Biology subjects

Yao, B.

Publications and source records attributed to Yao, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Temporal RNA splicing dysregulation in a human multicellular model of C9orf72 ALS

Amyotrophic lateral sclerosis (ALS) is a complex and heterogenous disease, comprising of familial and sporadic forms, numerous genetic mutations, and a wide range of affected cell types and proposed dysregulated processes. While the usage of in vitro cell cultures, mouse models, and postmortem patient tissue have advanced our understanding of ALS disease mechanisms, the temporal progression of the disease and how early molecular changes evolve remain poorly understood. Here, we establish human spinal cord organoids (hSpO) derived from C9orf72 patient induced pluripotent stem cells (C9-ALS hiPSCs) as a platform to model longitudinal changes across the disease trajectory. We show that C9-ALS hSpO recapitulate hallmark C9orf72-related pathologies, increased cell death, and progressive neuronal dysfunction. We further identify RNA splicing dysregulation as an early and persistent feature of C9-ALS, with splicing signatures in hSpO closely mirroring those observed in postmortem patient tissue. Finally, prediction of RNA binding proteins (RBPs) that induce early splicing changes in C9-ALS reveals signatures consistent with a broad RBP network dysfunction. Together, this work establishes hSpO as an in vitro system for investigating early disease mechanisms of ALS in a human multicellular context and highlights dysregulated events that precede cell death.

neuroscience↗

bioRxiv2026

SpatialOmicsLab: an integrated research environment for AI co-scientists in spatial transcriptomics

Spatial profiling is moving from method development into a central platform for biological discovery and clinical translation, but analyzing such complex data has become the bottleneck of scientific discovery. Translating spatial transcriptomic measurements into biological insight requires reproducible workflows that integrate diverse analytical methods. Although language-model agents can plan and execute multistep analyses, fragmented data and software ecosystems hinder their use as reliable research assistants. In this study, we introduce SpatialOmicsLab, an integrated research environment for spatial transcriptomics comprising 113 curated computational tools, 63 reference datasets and 4,614 task specifications. We also introduce ST-Coscientist, an AI co-scientist that uses this environment to assemble, execute and refine spatial transcriptomic analyses. Within SpatialOmicsLab, ST-Coscientist outperformed expert-led workflows and other scientific agents across six benchmark settings spanning spatial clustering, cell-type deconvolution and spatially variable gene detection. It constructed alignment-aware workflows for three-dimensional MERFISH data and addressed biological questions by selecting and analysing relevant reference datasets. Procedural memory increased the success rate of integrating and validating additional analytical tools from 48% to 95%. Together, SpatialOmicsLab and ST-Coscientist establish an extensible ecosystem for reproducible spatial transcriptomic research, enabling AI co-scientists to address evolving biological questions with an expanding analytical repertoire.

bioinformatics↗

bioRxiv2026

Spatial Topology Reveals Biologically Distinct Recurrent Motifs in Colorectal Cancer

Most spatial transcriptomic analyses of solid tumors focus on individual cell states or single-sample spatial domains rather than on recurrent multicellular tissue architectures shared across patients, and typically depend on predefined cell-type annotations or compartment definitions. We developed STORM (Spatial Topology analysis of Recurrent Motifs), an unsupervised graph-attention variational autoencoder that learns recurrent spatial motifs directly from cell-level graph structure and molecular profiles without cell-type labels, manual annotation, or predefined compartments. We applied to 32 Xenium sections from 16 patients with paired early-onset (EOCRC) and average-onset (AOCRC) colorectal cancer, STORM identified 10 recurrent motifs that self-organized into tumor-parenchymal, stromal, and immune macro-compartments. Among these, the Desmoplastic Fibrotic Barrier (DFB) motif, a CAF- and ECM-rich boundary architecture, was associated with restricted CD8+ T-cell geodesic access to tumor cores independently of CD8+ abundance, as demonstrated by abundance-normalized neighborhood enrichment statistics and within-sample mixed-effects models. EOCRC selectively amplified this barrier-exclusion architecture, exhibiting tighter tumor parenchyma, denser DFB shells, and a DFB-specific ECM activation program that yielded an age-specific prognostic signature in TCGA-COAD. Translation of motif macro-classes to H&E images via a Vision Transformer classifier produced an image-derived DFB-barrier composite that predicted overall survival in advanced-stage TCGA colorectal cancer. STORM provides an annotation-free framework for discovering recurrent spatial motifs and identifies a fibroblast barrier architecture whose topological association with immune exclusion is independent of effector abundance, amplified in early-onset disease, and translatable to a deployable pathology-based prognostic biomarker.

bioinformatics↗

bioRxiv2026

Mesothelial Plasticity Specifies a Pleural Immune Circuit that Orchestrates Lung Regeneration

Following injury, predisposition towards regenerative repair and away from degenerative remodeling is central to organismal health, yet the upstream determinants that instruct this fate choice remain poorly understood. Here, we identify mesothelial cell plasticity as a central determinant between regeneration and degeneration by comparing mouse models of pneumonectomy (PNX) versus chronic lung allograft dysfunction (CLAD). While mesothelial cells expand in both settings, following PNX, these cells undergo differentiation through multiple transitional states, culminating in an inflammatory population that orchestrates monocyte recruitment and subsequent tissue regeneration. In contrast, in CLAD, mesothelial cells enrich in an extracellular matrix-enriched state that lacks pro-regenerative signaling capacity. Using single cell epigenomic profiling and in vivo genetics, we define a mesothelium-specific TWIST1-CCL2 axis that governs mesothelium plasticity and signaling, monocyte recruitment and lung regrowth. Together, these findings demonstrate that context-dependent reprogramming of a pleural population, known to protect organs, can be leveraged to drive regeneration.

Developmental Biology↗

bioRxiv2026

Temporal Biodynamics: An AI Platform for Identification of Stage-Relevant Targets and Biomarkers

Temporal modeling of disease progression is poised to revolutionize the process of target identification, leading to better characterization of and intervention at the critical early stages of chronic conditions. Temporal Biodynamics is an artificial intelligence-driven platform that leverages within-tissue heterogeneity in cross-sectional cohorts to assemble a single, continuous trajectory of transcriptomic changes between health and disease. We demonstrate that the platform enriches for known disease-associated genes and proteins by more than 50% over the conventional case-control comparisons. When compared to other published pseudotime methods, our models were better at extracting disease-relevant signals in the presence of confounders and co-morbidities. The Temporal Biodynamics platform enables rich profiling of a disease continuum, providing temporal insights that are otherwise hidden by the traditional discrete staging of chronic diseases. This includes detecting cascades of molecular events, providing clues regarding causality, and increasing confidence in blood-based protein biomarkers using tissue-based context.

bioinformatics↗

bioRxiv2026

NEO-AAV: an engineered extracellular vesicle-enveloped AAV platform for activation-coupled T cell transduction and CAR-T cell generation

Efficient delivery of genetic cargo to primary human T cells remains a critical barrier for cell-based immunotherapies. AAVs are widely used but limited by immune neutralization and poor T cell transduction efficiency. We present NEO-AAV, an engineered fusion protein (PH-ALG2-PKD12) that recruits AAV capsids into endogenous extracellular vesicles (EVs) via PI(4,5)P2-directed membrane targeting, multivalent PKD12-capsid clustering, and ALG2-mediated ESCRT machinery recruitment, yielding EV-enveloped particles with improved AAV loading compared with passive EV-AAV controls. NEO-AAV displayed enhanced resistance to an anti-AAV6 neutralizing antibody (ADK6), maintaining transduction at concentrations that neutralized naked AAV6 and outperformed passively formed EV-AAV6. Surface display of a CD7/CD3/CD28 tri-chimera enabled single-step activation and transduction of CD7 T cells within PBMCs, reaching [~]38% eGFP cells without exogenous pre-activation. As a proof-of-concept, NEO-AAV generated functional CAR-T cells from primary human PBMCs, exhibiting antigen-specific IFN-{gamma} release and cytotoxicity against CD19+ target cells. CAR expression peaked at day 5 and declined by day 15, consistent with episomal AAV kinetics, framing NEO-AAV as an activation-coupled delivery module rather than a durable solution. Together, NEO-AAV provides a programmable EV-enveloped AAV platform with improved immune shielding and single-step T cell transduction, offering a building block for immune cell engineering with in vivo potential.

bioengineering↗

bioRxiv2026

An extracellular vesicle biogenesis-inspired engineering platform for efficient protein delivery and therapeutic base editing

Efficient and controllable delivery of genome-editing proteins remains a central challenge for therapeutic translation of gene-editing technologies. Extracellular vesicles (EVs) offer an attractive non-viral delivery modality due to their biocompatibility and large capacity for cytosolic cargo delivery. Yet, rational strategies to achieve controlled and programmable protein loading are still lacking. Here, we present NEO-TOP-EVs, an EV biogenesis-guided engineering platform that systematically integrates key features of three design principles inspired by vesicle formation: 1) PI(4,5)P2-mediated plasma membrane targeting, 2) ESCRT-dependent membrane scission, and 3) self-assembly-driven cargo clustering for enabling efficient encapsulation of genome-editing ribonucleoproteins. Together, the NEO design increased cargo incorporation and enhanced functional delivery of gene editing modalities under particle-normalized conditions. Using NEO-TOP-EVs, we achieve efficient delivery of Cas9 and adenine base editor ribonucleoproteins without nucleic acid templates. In an in vitro proof-of-concept, delivery of an adenine base editor targeting proprotein convertase subtilisin/kexin type 9 (PCSK9) induces efficient splice-site disruption, resulting in reduced PCSK9 expression and enhanced LDL receptor activity. Proof-of-concept in vivo experiments provide preliminary evidence of functional Cre protein delivery to the liver. Together, these findings establish NEO-TOP-EVs as a modular platform for protein-based genome editing, demonstrating how biogenesis-informed EV engineering yields functional protein delivery at levels relevant to therapeutic development.

bioengineering↗

bioRxiv2026

EnzCast: Prediction of Patient-Specific Enzymatic Kinetics through Multi-Modal Deep Learning and Isoform-Resolved Bayesian Inference based on Single-Cell Transcriptomics

Enzyme kinetic parameters underpin mechanistic biology but remain sparse in physiological context. We present EnzCast, a multi-modal framework jointly predicting Km, kcat, kcat/Km, and Ki from protein sequence, 3D structure, substrate chemistry, and experimental conditions, paired with IsoKin, an isoform-resolved Bayesian framework converting EnzCast priors into patient-specific in vivo kinetics. Trained on KinBench, the largest curated kinetics database, task-adaptive EnzCast achieved R2 = 0.413, 0.455, 0.227, and 0.105 for Km, Ki, kcat, and kcat/Km, surpassing all baselines on catalytic tasks. Systematic condition scans recovered compartment-specific pH direction inversion and pathway-level temperature responses. In a 20-patient colorectal cancer single-cell cohort, IsoKin reduced posterior uncertainty by 73.3% and 77.3%, revealing cell-type-specific rewiring. Orthogonal validation--scFEA flux, DepMap essentiality (permutation P = 0.0008) and TCGA survival-- provided mixed but directionally consistent support. Together, EnzCast and IsoKin bridge in vitro prediction, condition-aware biochemical interrogation and patient-resolved in vivo inference.

bioinformatics↗

bioRxiv2026

PA-SfM: Tracker-free differentiable acoustic radiation for freehand 3D photoacoustic imaging

Three-dimensional photoacoustic imaging (3D PAI) has shown great potential in preclinical studies and clinical implementations, while it commonly has sparse sensor arrays, limiting both spatial sampling and the field of view (FOV). Freehand scanning could overcome these limitations through anatomy-adaptive scanning that extends spatial coverage and provides complementary views beyond the fixed array aperture, but its flexibility is conventionally constrained by the need for motor feedback or external pose tracking. Here we introduce PA-SfM, a tracker-free structure-from-motion (SfM) framework that recovers multiple poses directly from PA measurements with superior precision, enabling freehand 3D PAI without external pose measurements. PA-SfM integrates a differentiable acoustic radiation model with hierarchical optimization and rigid array constraints, it jointly estimates inter-view transformations and reconstructs 3D volumes. The performance of PA-SfM was demonstrated in numerical simulations, animal studies and human imaging experiments. Compared with motor encoder-based fusion of the mechanically scanned system, PA-SfM produced sharper and more continuous vascular reconstructions with an expanded FOV. In controlled quantitative validations, PA-SfM achieved high reconstruction fidelity, with PSNR values over 40 dB and SSIM values above 0.98 relative to ground truth or known pose reference reconstructions. These results demonstrate that PA-SfM enables robust freehand 3D PAI, with particular potential for clinical implementation of large-scale freehand imaging. The source code is publicly available at https://github.com/JaegerCQ/PA-SfM.

bioengineering↗

bioRxiv2026

Leveraging Large Language Models to Extract Prognostic Pathology Features in Ewing Sarcoma

BackgroundCurrent risk stratification for Ewing sarcoma relies heavily on clinical factors such as metastatic status, failing to capture histologic heterogeneity as a potential prognostic indicator. Although pathology reports contain rich biological data, this information remains locked in unstructured narrative text, limiting large-scale retrospective analyses. We aimed to validate the utility of Large Language Models (LLMs) for scalable data abstraction and to identify prognostic histologic features from a large multi-institutional cohort. MethodsWe conducted a retrospective cohort study using data from six Childrens Oncology Group (COG) clinical trials. We utilized an LLM-based pipeline (OpenAI o3) to extract structured variables--including immunohistochemical (IHC) markers and CD99 staining patterns--from digitized, Optical Character Recognition (OCR)-processed pathology reports. Extraction accuracy was validated against a human-annotated ground truth (n=200) and cross-validated against senior experts (n=48). We assessed the association between extracted features and Overall Survival (OS) using Kaplan-Meier analysis and multivariable Cox proportional hazards regression, adjusting for metastatic status. FindingsWe analyzed 931 diagnostic pathology reports from 185 institutions spanning over 21-years. The LLM achieved a weighted average accuracy of 94% across 17 IHC markers; in a cross-validation subset, the LLM outperformed human annotators (weighted average accuracy over 15 IHC markers: LLM o3: 98.1%, a pediatric resident 91.4%, and a pediatric oncologist 95.9%). Survival analysis identified Neuron-Specific Enolase (NSE) and S100 as significant prognostic biomarkers. After adjusting for metastatic status, NSE positivity was associated with significantly inferior survival (HR 2.15, 95% CI 1.15-4.02, p=0.016); this risk was most pronounced in patients with non-metastatic disease (HR 5.64, p=0.0055). Conversely, S100 positivity was associated with improved survival (HR 0.58, 95% CI 0.34-1.00, p=0.046). InterpretationLLM-assisted extraction of pathology variables is highly accurate and scalable, capable of unlocking "dark data" from historical clinical trials. We identified NSE as a potent risk factor and S100 as a protective marker in Ewing sarcoma, particularly in localized disease. These findings suggest that AI-derived histologic data can refine risk stratification and, if validated, warrant inclusion in future prospective trials. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and Google Scholar for articles published up to December 2025, using terms such as "Ewing sarcoma", "prognosis", "risk stratification", "large language models", "natural language processing", and "pathology report extraction". Current risk stratification for Ewing sarcoma relies predominantly on clinical variables, specifically the presence of metastatic disease and tumor size. While histologic heterogeneity is well-documented, it is rarely incorporated into risk models because extracting structured data from narrative pathology reports is labor-intensive. Small, single-institution studies have suggested potential prognostic roles for markers like Neuron-Specific Enolase (NSE) or S100, but results have been inconsistent and limited by small sample sizes. Furthermore, while Large Language Models (LLMs) have demonstrated potential for extracting clinical data, their application to "rescue" data from legacy, multi-institutional clinical trial documents (scanned PDF images) for rare disease biomarker discovery has not been validated at scale. Added value of this studyTo our knowledge, this is the largest study to utilize AI to extract histologic data from Ewing sarcoma pathology reports, aggregating 931 patients from six distinct Childrens Oncology Group (COG) clinical trials spanning over 21 years. We validated an LLM-based pipeline that converted noisy, optically character-recognized text from diverse institutions into structured data with 98.1% accuracy, outperforming human annotators. This scalable approach revealed that NSE positivity is a significant independent risk factor for mortality (HR=2.15), particularly in patients with non-metastatic disease where it confers a more than 5-fold increased risk of death. Conversely, we identified S100 positivity as a protective factor associated with improved survival (HR=0.58). Implications of all the available evidenceThis study demonstrates that LLMs can reliably unlock "dark data" from historical clinical trials, rendering vast archives of unstructured medical documents accessible for retrospective analysis. The identification of NSE and S100 as robust prognostic biomarkers suggests that these widely available immunohistochemical stains provide valuable information beyond standard diagnostic information. These findings support the integration of automated data extraction tools in research workflows and suggest that NSE and S100 status should be considered in the design of future risk-stratified clinical trials for Ewing sarcoma.

bioinformatics↗

bioRxiv2025

A trade-off mechanism underpins the evolution of a young two-gene sex-determining system in plants

Sexual systems in animals and plants are remarkably diverse, with dioecy having evolved independently in numerous lineages. In plants, dioecy often evolved more recently than in the best-studied animal systems, making plants especially important for understanding how separate sexes evolved independently from functionally hermaphrodite ancestors. Despite long-standing theories of developmental trade-offs in sex allocation, the underlying genetic mechanisms remain elusive. Here, we show that the XY sex determination system in the dioecious plant species Eurycorymbus cavaleriei in Sapindaceae involves two Y-linked mutations that act jointly within the developmental male-female trade-off: YUN{Delta}, a truncated allele that lowers the dosage of the D-class MADS-box gene YUN, and SUNMAO, a novel sRNA locus that silences the X-linked SUN allele. In females, SUN stabilizes the HD-ZIP transcription factor KUN, which is a known sex determinant in another dioecious plant, thereby promoting femaleness by increasing YUN expression; loss of SUN expression, together with the effect of YUN{Delta}, shifts development toward males. Two interlocking regulatory loops in this "SKY" module (SUN-KUN-YUN) fine-tunes YUN dosage. This dioecious system in E. cavaleriei likely evolved by sequential mutations in genes acting in the predicted male-female trade-off system, with their close linkage reflecting a translocation, and later recombination-suppressing inversions.

plant biology↗

bioRxiv2025

Write and read: Harnessing Synthetic DNA Modifications for Nanopore Sequencing.

An exciting feature of nanopore sequencing is its ability to record multi-omic information on the same sequenced DNA molecule. Well-trained models allow the detection of nucleotide-specific molecular signatures through changes in ionic current as DNA molecules translocate through the nanopore. Thus, naturally occurring DNA modifications, such as DNA methylation and hydroxymethylation, may be recorded simultaneously with the genetic sequence. Additional genomic information, such as chromatin state or the locations of bound transcription factors, may also be recorded if their locations are chemically encoded into the DNA. Here, we present a versatile "write-and-read" framework, where chemo-enzymatic DNA labeling with unnatural synthetic tags results in predictable electrical fingerprints in nanopore sequencing. As a proof-of-concept, we explore a DNA glucosylation approach that selectively modifies 5-hydroxymethylcytosine (5hmC) with glucose or glucose-azide adducts. We demonstrate that these modifications generate distinct and reproducible electrical shifts, enabling the direct detection of chemically altered nucleotides. We further demonstrate that enzymatic alkylation, such as the enzymatic transfer of azide residues to the N6 position of adenines, also produces characteristic nanopore signal shifts relative to the native adenine and 6-methyladenine. Beyond direct nucleotide detection, this approach introduces new possibilities for bio-orthogonal DNA labeling, enabling an extended alphabet of sequence-specific detectable moieties. The future use of programmable chemical modifications for simultaneous analysis of multiple omics features on individual molecules opens new avenues for genetic research and discovery.

bioengineering↗

bioRxiv2025

mcDETECT: Decoding 3D Spatial Synaptic Transcriptomes with Subcellular-Resolution Spatial Transcriptomics

Spatial transcriptomics (ST) is a powerful tool for studying the molecular basis of brain diseases. However, most current analyses focus only on nuclear or somatic expression, overlooking distally localized mRNAs, known as the "dark transcriptome." These transcripts, which make up nearly 40% of the brain transcriptome, are packaged into RNA granules and transported to polarized neuronal structures critical for neuronal function. Here, we present mcDETECT, a machine learning framework that leverages in situ ST (iST) data to detect RNA granules and resolve their RNA composition. Applying mcDETECT to simulated and real datasets from multiple iST platforms, we demonstrate robust granule detection and molecular subtyping. Granule-informed downstream analysis further revealed functionally distinct neuronal substates not captured by somatic expression alone. In an Alzheimers disease (AD) mouse model, mcDETECT uncovered alterations in RNA granule distributions and associated neuronal substates prior to measurable neuronal loss, highlighting potential therapeutic targets for early AD pathology.

bioinformatics↗

bioRxiv2024

Phosphotyrosine Signal Profiling of Clinical CAR-T Reveals Tonic Signaling Associated with Therapeutic Efficacy

Chimeric Antigen Receptor T (CAR-T) therapies have revolutionized the treatment of cancers such as relapsed and refractory B cell malignancies. However, the precise therapeutic mechanism of CAR-T action remain to be elucidated. In this study, we systematically analyzed CAR signaling via phosphotyrosine (pTyr) proteomics in CAR-T cells from both clinical patients and healthy donors. We found that CAR-T products from clinical patients displayed heightened tyrosine phosphorylation, particularly in the JAK-STAT, MAPK and TCR signaling cascades. We also identified the significantly regulated pTyr sites in primary CAR-T cells under tonic signaling or upon stimulation by antigen-presenting CD19-K562 cells. Although both CD28{zeta} and 4-1BB{zeta} CAR-T cells exhibited comparable pTyr changes, CD28{zeta} CAR-T cells displayed a more pronounced activation in the TCR signaling pathway. Additionally, comparative analysis between clinical and primary CAR-T cells suggested that CAR-T products were subject to heightened tonic signaling, which may be related to therapeutic relapse. Our findings reveal the state of clinical CAR-T products and refine the CAR-T signal transduction network, providing comprehensive insights and informed guidance for CAR-T therapies.

immunology↗

bioRxiv2024

Age-Related Changes in Functional Balance Ability Predict Alpha Activity During Multisensory Integration

The increased multisensory integration and weaker attentional control experienced by older adults during audiovisual processing can result in inaccurate perceptions of their dynamic, everyday environment. These inaccurate representations of our environment can contribute to increased fall risk in older adults. A neural correlate of the attentional difference between younger and older adults could be oscillatory alpha activity (8-12Hz), indexing inhibitory processes during multisensory integration. The current study investigated whether age-related changes in alpha activity underlie weaker attentional control in older adults during a multisensory task, and if alpha associates with fall risk. Thirty-six younger (18-35 years old) and thirty-six older (60-80 years old) adults completed a cued-spatial-attention stream-bounce task, assessing audiovisual integration when attending to validly-cued or invalidly-cued locations, at 0ms or 300ms stimulus-onset asynchronies. Oscillatory alpha activity was recorded throughout using EEG to index participants inhibitory abilities. Functional ability and balance were measured to index fall risk. Multiple linear regression models revealed that even when attending to the validly-cued location, less accurate multisensory integration was exhibited by older adults compared to younger adults, suggesting that older adults demonstrate weaker top-down modulation of multisensory integration through failing to inhibit task-irrelevant information. However, alpha power across the trials did not predict the extent of multisensory integration within the task. A significant interaction between age and functional ability scores predicted alpha power, suggesting that older adults may rely on attentional mechanisms for functional ability more than younger adults do. Potential implications in the design of clinical treatments to reduce falls are discussed.

neuroscience↗

bioRxiv2024

Structural Basis of GPCR-G Protein Pre-coupling and Activation: Insights from CCR1-Gi Complex

GPCR-mediated G protein activation cycle contains obligated step of the receptor-G protein assembly before G protein activation (pre-coupling state). However, transient nature of this pre-coupling state has prevented structural and mechanistic understanding of this essential step of G protein activation cycle. In this study, we discover that CCR1 has high level of pre-coupling state, which allows its rapid response to chemokines. Taking advantage of this observation, we uncover molecular mechanism of the pre-coupling state by solving the cryo-electron microscopy structure of the chemokine receptor CCR1 in complex with its cognate G protein (Gi) in a pre-coupled, inactivated state. This structure reveals that CCR1 adopts a conformation distinct from both its fully active and inactive states, with the G proteins 5 helix partially inserted into the receptors intracellular cavity. Notably, the C-terminal four residues of the G subunit are disordered in this pre-coupled state, contrasting with their well-defined -helical structure in the fully active complex. Functional assays demonstrate that while deletion of these four C-terminal G residues does not affect pre-coupling, it abolishes G protein activation upon agonist binding. This finding highlights the critical role of these residues in GPCR-mediated G protein activation, but not in initial recruitment. Furthermore, our studies indicate that the ability to form pre-coupled complexes is conserved among chemokine receptors, suggesting a common mechanism for rapid signal transduction in this GPCR subfamily. These results provide the first structural evidence for GPCR-G protein pre-coupling and offer molecular insights into the transition from inactive to active states. Our findings fill the long-missing gap in understanding GPCR-mediated G protein activated cycle.

biophysics↗

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

Label-Free Electrochemical Imaging of Single Extracellular Vesicles

Extracellular vesicles (EVs) are pivotal in various biological processes and diseases, yet their small size and heterogeneity pose challenges for single EV quantification. We introduce interferometric electrochemical microscopy (iECM), a sensitive label-free technique that combines interferometric scattering and electrochemical impedance imaging. This method enables the quantification of the impedance of single EVs, providing unique insights into their electrochemical properties, and allows for the simultaneous measurement of size and real-time monitoring of antibody binding. Notably, we discovered that the impedance spectra of EVs can serve as unique fingerprints to differentiate EVs from cancerous and healthy cells, offering potential advancements in clinical diagnostics and the profiling of extracellular biomarkers.

biophysics↗