Biology subjects

Mao, S.

Publications and source records attributed to Mao, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Predicting cell-state-specific gene regulation from DNA across species

DNA encodes the cell's phenotype, but the same DNA sequence is interpreted differently in every cell type, developmental stage, and activity state. Existing deep-learning models solve only half of this puzzle: sequence-to-function models read DNA but treat cell state as a fixed label, while single-cell foundation models represent cell state but not sequence. We developed Bolero, a sequence-to-function model whose parameters condition on atlas-scale cell-state embeddings and additional biological variables to predict cell-state-specific chromatin accessibility and transcript abundance from DNA. Trained on 10.7 million cells from 33 datasets across six mammals, Bolero matched or exceeded specialised models and resolved regulatory differences between closely related cell states, including cortical regional and developmental gradients. Extrapolating cell-state embeddings extended Bolero's predictions to cell states that are absent from training. Cell-state conditioning facilitated tracing of the effects of non-coding variants to the cell types in which they act. Using an AP-1 activity score as a further continuous conditioning axis, Bolero predicted activity-regulated chromatin changes without requiring actual stimulation data. Across human cell types, we reconstructed the cooperative grammar of motif identity and spacing underlying predicted AP-1 responses. Bolero further predicted cell-type-specific chromatin accessibility and AP-1 responses in species excluded from training, extending comparative genomics from sequence conservation to predicted functional conservation. Across 84 mammals, we found that predicted AP-1 responses were less conserved than chromatin accessibility at cis-regulatory elements, driven by the human-specific sequence variants on AP-1 and cooperating motifs. By combining reference genomes with reference cell atlases, Bolero allowed genome sequences, either native or mutated, to be queried across cell states, perturbations, and different species for their chromatin and expression signatures.

genomics↗

bioRxiv2026

The Onset of Breathing at Birth Switches Off Regenerative Capacity

Many mammalian tissues, including the central nervous system (CNS), lose regenerative capacity in adulthood, but when and how this transition is initiated remains unclear. By monitoring mTOR activity, an evolutionarily conserved readout of cellular growth and intrinsic regenerative potential, we found that mTOR signaling declines within minutes after birth in murine CNS neurons and in other organs such as the heart and skeletal muscles. Focusing on retinal ganglion cells (RGCs), we show that this abrupt decline is driven primarily by CO2 exhalation following the onset of breathing after birth. Using real-time in vivo imaging of RGCs during the earliest postnatal period, we further discover that breathing-induced CO2 exhalation rapidly causes intracellular alkalinization, activates an AMPK-family kinase, SNF-related kinase (SNRK), which in turn suppresses mTOR signaling. Importantly, this process remains reversible in adulthood: CO2 inhalation or Snrk deletion restores mTOR activity in RGCs and enables robust regeneration of their axons after injury. Similar CO2 inhalation-induced mTOR reactivation occurs across multiple cell types and organs, suggesting a general mechanism by which breathing-associated CO2/pH signaling shapes the regenerative capacity across life and pointing to a potential CO2-based strategy for promoting tissue regeneration.

neuroscience↗

bioRxiv2026

EIDD-3525 is a broad-spectrum antiviral against alphavirus infection that targets the nsP4 RNA-dependent RNA polymerase

Mosquito-transmitted alphaviruses, including chikungunya (CHIKV), Mayaro, Eastern equine encephalitis, and Venezuelan equine encephalitis viruses, cause severe arthritic or neurologic disease in humans. There are currently no approved antiviral treatments against alphavirus infection. Here, we report that the purine ribonucleoside EIDD-3525 is a potent inhibitor of alphavirus replication in vitro with broad spectrum antiviral activity against the Alphavirus genus. Using a trans-replicase assay, we find that EIDD-3525 blocks alphavirus RNA replication. Mutational profiling suggests that EIDD-3525 targets the alphavirus nsP4 RNA-dependent RNA polymerase that is conserved across medically important alphaviruses. In a pre-clinical mouse model of CHIKV infection and disease, oral treatment with EIDD-3525 reduced viral tissue burdens and the severity of virus-induced disease. Collectively, our findings support the continued development of the purine ribonucleoside EIDD-3525 as an orally available antiviral therapeutic for use against a broad group of alphaviruses.

microbiology↗

bioRxiv2026

miR-34/449 miRNAs regulate choroid plexus ciliogenesis to control cerebrospinal fluid production

A developmental increase in cerebrospinal fluid (CSF) production during development is essential for neuronal growth and ventricular expansion. A key regulator of CSF production is the specialized sensory multicilia of the choroid plexus (ChP), which mediate non-canonical Sonic hedgehog (Shh) signaling to suppress water channel and ion transporter expression, thereby limiting CSF production. ChP multicilia progressively shortens during development, attenuating Shh signaling and promoting CSF production. Here, we identify miR-34/449 miRNAs as essential regulators of ChP multiciliogenesis. Whereas mutations in canonical ciliogenesis genes elevate CSF production and contribute to hydrocephaly, deletion of miR-34/449 reduces CSF volume and causes microcephaly. Loss of miR-34/449 miRNAs causes excessive basal body amplification, defective basal body docking, and failure of developmental multiciliary shortening. Consequently, miR-34/449-deficient ChP cilia remain abnormally long and fail to attenuate Shh signaling, resulting in sustained repression of water channel and ion transporter expression and reduced CSF production. Mechanistically, miR-34/449 miRNAs directly target Gmnc, a master transcriptional regulator of multiciliogenesis, to restrain basal body amplification and promote basal body docking. Together, our findings identify miR-34/449 miRNAs as critical regulators of ChP multiciliogenesis and establish the developmental remodeling of ChP multicilia as a mechanism to couple Shh signaling dynamics to developmental control of CSF production.

developmental biology↗

bioRxiv2026

Lineage recording reveals hijacked hepatic progenitor states as a common origin of HCC and ICC

HighlightsCERAMIC enables continuous and high-capacity lineage tracing of liver tumor initiation Fatty liver-associated hepatocytes acquire regenerative and premalignant cell states before malignant transformation Lineage reconstruction identifies Hep_Bi-zonal cells as the cellular origin of liver tumor initiation Transcriptional and regulatory programs distinguish tumor-fated hepatocytes from failed-to-transform lineages Peroxisomal metabolism is required for progenitor-state formation and liver tumor initiation Spatial remodeling identifies a macrophage niche associated with tumor-fated hepatocytes Dual ontogenies and functional specialization of lipid-associated macrophages shape the tumor-fated hepatocyte niche Fatty liver disease predisposes to primary liver cancer, yet the lineage routes and niche mechanisms that select rare tumor-fated hepatocytes remain unclear. Here we developed CERAMIC, a high-capacity CRISPR-Cas9 lineage recorder that co-recovers editing scars and transcriptomes from single cells, and applied it to an AKT/NRAS-driven model of MASLD-associated liver tumor initiation. Longitudinal lineage, single-cell and spatial analyses revealed a hierarchical trajectory in which bipotential bi-zonal hepatocytes (Hep_Bi-zonal), rather than pericentral-like hepatocytes (Hep_CVlike), generated regenerative and neoplastic hepatocyte progenitor states that progressed toward both hepatocellular carcinoma and intrahepatic cholangiocarcinoma lineages. Tumor-fated cells preferentially expanded along a remodeled midlobular-periportal axis and depended on ACOX1-mediated peroxisomal beta-oxidation to withstand lipotoxic and oxidative stress. Spatial and lineage analyses further identified a sequential lipid-associated macrophage niche, in which monocyte-derived LAMs engaged tumor-fated hepatocytes through an LGALS9-P4HB axis, and P4HB inhibition suppressed tumor expansion. These findings define liver tumor initiation as a lineage-restricted process licensed by peroxisomal metabolic adaptation and macrophage-derived niche signals.

cancer biology↗

bioRxiv2026

OmicOS: A Comprehensive Omics Ecosystem Infrastructure and Agent System for the AI Era

Biology has accumulated a vast ecosystem of omics methods, but much of this ecosystem remains built for expert humans rather than scientific agents. Methods are scattered across Python packages, R/Bioconductor and CRAN workflows, command-line tools, incompatible data containers and implicit object states, making even routine analyses difficult for an AI system to choose, execute and verify reliably. Here we introduce OmicOS, a comprehensive omics ecosystem infrastructure and agent system that turns OmicVerse V2, an open-source omics community, into an executable foundation for agentic biology. OmicVerse V2 provides the community substrate: scalable AnnDataOOM-compatible rust backends, agent-friendly Python algorithms for single-cell, spatial, bulk and multi-omics analysis, interfaces to single-cell foundation models, and Python-native reconstructions of historically R-centred Bioconductor/CRAN-style workflows. OmicOS makes this substrate actionable by registering analytical functions as state-aware capability contracts, allowing agents to inspect live data objects, select valid methods, execute controlled workflows and record provenance. The result is not a fixed pipeline, but a programmable omics environment in which agents compose real analyses from verified community methods rather than inventing tools. Across external and purpose-built benchmarks, OmicOS ranked first among the evaluated systems, reaching 81.2% on BiomniBench. Adding OmicVerse to a minimal agent improved task completion by up to 34.2 percentage points with qwen-3.6-35b, and controlled ablations showed that the gains came from registry-grounded execution rather than from larger models, documentation retrieval or unrestricted tool exposure. The same infrastructure scaled to atlas-sized data, reproduced R-centred workflows in Python and converted external pathology software into agent-usable skills. In a discovery task starting from a whole-body spatial map and the term "Alzheimers disease", OmicOS composed a non-canonical workflow that integrated spatial expression, genetic association, eQTL and colocalization evidence to nominate a colon epithelial risk axis centred on PICALM, CD2AP and CR1. Together, OmicVerse and OmicOS define an open foundation for AI-era omics, showing how a community of biological methods can be transformed into a reliable, extensible and agent-operable system for discovery. HighlightO_LIOmicVerse 2.0 consolidates 694 methods spanning 11 omics domains into agent-callable high-level APIs. C_LIO_LIRebuildR automatically reconstructs and evolves R/Bioconductor methods as Python-native implementations under output-equivalence gates. C_LIO_LIOmicOS establishes a state-of-the-art omics agent harness, ranking first on general omics benchmarks across models and substantially improving the analytical capability of local open-source models. C_LIO_LICompositional use of ecosystem modules nominates a colon epithelial axis associated with Alzheimers disease risk. C_LIO_LIExternal algorithm packages supporting automatic iterative evolution can be integrated into the OmicOS ecosystem. C_LI

bioinformatics↗

bioRxiv2026

Dissecting inter-focal heterogeneity and convergent immunosuppressive ecosystems in multifocal hepatocellular carcinoma through single-cell and spatial multi-omics

Background Multifocal hepatocellular carcinoma (HCC) comprises spatially separated tumor foci with distinct evolutionary trajectories, posing a major therapeutic challenge. Whether divergent lesions retain shared immune-evasion mechanisms beyond lesion-specific heterogeneity and offer cross-focal therapeutic opportunities remains unknown. Objective To resolve malignant and microenvironmental heterogeneity across tumor foci and identify conserved immunosuppressive programs underlying multifocal HCC progression. Design Matched tumour foci and peritumoural tissues from patients with multifocal HCC underwent integrated single-cell and spatial multi-omics profiling, including scFAST-seq (n=15), scATAC-seq (n=12), and Stereo-seq (n=14). In-house RNA-seq (n=13), public scRNA-seq (HRA001748) and RNA-seq datasets (TCGA-LIHC, OEP000321) were used for validation. Multiplex immunohistochemistry (n=96) and functional assays validated key findings. Results Multifocal HCC exhibited extensive inter-focal heterogeneity in malignant genomic, transcriptional, epigenetic, and evolutionary states, accompanied by diverse immune and stromal landscapes. Despite this divergence, spatially separated tumor foci converged on shared malignant states marked by elevated TMED2 expression and conserved immunosuppressive ecosystems characterized by regulatory T-cell (Treg) and SPP1+/SPRED1+ macrophage enrichment, impaired cytotoxic CD8+ T-cell immunity, and recurrent stromal-vascular remodeling. Mechanistically, malignant hepatocytes engaged SPP1+/SPRED1+ macrophages through C3/C5 complement signaling across divergent tumor foci, while macrophages and Tregs contributed to CD8+ T-cell dysfunction through CD274-PDCD1, CD80/CD86-CTLA4/CD28, and MHC-I-CD8 interactions. A tumor-intrinsic TMED2-ADAR1-dependent A-to-I RNA editing program promoted C3/C5 complement expression and secretion, linking malignant heterogeneity to conserved tumor-immune signaling. Conclusions Despite divergent malignant evolution, multifocal HCC converges on shared immunosuppressive ecosystems. The TMED2-ADAR1-C3/C5 complement axis represents a conserved tumor-immune vulnerability that may enable therapeutic strategies targeting common immune-evasion mechanisms across heterogeneous tumor foci.

cancer biology↗

bioRxiv2026

HalluCodon enables species-specific codon optimization using multimodal language models

Codon optimization is widely used in transgenic crop development, plant synthetic biology, and molecular farming to improve heterologous protein expression in plant cells. Increasing availability of plant omics data now enables optimization strategies that account for species-specific sequence features. We developed HalluCodon, a customizable framework that uses multimodal language models to design coding sequences tailored to individual plant species. The framework allows users to fine tune pre-trained protein and RNA language models with their own datasets to build species-specific codon optimization models. The current implementation includes base models trained on coding sequences and proteomes from fifteen plant species. HalluCodon generates coding sequences through a hallucination-based design strategy guided by two predictive modules that evaluate coding sequence naturalness (CodonNAT) and expression potential (CodonEXP). Benchmark tests using representative proteins show that the generated sequences reproduce host-specific codon usage patterns and support high expression levels in plant systems.

bioinformatics↗

bioRxiv2026

Hyperactive STAT1 Promotes T Follicular Helper Type 1 Cell Differentiation to Trigger Autoimmunity

ABSTRACTSHeterozygous gain-of-function (GOF) mutations in signal transducer and activator of transcription 1 (STAT1) cause an inborn error of immunity characterized by immune dysregulation, recurrent infections and various autoimmune manifestations. However, the precise pathogenic mechanism by which STAT1 GOF contributes to autoimmunity remains elusive. In our cohort, STAT1-GOF patients exhibit biased circulating follicular helper T (cTfh) populations with CXCR3+ Tfh1-like features. Using a Stat1 GOF mouse model that spontaneously developed autoimmunity, we found that overactivated STAT1 promotes Tfh differentiation and disrupted T cell-dependent humoral responses with skewed immunoglobulin class switching towards IgG2. Furthermore, STAT1 GOF directly targets to Tfh and Th1 cell signature genes and thereby drives the development of Tfh1 cells with excessive IFN-{gamma} production, which implicated in autoantibody production and the development of autoimmunity. Notably, IFN-{gamma} neutralization significantly alleviated autoimmune cellular responses and autoantibody levels in mutant mice, highlighting IFN-{gamma} blockade as a promising targeted therapy for the STAT1-GOF patients with autoimmunity. Our findings suggest that proper regulation of STAT1 activity within a reasonable magnitude is crucial for ensuring optimal host-protective humoral immunity. One-sentence summaryOveractivated STAT1 promotes Tfh1 differentiation to drive autoimmunity.

immunology↗

bioRxiv2026

Somatic mutation in human cerebellum illustrates neuron type-specific patterns of age-related mutation

Human neurodegenerative disorders are characterized by exquisite specificity for neuronal types, but the basis of this is unknown. Here, we show that cerebellar granule neurons (GN)--the most abundant neuronal type in the human brain--accumulate somatic mutations in patterns highly distinct from cerebral cortical neurons, and more closely resembling oligodendroglia and other dividing cells. We find shared mutational signatures between normal aging GNs and medulloblastoma subtypes, suggesting the GN lineage as a tumor cell of origin. Whole-genome sequence of multiple single GNs from the same donor allowed analysis of specific times of neurogenesis, revealing a rich lineage tree that includes GNs that become postmitotic 2 years or more after birth, yet migrating postnatally to populate both the cerebellar vermis and the distant cerebellar hemisphere. Our results show that neuronal type-specific somatic mutation patterns enlighten normal development, cancer origins and potentially the cell type-specificity of neurodegeneration.

neuroscience↗

bioRxiv2026

Asymmetric partition of the O-GlcNAcome in mitosis ensures binary cell fate decision

Many cellular components undergo biased segregation during stem cell division, but whether such order extends to the proteome has remained unknown. Using our real-time O-GlycoTracer, we find that O-GlcNAcylated proteins (the O-GlcNAcome) segregate asymmetrically during Drosophila neuroblast mitosis, predisposing the two daughters to distinct fates. The daughter that preserves stem cell identity inherits most of the O-GlcNAcome. This asymmetric partition requires putative O-GlcNAc readers, notably 14-3-3 proteins. We further identify the nuclear pore complex (NPC) as a major O-GlcNAc substrate in neuroblasts; Nup153, located in the nuclear basket, shows O-GlcNAc-dependent biased inheritance. Perturbing multiple steps in this segregation pathway disrupts neuroblast differentiation, reduces brain size, and causes adult learning deficits. These findings reveal a coordinated O-GlcNAc-driven mechanism for proteome-level asymmetric inheritance during neural stem cell division, with implications for neurodevelopmental disorders linked to OGT or 14-3-3 mutations.

cell biology↗

bioRxiv2025

SpaTranslator: A deep generative framework for universal spatial multi-omics cross-modality translation

Recent advances in spatial omics technologies have enabled the simultaneous analysis of multiple molecular patterns in tissue sections, offering unprecedented insights into cellular microenvironments. However, the high cost of measurements and the sparsity of data restrict the availability of paired spatial multi-omics datasets. Here, we present SpaTranslator, a deep generative framework that integrates graph neural networks with an adversarial variational generative model to fully capture spatial characteristics and enable effective cross-modality translation of spatial omics data, enabling simulation of paired spatial multi-omics data from single-omics measurements. Extensive experiments demonstrate that SpaTranslator consistently outperforms baseline methods in both clustering accuracy and biological coherence across various real-world scenarios, including spatial transcriptomics-epigenomics and spatial transcriptomics-proteomics translation tasks. Furthermore, SpaTranslator provides biologically meaningful insights through marker genes and proteins recovery, motif enrichment analysis, and gene regulation inference. Our work offers an effective and adaptable solution for spatial multi-omics cross-modality translation, supporting a broad range of biological and biomedical research.

bioinformatics↗

bioRxiv2025

A Spatially Resolved Atlas of Alternative Polyadenylation Across 18 Human Tissues and 76 Disease States

Alternative polyadenylation (APA) is a key regulator of gene expression and cellular dynamics, yet systematic investigations of spatially resolved APA across diverse human tissues remain limited. Here, we developed SpatialAPA (https://github.com/Omicslab-Zhang/spatialAPA), a framework that benchmarks multiple APA identification methods and integrates spatial APA data with gene expression and cellular dynamics at spatial resolution. Applying SpatialAPA to 363 spatial transcriptomic data from 56 projects across 18 human tissues and 76 diseases, we constructed a spatially resolved APA atlas comprising 346,932 APA events across 52,175 genes. This atlas reveals organ-specific APA patterns and provides new insights into how APA regulates tissue homeostasis and disease progression beyond transcriptional control. To ensure cross-sample comparability, we applied batch correction, while spatial cell deconvolution uncovered cell-type-specific dynamics and interactions. In triple-negative breast cancer, integrated spatial and single-cell analyses identified TSPAN8-positive epithelial subpopulations whose distinct APA regulation and transcriptional programs drive differentiation and malignant progression. To facilitate community access, we developed an online platform (http://www.biomedical-web.com/spatialAPAdb/home) for exploring APA, gene expression, and cellular dynamics in health and disease. Together, this study establishes the first comprehensive spatial APA atlas, providing a valuable resource and analytical framework for investigating molecular mechanisms and therapeutic targets.

bioinformatics↗

bioRxiv2025

STARNet enables spatially resolved inference of gene regulatory networks from spatial multi-omics data

Biological tissues are composed of distinct microenvironments that spatially orchestrate gene expression and cell identity. However, the regulatory principles governing domain-specific cellular functions remain poorly understood due to the lack of effective methods for mapping gene regulatory networks (GRNs) in situ. To address this gap, we introduce STARNet, a representation learning approach that leverages heterogeneous hypergraph modeling of spatial transcriptomic and epigenomic data to resolve tissue-domain-specific regulatory interactions. By integrating graph neural networks with contrastive learning in a self-supervised framework, STARNet learns unified embeddings that preserve both multi-modal molecular features and anatomical spatial context, enabling accurate and domain-resolved GRN reconstruction within complex tissues. Benchmarking on both simulated and real datasets demonstrates that STARNet achieves state-of-the-art performance. We further demonstrate its broad applicability across diverse biological contexts, including neural development, genetic disease risk, and drug-induced developmental toxicity. In the mouse brain, it delineates region-specific regulatory networks and reconstructs spatiotemporal programs underlying neural stem cell differentiation. In human genetics, it provides a mechanistic link between genotypes and phenotypes by showing how genome-wide association study (GWAS) variants for complex diseases perturb hippocampus-specific GRNs. In developmental toxicology, STARNet reveals that drug-induced disruptions of GRNs in defined embryonic regions underlie tissue-specific vulnerability. Collectively, STARNet offers a powerful and versatile framework for resolving the spatial regulatory logic of complex tissues, providing multi-angle insights into tissue patterning, development, and disease mechanisms.

bioinformatics↗

bioRxiv2025

Structome-AlignViewer: On Confidence Assessment in Structure-Aware Alignments

Protein structure-based comparison provides a framework for uncovering deep evolutionary relationships that can escape conventional sequence-based approaches. Encoding three-dimensional protein structures using a simplified structure-aware alphabet, can lead to compact, comparable strings that retain key spatial relationships. Although this enables comparison, structure-aware alignments can experience misaligned regions, particularly when comparing proteins with substantial divergence in fold architecture. To address this, a web-based resource, Structome-AlignViewer, is introduced in this work for evaluating the quality of structure-aware alignments through both spatial mapping of alignment columns to protein structures and quantitative confidence scoring. Confidence is computed from pairwise structural substitutions between adjacent inputs and normalized within each alignment to highlight relatively well-supported columns. To provide broader context, thousands of alignments from established structural classification systems were analysed, allowing for an empirical comparative statistic to be derived to assess alignment quality. Option to exclude gap-rich regions enable users to refine alignments and focus on conserved structural cores. This approach provides an interpretable method for assessing structural alignment quality and supports more robust comparative and evolutionary analyses. Structome-AlignViewer is freely available at https://biosig.lab.uq.edu.au/structome_alignviewer/.

bioinformatics↗

bioRxiv2025

Cell-type-specific patterns and consequences of somatic mutation in development and aging brain

Elucidating the role of somatic mutations in cancer, healthy tissues, and aging depends on methods that can accurately characterize somatic mosaicism across different cell types, as well as assay their impact on cellular function. Current technologies to study cell-type-specific somatic mutations within tissues are low-throughput. We developed Duplex-Multiome, incorporating duplex consensus sequencing to accurately identify somatic single-nucleotide variants (sSNV) from the same nucleus simultaneously analyzed for single-nucleus ATAC-seq (snATAC-seq) and RNA-seq (snRNA-seq). By introducing strand-tagging into the construction of snATAC-seq libraries, duplex sequencing reduces sequencing error by >10,000-fold while eliminating artifactual mutational signatures. When applied to 98%/2% mixed cell lines, Duplex-Multiome identified sSNVs present in 2% of cells with 92% precision and accurately captured known sSNV mutational spectra, while revealing unexpected subclonal lineages. Duplex-Multiome of > 51,400 nuclei from postmortem brain tissue captured sSNV burdens and spectra across all major brain cell types and subtypes, including those difficult to assay by single-cell whole-genome sequencing (scWGS). This revealed for the first time that diverse neuronal and glial cell types show distinct rates and patterns of age-related mutation, while also directly discovering developmental cell lineage relationships. Duplex-Multiome identified clonal sSNVs occurring at increased rates in glia of certain aged brains, as well as clonal sSNVs that correlated with changes in expression of nearby genes, in both neurotypical and autism spectrum disorder (ASD) individuals, directly demonstrating that somatic mutagenesis can contribute to gene expression phenotypes. Duplex-Multiome can be easily adopted into the 10X Multiome protocol and will bridge somatic mosaicism to a wide range of phenotypic readouts across cell types and tissues.

genomics↗

bioRxiv2025

Neurons accumulate disease-specific somatic genomic changes across taupathologic states in Alzheimer's disease

Tau deposition within neurons marks Alzheimers disease (AD) neuropathology, suggesting that tau may contribute to cellular dysfunction and death. In AD, somatic mutations accumulate in neurons, with features that suggest deleterious effects on cellular function. To examine the relationship between tau and somatic mutation, we isolated neurons according to tau pathology and performed single-cell whole-genome sequencing on tau+, tau-, and tau-agnostic neurons from 13 individuals with AD, as well as neurons from 15 control individuals. We found that AD neurons, regardless of tau status, exhibited an increased burden of somatic single-nucleotide variants (sSNVs) and insertions and deletions (sIndels). Mutational signature analyses revealed disease-specific patterns, including sIndels characterized by two-basepair (2bp) deletions, indicating shared mutagenic mechanisms in AD neurons across tau pathologic cell states. Somatic mutations are associated with tissue-wide tau pathology, suggesting that tangles do not confer cell-autonomous genotoxicity to neurons and that non-tangle components drive somatic mutation in AD.

genomics↗

bioRxiv2025

Optimal distributions of receptors on arbitrarily shaped cell surfaces

Efficient absorption of signaling molecules and carbohydrates through receptors on cell surfaces is crucial for various biological processes. While ubiquitous patterns of receptor distributions, including polar localization in rod-shaped cells, have been widely observed experimentally, their underlying evolutionary advantage is unclear. In this work, we study how spatial distributions of receptors on cell surfaces affect the total flux entering the cell. We innovate a method by which one can calculate the fluxes through all receptors using linear equations, which applies to arbitrarily shaped cells. Our theories recover previous results for spherical cells and further show that the flux through each receptor is spatially dependent in non-spherical cells. In particular, the fluxes are the highest near the poles in rod-shaped cells and the highest near the invagination in defective spherical cells. Surprisingly, we prove that the optimal receptor distribution on an arbitrarily shaped cell maximizing the total flux is precisely the charge density distribution on an ideal conductor of the same shape, which agrees with numerical simulations. Our work unveils the evolutionary origin of receptor localizations.

biophysics↗