Biology subjects

Wei, Y.

Publications and source records attributed to Wei, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

A Cross-kingdom Effector Modulates EDS1-dependent TIR-NLR-mediated Plant Immunity

In the ongoing battle between plants and pathogens, successful pathogens promote colonization by evolving new effectors that manipulate counteractive host responses, such as intracellular nucleotide-binding leucine-rich repeat (NLR)-triggered immunity. Here, we identify a secreted effector, PbSTMI, from the unique intracellular protist Plasmodiophora brassicae, that is conserved across divergent plant pathogens and broadly suppresses ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1)-dependent plant immunity. PbSTMI directly binds EDS1 and promotes its proteasome-mediated degradation, thereby diminishing downstream EDS1 oligomerization required for resistance. PbSTMI overexpression suppresses toll/interleukin-1 receptor (TIR) domain-containing NLR (TIR-NLR)-driven autoimmunity in Arabidopsis and further attenuates salicylic acid-mediated signaling in the autoimmune background. It blocks flg22-induced accumulation of EDS1 and regulates SA-mediated PAMP-triggered immunity (PTI) responses. Oligomerization among PbSTMI and its paralogs suggests coordinated immune suppression during different stages of P. brassicae infection and clubroot disease progression. PbSTMI suppression of EDS1-dependent TIR-NLR-mediated pathways increases susceptibility to a broad range of phytopathogens, including the biotrophs P. brassicae and Erysiphe cichoracearum and the hemibiotrophic Colletotrichum higginsianum. Together, these findings reveal a conserved pathogen strategy that disarms a central immune hub, tipping the balance of the plant-pathogen arms race decisively in favor of the pathogen.

plant biology↗

bioRxiv2026

Sequence-Defined Digital Bottlebrush Polymers for Programmable Oligonucleotide Delivery

Oligonucleotide therapeutics hold transformative potential, yet their clinical translation is hindered by delivery barriers, including rapid renal/hepatic clearance and poor organ specificity. Bottlebrush polymers conjugates have emerged as a promising vector to address these limitations, but conventional architectures with uniform backbones can only achieve an unmodifiable, rigid biodistribution profile. Here, we report a library of sequence-defined "digital" bottlebrush polymers, precisely engineered with controlled placements of chemical motifs that modify physiochemical properties - including lipids, cholesterol, and cationic groups - along a polyphosphodiester backbone. Systematic evaluation of the digital bottlebrush polymer library reveals distinct structure-property relationships and enables organ-biased systemic delivery to several traditionally difficult-to-reach tissues, including muscle and skin. In a mouse model of rheumatoid arthritis, a single dose of a spleen-homing polymer-conjugated antisense oligonucleotide targeting TNF- achieves potent knockdown and drives full functional recovery. These findings establish a versatile design framework for tailoring bottlebrush polymers to specific therapeutic applications.

bioengineering↗

bioRxiv2026

Comparative single-cell transcriptomic roadmap of mammalian fetal ovarian development

Early ovarian development establishes the cellular basis of female reproduction, yet its cellular composition and developmental dynamics remain poorly characterized across mammalian species. Here, we generated a cross-species single-cell transcriptomic atlas of early female gonadal development in cattle (E38-E112), human (PCW6-16), and mouse (E11.5-E18.5), integrating 107,930 cells across comparable developmental windows, spanning sex determination and early ovarian differentiation. We identified 11 shared gonadal cell types across three species, including germ cells and granulosa cells, and uncovered a previously undescribed bovine-specific cell population with steroidogenic features, suggesting species differences in ovarian somatic lineage development. Epigenetic regulators exhibited dynamic activation in germ cells and granulosa cells. Developmental trajectory analysis revealed shared and species-specific genes associated with germ cell and granulosa cell development, including conserved dynamically expressed genes such as TFAP2C and ZCWPW1 in germ cells and FOS and JUNB in granulosa cells. Cross-species cell type classification using a machine learning support vector machine (SVM) model revealed transcriptionally conserved cell types, such as immune cells and germ cells, while granulosa cells showed substantial cross-species divergence, associated with extracellular matrix-related gene expression. Together, our study provides a comparative framework for understanding conserved and divergent mechanisms of early ovarian development across mammals.

developmental biology↗

bioRxiv2026

linearPOA: A parallel, memory-efficient framework for Partial Order Alignment with linear space complexity

Multiple sequence alignment (MSA) is a fundamental problem in computational bioinformatics, playing a critical role in genome biology, especially in long read sequencing and assembly. One solution for representing and solving MSA is Partial Order Alignment (POA), which employs Directed Acyclic Graphs (DAGs) to represent sequence relationships. However, when facing the ultra-long, error-prone reads (e.g., >100 kbps), existing POA algorithms with quadratic space complexity become impractical due to excessive memory consumption. This paper introduces the linearPOA, which based on divide-and-conquer strategy to solve the POA, aimed at saving memory compared to quadratic space complexity algorithms like SPOA, abPOA and TSTA. Particularly notable is its capability to save up to 102.74 times memory usage when aligning sequences with 100 kbp reads, compared to the abPOA method using non-heuristic methods. The algorithm was implemented within the linearPOA library, providing functionality for POA and foundational support for sequencing analysis, like error correction for reads. The linearPOA algorithm provides memory-efficient algorithms for long-read sequencing, especially in directly assembling long reads like 100 kbp reads. AvailabilityThe linearPOA library is freely available at https://github.com/malabz/linearPOA, and the data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.15637837. Supplementary informationSupplementary information are available at BioRxiv online.

bioinformatics↗

bioRxiv2026

CMS: Achieving Uniform and High-Quality Sequencing across Challenging Non-canonical Genomic Regions

High-throughput sequencing is essential in modern biological research, yet low-complexity sequences remain challenging as they form structurally complex, non-canonical (non-B) DNA conformations that impede sequencing enzyme read-through. This leads to a long-standing trade-off: maximizing coverage introduces false positives (FP), while stringent filtering causes coverage loss and false negatives (FN). To address this, we developed CMS (Cross Mountains and Seas) on GeneMind sequencing platforms by optimizing its chemistry and enzymatic systems to traverse these secondary structures with high fidelity. Benchmarking across whole-genome (WGS) and whole-exome (WES) sequencing demonstrates that CMS addresses the trade-off by simultaneously enhancing both coverage uniformity and accuracy, notably achieving an approximately 100-fold reduction in low-coverage bins for WGS and a 70% reduction in FN insertions/deletions (INDELs) within complex non-B regions. Specifically, a synthetic G-quadruplex (G4) motif sequencing experiment demonstrates that CMS maintains a 1:1 strand ratio, effectively handling G4-induced biases where benchmarked platforms exhibit extensive depletion. These findings establish CMS as a reliable technology for the precise characterization of structural-challenging but functional-essential genome regions.

genomics↗

bioRxiv2026

Ribosome biogenesis bottlenecks reveal vulnerabilities in cancer

Cell growth requires elevated protein synthesis, which depends on the production of ribosomes. Ribosome biogenesis is a complex, multi-step pathway in which newly transcribed precursor ribosomal RNA (rRNA) undergoes coordinated processing and assembly in the nucleolus to produce the small and large ribosomal subunits (SSU and LSU).1-3 Oncogene activation stimulates rRNA transcription and processing, giving rise to enlarged nucleoli that produce thousands of ribosomes every minute.4,5 However, efficient ribosome production requires tight coordination across numerous maturation steps, and it remains unclear if elevated rDNA transcription is proportionally converted into mature ribosomes, or whether imperfect coordination constrains the output yield. Here, we quantify pre-rRNA transcription (input) and compare it with newly-assembled cytoplasmic ribosomes (output), revealing that oncogene activation reduces the efficiency of ribosome production. Using a quantitative pulse-chase sequencing approach with mathematical modeling to resolve rRNA maturation kinetics, we found that oncogene activation creates late-stage processing bottlenecks, characterized by delayed precursor maturation and increased degradation. Perturbation of late-stage ribosome biogenesis factors preferentially impaired oncogene-driven cell growth, and limited tumor growth in mouse models, suggesting that these bottlenecks represent selective vulnerabilities in cancer, created by imbalanced biosynthetic flux. Together, these findings reveal that oncogene-driven ribosome production is imperfectly coordinated across maturation steps, and suggest that capacity limits in multi-step assembly pathways may be therapeutically exploitable in cancer and other diseases.

cell biology↗

bioRxiv2026

U-Probe: universal agentic probe design for imaging-based spatial-omics

Probe design for fluorescence in situ hybridization (FISH) underpins spatial transcriptomics, three-dimensional genome studies, and clinical diagnostics, yet remains constrained by two challenges: dependence on expert knowledge for parameter selection and quality evaluation, and the inability of existing tools to accommodate the diverse probe architectures introduced by rapidly emerging methods. Here we present U-Probe, a universal and agentic probe design platform. U-Probe employs a declarative configuration system with a directed acyclic graph (DAG)-based assembly engine that supports arbitrary probe structures from established protocols such as MERFISH, seqFISH, and MiP-seq to entirely novel architectures without code modifications. Integrated LLM-based AI agents enable conversational design workflows, allowing users to specify experimental goals in natural language and receive synthesis-ready probe sequences. We validated U-Probe in three scenarios: agent-driven MiP-seq panel design for influenza-infected mouse lung tissue, genome-tiling DNA-FISH for herpesvirus detection, and a novel RCA-based ligation probe for single-nucleotide mutation discrimination. U-Probe is available as an open-source tool with CLI, Web, and agent interfaces.

bioinformatics↗

bioRxiv2026

VeloTrace Reconciles Divergent Velocity and Trajectory in Single-cell Transcriptomics with Deep Neural ODE

Cellular identity and fate transitions are governed by continuous molecular processes that form dynamic trajectories within a high-dimensional transcriptomic landscape. Existing methods attempt to model these dynamics from two complementary perspectives: trajectory inference and velocity modeling. Ideally, velocity and trajectory are dual aspects of transcriptomic dynamics where velocity is tangent to trajectory everywhere. This inherent connection between velocity and trajectory is currently absent in transcriptomic analysis. Splicing velocity are precision-limited to inadequately-sequenced genes, while trajectory inference prioritizes the modeling of global trends while omitting local dynamics. This divergence breaks the geometric continuity between local velocities and global trajectories, hindering the reliable interpretation of developmental dynamics. To reconcile trajectory inference and RNA velocity, we introduce VeloTrace, a framework that unifies them through Neural Ordinary Differential Equations (NeuralODEs). VeloTrace learns a continuous-time velocity field whose integral curves constitute the trajectory itself, while ensuring that velocities are tangent to integral paths everywhere. Leveraging a splicing quality score, VeloTrace incorporates high-quality splicing velocity as partial supervision for velocity orientation and grounding. During optimization, VeloTrace incorporates a Monte Carlo multi-time-frame supervision strategy to ensure coherence between local and global trajectorys and suppress sequencing-induced stochastic diffusion. Through refining the velocity field and cell-specific parameters for pseudo-time, expression, and velocity, VeloTrace reconstructs a smooth, local-and-global-coherent velocity-vector-guided flow in the transcriptomic latent space. This strategy ensures a complementary integration of velocity and trajectory, imputing the transcriptional kinetics for genes of insufficient strength, whose kinetics cannot be accurately portrayed by splicing velocity. In simulation benchmarks, VeloTrace captured the transcriptional dynamics of all expressed genes, even those with inadequate sequencing coverage, producing velocity directions that were most consistent with the true direction and every-where tangential across the entire process, outperforming state-of-the-art methods, including scVelo, UniTVelo, VeloVI and scTour. VeloTrace uniquely reconciles RNA velocity and trajectory inference, creating a velocity field where each cell can infer past and future transitions from its current state. Moreover, VeloTrace extends reliable velocity estimation to a broader set of genes. When applied to mouse neural stem cell differentiation data, it successfully recovers dynamics of driver genes for two developmental lineages, including those with low expression, shedding light on their regulatory roles during differentiation. This unified framework lays the foundation for more accurate modeling of gene regulation and cell fate decisions in complex biological systems.

bioinformatics↗

bioRxiv2026

Bi-cross-validation: a data-driven method to evaluate dynamic functional connectivity models in fMRI

Dynamic functional connectivity (dFC) models have become increasingly popular over the past decade for characterising time-varying interactions between brain regions. However, assessing and comparing dFC models remains challenging. Here, we introduce bi-cross-validation as a general framework for evaluating dFC models and selecting key hyperparameters, such as the number of states. By jointly partitioning the data across subjects and brain regions, bi-cross-validation enables out-of-sample evaluation without re-estimating latent states on the same data used for testing, thereby avoiding circularity. Using simulated data with known ground-truth dynamics, we show that bi-cross-validation favours models that accurately capture the underlying state structure. Applying the framework to real resting-state fMRI data, we demonstrate that bi-cross-validation naturally balances goodness-of-fit against model complexity, with performance improving and then declining as model complexity increases. Finally, we use bi-cross-validation to directly compare static and dynamic FC models, showing that dynamic models underperform static models at low spatial dimensionality, but outperform static models at sufficiently high dimensionality. Together, these results establish bi-cross-validation as a principled tool for dFC model selection, evaluation, and comparison.

neuroscience↗

bioRxiv2026

In-Situ ssDNA Isolation from dsDNA Sources as a Streamlined Pathway to DNA Origami Assembly and Testing

Scaffolded DNA origami has become a valuable nanoscale tool for applications in biomedical and physical sciences. Critical to leveraging the modular and programmable properties of DNA origami nanodevices is access to the scaffold strand, a long single-stranded DNA (ssDNA) of precise length and sequence, which is folded into a compact shape via piecewise base-pairing with many staple strands, short ssDNA oligonucleotides. Current methods to produce and manipulate long ssDNA scaffolds can be costly, time-consuming, and cumbersome. In contrast, methods to produce and manipulate the sequence of double-stranded DNA (dsDNA) are efficient and scalable. Here, we present a method for the rapid isolation of target ssDNA sequences from a variety of dsDNA sources using oligonucleotides as blocking strands that bind continuously to the undesired strand, thereby releasing the target scaffold strand. We report successful ssDNA isolation from linear and supercoiled dsDNAs of various sequences and lengths, ranging from 769 to 15,101 nucleotides. In addition to isolating ssDNA, we demonstrated this approach enables folding of DNA origami directly from dsDNA templates using both blocking and staple strands in a single-pot thermally controlled reaction. Furthermore, we explore multi-scaffold and gene-encoding DNA origami structures, expanding the framework for application-based designs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/709872v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@f7ada7org.highwire.dtl.DTLVardef@a9bab8org.highwire.dtl.DTLVardef@121b695org.highwire.dtl.DTLVardef@5a1dea_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2026

VarDCL: A Multimodal PLM-Enhanced Framework for Missense Variant Effect Prediction via Self-distilled Contrastive Learning

Missense variants are a common type of genetic mutation that can alter the structure and function of proteins, thereby affecting the normal physiological processes of organisms. Accurately distinguishing damaging missense variants from benign ones is of great significance for clinical genetic diagnosis, treatment strategy development, and protein engineering. Here, we propose the VarDCL method, which ingeniously integrates multimodal protein language model embeddings and self-distilled contrastive learning to identify subtle sequence and structural differences before and after protein mutations, thereby accurately predicting pathogenic missense variants. First, leveraging sequence and structural information before and after mutations, VarDCL generates sequence-structural multimodal features via different language models. It incorporates both global and local perspectives of feature embeddings to provide the model with dynamic, multimodal, and multi-view input data. Additionally, a Self-distilled Contrastive Learning (SDCL) module was proposed to enable more effective information integration and feature learning, enhancing the models ability to detect sequence and structural changes induced by mutations. Within this module, the multi-level contrastive learning framework excels at capturing information differences before and after mutations within the same modality; meanwhile, the feature self-distillation mechanism effectively utilizes high-level fused features to guide the learning of low-level differential features, facilitating information interaction across different modalities. The VarDCL framework not only ensures the models capacity to learn dynamic changes pre- and post-mutation but also significantly improves cross-modal information interaction between sequence and structure, thereby remarkably boosting the models performance in distinguishing pathogenic mutations from benign ones. To validate the effectiveness of VarDCL, extensive experiments were conducted. The ablation study demonstrates that all key components of VarDCL contribute significantly. On an independent test set containing 18,731 clinical variants, VarDCL achieved an AUC of 0.917, an AUPR of 0.876, an MCC of 0.690, and an F1-score of 0.789, outperforming 21 state-of-the-art existing methods. Benchmark analysis shows that VarDCL can be utilized as an accurate and potent tool for predicting missense variant effects.

bioinformatics↗

bioRxiv2026

Distinct cellular DNA methylation mechanisms underlie common and rare genetic risk for brain disorders

Noncoding genetic variation contributes to brain disorder risk, but the mechanisms through which it acts in specific brain cell types remain unclear. DNA methylation (DNAm), a highly cell type-specific regulatory layer in the brain, may mediate noncoding genetic risk, yet whether methylation at CG (mCG) and neuron-enriched non-CG (mCH) dinucleotides contribute differently to that risk remains unknown. Here we develop a deep learning framework that predicts DNAm from DNA sequence and estimates variant effects across 186 brain cell subtypes in both mCG and mCH, leveraging single-nucleus DNAm profiles from 46 brain regions. The models reveal distinct transcription factor (TF) programs underlying the two methylation contexts, with mCH-associated TFs showing stronger evolutionary constraint. Predicted variant effects agree closely with cell type-matched mQTLs in both direction and magnitude. Common variants predicted to affect mCG, particularly in excitatory neurons, show substantially greater heritability enrichment for brain-related traits than variants affecting mCH. By contrast, noncoding de novo mutations in autism preferentially perturb mCH, but not mCG, at conserved neuronal regulatory regions. This pattern is replicated across two independent cohorts totaling 5,782 probands and 4,053 unaffected siblings. Together, these findings indicate that common and rare noncoding variants contribute to brain disorders through distinct DNA methylation mechanisms.

genetics↗

bioRxiv2026

Enteric sensory neurons for nutrient detection and gut motility

The enteric nervous system (ENS) orchestrates gastrointestinal reflexes and brain-gut communication via molecularly diverse neurons. Among these, intrinsic primary afferent neurons (IPANs) are essential for detecting luminal nutrients and irritants, yet their molecular identities, sensory properties, and functions remain poorly resolved. Here, we establish a segment-resolved single-cell atlas of the murine ENS, including a comprehensive characterization of the gastric ENS. This resource defines a refined taxonomy of enteric neurons and glia and enabled the development of a genetic toolkit for molecularly defined IPANs. Using chemogenetics and calcium imaging, we discovered that myenteric neurons detect a wide range of nutrients, irritants, and cytokines. Nutrient detection depends on a functional connection between chemosensory epithelial cells and enteric neurons mediated by 5-HT--HTR3 axis. Through optogenetic analysis, we demonstrated segment-specific regulation of gut motility by different IPANs. Our work establishes a genetic and physiological framework for enteric-specific sensory mechanisms.

neuroscience↗

bioRxiv2026

A vagal gut-brain axis for feeding induced gastric secretion

Feeding begins with food-seeking and ends with nutrient absorption and waste elimination. Although meal-related sensory cues are known to shape feeding behavior, how they engage neural circuits to control digestion remains unclear. We dissected vagal gut-brain-gut pathways that mediate meal-induced gastric secretion. Vagal afferent neurons innervating the stomach were necessary and sufficient for secretion. Among those, Glp1r and Sst neurons robustly drove acid, pepsin, and gastrin secretion, whereas Oxtr and Vip neurons had little effect. Piezo2 was expressed in Glp1r mechanosensory afferents, and conditional deletion of Piezo channels attenuated distension-evoked secretion. On the efferent limb, genetically defined vagal motor neuron subsets differentially recruited secretory outputs: Cck neurons broadly increased acid, pepsin, and gastrin, whereas Grp neurons preferentially drove acid secretion. Finally, enteric circuit dissection revealed modular output control: Calb2 enteric neurons potently drove secretion with minimal motility effects, Grp enteric neurons coupled secretion to motility, and Cysltr2 enteric neurons selectively modulated acid output. Together, these results define a cell-type resolved vagal-enteric architecture for gastric control and show that secretory and motor programs can be segregated across dedicated enteric neuron subtypes.

neuroscience↗

bioRxiv2026

Postnatal conversion of methylcytosine to hydroxymethylcytosine reconfigures the human neuronal epigenome

Healthy brain development requires a coordinated process of postnatal cellular maturation throughout the first two decades of life that transforms neuronal morphology, connectivity, physiology, and gene expression. The maturation and stable maintenance of neuron identity is driven, in part, by large-scale reconfiguration of the neuronal DNA methylome. Neurons have uniquely high levels of 5-hydroxy-methyl-cytosine (hmC) compared to other cell types, yet the relative contributions of 5hmC and 5-methyl-cytosine (mC) remain unknown because most experimental assays do not distinguish these marks. We measured mC and hmC using bisulfite- and oxidative-bisulfite sequencing in excitatory and inhibitory neurons, along with mRNA and histone modifications, from the prefrontal cortex of 103 human donors, ranging from 38 days to 77 years of age. Up to half of all CG dinucleotides convert from mC to hmC in a gradual process extending throughout the first decade of life, dramatically reshaping the neuronal methylome. Asymmetric enrichment of hmC on the sense strand of actively transcribed genes increases in a linear, clock-like fashion throughout the lifespan, indicating a mechanistic link between transcription and hmC. We found that sex differences in X-linked DNA methylation in the human brain are primarily driven by hmCG rather than mCG, suggesting an important role for hmC in X-chromosome inactivation (XCI) and escape gene expression. We found key changes in 5hmC at dynamic cis-regulatory elements marked by changing cell type-specific levels of active and repressive histone modifications. Collectively, our findings reveal the dynamic trajectory of hmC in human neurons across the lifespan and highlight the association of DNA hydroxymethylation with transcription, chromatin state, and sex-specific gene regulation.

neuroscience↗

bioRxiv2026

Minipoa: A minimizer-based method for fast and memory-efficient partial order alignment

Partial order alignment (POA) has emerged as a fundamental component in long-read error correction, assembly and pangenomics. However, conventional POA algorithms are limited by high time and memory requirements, making them inefficient for large-scale datasets. Here, we present minipoa, a fast and memory-efficient POA tool that incorporates seed-chain-align heuristics, adaptive or static banding strategies, and single-instruction multiple-data optimizations. Minipoa achieves up to a 5-fold speedup over abPOA, reduces memory usage by up to 16-fold, and improves correction accuracy, while maintaining strong performance on both PacBio and ONT simulated datasets, and can be readily integrated into existing long-read error correction and assembly workflows. In multiple sequence alignment datasets, minipoa demonstrates superior computational efficiency and alignment accuracy compared with all other tested tools, achieving Total Column scores up to 2.5-fold higher than MAFFT in low-similarity scenarios. Moreover, minipoa enables multiple sequence alignment of megabase-long genomes and million-sequence datasets, demonstrated by 342 Mycobacterium tuberculosis sequences and one million SARS-CoV-2 sequences respectively. Collectively, minipoa is well positioned to become a cornerstone in the era of large-scale pangenomics.

bioinformatics↗

bioRxiv2026

Brain-wide Organization of Post-Synaptic Sites: Three Principles

Synapses are fundamental units of neural computation and structural plasticity in the brain, yet their brain-wide spatial organization in mammals remains largely unknown. Building on our previous mapping and modeling of dendritic spines and axonal boutons, we developed a statistical approach to generate over 17.99 million predicted post-synaptic sites based on the potential arbor contacts (PPSSPAC) across the dendritic arbors of 155,743 neurons spanning the entire mouse brain. By analyzing the topological features of these sites, we identified three key organizational principles. First, PPSSPAC exhibits a distinct region-specific spatial pattern at the dendritic branch level, preferentially enriching near the distal ends of dendritic segments, in contrast to the valley-like distribution of pre-synaptic sites along axonal branches. Cross-species validation against independent electron microscopy datasets from mouse and human brains, revealed a conserved and previously unquantified pattern of synaptic organization. Second, the spatial distribution of PPSSPAC effectively captures variations across cell types in the hippocampus, enabling neuron classification based on synaptic architecture. This serves as a complementary mesoscale descriptor to existing classifications based on global neuronal connectivity (source-target relationships) or local dendritic microenvironments. Third, the spatial distribution of PPSSPAC, rather than synapse density, shows remarkable correlation with independent whole-brain electrophysiological activity. The spatial organization of PPSSPAC also correlates with several gene expression datasets. These results suggest the post-synaptic pattern could be mediator linking molecular signatures, neural dynamics, and connectivity types of cells. Our study reveals, for the first time, a structural substrate for hierarchical information processing in the brain, providing new insights into neural computation and a foundation for brain digital twin models to support next-generation AI infrastructure.

neuroscience↗

bioRxiv2025

Clinical feasibility of spatial transcriptomics using discarded tissue from diagnostic breast biopsies

Spatial transcriptomics holds potential to transform cancer diagnostics, yet significant barriers still limit its clinical translation. First, access to primary patient tissue is often restricted by logistical challenges and patient hesitancy. Second, it remains uncertain whether high-quality spatial transcriptomics data can be generated from clinical biopsy sections as these are collected primarily for diagnostic purposes that do not prioritize RNA-quality. We investigated whether discarded tissue slices, generated during standard pathology procedures, could be repurposed for spatial transcriptomics and alleviate concerns about both tissue availability and quality. Here, we established a pipeline to collect and perform spatial in situ transcriptomics on discarded biopsy material from a breast cancer patient, and digitized matched pathology images, including hematoxylin and eosin and traditional histochemistry stains from adjacent sections. Our results show that spatial transcriptomics data from discarded tissue are concordant with the original pathology report, while also providing additional insights such as accurate cell type annotation, detailed spatial architecture, and quantification of biological processes relevant to breast cancer progression. Altogether, our approach using discarded pathology tissue sections provides a practical and scalable solution that would maximize the scientific value of existing clinical specimens and enable high-resolution tumor microenvironment mapping.

genomics↗