Biology subjects

Cai, Z.

Publications and source records attributed to Cai, Z..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Systematic identification and characterization of virus lncRNAs suggests extensive structural mimicry of host lncRNAs

Virus long non-coding RNAs (vlncRNAs) play crucial roles in viral infections, yet their identification and characterization remain limited. This study identified 5,053 novel vlncRNAs across 25 viral species using third-generation sequencing, with two from Influenza A virus and Vesicular stomatitis virus validated by RT-qPCR. Most vlncRNAs originated from dsDNA viruses. Only 1% having annotated RNA families, suggesting many novel RNA structures. Although only seven vlncRNAs shared sequence similarities with human lncRNAs (hlncRNAs), 772 vlncRNAs from 15 human viruses structurally mimicked hlncRNAs. These vlncRNA and hlncRNAs bound to similar miRNAs, potentially acting as miRNA sponges to promote essential life process. Splicing analysis showed vlncRNAs had a prevalence of alternative first exon. Finally, we developed vlncRNAbase (http://computationalbiology.cn/vlncRNAbase/#/) to store and organize the newly identified and known vlncRNAs. Overall, the study provides a valuable resource for further investigation into vlncRNAs and deepens our understanding of the diversity, structure and function of the molecule.

bioinformatics↗

bioRxiv2024

Single-Cell Resolution DESI Mass Spectrometry Imaging through 10-Fold Sample Expansion

Desorption electrospray ionization mass spectrometry imaging (DESI MSI) is a valuable tool for label-free, spatially resolved molecular analysis of biological tissues. However, its spatial resolution has been limited to tens of micrometers due to constraints in spray geometry and solvent flow, hindering single-cell imaging. Inspired by expansion microscopy, we developed a tenfold DESI expansion MSI (10x-DESI ExMSI) technique to achieve cellular-level resolution. In this study, tissue sections were embedded in a swellable polyelectrolyte gel, and physically enlarged by approximately tenfold without compromising molecular integrity. After drying of the sample, mass spectrometry imaging was performed using a custom-built DESI ion source coupled with an Orbitrap mass spectrometer. Using mouse brain tissues as representative samples, 10x-DESI ExMSI achieved a spatial resolution of approximately 5 {micro}m, comparable to the highest levels attained by advanced commercial MALDI-MSI systems. Enhanced resolution allowed clear visualization of fine structural details, such as the granule cell layer of the dentate gyrus and the pyramidal layer of the Cornu Ammonis, which were unresolved in unexpanded samples under a 100 m step size. Lipid profiling demonstrated high retention and detection efficiency, confirming the techniques suitability for comprehensive molecular imaging. The 10x-DESI ExMSI significantly advances DESI MSI by enabling single-cell level resolution without modifying existing mass spectrometers. The method is simple, cost-effective, and easily implementable in most laboratories, making high-resolution DESI MSI accessible for a wide range of biological applications. This approach overcomes longstanding limitations of DESI imaging and holds great potential for detailed molecular studies at the cellular level.

biochemistry↗

bioRxiv2024

Ten-Fold Expansion MALDI Mass Spectrometry Imaging of Tissues and Cells at 500 nm Resolution

Achieving high spatial resolution in matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is crucial for detailed molecular mapping in biological tissues and cells. However, conventional MALDI-MSI platforms are typically limited to spatial resolutions of 5-20 m, restricting their ability to visualize fine subcellular structures. Here, we present a novel ten-fold expansion MALDI-MSI (10X ExMSI) method that attains 500 nm spatial resolution without the need for specialized optics or customized instrumentation. By physically expanding biological samples using a swellable hydrogel matrix, our method maintains high retention and broadens the detection range of lipids, ensuring comprehensive lipid profiling. We demonstrated the effectiveness of 10X ExMSI by visualizing intricate subcellular structures within mouse brain tissues, including individual nuclei, astrocytes, Purkinje cells, and, notably, dendritic arborizations-features previously unresolvable using mass spectrometry imaging. Applying 10X ExMSI to single-cell analysis of cultured cells revealed detailed spatial distributions of lipids at the subcellular to organelle level. Notably, the method is fully compatible with standard commercial MALDI-MSI platforms, enabling widespread adoption without significant additional investment. The 10X ExMSI offers a transformative tool for high-resolution molecular imaging, opening new avenues for molecular analysis in diverse biological and biomedical fields, including neuroscience, pathology, and cellular biology.

biochemistry↗

bioRxiv2024

Federated deep learning enables cancer subtyping by proteomics

Artificial intelligence applications in biomedicine face major challenges from data privacy requirements. To address this issue for clinically annotated tissue proteomic data, we developed a Federated Deep Learning (FDL) approach (ProCanFDL), training local models on simulated sites containing data from a pan-cancer cohort (n=1,260) and 29 cohorts held behind private firewalls (n=6,265), representing 19,930 replicate data-independent acquisition mass spectrometry (DIA-MS) runs. Local parameter updates were aggregated to build the global model, achieving a 43% performance gain on the hold-out test set (n=625) in 14 cancer subtyping tasks compared to local models, and matching centralized model performance. The approachs generalizability was demonstrated by retraining the global model with data from two external DIA-MS cohorts (n=55) and eight acquired by tandem mass tag (TMT) proteomics (n=832). ProCanFDL presents a solution for internationally collaborative machine learning initiatives using proteomic data, e.g., for discovering predictive biomarkers or treatment targets, while maintaining data privacy. Statement of SignificanceA federated deep learning approach applied to human proteomic data, acquired using two distinct proteomic technologies from 40 tumor cohorts from eight countries, enabled accurate cancer histopathological subtyping while preserving data privacy. This approach will enable privacy-compliant development of large-scale proteomic AI models, including foundation models, across institutions globally.

cancer biology↗

bioRxiv2024

TOMM40-APOE chimera linking Alzheimer's highest risk genes: a new pathway for mitochondria regulation and APOE4 pathogenesis

The patho-mechanism of apolipoprotein variant, APOE4, the strongest genetic risk for late-onset Alzheimers disease (AD) and longevity, remains unclear. APOEs neighboring gene, TOMM40 (mitochondria protein transport channel), is associated with brain trauma outcome and aging-related cognitive decline, however its role in AD APOE4-independently is controversial. We report that TOMM40 is prone to transcription readthrough into APOE that can generate spliced TOMM40-APOE mRNA chimera (termed T9A2) detected in human neurons and other cells and tissues. T9A2 translation tethers APOE (normal APOE3 or APOE4) to near-full-length TOM40 that is targeted to mitochondria. Importantly, T9A2-APOE3 boosts mitochondrial bioenergetic capacity and decreases oxidative stress significantly more than T9A2-APOE4 and APOE3, and lacking in APOE4. We describe detailed interactomes of these actors that may inform about the activities and roles in pathogenesis. T9A2 uncovers a new candidate pathway for mitochondria regulation and oxidative stress-protection that are impaired in APOE4 genotypes and could initiate neurodegeneration.

cell biology↗

bioRxiv2024

The Composition of the Bacterial Community in Raw Milk from Holstein Dairy Cattle Correlated with the Occurrence of Klebsiella pneumoniae Clinical Mastitis Infections

Klebsiella pneumoniae is a common, opportunistic bacterial pathogen that can cause severe clinical mastitis in dairy cattle. Optimizing the bovine udder microbiome to resist mastitis pathogens is a growing area of research; however, previous work has not examined which members of the mammary microbiome may have antagonistic interactions with K. pneumoniae. In this study, we collected quarter-level milk samples from Holstein dairy cows in Quebec, Canada every two weeks for 14 months and analyzed differences in the milk microbiome between samples that were collected from healthy quarters, quarters that developed subclinical mastitis, and quarters that experienced K. pneumoniae clinical mastitis (KP-CM) (n = 512 milk samples). The occurrence of subclinical mastitis did not cause significant differences in the alpha-diversity of the milk microbiome, nor did subclinical mastitis alter the interactions between taxa in the microbiome. However, the occurrence of KP-CM caused reductions in Shannon diversity in raw milk relative to healthy milk and altered the interactions between taxa. Specifically, K. pneumoniae showed negative interactions with the genus Aerococcus. The negative interactions between Aerococcus spp. and K. pneumoniae in the context of the bovine milk microbiome should be analyzed further.

microbiology↗

bioRxiv2024

SIRT4 enhances the cytotoxicity of NK cells toward hepatic stellate cells and reverses liver fibrosis via AMPKα/P-p53/NKG2DL pathway

Natural killer (NK) cells exhibit antifibrotic properties in liver fibrosis (LF) by suppressing activated hepatic stellate cells (HSCs). SIRT4, a mitochondrial regulatory protein, plays a crucial role as a link between energy metabolism and cell viability. However, the role of SIRT4 in the cytotoxicity of NK cells toward HSCs remains unexplored. In this study, we found that SIRT4 was markedly downregulated in both mouse models and patients with LF. The loss of SIRT4 reduced the cytotoxicity of NK cells against activated HSCs, while its overexpression enhanced this cytotoxicity. Mechanistically, SIRT4 activates AMPK to promote p53 phosphorylation and facilitates its nuclear translocation, which induces the transcription of ULBP1 and ULBP2, members of the NK group 2D Legend (NKG2DL) family of molecules. Finally, overexpression of SIRT4 activated mouse hepatic NK cells and reversed LF by constructing adeno-associated viruses (AAV) that specifically target HSCs. Thus, SIRT4 is essential for the cytotoxicity of NK cells toward HSCs, and AAV8-pGAFP-SIRT4 may serve as a therapeutic approach for managing LF. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/615768v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@1e6e987org.highwire.dtl.DTLVardef@edcc2dorg.highwire.dtl.DTLVardef@1f7c2bcorg.highwire.dtl.DTLVardef@111d81d_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2024

Growth and defence in a unicellular microalga: the coexistence of r and K strategies

r (high reproductivity but stress-sensitive offspring) and K (low reproductivity but high stress-resistant offspring) strategies are two classical life history theories. Contrary to the belief that one species only has a single strategy (i.e., either r or K), we present experimental evidence for the coexistence of both strategies in the isogenic microalga Haematococcus lacustris. Under standard conditions, swimming vegetative cells (SVC) normally grew and divided via binary fission (r strategy) and gradually shifted into non-swimming cysts (NSC) over time. Intriguingly, unlike the prevailing notion that NSC cannot propagate under stress, they were found to reproduce via multiple fission at a barely detectable growth rate, resulting in stress-resistant, non-mobile daughter cells and demonstrating a complete life history (K strategy). Collectively, our findings indicate that H. lacustris adopts both r and K strategies throughout its life history. This research enhances the understanding of the adaptation and survival of microbial populations in changing environments.

ecology↗

bioRxiv2024

Constructing a Norm for Children's Scientific Drawing: Distribution Features Based on Semantic Similarity of Large Language Models

The use of childrens drawings to examining their conceptual understanding has been proven to be an effective method, but there are two major problems with previous research: 1. The content of the drawings heavily relies on the task, and the ecological validity of the conclusions is low; 2. The interpretation of drawings relies too much on the subjective feelings of the researchers. To address this issue, this study uses the Large Language Model (LLM) to identify 1420 childrens scientific drawings (covering 9 scientific themes/concepts), and uses the word2vec algorithm to calculate their semantic similarity. The study explores whether there are consistent drawing representations for children on the same theme, and attempts to establish a norm for childrens scientific drawings, providing a baseline reference for follow-up childrens drawing research. The results show that the representation of most drawings has consistency, manifested as most semantic similarity>0.8. At the same time, it was found that the consistency of the representation is independent of the accuracy (of LLMs recognition), indicating the existence of consistency bias. In the subsequent exploration of influencing factors, we used Kendall rank correlation coefficient to investigate the effects of "sample size", "abstract degree", and "focus points" on drawings, and used word frequency statistics to explore whether children represented abstract themes/concepts by reproducing what was taught in class. It was found that accuracy (of LLMs recognition) is the most sensitive indicator, and data such as sample size and semantic similarity are related to it; The consistency between classroom experiments and teaching purpose is also an important factor, many students focus more on the experiments themselves rather than what they explain. In addition, most children tend to use examples they have seen in class to represent more abstract themes/concepts, indicating that they may need concrete examples to understand abstract things.

scientific communication and education↗

bioRxiv2024

A Newborn F-box Gene Blocks Gene Flow by Selectively Degrading Phosphoglucomutase in Species Hybrids

The establishment of reproductive barriers such as postzygotic hybrid incompatibility (HI) remains the key to speciation. Gene duplication followed by differential functionalization has long been proposed as a major model underlying HI, but few supporting evidence exists. Here, we demonstrate that a new-born F-box gene, Cni-neib-1, of the nematode Caenorhabditis nigoni specifically inactivates an essential phosphoglucomutase encoded by Cbr-shls-1 in its sister species C. briggsae and their hybrids. Zygotic expression of Cni-neib-1 specifically depletes Cbr-SHLS-1, but not Cni-SHLS-1, in approximately 40 minutes starting from gastrulation, causing embryonic death. Cni-neib-1 is one of thirty-three paralogues emerging from a recent surge in F-box gene duplication events within C. nigoni, all of which are evolving under positive selection. Cni-neib-1 undergoes turnover even among C. nigoni populations. Differential expansion of F-box genes between the two species could reflect their distinctive innate immune responses. Collectively, we demonstrate how recent duplication of genes involved in protein degradation can cause unintended destruction of targets in hybrids that leads to HI, providing an invaluable insight into mechanisms of speciation. Significance statementUnderstanding how reproductive isolation forms and triggers speciation remains a central topic in evolutionary biology. In this study, the authors reveal a novel molecular mechanism that obstructs gene flow between the nematodes Caenorhabditis briggsae and C. nigoni. A recently evolved, species-specific F-box gene in C. nigoni selectively depletes an essential phosphoglucomutase enzyme in C. briggsae, leading to embryonic lethality in hybrids. This mechanism effectively prevents gene flow from C. nigoni to C. briggsae. This discovery underscores how a newborn gene, arising from recent gene duplication, can contribute to reproductive isolation by inadvertently disrupting a functionally important protein in hybrid offspring.

evolutionary biology↗

bioRxiv2024

NIRSTORM: a Brainstorm extension dedicated to functional Near Infrared Spectroscopy (fNIRS) data analysis, advanced 3D reconstructions, and optimal probe design.

SignificanceWe propose NIRSTORM, a software package built within Brainstorm environment, enabling full data analysis of functional Near InfraRed Spectroscopy (fNIRS) data from experiment planning to 3D reconstruction of hemodynamic fluctuations on the cortical surface using optical tomographic approaches. NIRSTORM enables the integration of fNIRS analysis within a multimodal setup making it easy to study fNIRS in combination with other multimodal data such as electroencephalography (EEG) or magnetic resonance imaging (MRI). AimNIRSTORM aims to provide an easy-to-use and fully modular toolbox for fNIRS analysis from experimental planning to optical tomography 3D reconstruction extending Brainstorm capacity for multimodal analysis. ApproachNIRSTORM was developed in MATLAB(R) and integrated as a plugin of the software Brainstorm. Brainstorm is a GUI-oriented, widely used software originally dedicated to statistical analysis and source imaging of EEG and magnetoencephalography (MEG) data. ResultsIn addition to conventional fNIRS preprocessing steps, including standard channel space and statistical analyses, NIRSTORM provides advanced methods dedicated to optimal probe placement, allowing personalized fNIRS study designs and accurate near-infrared optical tomography within the Maximum Entropy on the Mean (MEM) framework. ConclusionNIRSTORM is an open-access, user-friendly plugin extending the capacity of Brainstorm, for fNIRS analysis, therefore narrowing the gap between EEG/MEG and hemodynamics.

bioengineering↗

bioRxiv2024

Circuit firing homeostasis following synaptic perturbation ensures robust behavior

Homeostatic regulation of excitability and synaptic transmission ensures stable neural circuit output under changing conditions. We find that pre- or postsynaptic weakening of motor neuron (MN) to muscle glutamatergic transmission in Drosophila larva has little impact on locomotion, suggesting non-synaptic compensatory mechanisms. In vivo imaging of MN to muscle synaptic transmission and MN activity both show that synaptic weakening increases activity in tonic type Ib MNs, but not in the phasic type Is MN that innervate the same muscles. Additionally, an inhibitory class of pre-MNs that innervates type Ib--but not Is--MNs decreases activity. Our experiments suggest that weakening of MN evoked synaptic release onto the muscle is compensated for by an increase in MN firing due to a combined cell-autonomous increase in excitability and decreased inhibitory central drive. Selectivity for type Ib MNs may serve to restore tonic drive while absence of firing adjustment in the convergent Is MN can maintain the contraction wave dynamics needed for locomotion.

neuroscience↗

bioRxiv2024

A Self-Assembling Immune-Featured Osteosarcoma Patient/PDX Derived Organoid Model and Biobank for Personalized Immune Therapy

Osteosarcoma (OS) exhibit intra- and inter-heterogeneity, complicating the exploration of effective therapeutic strategies. Traditional in vitro and in vivo models are limited in inheriting biological and genomic heterogeneities of OS patients, even in inheriting the features on tumor microenvironment. The prolonged generation time of current models makes the drug development of OS slow and is not suitable to clinically rapid timing. Here, we introduce methods for generating and biobanking patient/PDX-derived osteosarcoma organoids (OS PD(X)Os) that recapitulate the histological, biological and genomic features of their paired OS patients. OS PD(X)Os can be generated quickly with high reliability in vitro or transplanted to immunodeficient mice. We further demonstrate an immune-featured OS PD(X)O (named iOS) model and its method for testing personalized chemotherapy response, personalized immune therapeutic strategy and target drug development, such as a novel PRMT5MTA inhibitor ARPN2169 on MTAP-deleted OS. Our studies show that iOS models maintain many typical features of OS and could be rapidly employed to investigate patient-specific therapeutic strategies. Additionally, our biobank establishes a rich resource for basic, translational and even clinical OS researches.

cancer biology↗

bioRxiv2024

Computing hematopoiesis plasticity in response to genetic mutations and environmental stimulations

Cell plasticity (CP), describing a dynamic cell state, plays a crucial role in maintaining homeostasis during organ morphogenesis, regeneration and damage-to-repair biological process. Single-cell-omics datasets provide unprecedented resource to empowers analysis on CP. Hematopoiesis offers fertile opportunities to develop quantitative methods for understanding CP with rich supports from experimental ground-truths. In this study we generated high-quality lineage-negative (Lin-) single-cell RNA-sequencing datasets under various conditions and introduced a working pipeline named Snapdragon to interrogate naive and disturbed plasticity of hematopoietic stem and progenitor cells (HSPCs) with mutational or environmental challenges. Utilizing embedding methods UMAP or FA, a continuum of hematopoietic development is visually observed in wildtype where the pipeline confirms a very low Proportion of hybrid-cells (Phc, with bias range: 0.4-0.6) on a transition trajectory. Upon Tet2 mutation, a driver of leukemia, or treatment of DSS, an inducer of colitis, Phc is increased and plasticity of HSPCs was enhanced. Quantitative analysis indicates that Tet2 mutation enhances HSC self-renewal capability while DSS treatment results in an enhanced myeloid-skewing trajectory, suggesting their similar but different consequences. We prioritized several transcription factors (i.e the EGR family) and signaling pathways (i.e. receptors IL1R1 and ADRB, inflammation and sympathy-sensing respectively) which are responsible for Phc alterations. CellOracle-based simulation suggests that knocking-out EGR regulons or pathways of IL1R1 and ADRB partially reverses Phc promoted by Tet2 mutation and inflammation. In conclusion, the study provides high-quality datasets with single-cell transcriptomic matrices for diversified hematopoietic simulations and a computational pipeline Snapdragon for quantifying disturbed Phc and CP. (247 words) HighlightsO_LITo guide CP analysis, we introduce a quantizable parameter Phc and a pipeline Snapdragon, which discriminate naive and disturbed hematopoiesis; C_LIO_LIThe Snapdragon pipeline analysis on Tet2+/-Lin- cells demonstrates many novel insights, including enhanced HSC plasticity and increased PHC; similar trends are observed in inflammatory Lin- cells; C_LIO_LIRegulon analysis suggests that transcriptional factor EGR1 is significantly activated to elevated the HSC plasticity and change hematopoietic trajectory; C_LIO_LIStress-response-related signaling pathways mediated by receptors IL1R1 or ADRB were obviously activated in the challenged hematopoiesis; C_LIO_LICellOracle-based simulation suggests that knocking-out EGR regulons or pathways of IL1R1 and ADRB partially reverses Phc promoted by Tet2 mutation and inflammation. C_LI

developmental biology↗

bioRxiv2024

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

Multi-omic characterization and integration remains a challenge due to data complexity and sparsity. Addressing this, our study introduces an unsupervised deep learning model, MOVE (Multi-Omic Variational Encoder), specifically designed to integrate and augment the Cancer Dependency Map (DepMap). Harnessing orthogonal multi-omic information, this model successfully generates molecular and phenotypic profiles, resulting in an increase of 32.7% in the number of multi-omic profiles and thereby generating a complete DepMap for 1,523 cancer cell lines. The synthetically enhanced data increases statistical power, uncovering less studied mechanisms associated with drug resistance, and refines the identification of genetic associations and clustering of cancer cell lines. By applying SHAP for model interpretation, MOVE reveals multi-omic features essential for cell clustering and biomarker identification related to drug and gene dependencies. This understanding is crucial for the development of much-needed, effective strategies in prioritizing cancer targets.

systems biology↗

bioRxiv2024

Coral hosts actively accumulate inorganic carbon for zooxanthellae to maintain symbiotic relationships

High primary productivity of coral reefs is widely attributed to the mutualistic symbiosis between coral hosts and their microalgal partners (Symbiodiniaceae). Although the mechanisms maintaining this symbiosis have been considerable investigated, how the symbiont microalgae within coral get sufficient CO2 for photosynthesis still remains inadquately explored. Here, we hypothesized that corals may actively accumulate dissolved inorganic carbon (DIC) for microalgae to maintain the symbiosis. Carbon (HCO3- and glucose) supply and consumption were evaluated in the scleractinian coral (Goniopora lobata) and its symbiont under light and dark conditions. Results suggest that Symbiodiniaceae were high DIC consumers, requiring about 2-3 fold more DIC than free-living species. The corals were high DIC producers, with internal concentrations up to 4.2 fold higher than in the surrounding seawater. In the absence of microalgae utilization, the excess DIC they produced appeared detrimental to their own growth. Moreover, transcriptomic analysis identified several DIC enrichment pathways are evolved in corals to attact the Symbiodiniaceae, including CO2 concentrating mechanisms, respiration, calcification and phosphoenolpyruvate carboxylase. Increased the CO2 dosage in seawater may induce coral symbiosis bleaching. Our findings can deeper reveal on the mechanisms sustaining coral symbiosis, and may help to predict how some corals respond to DIC imbalance under climate changes.

ecology↗

bioRxiv2024

Repetitive mild closed-head injury induced synapse loss and increased local BOLD-fMRI signal homogeneity

Repeated mild head injuries due to sports, or domestic violence and military service are increasingly linked to debilitating symptoms in the long term. Although symptoms may take decades to manifest, potentially treatable neurobiological alterations must begin shortly after injury. Better means to diagnose and treat traumatic brain injuries, requires an improved understanding of the mechanisms underlying progression and means through which they can be measured. Here, we employ a repetitive mild closed-head injury (rmTBI) and chronic variable stress (CVS) mouse model to investigate emergent structural and functional brain abnormalities. Brain imaging is achieved with [18F]SynVesT-1 positron emission tomography, with the synaptic vesicle glycoprotein 2A ligand marking synapse density and BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI). Animals were scanned six weeks after concluding rmTBI/Stress procedures. Injured mice showed widespread decreases in synaptic density coupled with an increase in local BOLD-fMRI synchrony detected as regional homogeneity. Injury-affected regions with higher synapse density showed a greater increase in fMRI regional homogeneity. Taken together, these observations may reflect compensatory mechanisms following injury. Multimodal studies are needed to provide deeper insights into these observations.

neuroscience↗

bioRxiv2024

Spontaneous HFO Sequences Reveal Propagation Pathways for Precise Delineation of Epileptogenic Networks

AbstractEpilepsy, a neurological disorder affecting millions worldwide, poses great challenges in precisely delineating the epileptogenic zone - the brain region generating seizures - for effective treatment. High-frequency oscillations (HFOs) are emerging as promising biomarkers; however, the clinical utility is hindered by the difficulties in distinguishing pathological HFOs from non- epileptiform activities at single electrode and single patient resolution and understanding their dynamic role in epileptic networks. Here, we introduce an HFO-sequencing approach to analyze spontaneous HFOs traversing cortical regions in 40 drug-resistant epilepsy patients. This data- driven method automatically detected over 8.9 million HFOs, pinpointing pathological HFO- networks, and unveiled intricate millisecond-scale spatiotemporal dynamics, stability, and functional connectivity of HFOs in prolonged intracranial EEG recordings. These HFO sequences demonstrated a significant improvement in localization of epileptic tissue, with an 818.47% increase in concordance with seizure-onset zone (mean error: 2.92 mm), compared to conventional benchmarks. They also accurately predicted seizure outcomes for 90% AUC based on pre-surgical information using generalized linear models. Importantly, this mapping remained reliable even with short recordings (mean standard deviation: 3.23 mm for 30-minute segments). Furthermore, HFO sequences exhibited distinct yet highly repetitive spatiotemporal patterns, characterized by pronounced synchrony and predominant inward information flow from periphery towards areas involved in propagation, suggesting a crucial role for excitation-inhibition balance in HFO initiation and progression. Together, these findings shed light on the intricate organization of epileptic network and highlight the potential of HFO-sequencing as a translational tool for improved diagnosis, surgical targeting, and ultimately, better outcomes for vulnerable patients with drug-resistant epilepsy. One Sentence SummaryPathological fast brain oscillations travel like traffic along varied routes, outlining recurrently visited neural sites emerging as critical hotspots in epilepsy network.

neuroscience↗