Biology subjects

Li, Z.

Publications and source records attributed to Li, Z..

At least 217 records · Page 12Linked to original sources

bioRxiv2026

Estrogen receptor-positive cell line xenograft models recapitulate metastatic dissemination and endocrine response of invasive lobular breast carcinoma

HighlightsO_LIER+, CDH1-deficient ILC xenografts recapitulate single-file histology in vivo C_LIO_LISpontaneous metastases faithfully mirror unique ILC clinical dissemination patterns C_LIO_LIER+ leptomeningeal metastases confirmed histologically in ILC xenograft models C_LIO_LIFulvestrant inhibits ILC tumor growth and metastatic burden, improving survival C_LIO_LIER signaling is enriched in ILC brain metastases, with upregulation of RET C_LI Invasive lobular breast carcinoma (ILC), the most common special histological subtype of breast cancer, is characterized by nearly universal expression of estrogen receptor alpha (ER) and unique sites of metastases, neither of which is fully recapitulated by genetically engineered mouse models. Using reporter-labeled ILC mouse xenografts, herein we used mammary fat pad, tail vein and intracardiac orthotopic growth to analyze spontaneous and experimental metastasis and gene expression. We observed ER-positive primary tumors with single-file histology and collagen deposition, and spontaneous metastasis from the mammary fat pad to bones, ovaries, and brain including the leptomeninges, thereby closely mirroring the growth and metastatic spread of human ILC. Brain metastases showed strong ER staining, confirmed by sequencing analyses which identified estrogen signaling as top activated pathway, and the lesions exhibited robust response to endocrine therapy. In summary, we report endocrine responsive mammary fat pad, tail vein and intracardiac xenografts that faithfully demonstrate unique ILC features and can serve as invaluable pre-clinical translational platforms for validating candidate ILC genetic drivers and testing novel therapeutics.

cancer biology↗

bioRxiv2026

UniST: A Unified Computational Framework for 3D Spatial Transcriptomics Reconstruction

Spatial transcriptomics (ST) enables the measurement of gene expression in its native spatial context, yet most ST datasets are acquired as two-dimensional (2D) sections. Consequently, the underlying three-dimensional (3D) organization of tissues is only partially observed, and 3D ST data generated from serial sections are typically sparse and heterogeneous, with substantial tissue loss and missing measurements. These limitations pose major analytical challenges for reconstructing coherent 3D tissue architecture, rather than issues of experimental scalability alone. Here, we present UniST, a unified generative artificial intelligence (AI) framework designed to computationally reconstruct dense and continuous 3D ST landscapes from sparse serial sections, without altering the underlying experimental ST technologies. UniST integrates three complementary modules: kernel point convolution with cross-attention layers for point cloud upsampling, optical flow-based interpolation for continuous slice reconstruction, and a graph autoencoder with implicit neural representations for gene expression imputation. Together, these components densify sparse slices, resolve discontinuities, and map spatial coordinates to high-dimensional transcriptomics. Across multiple ST platforms and tissue contexts, UniST accurately restored structural continuity and biologically meaningful expression patterns. In a mouse embryo dataset, UniST reconstructed a dense 3D heart architecture from sparsely sampled slices. In two 3D human cancer tissues, UniST recovered critical spatial features, including tumor-immune boundaries and tertiary lymphoid structures, that were fragmented in the original data. By providing a generalizable computational solution that complements existing ST acquisition protocols, UniST facilitates cost-efficient and scalable reconstruction of 3D ST landscapes, enabling more faithful investigation of tissue organization and disease biology.

bioinformatics↗

bioRxiv2026

HMA proteins produce 2',3'-cNMP signaling molecules and activate CNL-mediated immunity in rice

Plants deploy a sensor repertoire, usually NLRs, to perceive pathogen effectors and activate executor NLR-triggered immunity. Here we reveal that rice HMA proteins function as sensors detecting the Magnaporthe oryzae MAX effector AvrPigm to trigger broad-spectrum blast resistance mediated by the CNL PigmR. AvrPigm is conserved with multiple copies in blast genomes and targets HMA proteins, facilitating HMA translocation into the cytoplasm. Cryo-EM structure of the HPP04HMA reveals a filament-like oligomer with ssDNA/RNA bound inside the filament, which display cyclic nucleotide synthase activity to generate 2',3'-cNMP in a CNL-dependent manner. We further discovered that the LRR domains of the executor CNLs perceive 2',3'-cNMP to mount immunity. Our study thus establishes an unrecognized immunity mode with a distinct repertoire of sensor receptors that produce signaling molecules that are perceived by the LRR domains of executor NLRs to mediate immunity, shedding new light on the sensor-executor conception and immune activation in plants. HighlightsO_LIHMA proteins and CNL receptors form a new sensor-executor mode in plant immunity C_LIO_LICryo-EM reveals the filament-like structure of HMA-ssRNA/ssDNA C_LIO_LIHMA proteins are cyclic nucleotide synthetases to generate 2',3'-cNMP C_LIO_LILRR domains of CNLs perceive 2',3'-cNMP to mediate immunity C_LI

plant biology↗

bioRxiv2026

LSD1 ablation promotes mammary tumor metastasis by attenuating NK cell-mediated anti-tumor immunity

Epigenetic deregulation can alter the expression of cancer-related genes in tumor cells and may promote metastasis by influencing interactions between tumor cells and their immune microenvironment. However, the underlying immune mechanisms remain poorly understood. LSD1 (KDM1A) is a histone demethylase that has been proposed to function as a tumor and metastasis suppressor in breast cancer. Here, using the MMTV-PyMT breast cancer mouse model, we show that natural killer (NK) cells play a critical role in suppressing tumor cell metastasis to the lung, and that ablation of LSD1 leads to increased lung metastasis. This phenotype is accompanied by pronounced upregulation of immune-related genes, including major histocompatibility complex class I (MHC-I) genes, in tumor cells and by extensive remodeling of the tumor immune microenvironment, characterized by reduced abundance and maturation of NK cells. Consistent with these observations, NK cells exhibit reduced cytotoxicity toward Lsd1-null PyMT tumor cells. Notably, NK cell-mediated killing can be restored by disrupting expression of the non-classical MHC-I molecule Qa-1, a ligand for the inhibitory NK receptor CD94/NKG2A, in tumor cells. In transplantation experiments, Lsd1-null PyMT tumor cells formed significantly larger lung metastatic lesions than Lsd1-wildtype tumor cells in SCID mice, which possess functional NK cells, but not in NSG mice that lack NK cells. Collectively, these findings suggest that epigenetic deregulation in LSD1-deficient mammary tumor cells reprograms the tumor immune microenvironment, resulting in impaired NK cell-mediated tumor surveillance and enhanced metastatic progression.

cancer biology↗

bioRxiv2026

SuperSurv: A Unified Framework for Machine Learning Ensembles in Survival Analysis

This paper introduces SuperSurv, a user-friendly R package for building, evaluating, and interpreting ensemble models for right-censored survival data. Although many survival modeling methods are available, existing tools are often model-specific and lack a unified platform for systematically integrating, comparing, and ensembling heterogeneous learners. SuperSurv addresses this gap by providing a unified interface for diverse survival learners, including models that return full survival curves as well as methods that produce only risk scores. All learner outputs are mapped to calibrated survival probability curves on a common evaluation time grid, enabling direct comparison and ensemble construction across heterogeneous model classes. SuperSurv implements stacking of survival models using inverse-probability-of-censoring weighted (IPCW) Brier risk to estimate ensemble weights in the presence of right censoring. The framework integrates hyperparameter tuning, time-dependent benchmarking metrics, and visualization tools for survival model evaluation. In addition, the package provides post-hoc interpretability utilities based on SHAP(SHapley Additive exPlanations) values and supports covariate-adjusted restricted mean survival time (RMST) contrasts through g-computation. By bridging the gap between theoretical rigor and clinical application, SuperSurv offers researchers a comprehensive ecosystem for modern survival analysis. The SuperSurv package is open-source and available on CRAN at https://CRAN.R-project.org/package=SuperSurv and on GitHub at https://github.com/yuelyu21/SuperSurv. An empirical example using the METABRIC breast cancer dataset demonstrates a complete workflow from model training and benchmarking to explainability and clinically interpretable survival contrasts.

bioinformatics↗

bioRxiv2026

HitAnno: Atlas-level cell type annotation based on scATAC-seq data via a hierarchical language model

The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) has emerged as a core technology for dissecting cellular epigenomic heterogeneity and gene regulatory programs. With the emergence of atlas-level scATAC-seq datasets, cell type annotation increasingly faces challenges arising from unprecedented data scale and increased cell-type diversity, which together place stringent demands on model reliability and robustness. Here, we introduce HitAnno, a hierarchical language model capable of accurate and scalable cell type annotation in atlas-level scATAC-seq data. Leveraging selected cell-type-specific peaks to construct "cell sentences", HitAnno employs a two-level attention mechanism that captures accessibility profiles hierarchically. Extensive evaluations show that HitAnno robustly annotates both major and rare cell types across multiple settings, including intra-dataset, cross-donor and inter-dataset annotation. The hierarchical attention mechanisms of the model reveal co-accessibility patterns among peaks and dependencies across higher-order peak sets, ensuring an interpretable annotation process. Training on a 31-cell-type human atlas, HitAnno can directly annotate new query datasets without retraining and is accessible through an online interface. Our model identifies heterogeneous subgroups within mixed labeled cells from unseen datasets, demonstrating its potential to assist researchers in refining existing cell atlases.

bioinformatics↗

bioRxiv2026

Digital Twin Brain simulation and manipulation of a functional brain network underlying mental illness

Linking synaptic-level perturbations to distributed brain-network dynamics remains a central challenge for understanding and treating mental illness. Although recent whole-brain models can reproduce individual brain activity patterns, they largely function as descriptive simulators rather than mechanistic, intervention-capable systems. Here we present an intervention-capable digital twin of the human brain, integrating individual neuroanatomy and task-evoked dynamics within a neuronal-scale framework. Individualised digital twin brains recapitulate a participant-specific compact cortico-subcortical network phenotype that captures transdiagnostic psychopathology across population and clinical cohorts. In silico modulation of excitatory and inhibitory synaptic conductance produces bidirectional, heterogeneous network responses across individuals. Population-scale simulations stratify individuals and predict longitudinal symptom trajectories from DTB-derived response profiles. Independent pharmacological functional MRI data further validate the predicted baseline-dependent network responses in vivo. Together, these findings establish digital brain models as experimental platforms for mechanistic perturbation, behavioural prediction and stratification, providing a foundation for precision neuroscience and psychiatry.

neuroscience↗

bioRxiv2026

A tunnel microtract organ for T cell progenitor homing is formed by neural crest morphogenesis via Sox10-Cdc42 axis

Thymic colonization by T cell progenitors (TCPs) is essential for adaptive immunity, yet the guiding tissues remain elusive. Here, we unveiled a tunnel microtract (TMT) as an organ indispensable in TCPs homing in both zebrafish and mouse. Disruption of TMT leads to compromised T cell development. Specifically, the zebrafish TMT were positioned bilaterally beneath the fifth branchial levator muscle, connecting the thymus and kidney. They are semi-coiled, non-vascular, non-lymphatic tubes of epithelial signatures. Impressively, Sox10 activates Cdc42 to promote F-actin remodeling in neural crest cells (NCCs), leading to precise elongation and tight packaging of a low-permeability TMT. Remarkably, a homologous CD34 TMT was observed in mice, which bilaterally enveloped the embryonic thymus and extended into the thyroid cartilage. Sox10-Cdc42 signalling functioned recapitulatively in NCC morphogenesis during its construction. These findings establish TMT as an unappreciated NCC-derived organ in TCPs homing, with implications in T cell development and immune disorders.

developmental biology↗

bioRxiv2026

Multiomic Spatial Imaging Assay (MSIA) -- A High-plex In Situ Detection Method for mRNAs, Proteins, and Protein-Protein Interactions using Manual And Semi-Automated Workflows

We report Multiomics Spatial Image Analysis (MSIA), a suite of technologies that include streamlined manual and semi-automated workflows to image 100s of RNAs from fresh frozen and formalin-fixed paraffin-embedded (FFPE) samples, contextualize RNAs with select protein markers including imaging synaptic protein interactions, a data analysis pipeline that includes corrections for chromatic aberration, algorithms for image stitching, deep learning models for image analysis (cell segmentation and RNA dot detection using hundreds of RNAscope images) from multiple stain-image cycles and proof of concept for a language model trained on extensive Parkinsons Disease (PD) literature. We used the language model to identify potential new biomarkers that were confirmed in MPTP-treated mouse model of PD. Furthermore, by incorporating more stain-image cycles alongside an additional fluorescent channel and advanced error-correcting decoding schemes, the MSIA workflow demonstrates the scalability to potentially quantify over 1,000 genes.

neuroscience↗

bioRxiv2026

Distributed neural signatures of discomfort induced by transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a cornerstone tool for causal inference in human brain function and an increasingly used neuromodulation therapy, yet it induces well-recognized discomfort that may systematically bias measured outcomes. Despite its ubiquity, a critical gap remains in understanding how TMS-induced discomfort is represented across the brain and to what extent it contributes to TMS-evoked neural responses. Using concurrent TMS-fMRI across 11 cortical targets, we collected an unprecedented dataset (165 participants; 1,535 runs) spanning healthy participants and those with elevated affective symptoms. Cross-validated multivariate modeling revealed that TMS-induced discomfort engages distributed cortical and subcortical regions across sensorimotor, attentional, default mode, and limbic networks, with both shared and group-specific patterns. Discomfort-related activity accounted for approximately 12% and 25% of TMS-evoked responses in healthy and elevated-symptom groups, respectively, and varied systematically across stimulating sites. These findings identify TMS-induced discomfort as a substantial and previously under-characterized component of TMS-evoked neural responses, underscoring the need to explicitly measure and model it. By providing a whole-brain map of regional contributions associated with TMS-induced discomfort and an analytic framework to dissociate direct neuromodulatory effects from discomfort-related responses, this work improves the interpretability of TMS-evoked signals and supports more rigorous causal inference and therapeutic applications.

neuroscience↗

bioRxiv2026

Sex-specific Genetic Regulatory Effects in Chickens

Sexual dimorphism is a defining vertebrate feature, yet its sex-specific molecular architecture remains poorly understood. Here we established a sex-balanced, uniformly reared chicken cohort to map this landscape, integrating individual whole-genome sequencing with 7,969 bulk and 779,380 single nucleus transcriptomes across 32 tissues from 280 birds. We identified 495,098 independent expression quantitative trait loci for 20,194 genes, including 10,937 loci modulated by cell-type composition. Notably, 340 genes were regulated by 449 loci in a sex-dependent manner, significantly enrichment in endocrine tissues like adipose and the adrenal gland. Furthermore, we fine-mapped 1,219 structural variants, demonstrating their unique roles to tissue- and sex-specific expression beyond SNPs. Ultimately, we showed the utility of these regulatory effects in elucidating the molecular basis of metabolism and complex traits in both chickens and humans. This comprehensive atlas of regulatory effects provides profound insights into the genomic and molecular basis of sexual dimorphism in vertebrates.

genomics↗

bioRxiv2026

A long-read human pangenome initiative for comprehensive interpretation of nuclear-embedded mitochondrial DNA

Nuclear-embedded mitochondrial DNA segments (NUMT) preserve a record of ongoing mitochondrial-to-nuclear DNA transfer during evolution, with important implications for disease mechanisms and genome organization. Here, we developed a Pangenome Graph-based NUMT Detection (PG-NUMT) approach to improve NUMT detection sensitivity by 2.52-fold compared to short-read based approaches and comprehensively resolved seven concatenated mega-NUMTs with a maximum length of 127.7 kbp. We generated a high-resolution human NUMT map comprising 774 fixed, 280 polymorphic, and 123 pericentromeric NUMTs, alongside 74 superpopulation-stratified NUMTs. Both fixed and polymorphic NUMTs are hypermethylated and enriched within segmental duplications, whereas fixed NUMTs preferentially localize to intergenic regions and avoid transposable elements. Interestingly, NUMTs derived from the 3-end of the mtDNA D-loop are less frequently fixed in human genomes and exhibit potential cis-regulatory activity in the nuclear genome. These observations suggest that selective pressures shape the genomic features of fixed NUMTs. Seven NUMTs associated with gene expression or alternative splicing were further identified, suggesting potential modulatory functions of common NUMTs. Using 20 complete non-human primate genomes, we identified variable NUMT insertion rates across primate lineages (1.5-18.7 insertions per million years), with particularly high rates in the Pan lineage. Notably, we uncovered two NUMT-derived variable number tandem repeats (VNTRs), establishing NUMTs as a novel source of VNTRs. In summary, the integrated analysis enhances our understanding of NUMT genomic architecture, population dynamics, and evolutionary implications in human and non-human primates, establishing NUMTs as dynamic genomic components of biomedical relevance.

genomics↗

bioRxiv2026

Transforming Histology into Virtual Multiplex Immunofluorescence to Decode Prognostic Spatial Immunity in Hepatocellular Carcinoma

The spatial organization of the tumor immune microenvironment (TIME) drives hepatocellular carcinoma (HCC) prognosis but remains unquantifiable on routine H&E slides. Here, we present HCCExplorer, a deep learning framework that translates H&E into virtual multiplex immunofluorescence (mIF) and uses multi-modal graph learning to decode spatial survival signals. Trained on 30 H&E-mIF slide pairs, HCCExplorer evaluated a 1,813-slide multi-center cohort. It achieved superior overall survival stratification over clinical indices and state-of-the-art pathology and protein foundation models, yielding a concordance index of 0.71 and a Hazard Ratio (HR) of 15.46 (P < 0.001), maintaining stability across three external cohorts. Beyond risk stratification, interpretation of model features identified M1 macrophage infiltration as a protective determinant (HR=0.40, P < 0.05). Furthermore, it uncovered a protective "Containment Niche" at the invasion frontier (HR=0.02, P < 0.01), featuring macrophages co-localizing with Foxp3+ Tregs and CD4+ T cells. Ultimately, HCCExplorer provides actionable, spatially-resolved biomarkers from conventional histology for precision HCC management.

pathology↗

bioRxiv2026

Fusion of lipid membranes: an alternative pathway

Membrane fusion is ubiquitous in myriad biological processes, including presynaptic neurotransmitter release, myoblast fusion, virus entry, and fertilization1-3. It is often considered in the presence of fusogenic proteins, which mediate the fusion by pulling the membranes into contact to overcome the high energy barrier imposed by hydration repulsion4-7. Here we uncover an alternative pathway - membrane fusion arising from electroporation induced by transmembrane potentials8,9 in absence of proteins. Using biased molecular dynamics simulations to carry out extensive potential-of-mean-force calculations10, we show how electroporation of opposing membranes promotes membrane fusion via formation of an intermembrane splayed lipid and peripore stalk, without any protein involvement. We validate our mechanism by demonstrating experimentally, via video microscopy and second harmonic generation imaging11 of interacting giant unilamellar vesicles (GUVs), that GUV fusion occurs in the presence of transmembrane potentials but not in their absence. The magnitude of these transmembrane potentials is biologically relevant, prevalent in transient states near the surfaces of cell membranes8,12, underscoring the potential importance of this mechanism in biological events.

biophysics↗

bioRxiv2026

MG53 Protects Against Intestinal Inflammation by Inhibiting NLRP3 Inflammasome Activation

Background and ObjectivesInflammatory bowel disease (IBD) involves dysregulated immune responses and chronic intestinal inflammation. The nod-like receptor pyrin domain-containing 3 (NLRP3) inflammasome plays a critical role in IBD pathogenesis, but regulatory mechanisms remain not fully understood. Mitsugumin 53 (MG53, also known as TRIM72), originally identified as a critical membrane repair protein, has emerged as a novel regulator of inflammatory processes. To investigate the protective role of MG53 in colitis and elucidate its mechanisms in regulating NLRP3 inflammasome activation in IBD. MethodsWe used dextran sodium sulfate (DSS)-induced colitis models comparing MG53 knockout (MG53-/-) and wild-type (WT) mice, assessing disease severity, MG53 tissue uptake, and therapeutic effects of recombinant human MG53 (rhMG53). In vitro studies examined rhMG53s effects on NLRP3 inflammasome activation, caspase-1 cleavage, interleukin-1{beta} (IL-1{beta}) secretion, and MG53-NLRP3 interactions. ResultsMG53-/- mice showed more severe colitis with increased weight loss, higher disease activity scores, shortened colons, and greater inflammation. DSS treatment induced the accumulation of circulating MG53 in inflamed colonic tissue. rhMG53 administration ameliorated colitis severity in MG53-/- mice and dose-dependently suppressed NLRP3 inflammasome activation in vitro. MG53 interacted with NLRP3 and reduced apoptosis-associated speck-like protein containing a CARD (ASC) speck formation and NLRP3 oligomerization without affecting upstream signaling or NLRP3 stability. ConclusionMG53 is a physiological regulator of NLRP3 inflammasome activation that protects against colitis, suggesting therapeutic potential for IBD.

immunology↗

bioRxiv2026

Event2Flow: Scalable imaging of peripheral and cerebral hemodynamics with event-based vision sensors

Accurate blood flow mapping over mesoscale fields of view is essential for understanding physiological and pathological processes, yet conventional optical methods often rely on bulky high-speed cameras that generate massive datasets with excessive computation burden. Here, we introduce Event2Flow, a compact and data-efficient framework leveraging event-based vision sensors, which asynchronously capture brightness changes with sub-millisecond latency and minimal data redundancy. Event2Flow supports multiple contrast mechanisms for flow measurement, including speckle fluctuation and particle tracking. By correlating the event count with flow velocity through simulations and experiments, we first demonstrate its application in laser speckle imaging for noninvasive mapping of mouse ear vasculature and ethanol-induced hemodynamic changes. When integrated with widefield fluorescence localization microscopy and point spread function engineering, Event2Flow further enables kilohertz-rate particle tracking for rapid 3D velocity quantification in transcranial brain imaging and snapshot flow direction estimations using event polarity. Overall, Event2Flow offers a scalable alternative to conventional high-speed imaging systems for vascular and neuroimaging applications.

neuroscience↗

bioRxiv2026

Toward Standardized Ex Vivo Joint Models: Impact of Glucose and Oxygen Levels for Enhanced Tissue Maintenance

Ex vivo models bridge in vitro and in vivo systems by preserving native extracellular matrix architecture and multicellular interactions. In articular joint research, osteochondral-synovial co-cultures are particularly valuable for studying bone-cartilage crosstalk and synovial inflammatory regulation. However, a lack of standardized culture conditions regarding glucose and oxygen, two key regulators of cellular metabolism, limits reproducibility and translational relevance. This study aims to define how glucose and oxygen conditions influence joint tissues maintenance in an ex vivo model. Bovine osteochondral explants and synovium are harvested from the stifle joint and co-cultured using either high glucose DMEM (HG, 4.5 g/L) or low glucose DMEM (LG, 1 g/L) under hyperoxic (21% O2) or physioxic (5% O2) conditions. Cell viability, gene expression, and metabolomic profiles are evaluated across tissues. LG conditions increase cell death in the deep zone of cartilage and in subchondral bone. Gene expression and metabolomic analyses reveal tissue-specific effects of glucose and oxygen. In cartilage and bone, glucose-dependent gene regulation and metabolic changes occur under hyperoxia but are largely absent under physioxia, indicating buffering of glucose responses. Gene-specific sensitivity to glucose and oxygen is observed in bone and synovium; however, glucose-induced metabolic responses persist under physioxia only in synovium. Overall, these findings identify oxygen and glucose as critical modulators of joint tissue physiology and support the use of HG, physioxic culture conditions to improve cell viability and stabilize molecular outcomes in ex vivo joint models. This optimized ex vivo model provides platforms for investigating mechanisms relevant to joint-related diseases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/704322v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1393324org.highwire.dtl.DTLVardef@4c9393org.highwire.dtl.DTLVardef@16cc00eorg.highwire.dtl.DTLVardef@b4d9ea_HPS_FORMAT_FIGEXP M_FIG C_FIG This study evaluates the effects of glucose concentration and oxygen tension in an ex vivo joint co-culture system to define optimal culture conditions. High glucose medium and physioxic conditions support tissue viability, preserve homeostasis, and enhance the physiological relevance of the ex vivo model.

cell biology↗

bioRxiv2026

Visual experience drives functional reorganization of cortical GABAergic circuits

Sensory experience shapes the development and function of neocortical circuits. Although repeated exposure to sensory stimuli can alter cortical responses, the precise relationship between sensory experience and circuit interactions remains unclear. Here, we examine how cortical circuits are shaped by repeated exposure to varied visual stimuli. We find that visually evoked beta activity, a prominent pattern of visually evoked activity that relies on somatostatin-expressing (SST) GABAergic interneurons, is robustly potentiated by visual experience. Enhanced visual experience leads to increased responses in SST interneurons and suppressed responses in vasoactive intestinal peptide-expressing (VIP) interneurons. In association with this rebalancing of cortical inhibitory activity, experience increases the recruitment and visual selectivity of excitatory neurons. This enhancement is associated with increased SST-IN influence on nearby pyramidal neurons and requires SST-IN activity during visual experience. Visual experience thus selectively reorganizes adult dendrite-targeting inhibitory circuits, promoting network synchrony and enhancing sensory encoding by excitatory neurons.

neuroscience↗