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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 1,621 records · Page 90Linked to original sources

bioRxiv2026

The STA1-DOT2 interaction promotes nuclear speckle formation and splicing robustness in growth and heat stress responses

Pre-mRNA splicing is carried out by the spliceosome, a large and dynamic ribonucleoprotein complex. The spliceosome is known to be stored in nuclear speckles (NS), which are now recognized as active subnuclear organelles for splicing. However, it remains poorly understood how spliceosomal protein-protein interactions are functionally coupled to NS organization to maintain splicing robustness in plants. Here, we report the functional significance of a specific interaction between two U4/U6{middle dot}U5 tri-snRNP components of the spliceosome, STA1 and DOT2, in regulating NS organization, pre-mRNA splicing, and heat stress responses in Arabidopsis. We identified a missense mutation in DOT2 (a Snu66/SART1 homolog) from a genetic suppressor of the PRP6 homolog mutant sta1-1 (named S307). This mutation restored the weakened interaction between STA1 and DOT2 in the sta1-1 mutant background. Genetic, biochemical, and cell biological analyses showed that variation in the strength of the STA1-DOT2 interaction was closely associated with changes in NS formation, splicing efficiency, as well as growth and heat tolerance. Pharmacological inhibition of STA1-associated NS formation by tubercidin recapitulated sta1-1-like phenotypes and splicing defects, supporting a functional link between NS organization and splicing outcomes. In addition, heat-induced weakening of the STA1-DOT2 interaction was accompanied by reduced NS formation and increased intron retention at the transcriptome-wide level including key heat-responsive transcripts. Based on these observations, we propose that the STA1-DOT2 interaction, likely reflecting the assembly state of the U4/U6{middle dot}U5 tri-snRNP, functions as a heat-sensitive interaction node that couples spliceosome assembly to NS organization and splicing robustness under stress conditions.

plant biology↗

bioRxiv2026

Wash-Free Multi-Target Super-Resolution Microscopy with Photocaged DNA Labels

Super-resolution microscopy with DNA-fluorophore labels is primed for multi-target imaging of cell biological samples. However, direct interaction with the sample is required to exchange or add DNA-fluorophore labels in each imaging round, which can impair the accuracy of the imaging data at the nanometer scale. To bypass this requirement, we introduce a wash-free method that employs DNA oligonucleotides equipped with photocaging groups. Irradiation with light removes these photo-modulatable groups and changes the hybridization properties of DNA labels, enabling light-modulated targeting. We demonstrate this concept by imaging various cellular targets with confocal microscopy, single-molecule localization microscopy, and stimulated emission depletion (STED) microscopy.

biophysics↗

bioRxiv2026

Transcriptomic profiling of human γδ T cells reveals non-linear immune aging characterized by childhood transitions and relative stability in adulthood

{gamma}{delta} T cells are one of the first T cell subsets developing in early ontogeny and show various effector functions in immune homeostasis and response in the young and the old. However, their maturation trajectories from infancy to children, adults and elderly have not been systematically defined. Here, we generated a single-cell transcriptome atlas of 106,711 {gamma}{delta} T cells from 223 individuals spanning infancy to old age. Our analysis reveals that {gamma}{delta} T cell aging is non-linear, characterized by pronounced childhood transitions followed by relative stability throughout adulthood despite marked inter-individual variability. In childhood, changes from developmental and mitochondrial programs toward cytotoxicity and inflammaging were evident. This includes maturation trajectories from GZMK intermediates to GZMB+Perforin+ effectors at both RNA and protein levels. Taken together, our study delineates the aging trajectories of human {gamma}{delta} T cells, establishes {gamma}{delta} T cells as a cellular paradigm of non-linear immune aging, and provides a comprehensive resource for investigating {gamma}{delta} T cell biology across the human lifespan.

immunology↗

bioRxiv2026

Cysteine Glutathionylation as a Global Dynamic Regulator of Protein Active Site Accessibility and Protein Complex Formation

Protein-glutathionylation is traditionally viewed as a protective mechanism that shields cysteine-residues from irreversible oxidative damage. Its broader functional roles remain poorly understood, in part due to technical limitations in detecting this modification at scale. Here, we develop and leverage a new mass-spectrometry approach that preserves protein-glutathionylation, thereby revealing its widespread distribution across the proteome in cell models, worms, mice and human cardiac tissues. In all cases, we find that glutathionylation sites are enriched at both protein-protein interfaces and protein-active sites, and are highly conserved across species. We find that glutathionylation is dynamically redistributed in response to environmental challenges, thereby driving remodelling of cellular protein-protein interaction (PPI) networks, and access to protein active sites, with functional and phenotypic consequences. Finally, we show that glutathionylation accumulates on key cardiac-sarcomeric proteins in aged-mice and human cardiomyopathy biopsies, revealing its role in cardiovascular dysfunction. These findings reposition glutathionylation as a crucial regulatory PTM, akin to phosphorylation, that orchestrates adaptive cellular responses. This work redefines the role of glutathionylation, with broad implications for cell biology and disease.

molecular biology↗

bioRxiv2026

Bifunctional Lipid-Protein Crosslinking Efficiency and Reaction Products

Bifunctional diazirine lipids are valuable tools for mapping protein-lipid interactions and cellular localization by photocrosslinking. Yet, the crosslinking efficiency of these probes has not been systematically evaluated. Here, we use the lipid transfer protein STARD10, which binds phospholipids in a 1:1 stoichiometry within a hydrophobic pocket, to measure the upper limit of the photo-crosslinking efficiency of bifunctional lipid probes. We characterize reaction products using native and denaturing mass spectrometry. Our results show that approximately 5% of photoactivated lipids form covalent protein-lipid crosslinks, while the majority follow intramolecular reaction trajectories, resulting in the formation of products featuring alkene, ketone and hydroxyl moieties. These findings provide essential context for the use of bifunctional probes to uncover the cell biology of lipids and highlight the need for continuous improvement to experimental workflows. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/700185v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@15d1641org.highwire.dtl.DTLVardef@6024e0org.highwire.dtl.DTLVardef@1503dcorg.highwire.dtl.DTLVardef@1b067bd_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

SARS-CoV-2 membrane protein biogenesis

Viral protein biogenesis underpins every viral life cycle stage, and elucidating these processes could reveal fundamental principles of virus-host interaction, and vulnerabilities amenable to therapeutic targeting. Here we apply biophysical, molecular, and cell biology techniques to investigate the insertion, folding, and oligomerization of the SARS-CoV-2 M protein. We describe the sequential co-translational insertion of the hydrophobic core, and demonstrate that the cytosolic C-terminal domain undergoes slower adoption of its tertiary structure. Additionally, we characterize how the transmembrane domain bundle facilitates M-protein oligomerization. Our results reveal a hydrophobic residue cluster that is essential for protein folding and co-translational dimerization. Additionally, we identify the cellular machinery responsible for targeting and inserting the M protein into the ER membrane, and chaperones and cofactors that may contribute to proper folding.

molecular biology↗

bioRxiv2026

Generative modeling reveals the connection between cellular morphology and gene expression

The understanding of how transcriptional programs give rise to cellular morphology, and how morphological features reflect and influence cell identity and function remains limited. This is due in part to the lack of large-scale datasets pairing the two modalities as well as the absence of computational frameworks capable of modeling their cross-modal structure. Here, we introduce COSMIC, a bidirectional generative framework that enables quantitative decomposition of transcriptional variance reflected in morphology and morphological variance explained by gene expression. COSMIC builds on a foundation model trained on over 21 million segmented nuclei and couples it with existing transcriptomic embeddings. To enable cross-modal learning, we leveraged a newly generated multimodal dataset acquired using IRIS, a technology that captures high-resolution images and transcriptomes from the same single cells at scale. COSMIC accurately modeled cell type identity, as well as continuous dynamics such as cell-cycle progression, establishing a quantitative link between morphological phenotypes and underlying gene expression. In prostate cancer cells, COSMIC identified morphological and transcriptomic differences between chemotherapy drug treatment-responsive and -resistant cells, and revealed morphology-associated genes linked to tumor state. Together, these results demonstrate that generative modeling powered by paired single-cell measurements can capture the bidirectional flow of information between cellular form and gene expression, opening new avenues for mechanistic discovery and predictive modeling in both basic and translational cell biology.

bioinformatics↗

bioRxiv2026

Unifying the genetic landscape of common and rare diseases via latent neighborhoods

Resolving disease mechanisms and identifying safer drug targets remains challenging due to difficulties in integrating inconsistent sources of genetic evidence. Here, we developed a deep-learning (DL) method for obtaining latent representations of human diseases and other terms by coupling a variational autoencoder (VAE) to network embeddings from graph representation learning on a protein interaction network. We apply this approach to map the landscape of human disease, by systematically integrating evidence for [~]42,000 traits and terms from sources such as clinical reports (ClinVar), genome-wide association studies (GWAS), mouse phenotypes and gene ontology. Similar diseases share their latent neighborhoods with the sources of evidence highlighting different aspects of the same cell biology for its role in disease. By decoding latent neighborhoods, we unify the sources of evidence and integrate across diseases to prioritize genes for common and rare diseases. Through examples for diabetes, hearing loss, and familial long QT syndrome, we illustrate how the methodology can be applied to develop hypotheses for disease mechanisms and to propose experimental models, measurements, targets and potential drugs that may improve diagnosis and treatments.

genetics↗

bioRxiv2026

Control of motility and cell shape of Haloferax volcanii is linked by a transcriptional regulator

Archaea rely on motility and morphological plasticity to navigate their environments, yet the transcriptional regulation of these processes remains poorly understood. In Haloferax volcanii, archaellum-dependent motility is transcriptionally regulated, but an EarA-like master regulator is absent. Here, we identify CsmR as a transcriptional regulator that links archaellum biogenesis and cell-shape transitions in H. volcanii. Deletion of csmR abolished detectable motility, whereas overexpression increased motility and promoted a sustained rod-like morphology. Comparative transcriptomics defined a CsmR-associated regulon that includes archaellum and chemotaxis genes as well as rod-shape determinants (e.g., Sph3 and RdfA), and upstream motif enrichment supports a direct role for CsmR in transcriptional control. Furthermore, csmR and cirA, a KaiC-like regulator, share extensive transcriptional overlap, with CirA likely fine-tuning CsmR-mediated regulation through post-translational modification. These findings establish CsmR as a key integrator of motility and cell shape regulation in Haloferax volcanii, suggesting that haloarchaea coordinate these fundamental processes through an unidentified transcriptional network. Moreover, Northern blotting and cell shape observation suggest that transcription factor RosR is involved in the regulation of an sRNA that shares extensive overlap with the cirA gene, possibly fine-tuning the effect of CirA on the regulation of the archaellum cluster and the rod shape determinants sph3 and rdfA. Understanding this interplay provides new insights into archaeal adaptability and may reveal broader regulatory principles in prokaryotic cell biology.

microbiology↗

bioRxiv2026

Whole-Brain Co-Mapping of Gene Expression and NeuronalActivity at Cellular Resolution in Behaving Zebrafish

The brains capabilities rely on both the molecular properties of individual cells and their interactions across brain-wide networks. However, relating gene expression to activity in individual neurons across the entire brain remains elusive. Here we developed an experimental-computational platform, WARP, for whole-brain imaging of neuronal activity during behavior, expansion-assisted spatial transcriptomics, and cellular-level registration of these two modalities. Through joint analysis of whole-brain neuronal activity during multiple behaviors, cellular gene expression, and anatomy, we identified functions of molecularly defined populations--including luminance coding in a cckb-pou4f2 midbrain population and task-structured activity in pvalb7-eomesa hippocampal-like neurons--and defined over 2,000 other function-gene-anatomy subpopulations. Analysis of this unprecedented multimodal dataset also revealed that most gene-matched neurons showed stronger activity correlations, highlighting a brain-wide role for gene expression in functional organization. WARP establishes a foundational platform and open-access dataset for cross-experiment discovery, high-throughput function-to-gene mapping, unification of cell biology and systems neuroscience, and scalable circuit modeling at the whole-brain scale.

neuroscience↗

bioRxiv2026

Single-cell analysis of sterol-induced Ca2+ signaling in human astrocytes by dynamic mode decomposition

Ca2+ signaling in astrocytes is a central mechanism of intercellular communication in the brain and plays a key role in regulating neuronal excitability, synaptic plasticity, and energy metabolism. Disruption of astrocytic Ca2+ dynamics is a characteristic of neurodegenerative diseases, as are deviations in cholesterol trafficking and metabolism, which are essential for maintaining membrane structure and function. Although recent studies have begun to explore links between Ca2+ signaling and sterol homeostasis in astrocytes, unbiased analytical workflows and mechanistic insight into how cholesterol and related sterols regulate astrocytic Ca2+ dynamics remain limited. Here, we apply dynamic mode decomposition to dissect and classify Ca2+ signals obtained from time-lapse imaging of human astrocytes. Using both synthetic and experimental datasets, we show that delay-embedded dynamic mode decomposition combined with clustering separates heterogeneous Ca2+ activity into distinct dynamical states. This analysis reveals that increasing cholesterol levels shift astrocytes toward more active oscillatory states, whereas acute cholesterol depletion suppresses Ca2+ activity. In addition, pretreatment with the oxysterols 24-, 25-, and 27-hydroxycholesterol impaired cholesterolinduced Ca2+ oscillations. Together, this work presents a general computational framework for decomposing and analyzing complex spatiotemporal Ca2+ signals, with broad applicability to quantitative imaging in cell biology.

biophysics↗

bioRxiv2026

Phenotypic diversity of yeasts curated in the 5th edition of The Yeasts: A data-driven visualization approach

Yeasts have long served as key experimental systems in genetics, cell biology, and fermentation research; however, these studies have largely focused on a limited number of model species. In contrast, yeasts are a fungal group with remarkable physiological and ecological diversity. To provide a global overview of yeast diversity, we compiled and visualized phenotypic information for approximately 1,300 yeast species documented in The Yeasts (5th edition), including carbon utilization profiles, fermentation capacity, growth temperature ranges, and reported isolation sources. Taxonomic reconciliation revealed extensive reannotation, with approximately 44% of the species undergoing name changes and recognized genera increasing from 143 to 233. Integrative analyses revealed pronounced phylogenetic structuring of metabolic breadth. Many basidiomycetous yeasts, particularly Agaricomycotina, exhibited broader, generalist-like carbon utilization, whereas ascomycetous yeasts, especially Saccharomycotina, more frequently displayed sugar-centered, specialist-like narrower profiles; model yeasts such as Saccharomyces cerevisiae and Schizosaccharomyces pombe fell at the narrow end of this spectrum. Fewer than half of all species fermented glucose, a trait largely confined to Saccharomycotina. In addition, nearly one-fifth of species failed to grow at 30 {degrees}C, the standard laboratory temperature. By reconstructing and visualizing decades of dispersed taxonomic knowledge accumulated in The Yeasts, this study reframes yeasts not merely as laboratory model organisms but as metabolically diverse fungi whose phenotypic diversity reflects diverse ecological contexts. The analytical framework presented here provides a foundation for integrating standardized quantitative phenotypes and newly described species and offers a starting point for exploring the latent ecological and metabolic potential of yeast diversity.

microbiology↗

bioRxiv2026

The sugar-beet cyst nematode effector Hs2B11 targets the Arabidopsis serine protease inhibitor AtPR-6 to favor parasitism

Cyst nematodes secrete effector proteins to manipulate host cell biology and suppress immunity, yet the mechanisms underlying these interactions remain largely unexplored. In this study, we characterize the function of Hs2B11, a Heterodera schachtii effector that was previously shown to be expressed in the dorsal gland of sugar-beet cyst nematodes (BCN). Here, we report that in Arabidopsis thaliana Hs2B11 functions as an immune regulator that modulates the production of elicitor-induced oxidative species, likely to favor parasitism. To elucidate the molecular basis of this immune suppression, we performed a yeast-two-hybrid screen and identified the host serine protease inhibitor AtPR-6 as a direct interactor of Hs2B11. We show that AtPR-6 acts as a positive regulator of plant immunity; its expression is induced upon nematode infection and knock-out of AtPR-6 compromises oxidative species production leading to higher susceptibility to H. schachtii infection. Conversely, AtPR-6 overexpression enhances immune responses resulting in increased resistance to BCN infection. Detailed analysis of this interaction demonstrated that Hs2B11 interacts with AtPR-6 using its carboxyl-terminal domain. AlphaFold2 predicts that the C-terminal domain forms a beta-solenoid-like structure with a ladder of serine residues organized across one of its surfaces. We propose that using this interface, Hs2B11 targets AtPR-6 via molecular titration, preventing the inhibitor from regulating host proteases that control immune signaling. These findings highlight a counter-defense strategy where a nematode effector neutralizes a specific host protease inhibitor to subvert plant immunity.

plant biology↗

bioRxiv2026

Quantified duplications of proteins within complexes across eukaryotes

Protein complexes are central to cell biology and typically verified via a combination of interaction data, complete genome sequencing and comprehensive protein-coding gene predictions for reference eukaryotes. However this data is lacking for non-reference eukaryotes. Protein complexes can be predicted in species for which no interaction data is available by mapping orthology of verified protein complex components from reference eukaryotes to predicted proteomes. Studies that map conservation of protein complex components by orthology are often limited to a small number of protein queries, an under-representation of non-reference, microbial eukaryotes and are scattered across the literature. Here, I integrate orthology and protein interaction data by mapping proteins of experimentally verified complexes to orthogroups of proteins spanning 31 diverse eukaryotes. Proteins within complex-harbouring orthogroups are retained and distributed more evenly across taxa than non-complex orthogroups. I identified 184 universal orthogroups that included orthologs of known protein complex components from all 31 eukaryotes, consistent with a conserved core repertoire, likely present in the last eukaryotic common ancestor (LECA). I generated the protein complex orthology cartographer (PCOC) suite to find significant duplications and reductions of proteins in universal orthogroups across and between eukaryotes. This revealed both multi-copy and notably single-copy proteins, in all queried species, from the exosome, spliceosome, proteasome, small-ribosomal processome, tRNA synthetases, MCM complexes and RNA polymerase III. Case analyses of Naegleria gruberi and Guillardia theta highlight taxon-specific expansions and show how broader protist inclusion improves domain-wide inference of eukaryotic protein-complex evolution.

bioinformatics↗

bioRxiv2026

A Systems Framework for Quantifying Programmability and Persistence Across Mammalian Cell Types

Cellular therapies, toxicity screening, and regenerative medicine depend on selecting mammalian cell types with optimal lifespan, persistence post-transplant, immunogenicity, and chemical resilience. This review synthesizes data from over 50 immune, parenchymal, stem, and emerging engineered cell populations--including gamma-delta T cells, iNKT cells, CAR-macrophages, and hypoimmune iPSC derivatives--drawing from in vivo lifespan studies (including 1{blacksquare}C birth-dating and deuterium labeling), engraftment dynamics, immune rejection risk, and stress sensitivity profiles. We introduce a Programmability & Persistence Score (PPS; 0-20) that integrates these features into a unified metric, complemented by Pareto frontier analysis to visualize multi-objective trade-offs. High-PPS cell types (e.g., HLA-matched HSCs, hypoimmune iPSCs, chondrocytes) are suited for long-term regenerative applications, while low-PPS sentinels (e.g., neutrophils, enterocytes) serve acute assays. We discuss mathematical extensions including multi-criteria decision analysis, fuzzy membership functions, and Bayesian frameworks that address limitations of linear additive scoring. Together, these integrated profiles support cell selection for gene editing, organ-on-chip systems, in vivo cell programming, and immunotherapy, bridging cell biology with translational engineering.

systems biology↗

bioRxiv2026

The structure-interaction model of polymyxin lipopeptides with human oligopeptide transporter 2

Multidrug-resistant (MDR) Gram-negative bacteria pose a critical global health threat, while polymyxins remain a last-line therapy. However, their clinical use is limited by nephrotoxicity. Human oligopeptide transporter 2 (hPepT2) is a membrane transporter mediating the reabsorption of polymyxins in renal cells and contributes to their nephrotoxicity, but the molecular basis of their interaction remains unclear. Here, we investigated the structure-interaction relationship (SIR) of polymyxins with hPepT2 by integrating computational, chemical, and cell biology approaches. Bioinformatic modelling predicted an outward-facing hPepT2 structure and a potential transport pathway, with polymyxins interacting at the lateral opening, particularly E214, D215, D317, D342, and E622. Transporter mutagenesis and molecular analyses confirmed that D215 is critical for polymyxin binding, while other residues influence transporter turnover and/or expression. We subsequently synthesised polymyxin analogues with modifications at Dab1, Dab3, Dab5, and Dab9 of polymyxins, which reduced interactions with hPepT2. Notably, alanine substitution at Dab3 reduced nephrotoxicity in mice while retaining antibacterial activity. Overall, this proof-of-concept study demonstrates that the hPepT2-polymyxin SIR model provides a viable strategy for developing novel, safer lipopeptide antibiotics.

biochemistry↗

bioRxiv2026

Time-step restrictions for numerical approximations of the Poisson-Nernst-Planck (PNP) equations

The Poisson-Nernst-Planck (PNP) system is an accurate model of electrodiffusion of ionic species. It is commonly used in situations where nanoscale resolution is required, for instance close to ion channels in the membranes of biological cells. The inherent stiffness of the equations has made them challenging to solve and has limited the applicability of the system. In particular, the time step required for stable solutions has typically needed to be very short (nanoseconds), which makes simulations on the time scale of an action potential (milliseconds) difficult. Recently, it has been observed that avoiding operator splitting and instead solving the concentration equations and the electrostatic equation in a coupled manner relaxes the time-step limitation considerably. However, no theoretical explanation of this observation has been provided. Here, we aim to explain why the coupled scheme allows much larger time steps. We illustrate the mechanism by considering special cases that define necessary, but not sufficient, conditions for stability. We also show that these conditions remain relevant for the fully coupled PNP model in 3D.

biophysics↗

bioRxiv2026

COSMIC-Linked Ras Mutations at the Interface Between H-Ras and PI3KγRBD Frequently Generate Affinity Increases as Well as Affinity Decreases

The three conventional isoforms of the Ras G-protein (H-, K-, N-Ras) function as molecular on-off switches that regulate a wide array of signaling pathways, including the Ras-PI3K-PIP3-PDK1-AKT pathway that is central to innate immunity and normal cell growth, and is dysregulated in many disease states. Activation of the pathway by Ras requires adequate Ras-PI3K binding affinity. Here we focus on the interface of known structure in the H-Ras:PI3K{gamma} co-complex essential to multiple pathways including directed pseudopod growth in leukocyte chemotaxis. At this interface 10 H-Ras residues, all 100% conserved between the H-, K- and N-Ras isomers, contact the Ras binding domain of PI3K{gamma} (PI3K{gamma}RBD). To investigate the degree to which the native H-Ras:PI3K{gamma}RBD interface is optimized by evolution for maximal binding affinity, 8 interfacial Ras mutations selected from the COSMIC database and the literature were introduced at the contact positions. All 8 Ras mutations were observed to alter the H-Ras:PI3K{gamma}RBD binding affinity, with 4 mutations yielding significant affinity increases and 4 yielding significant affinity decreases. These findings indicate that the native H-Ras:PI3K{gamma}RBD interface provides intermediate, rather than maximal, binding affinity. Such intermediate affinity is consistent with the substantial binding plasticity of the conserved H-, N-, K-Ras effector docking surface, which has evolved to bind a diverse array of effectors. Furthermore, the findings provide evidence that COSMIC-linked mutations at the H-Ras:PI3K{gamma}RBD interface frequently generate affinity increases as well as decreases, with potential implications for molecular mechanisms of disease and for tool development in cell biology.

biochemistry↗