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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗

bioRxiv2026

Structural characterization the LlaI anti-phage defense system reveals insights into the evolution of nucleotide specificity and the organization of DNA binding in McrBC restriction complexes

Canonical McrBC enzymes are nucleotide-powered, motor-driven endonucleases that bind and cleave modified bacteriophage DNA. Non-canonical McrBC homologs like LlaI and BsuMI are distinguished by a unique three-gene organization and the ability to target DNA site-specifically. Here, we report the atomic-resolution crystal structures of the DNA-binding module LlaI.R1 and AAA+ motor LlaI.R2 from the Lactococcus lactis LlaI anti-phage defense system. The crystallized LlaI.R2 hexamer traps two distinct active site conformations that correlate to different states of the nucleotide hydrolysis cycle and reveal that the organization of the critical catalytic machinery present in canonical McrB homologs is also conserved in non-canonical R2 proteins. Although canonical McrB homologs are strictly GTP-specific, we find that the R2 proteins from LlaI and BsuMI do not discriminate between different nucleotides, even when in complex with their respective R1 partners. Using mutagenesis, we define surfaces on the LlaI.R1 structure that are critical for DNA-binding and interaction with LlaI.R2. These observations support computational modelling of the assembled LlaI restriction system bound to DNA. Together, our data provide new insights into the evolution of nucleotide specificity in McrBC restriction complexes and the molecular mechanisms governing McrBC-catalyzed DNA translocation and cleavage.

biochemistry↗

bioRxiv2026

FibrilNet maps conserved and tissue-specific molecular environments across systemic amyloidoses

Systemic amyloidoses are initiated by distinct amyloidogenic precursor proteins but frequently contain recurrent extracellular, complement, lipid-transport and matrix-remodelling components. Whether these recurrent proteins form a conserved systems-level environment across amyloid diseases, and how strongly that environment depends on precursor and tissue context, remains unresolved. We developed FibrilNet, a network framework that integrates experimentally defined amyloid proteomes with a human protein protein interaction graph and Gene Ontology derived semantic information. FibrilNet compares topology-only random walk with restart (RWR) with ontology aware semantic RWR in frozen leave-one-out module reconstruction and precursor-seeded prioritization tasks. The human graph contains 17,997 proteins and 925,977 physical interactions, with a 9-dimensional semantic representation of interaction context. In expanded cardiac transthyretin amyloidosis (ATTR), semantic-RWR increased mean reciprocal rank (MRR) from 0.00167 to 0.05015 and Recall@100 from 0.0199 to 0.3377, improving 132 of 151 held-out targets. Significant semantic gains were also observed in renal serum amyloid A amyloidosis (AA) and leukocyte chemotactic factor 2 amyloidosis (ALECT2). Across compact ATTR, light-chain amyloidosis (AL), AA and ALECT2 modules, APCS, VTN and TIMP3 formed a direct four-disease recurrent core, while APOE occurred in three of four modules. A tissue-aware ATTR analysis showed limited overlap between cardiac and neurologic modules (19 shared proteins; Jaccard 0.0569). In the hTTR-A97S peripheral-nerve model, semantic-RWR significantly improved reconstruction of the 202-protein mapped neurologic module, with the strongest evidence concentrated in the downregulated proteomic program. TTR-seeded propagation improved with semantic information but remained weak in absolute terms, separating precursor identity from the distributed downstream molecular environment. These results support a multilayer model in which a restricted conserved amyloid environment coexists with precursor-, tissue- and disease-specific organization

bioinformatics↗

bioRxiv2026

Profiling and modulating astrocyte borders at injected biomaterials in mice

Astrocyte border formation is a conserved neuroprotective response to neural tissue disruption, yet astrocyte border states at implanted biomaterials remain less well characterized than injury responses. Here, we developed the Astrocyte Border Characterization (ABC) Tool, which leverages a shear-thinning, injectable biomaterial to locally deliver astrocyte-specific RiboTag AAVs and small molecule regulators in the mouse striatum, enabling molecular profiling and phenotypic modulation of astrocyte border (AB) cells. Spatially precise delivery of AAV using the ABC Tool yielded enhanced specificity and robust RiboTag expression in AB cells from 7-70 days post injection. Temporal transcriptomic profiling of AB cells revealed predominantly acute, transient changes in genes governing dedifferentiation, proliferation, metabolic reprogramming, and inflammation regulation. Persistent changes accounted for only 14% of regulated genes but involved critical gain of functions in immune regulation and host defense that mirrored astrocyte border responses at chronic CNS injuries. Local delivery of indiscriminate or astrocyte-selective ablation molecules delayed, rather than prevented, border formation, ultimately yielding thicker astrocytes borders with increased inflammation and fibrosis at the biomaterial-tissue interface. Conversely, local delivery of {beta}-hydroxybutyrate (BHB) from the ABC Tool altered key aspects of the transcriptional reprogramming to attenuate chronic astrocyte reactivity and prevent biomaterial contraction without exacerbating inflammation or fibrosis. Our findings establish the ABC Tool as a bioassay for studying and manipulating astrocyte borders at implanted biomaterials and identify focal metabolic regulation as a strategy to modulate AB cell phenotypes and enhance the CNS biocompatibility of biomaterials.

neuroscience↗

bioRxiv2026

Data-driven spectroscopic dictionaries and detector-calibrated inference for photon-limited Raman hyperspectral imaging of living cells

Label-free Raman imaging of living cells is photon limited: at exposures compatible with cellular dynamics, single-pixel spectra carry about one count per channel on a dominant smooth background. We present an unmixing framework in which the decoder of a physics-constrained autoencoder is restricted to a data-driven spectroscopic dictionary: band centers,widths, and pseudo-Voigt shapes are measured from the dataset and fixed, and the network learns only nonnegative band amplitudes, a smooth B-spline background, and a per-pixel gain.First, on slit-scanning images of HeLa cells (532 nm) the dictionary yields spike-free component spectra that read as band tables, including a resonance-enhanced cytochrome-c-associated component matching literature spectra, and the most stable decomposition against the component number. Second, the dictionary and initialization calibrated at 1 s exposure perline transfer to 100 ms per line (12 s sweeps): cytochrome-c spectral identity survives a single sweep (correlation 0.92) while its map remains photon limited; the dictionary provides spectral physicality, and the transferred initialization prevents a structural collapse that global map correlations miss; in a measurement-derived phantom the dictionary estimator holds thecytochrome-c spectrum to 17-19{degrees} spectral angle at 100 ms, where classical factorizations and free decoders lose it (55-64{degrees}). Estimation on the count-equivalent detector output uses a calibrated shifted-Poisson quasi-likelihood. Third, evaluation must be time matched:correlation against a separately acquired reference saturates through slow specimen drift and acquisition mismatch rather than photon noise, and the self-consistency of learned denoisers is inflated by shared bias; time-matched self-consistency and independent cross-checks areproposed.

cell biology↗

bioRxiv2026

Linking continuous behavior to aesthetic enjoyment in a walkable virtual-reality museum tour: effects of agency and a painting-level analysis framework

Museum visits typically follow curator-defined routes that constrain how visitors shape their own experience, yet choice is widely held to heighten engagement, autonomy, and enjoyment. Virtual reality (VR) offers a setting in which to study these processes because it combines ecological immersion with precise, continuous behavioral measurement. We investigated (i) whether VR- derived behavioral signals are associated with self-reported enjoyment during a virtual museum tour, and (ii) whether the level of agency afforded to visitors influences enjoyment. Forty-eight adults completed a room-scale, life-size VR tour (8 * 4 m) of seven paintings from the Tel Aviv Museum of Art, each accompanied by a synchronized audio guide. Synchronized gaze and head- position streams were logged continuously (50 Hz) and segmented into painting-level viewing episodes using a trial-and-tile pipeline that intersects each painting's trial interval with an empirically defined spatial window in front of the canvas. Participants were randomly assigned to one of three agency conditions, Active (choice before every artwork), Semi-Active (choice for the first three), or Passive (fixed route),while the artwork sequence was held identical. Self- reported enjoyment at the tour and painting levels did not differ reliably across agency conditions. Among VR-derived measures, gaze engagement during the audio guide showed the clearest (though modest) association with painting-level liking, whereas locomotion and pacing measures were weak and inconsistent predictors. Agency nonetheless reliably modulated several gaze- and time-based viewing measures. The findings reveal a dissociation between subjective enjoyment and the micro-structure of viewing, and establish a reusable framework for full-tour, painting-level behavioral analysis in immersive settings.

neuroscience↗

bioRxiv2026

Zebrafish larval nitrogen excretion is flexible and resilient to loss of rhesus glycoproteins

Nitrogenous waste excretion is essential for all developmental stages of fish. Embryonic fish excrete urea, transitioning to cutaneous and later branchial ammonia excretion. In zebrafish, ammonia excretion involves rhesus glycoproteins Rhbg and Rhcgb in keratinocytes and ionocytes, but the developmental moment they appear in the gill remains unclear. Potential redundancy between Rhbg and Rhcgb in ammonia excretion is also not fully investigated, nor is the difference in response to low pH. We hypothesized that rhesus glycoproteins are partially redundant, and that they differ in their response to low pH as ammonia excretion enables ionocytes to exchange Na+ and H+ (Rh-NHE-metabolon). We predicted that a loss of rhbg or rhcgb induces compensatory responses. We characterized the transition from urea to branchial ammonia excretion from 0 to 8 days-post fertilization (dpf) and the response to pH 5.0 on the expression and localization of rhesus glycoproteins in control zebrafish and rhbg or rhcgb-crispants. Effects of high external ammonia (HEA, 500 M NH4Cl) and 10 mM HEPES-buffering were further characterized in rhcgb-crispants. Rhag and Rhbg appeared in the gill at 5 dpf, while Rhcgb appeared at 6 dpf. A loss of rhbg or rhcgb did not impact baseline N-excretion, illustrating that zebrafish can maintain ammonia excretion without the full complement of rhesus glycoproteins. We observed no compensatory increase in rhesus glycoproteins, but expression of the transporter hippocampus-abundant transcript 1b increased. HEA-exposed rhcgb-crispants switched to urea as primary nitrogen waste. Together, these findings underline the plasticity of the larval in dealing with nitrogenous waste.

physiology↗

bioRxiv2026

Tendon-derived injectable bio-instructive gel augments tenogenic differentiation of iMSC-SCX cells and extracellular matrix remodeling

In the US, 33 million musculoskeletal injuries have been reported per year, with 50% involving tendons and ligaments in both athletic and aging populations. Tendon repair often results in the formation of biomechanically inferior scar tissue rather than functional regeneration. Local cell therapy is garnering significant interest in tendon repair because it provides a targeted means to repopulate defects with potent therapeutic cells. Here, we developed a porcine tendon-derived thermoresponsive extracellular matrix hydrogel (TG) as an injectable, bio-instructive carrier for scleraxis-overexpressing induced mesenchymal stem cell derived tenocytes (iTenocytes), with the goal of improving cell retention and overall transplantation success. TG was compositionally distinct from purified collagen (PC, from rat tail type 1 collagen) and exhibited favorable material properties for a minimally invasive percutaneous strategy, including thermoresponsive gelation, shear-thinning injectability, retention at the injection site, and controlled biodegradation. In vitro, TG supported three-dimensional cell residence, promoted cell interconnectivity and redistribution at the matrix interface, and increased collagen type I release from iTenocytes compared to those embedded in PC. Transcriptomic and proteomic analyses further showed that TG enhanced programs associated with tenogenic maturation, extracellular matrix assembly, focal adhesion, mechano-transduction, and remodeling. In a rat Achilles tendon partial defect model, both optical imaging and histological analyses demonstrated retention of iTenocytes at the injection site. Additionally, confocal imaging demonstrated retention of iTenocytes within the defect site for up to 10 days. Together, these findings identify TG as a biofunctional injectable carrier that supports the tenogenic characteristics of iTenocytes and enhances their local persistence after transplantation.

cell biology↗

bioRxiv2026

Polystyrene Microplastics Accelerate Antibiotic Resistance Evolution and Exacerbate Pathogenicity in Acinetobacter Baumannii

Microplastics are pervasive environmental contaminants and are increasingly detected in contexts relevant to human health, yet their effects on antimicrobial resistance, host-pathogen interactions, and infection outcomes remain poorly understood. Here, we show that exposure to polystyrene microplastics alters both antibiotic resistance evolution and pathogenic behavior in Acinetobacter baumannii, a leading cause of multidrug-resistant hospital-acquired infections. Using experimental evolution under antibiotic selection, we demonstrate that microplastic exposure accelerates resistance emergence across multiple antibiotic classes. Although microplastic exposure did not uniformly enhance biofilm formation, it modestly impaired macrophage-mediated bacterial clearance, suggesting broader effects on bacterial adaptation and host interaction. In vivo, microplastic-associated infection resulted in more severe disease, characterized by increased lung tissue damage and reduced survival in a murine model of A. baumannii pneumonia. Together, these findings identify microplastics as ecological modifiers of bacterial adaptation, linking widespread plastic pollution to enhanced antimicrobial resistance and worsened infectious disease outcomes.

microbiology↗

bioRxiv2026

Rewiring of Integrin Signaling and Cell-cycle Deregulation Drive SMARCB1-Deficient Epithelioid Sarcoma

Epithelioid sarcoma (EPS) is an aggressive soft-tissue sarcoma characterized by loss of the chromatin-remodeling subunit SMARCB1. The oncogenic programs driving EPS remain poorly understood. Through CRISPR loss-of-function screens, we identified conserved dependencies on integrin signaling components and cyclin-dependent kinases (CDKs). Genetic disruption of integrin subunit alpha V (ITGAV)-centered signaling impaired epithelioid cluster formation and reduced MYC expression. SMARCB1 re-expression phenocopied these effects and revealed that SMARCB1 loss selectively represses context-dependent integrin subunits while preserving an ITGAV-centered pro-survival axis, associated with altered BAF complex occupancy. Analysis of EPS cell lines and primary tumors revealed frequent genetic or epigenetic inactivation of CDKN2A/p16, indicating that loss of cell-cycle control is a key cooperating event in EPS development and providing a mechanistic rationale for targeting CDK4/6. Together, these findings establish integrin-driven oncogenic signaling coupled with disruption of cell-cycle control as a central oncogenic program in EPS and identify actionable therapeutic vulnerabilities.

cancer biology↗

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗

bioRxiv2026

Ex vivo glioblastoma migration phenotypes define clinical recurrence and tumor heterogeneity

Glioblastoma's pronounced migratory capacity underlies its diffuse invasion, presenting a formidable barrier to successful treatment. Ex vivo characterization of glioblastoma cells isolated from freshly resected clinical samples under physiologically relevant conditions revealed two distinct migratory phenotypes, Fast Migrating (FM) and Slow Migrating (SM). These phenotypes reflect distinct mechanosensitivity profiles and are associated with pharmacological responses that support the motor clutch model of cell migration. Analysis of genes associated with these phenotypes revealed a transcriptomic signature that closely associated with in vitro cell migration, histological invasion in patient specimens, and clinical survival. Single-nucleus RNA sequencing revealed that FM and SM cells coexist within a single glioblastoma, with FM cells enriched at the periphery and SM cells localized to the tumor core. Collectively, our study demonstrates the utility of ex vivo glioblastoma characterization, allowing decoding of tumor heterogeneity and clinical prognostication as well as providing a framework for deconvoluting the complex cancer phenotype.

cancer biology↗

bioRxiv2026

VDAC1 regulates stress-associated matrix localization of DJ-1 to support mitochondrial homeostasis and neuronal survival

DJ-1 is a redox-sensitive protein implicated in early-onset Parkinson's disease, and its mitochondrial localization protects against oxidative stress, but the mechanisms regulating its submitochondrial targeting and functional impact on mitochondrial integrity remain poorly understood. We identify voltage-dependent anion channel 1 (VDAC1) as a regulator of the submitochondrial distribution of DJ-1 during stress. Endogenous DJ-1 interacted with VDAC1, and loss of VDAC1 reduced stress-induced DJ-1 accumulation within the mitochondrial matrix. VDAC1-deficient neurons exhibited mitochondrial fragmentation, impaired oxidative phosphorylation, reduced ATP levels, altered reactive oxygen species (ROS) responses, and increased sensitivity to MPP+;. Matrix-targeted, but not outer-membrane-targeted, DJ-1 rescued basal, ATP-linked, and maximal respiration, improved mitochondrial morphology, and enhanced neuronal survival. ATP synthase inhibition also rapidly increased mitochondrial DJ-1, suggesting bioenergetic stress promotes its mitochondrial accumulation. Our findings identify compartment-specific localization as a key determinant of DJ-1 function and establish VDAC1-dependent matrix targeting as a critical mechanism supporting mitochondrial integrity during stress.

neuroscience↗

bioRxiv2026

Unbiased and scalable reduction of diverse bacterial genomes

The genome is a complex, integrated system where the functions and regulatory interactions of its many components remain poorly understood. Genome minimization aims to reduce genomic complexity by removing non-essential elements to reveal the fundamental building blocks of cellular life. However, current minimization strategies are often slow and species-specific due to a reliance on prior information, and limited to producing single, isolated strains, which obscures the diverse ways a genome can adapt to large-scale DNA removal. Here we show the development and application of Stochastic Lineage-based Iterative Minimization (SLIM) a modular, high-throughput platform for unbiased genome reduction across phylogenetically diverse bacteria. We apply SLIM to generate a library of genome-reduced Escherichia coli lineages. We then interrogate the lineages, identifying both universal and lineage-specific transcriptional and translational reprogramming in response to deletions. We demonstrate that these expression dynamics drive environment-dependent fitness, allowing us to pinpoint a single gene deletion in one genome-reduced lineage as the driver of a measurable environmental growth defect. Beyond E. coli, we successfully deploy SLIM in phylogenetically distinct bacterial taxa to rapidly reduce the genomes of Shigella flexneri and Pseudomonas putida, distinct genus and order respectively from E. coli, without species-specific optimization. Our results establish a scalable, generalizable framework for navigating the vast landscape of minimized genomes, providing a powerful new tool for functional discovery and the rational design of synthetic genomic chassis.

synthetic biology↗

bioRxiv2026

Sequential Molecular Interactions Shape Aβ42 Aggregation, Propagation, and Toxicity

Protein aggregation is a context-dependent process in which the molecular environment can influence the properties of the resulting assemblies. In biological systems, these interactions can occur sequentially, as aggregates formed in one cellular or tissue context may encounter different molecular partners and act as seeds in subsequent aggregation events. Here, we used sequential seeding as a controlled experimental model of this temporal and contextual complexity to investigate how prion-like sequences from the gut microbiome modulate amyloid-{beta} aggregation across successive aggregation cycles. Combining kinetic, biophysical, conformational, and toxicity analyses, we show that early interactions with exogenous peptides modify the properties of first-generation A{beta}40- and A{beta}42-derived seeds, resulting in propagated A{beta}42 assemblies with distinct molecular and functional properties. These findings support an Interaction History model in which exogenous sequences bias the emergence of aggregate populations whose properties and subsequent propagation depend on the molecular contexts experienced during earlier aggregation events. Overall, our results present A{beta} aggregation as a history-dependent process and suggest that single-step assays may fail to capture aggregate diversity that emerges across successive aggregation cycles.

biochemistry↗

bioRxiv2026

Timing of transient darkness shapes carbon-nitrogen metabolism and sugar signaling in sugarcane

Fluctuating light is common in field environments. Yet, the mechanisms by which C4 crops coordinate carbon and nitrogen metabolism during short-term carbon deprivation remain poorly understood. Here, we imposed transient darkness at different phases of the diel cycle to assess how the timing of light loss affects photosynthesis, carbohydrate turnover, amino acid dynamics, and sugar-sensing pathways in commercial sugarcane leaves. Early-day darkness significantly impaired photosynthetic induction and revealed a temporal disconnect between stomatal and metabolic limitations, whereas midday and late-day treatments caused temporary, time-specific disruptions in carbon assimilation. These shifts altered the balance between sucrose preservation and catabolic mobilization, leading to treatment-dependent changes in starch reserves and free amino acids. Core circadian components largely maintained their phase relationships, but their amplitudes varied across treatments, consistent with partial decoupling from carbon status. Darkness also reorganized energy signaling, with SnRK1 and DIN6 responses associated with greater declines in sucrose. Notably, trehalose-pathway transcripts showed marked changes in network connectivity, with ScTPSIIG consistently emerging as a highly connected candidate associated with photosynthetic performance, water-use traits, sugar sensing, and amino acid metabolism. Overall, these results indicate that the timing of carbon limitation and residual sucrose availability shape distinct metabolic responses, while trehalose metabolism provides a candidate regulatory layer coordinating carbon-nitrogen adjustment during the diel cycle, highlighting class II TPS proteins as targets for functional investigation of metabolic resilience in sugarcane.

plant biology↗

bioRxiv2026

Constraining Palaeogeography and Palaeotides for the Cambrian using cnidarian medusae

The ocean tides influence key Earth system processes at a range of spatial and temporal scales. It is known that the geometry of ocean basins is the leading controller of tidal energetics, so well-constrained palaeogeographic reconstructions and tidal properties for Earths past are imperative when investigating other Earth system processes. Here, we present a novel way to constrain both deep-time tidal model results and reconstructions, by combining palaeoecology with sedimentology. We compare new palaeo-tidal model simulations for the Cambrian period, significant for the early origin and radiation of major animal fauna, to tidal proxies. One of the most abundant soft-bodied organisms preserved during this time are cnidarian medusae (jellyfish). A total of 17 cnidarian medusae localities were obtained through the literature, which had an adequate global distribution and occurred at regular intervals throughout the period of study. In some locations there were also estimates of palaeo-tidal range. Our results show a good agreement between the simulations and proxy data. In the few locations where there is disagreement, it is proposed that the palaeogeographic reconstructions are missing details, e.g., island chains, and our results allow for the palaeogeographic reconstructions to be improved. The proxy method presented is promising and can be applied to other time-periods with different marine fossils, particularly at evolutionary and extinction periods where the marginal marine environment is of importance.

paleontology↗

bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗