Aspiration (MA), a problem not successfully tackled so far. We estimate a cortex elastic modulus of Ec {approx} 15 kPa, an effective cortex thickness of tc {approx} 0.3 {micro}m and an active tension of{gamma} {approx} 0.4 nN/{micro}m. With these parameters, a JKR-like scaling of the separation force is recovered. Finally, the change of contact radius with applied force in a pull-off experiment was investigated. For small forces, a scaling similar to both BWdG and DMT is found.\n\nManuscript submitted to Biophysical Journal.

biophysics↗

bioRxiv2026

B-cell SIGLEC-5 engages T-cell components of the elastin receptor complex (ERC) to suppress inflammatory T-cell cytokines

SIGLECs remain poorly defined in human B-cell biology beyond SIGLEC-2/CD22 and SIGLEC-10. Here, we identify a previously unrecognized regulatory pathway involving the paired receptors SIGLEC-5 and SIGLEC-14 at the human B-T-cell interface. We show that activated B-cells differentially regulate these receptors: SIGLEC-5 is predominantly surface-expressed and induced by CD40 engagement, whereas SIGLEC-14 is primarily secreted and upregulated after both CD40 and TLR9 stimulation. We further identify EBP (elastin binding protein) and CTSA (cathepsin A) components of the elastin receptor complex (ERC), expressed by activated T-cells, as a novel ligand for both SIGLEC-5 and SIGLEC-14. Functionally, ERC-associated engagement of SIGLEC-5 on B-cells suppresses T-cell IFN-{gamma} and IL-17 expression, establishing SIGLEC-5 as a B-cell-expressed inhibitory SIGLEC that restrains inflammatory T-cell cytokine responses. SIGLEC-14 does not alter this suppression, as SIGLEC-5{square} B-cells from SIGLEC-14-sufficient and -null individuals show comparable inhibitory activity. These findings broaden SIGLEC-mediated adaptive immune regulation, with relevance to inflammatory and autoimmune disease.

immunology↗

bioRxiv2026

TGF-b/TIF1g signaling restrains CD8+ T cell expansion while preserving cytotoxic competence

Transforming growth factor b exerts complex and often opposing effects on CD8+ T cell biology, yet the molecular mechanisms that distinguish these functions remain poorly understood. Here, we identify TIF1g (TRIM33) as a critical signal threshold regulator that couples TGF b signaling to CD8+ T cell activation and cytotoxic function. In the presence of TGF b, TIF1g deficient CD8+ T cells bypass canonical suppressive signals and acquire a transcriptional program characterized by enhanced activation and proliferation. Despite this hyperactivated state, TIF1g deficient cells fail to acquire full effector function, exhibiting markedly reduced Granzyme B and Perforin expression under TGF b signaling. We confirmed this functional uncoupling in vivo using a secondary Listeria monocytogenes challenge model, in which TIF1g deficient CD8+ T cells underwent robust expansion yet failed to efficiently control bacterial infection because of impaired cytotoxic activity. In a lymphopenic homeostatic proliferation model, TIF1g deficiency disrupted cellular quiescence, leading to excessive proliferation and spontaneous effector differentiation. Consistent with these findings, loss of TIF1g accelerated B16 melanoma growth and was associated with reduced Perforin expression in tumor-infiltrating CD8+ T cells. Together, these findings identify TIF1g as a key regulator of TGF b mediated CD8+ T cell responses, uncoupling activation from cytotoxic differentiation and revealing a mechanism by which TGF b independently regulates proliferative and effector programs.

immunology↗

bioRxiv2020

Impact of explicit area scaling on kinetic models involving multiple compartments

BackgroundComputational modelling of cell biological processes is a frequently used technique to analyse the underlying mechanisms and to generally understand the behaviour of these processes in the context of a pathway, network or even the whole cell. The most common technique in this context is the usage of ordinary differential equations (ODEs) that describe the kinetics of the relevant processes in mechanistic detail. Here, it is usually assumed that the content of the cell is well-stirred and thus homogeneous - which is of course an over-simplification, but often worked in the past. However, many processes happen at membranes and thus not in 3D, but in 2D. The scaling of the rates of these processes poses a special problem, if volumes of compartments are changed. They will typically scale with an area, but not with the volume of the involved compartment. However, commonly, this is neglected when setting up models and/or volume scaling also sometimes automatically happens when using modelling software in the field. ResultsHere, we investigate generic as well as specific, realistic cases to find out, how strong the impact of the wrong scaling is for the outcome of simulations. We show that the importance of correct area scaling depends on the architecture of the reaction site and its changes upon volume alterations and it is hard to foresee, if it has a significant impact or not just by looking at the original model set-up. Moreover, scaled rates might exhibit more or less control over the behaviour of the system and therefore, accordingly, incorrect scaling will have more or less influence. ConclusionsWorking with multi-compartment reactions requires a careful consideration of the correct scaling of the rates when changing the volumes of the involved compartments. The error following incorrect scaling - often done by scaling with the volume of the respective compartments can lead to significant aberrations of model behaviour.

systems biology↗

bioRxiv2021

NDR1 and the Arabidopsis Plasma Membrane ATPase AHA5 are Required for Processes that Converge on Drought Tolerance and Immunity

NON-RACE-SPECIFIC DISEASE RISISTANCE1 (NDR1) is a key component of plant immune signaling, required for defense against the bacterial pathogen Pseudomonas syringae. Plant stress responses have overlapping molecular, physiological, and cell biology signatures, and given the central role of NDR1 during biotic stress perception and signaling, we hypothesized that NDR1 also functions in abiotic stress responses, including in a role that mediates signaling at the plasma membrane (PM) - cell wall (CW) continuum. Here, we demonstrate that NDR1 is required for the induction of drought stress responses in plants, a role that couples stress signaling in an abscisic acid-dependent manner. We show that NDR1 physically associates with the PM-localized H+-ATPases AHA1, AHA2, and AHA5 and is required for proper regulation of H+-ATPase activity and stomatal guard cell dynamics, providing a mechanistic function of NDR1 during drought responses. In the current study, we demonstrate that NDR1 functions in signaling processes associated with both biotic and abiotic stress response pathways, a function we hypothesize represents NDR1s role in the maintenance of cellular homeostasis during stress. We propose a role for NDR1 as a core transducer of signaling between cell membrane processes and intercellular stress response activation.

plant biology↗

bioRxiv2017

Monomerization of Far-Red Fluorescent Proteins

Anthozoa class red fluorescent proteins (RFPs) are frequently used as biological markers, with far-red emitting variants ({lambda}em ~ 600 - 900 nm) sought for whole animal imaging because biological tissues are permeable to light in this range. A barrier to the use of naturally occurring RFP variants as molecular markers is that all are tetrameric, which is not ideal for cell biological applications. Efforts to engineer monomeric RFPs have usually produced dimmer and blue-shifted variants, as the chromophore is sensitive to small structural perturbations. In fact, despite much effort, only four native RFPs have been successfully monomerized, leaving the vast majority of RFP biodiversity untapped in biomarker development. Here we report the generation of monomeric variants of HcRed and mCardinal, both far-red dimers, and describe a comprehensive methodology for the rapid monomerization of novel red-shifted oligomeric RFPs. Among the resultant variants, is mKelly1 (emission maximum: {lambda}em = 656 nm), which along with the recently reported mGarnet2 (1), forms a new class of bright, monomeric, far-red FPs.

biochemistry↗