Biology subjects

Zhou, J.

Publications and source records attributed to Zhou, J..

At least 181 records · Page 10Linked to original sources

bioRxiv2025

Improved Injury Detection Through Harmonizing Multi-Site Neuroimaging Data after Experimental TBI: A Translational Outcomes Project in NeuroTrauma (TOP-NT) Consortium Study

Multi-site neuroimaging studies have become increasingly common in order to generate larger samples of reproducible data to answer questions associated with smaller effect sizes. The data harmonization model NeuroCombat has been shown to remove site effects introduced by differences in site-related technical variance while maintaining group differences, yet its effect on improving statistical power in pre-clinical models of CNS disease is unclear. The present study examined fractional anisotropy data computed from diffusion weighted imaging data at 3 and 30 days post-controlled cortical impact injury from 184 adult rats across four sites as part of the Translational-Outcome-Project-in-Neurotrauma (TOP-NT) Consortium. Findings confirmed prior clinical reports that NeuroCombat fails to remove site effects in data containing a high proportion-of-outliers (>5%) and skewness, which introduced significant variation in non-outlier sites. After removal of one outlier site and harmonization using a global sham population, harmonization displayed an increase in effect size in data that displayed group level effects (p<0.01) in both univariate and voxel-level volumes of pathology. This was characterized by movement toward similar distributions in voxel measurements (Kolmogorov-Smirnov p<<0.001 to >0.01) and statistical power increases within the ipsilateral cortex. Harmonization improved statistical power and frequency of significant differences in areas with existing group differences, thus improving the ability to detect regions affected by injury rather than by other confounds. These findings indicate the utility of NeuroCombat in reproducible data collection, where biological differences can be accurately revealed to allow for greater reliability in multi-site neuroimaging studies. Significance StatementThis project demonstrates the utility of NeuroCombat in reducing site effects in multi-site rodent imaging. We also demonstrate that harmonization improves the ability to distinguish between sham and injured rats at the voxel level and increase statistical power and effect size in areas of injury. Multi-center studies are becoming more common to allow for increased efficiency in data collection, and with conservative approaches and analysis into the datasets, NeuroCombat can be utilized to improve study reliability and reproducibility.

neuroscience↗

bioRxiv2025

LPS/TLR4-Activating Exosomes from M1-Polarized Avian Macrophages Enhances IBV Vaccine-Induced Protective Immunity in Chickens

Infectious bronchitis virus (IBV) presents a substantial economic burden to poultry production due to extensive serotypic diversity and limited cross-protection afforded by conventional vaccines. This study evaluated exosomes derived from M1-polarized chicken macrophages (HD11M1-exo) as a novel adjuvant for IBV vaccination. HD11M1-exo, isolated from LPS-activated HD11 macrophages via ultracentrifugation, demonstrated significant immunomodulatory properties across multiple experimental systems. In vitro analyses revealed HD11M1-exo enhanced macrophage phagocytosis and cellular immune activation via the LPS/TLR4 signaling pathway. In embryonated eggs, HD11M1-exo pre-treatment induced TNF- upregulation, enhanced viral resistance, and reduced pathological damage. In chickens, HD11M1-exo administration elevated CD80/CD86 and TGF-{beta}4 expression in respiratory tissues and increased secretory IgA in lacrimal fluid. When combined with H120 vaccine, HD11M1-exo significantly augmented both humoral immunity (elevated serum IgY and mucosal IgA) and cellular responses (increased CD80/CD86 expression), surpassing commercial adjuvants in efficacy. Following viral challenge, HD11M1-exo+H120 immunized chickens exhibited significantly reduced viral loads and attenuated histopathology compared to controls. These data collectively suggest that exosome-based formulations may serve as effective adjuvants for enhancing poultry vaccine immunogenicity and protective efficacy.

immunology↗

bioRxiv2025

Integrative Multi-omics and Supervised Learning Identifies an Epithelial Signature for Radiotherapy Response in Colorectal Cancer

Colorectal cancer (CRC) is the third most diagnosed cancer globally, accounting for 9.6% of all cancer cases, and the second leading cause of cancer deaths. Radiotherapy is a common treatment, but can demonstrate dangerous side effects and varying patient outcomes that reflects on CRC cancer heterogeneity at genetic, epigenetic, transcriptomics and proteomic levels. Identifying biomarkers capable of effectively predicting CRC patient responses to radiotherapy remains paramount, and an unmet need. We channeled both unsupervised and supervised approaches to assess radiotherapy response for 233 patients of the S:CORT Consortium. Splitting the cohort, we first integrated matched RNA, CNA, mutation, and methylation profiles of 117 patients using multi-omics factor analysis (MOFA). We identified a new radiotherapy signature of 101 biomarkers associated with patients who demonstrate a complete response to radiotherapy, and validated the signature using a random forest classifier on the internal validation dataset, and an independent testing cohort. Our signature effectively predicted treatment outcomes, achieving 89% accuracy with strong discriminatory performance (ROC_AUC = 0.85; PR_AUC = 0.71) to differentiate patients with complete response to radiotherapy compared to incomplete responders. Assessing human and murine scRNAseq datasets underscores that the signature is predominantly expressed in CRC epithelial cells, which underpin CRC heterogeneity and cellular diversity. Our identified signature enables pre-treatment identification of CRC patients that are unlikely to achieve a complete response to radiotherapy, thereby sparing these patients from unnecessary radiation exposure and off-target damage effects.

bioinformatics↗

bioRxiv2025

Two telomere-to-telomere, gap-free genome assemblies and comparisons revealed the conserved key genes associated with sugar accumulation in Rubus genus

For the first time, we assembled two highly continuous, completely gap-free reference genomes of the Rubus subgenus: Rubus hirsutus Thunb. Penglei (XMM) and Rubus eustephanos Focke ex Diels Dahongpao (DHP), which are widely distributed in southern China with similar phenotypic traits (Figures 1A, 1B, and Figure S1), yet ripe fruits display distinct sugar accumulation levels (Table S1), making them ideal candidates for investigating the mechanisms underlying sugar accumulation in the Rubus genus. The XMM (213.53 Mb, 28,204 genes) and DHP (218.26 Mb, 28,569 genes) genomes exhibit close evolutionary relationships, diverging approximately 3.21 Mya. Comparative genomics identified extensive synteny, interspecific structural variations (translocations, inversions, segmental duplications), and presence/absence variation (PAV). Using Hi-C interaction heatmaps and Sanger sequencing, we validated interspecific structural inversions. Additionally, we identified a sugar transporter gene (MFS1), which is present in XMM but absent in DHP. Combined analysis of the gene family expansion/contraction and transcriptome identified two conserved key genes (RhSTP13 and RhSTP7) associated with sugar accumulation in Rubus genus and displayed distinct roles through transient expression assay. To facilitate functional genomics study, we also established a comprehensive Rubus database, RubusDB, a freely accessible repository consolidating all genomic, transcriptomic and phenotypic data of Rubus genus. These findings provide a foundational framework for elucidating the genetic basis of sugar accumulation, genome diversification, and trait improvement in Rubus species. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=170 SRC="FIGDIR/small/646780v2_fig1.gif" ALT="Figure 1"> View larger version (72K): org.highwire.dtl.DTLVardef@1baca20org.highwire.dtl.DTLVardef@2c01eaorg.highwire.dtl.DTLVardef@131dc00org.highwire.dtl.DTLVardef@62a04f_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFIGURE 1.C_FLOATNO Phenotypic characterization, de novo genome assemblies, variation, and evolution of Rubus hirsutus Thunb. Penglei (XMM) and Rubus eustephanos Focke ex Diels Dahongpao (DHP). (A) Illustration of XMM whole plants, stem, flower bud and flower, respectively. (B) Illustration of DHP whole plants, stem, flower bud and flower, respectively. (C) The chromosome karyotype analysis of XMM. Bar = 10 {micro}m. (D) Circular diagram of XMM and DHP reference genomes. (a) Chromosomes are represented by centromeres (dark red) and telomeres (blue); (b) CEN18 density; (c) CEN17 density; (d) Gene density; (e) TE density; (f) PAV density; (g) SNP density; (h) Collinear lines at the center of diagram highlight homoeologous chromosomes relationships and non-homoeologous regions. (E) Genomic alignments between XMM and DHP. Inversions, duplications and translocations are marked with orange, blue and green ribbons, respectively. (F) Identification of large inversions in chromosome 7 between XMM and DHP. The three heatmaps show the chromatin interaction matrix, including mapping Hi-C data of DHP against DHP genome (left), mapping Hi-C data of DHP against XMM genome (middle) and mapping Hi-C data of XMM against XMM genome (right). The lower panel illustrates gene alignments between XMM and DHP. (G) Synteny, structural rearrangements, monomer, and identity distribution in the 16-20 Mbp region of chromosome 6 between XMM and DHP. (H) Local genome synteny of chromosome 5, with a structural variation that is present only in XMM. The PAV region includes a gene related to sugar transporter (MFS1, XMM05G025540). (I) Gene-level matchings of MFS genes in the PAVs region between XMM and DHP. (J) Phylogenetic relationships and divergence times between raspberries and other Rosaceae species. The black numbers close to the divergence nodes indicate the divergence times and the red bars represent the 95% confidence intervals. The red and black numbers indicate expanded and contracted ortholog groups at the corresponding node. Scale bar corresponds to 10 Mya. C_FIG

genomics↗

bioRxiv2025

Transcription factor LHX2 suppresses astrocyte proliferation in the postnatal mammalian cerebral cortex

In the developing cerebral cortex astrocytes arise from progenitors in the ventricular and subventricular zones (V-SVZ), and also from local proliferation within the parenchyma. In the mouse neocortex, astrocytes that occupy upper versus deep layers (UL/DL) are known to be distinct populations in terms of molecular and morphological features. Transcription factor LHX2 is expressed both in V-SVZ gliogenic progenitors and in differentiated astrocytes throughout development and into adulthood. Here we show that loss of Lhx2 at birth results in an increased astrocyte proliferation in UL but not the DL of the cortex in the first postnatal week. Consistent with this, transcriptomic signatures of UL astrocytes increase. By 3 months, Lhx2 mutant astrocytes display upregulation of GFAP, and transcriptomic signatures associated with "reactive" astrocytes, in the absence of injury. These results demonstrate a novel role for Lhx2 in regulating proliferation and molecular features of cortical astrocytes. Summary StatementLoss of Lhx2 causes increased astrocyte proliferation in the upper but not deep layers of the mammalian cortex, and upregulation of reactive gliosis-like signatures in the absence of injury.

neuroscience↗

bioRxiv2025

Prior cocaine use disrupts identification of hidden states by single units and neural ensembles in orbitofrontal cortex

The orbitofrontal cortex (OFC) is critical to identifying task structure and to generalizing appropriately across task states with similar underlying or hidden causes. This capability is at the heart of OFCs proposed role in a network responsible for cognitive mapping, and its loss can explain many deficits associated with OFC damage or inactivation. Substance use disorder is defined by behaviors that share much in common with these deficits, such as an inability to modify learned behaviors in the face of new information about undesired consequences. One explanation for this similarity would be if addictive drugs impacted the ability of OFC to recognize underlying similarities, hidden states, that allow information learned in one setting to be used in another. To explore this possibility, we trained rats to self-administer cocaine and then recorded single unit activity in lateral OFC as these rats performed in an odor sequence task consisting of unique and shared positions. In well-trained controls, we observed chance decoding of sequence at shared positions and near chance decoding even at unique positions, reflecting the irrelevance of distinguishing these positions in the task. By contrast, in cocaine-experienced rats, decoding remained significantly elevated, particularly at the positions that had superficial sensory differences that were collapsed in controls across learning. These neural differences were accompanied by increases in behavioral variability at these positions. A tensor component analysis showed that this effect of reduced generalization after cocaine use also extended across positions in the sequences. These results show that prior cocaine use disrupts the normal identification of hidden states by OFC.

neuroscience↗

bioRxiv2025

Structural basis of substrate specificity of Helix pomatia AMP deaminase and a chimeric ADGF adenosine deaminase

HPAMPD, an enzyme enriched in the foot muscle of the mollusk Helix pomatia, exhibits deaminase activity on adenosine-5-monophosphate (AMP). HPAMPD is the first member of the adenosine deaminase-related growth factor (ADGF) family to prefer the nucleotide, AMP, over the nucleoside, adenosine. To investigate the substrate selectivity of HPAMPD, we determined its structure in the apo form and in complex with the adenosine analogs pentostatin (2- deoxycoformycin) and pentostatin-5-monophosphate. Structurally, HPAMPD adopts a fold similar to human ADA2, an ADGF family member. HPAMPD has acquired the ability to interact with the 5-monophosphate group of AMP through polar and charged residues located in three key structural elements: (1) the loop immediately following strand {beta}1, (2) the loop between helices H and I, and (3) the end of strand {beta}5 and its adjacent loop. We engineered a chimeric deaminase by integrating these elements from HPAMPD into another related mollusk nucleoside adenosine deaminase, the A. californica ADGF. The chimeric enzyme efficiently deaminates AMP, demonstrating a gained substrate specificity, while retaining the adenosine deamination activity of Aplysia ADGF. The phosphate-binding feature of HPAMPD is a hallmark of nucleotide deaminases, conserved among AMP and N6-methyl-AMP (6mAMP) deaminases. We discuss the human adenosine deaminases each with distinct substrate specificities for the nucleoside, the nucleotide (AMP), and the methylated form, 6mAMP. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/645602v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@be415forg.highwire.dtl.DTLVardef@b5f7a5org.highwire.dtl.DTLVardef@957514org.highwire.dtl.DTLVardef@397e9a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Human Body Single-Cell Atlas of 3D Genome Organization and DNA Methylation

Higher-order chromatin structure and DNA methylation are critical for gene regulation, but how these vary across the human body remains unclear. We performed multi-omic profiling of 3D genome structure and DNA methylation for 86,689 single nuclei across 16 human tissues, identifying 35 major and 206 cell subtypes. We revealed extensive changes in CG and non-CG methylation across almost all cell types and characterized 3D chromatin structure at an unprecedented cellular resolution. Intriguingly, extensive discrepancies exist between cell types delineated by DNA methylation and genome structure, indicating that the role of distinct epigenomic features in maintaining cell identity may vary by lineage. This study expands our understanding of the diversity of DNA methylation and chromatin structure and offers an extensive reference for exploring gene regulation in human health and disease.

genomics↗

bioRxiv2025

Group optimization methods for dose planning in transcranial electrical current stimulation (tES)

ObjectiveOptimizing transcranial electrical stimulation (tES) parameters--including stimulator settings and electrode placements--using magnetic resonance imaging-derived head models is essential for achieving precise electric field (E-field) distributions, enhancing therapeutic efficacy, and reducing inter-individual variability. However, the dependence on individually personalized MRI-based models limits their scalability in some clinical and research contexts. To overcome this limitation, we propose a novel group-level optimization framework employing multiple representative head models. ApproachThe proposed optimization approach utilizes computational modeling based on multiple representative head models selected to minimize group-level error compared to baseline (no stimulation). This method effectively balances focal stimulation intensity within targeted brain regions while minimizing off-target effects. We evaluated our method through computational modeling and leave-one-out cross-validation using data from 54 subjects and analyzed the effectiveness, generalizability, and predictive utility of anatomical characteristics. Main resultsOur approach demonstrated that group optimization significantly outperformed protocols derived from standard templates or randomly selected individual models, notably reducing variability in outcomes across participants. Additionally, correlations between anatomical features (e.g., head perimeter and tissue volumes) and E-field parameters revealed predictive relationships. This insight enables further optimization improvements through the strategic selection of representative head models that are electro-anatomically similar to the target subjects. SignificanceThe proposed group optimization framework provides a scalable and robust alternative to personalized approaches, substantially enhancing the feasibility and accessibility of model-driven tES protocols in diverse clinical and research environments. Data Access StatementThe data that support the findings of this study are available from the corresponding author, R.S., upon reasonable request.

biophysics↗

bioRxiv2025

Learning gene interactions and functional landscapes from entire bacterial proteomes

Unraveling complex gene interactions and understanding their functions in the genomes of bacteria will provide critical advancements in fields including bacterial genome evolution, microbiome studies, as well as drug and natural product discovery. This is a challenging problem due to the structural and functional complexity of bacterial genomes, and issues including poor gene annotation in non-model species. Language models (LMs) provide a feasible framework for learning the complex interactions among genes from a large number of publicly available, unannotated bacterial genomes. However, applications of language models have mostly been limited to developing models trained on short genomic sequences. Here, we introduce the first whole bacteria proteome foundation model to our knowledge. Our model was trained on ESM embeddings of tens of thousands of full-size proteomes and can generate contextual embeddings for individual proteins as well as embeddings representing the entire genome. We show that our model captures gene-gene interactions and genomic integrity. We further demonstrate that the learned embeddings can be used to achieve state-of-the-art performances for downstream tasks such as identifying operons, and predicting genotype-phenotype maps.

genetics↗

bioRxiv2025

SMART: an approach for accurate formula assignment in spatially-resolved metabolomics

Spatially-resolved metabolomics plays a critical role in unraveling tissue-specific metabolic complexities. Despite its significance, this profound technology generates thousands of features, yet accurate annotation significantly lags behind LC-MS-based approaches. To bridge this gap, we introduce SMART, an open-source platform designed for precise formula assignment in mass spectrometry imaging. SMART constructs a KnownSet database containing 2.8 million formulas linked by DBEdges derived from repositories such as HMDB, ChEMBL, PubChem, and BioEdges from KEGG biological reactant pairs. Using a multiple linear regression model, SMART extracts formula networks associated with the m/z of interest and scores potential candidates based on several criteria, including linked formulas, DBEdges/BioEdges, and ppm values. Benchmarking against reference datasets demonstrates that SMART achieves prediction accuracy rates of up to 92.4%. Applied to mass spectrometry imaging, SMART successfully annotated 986 formulas in developing mouse embryos. This robust platform enables systematic formula annotation within tissues, enhancing our understanding of metabolic heterogeneity.

bioinformatics↗

bioRxiv2025

Distinct Alpha Networks Modulate Different Aspects of Perceptual Decision-Making

Why do we sometimes perceive a faint stimulus but miss it at other times? One explanation is that conscious perception fluctuates with the brains internal state, influencing how external stimuli are processed. Ongoing brain oscillations in the alpha band (8-13 Hz), thought to reflect neuronal excitability levels1-5 and play a role in functional inhibition6,7, have been shown as a key contributor to such perceptual variability8,9. Under high alpha conditions, faint stimuli are more likely to be missed8. Some studies suggested alpha oscillations modulate perceptual criterion (c)10-14, shifting the threshold for interpreting sensory information; while others (including our prior work15) suggested alpha modulates sensitivity (d)15-19, changing the precision of sensory encoding. Few studies observed modulations in both metrics, making these results appear mutually exclusive. Most studies have focused solely on overall alpha activity-- whether within a region of interest or across the whole brain--and overlooked the coexistence of multiple distinct alpha networks20-26, which fluctuate in terms of predominance20,27,28 and adapt to behavioural demands29,30. Hence, it remained unclear whether different networks contributions to perception vary with their momentary state. Here, aiming to characterize how different alpha networks influence perceptual decision-making, we analyzed magnetoencephalography (MEG) data recorded while participants performed a visual detection task with threshold-level stimuli. We found that while the visual alpha network modulates perceptual sensitivity, the sensorimotor alpha network modulates criterion in perceptual decision-making. These findings reconcile previous conflicting results and highlight the functional diversity of alpha networks in shaping perception.

neuroscience↗

bioRxiv2025

Inference and visualization of complexgenotype-phenotype with gpmap-tools

Understanding how biological sequences give rise to observable traits, that is, how genotype maps to phenotype, is a central goal in biology. Yet our knowledge of genotype-phenotype maps in natural systems is limited due to the high dimensionality of sequence space and the context-dependent effects of mutations. The emergence of Multiplex assays of variant effect (MAVEs), along with large collections of natural sequences, offer new opportunities to empirically characterize these maps at an unprecedented scale. However, tools for statistical and exploratory analysis of these high-dimensional data are still needed. To address this gap, we developed gpmaptools https://github.com/cmarti/gpmap-tools), a python library that integrates a series of models for inference, phenotypic imputation, and error estimation from MAVE data or collections of natural sequences in the presence of genetic interactions of every possible order. gpmap-tools also provides methods for summarizing patterns of epistasis and visualization of genotype-phenotype maps containing up to millions of genotypes. To demonstrate its utility, we used gpmap-tools to infer genotype-phenotype maps containing 262,144 variants of the Shine-Dalgarno sequence from both genomic 5UTR sequences and experimental MAVE data. Visualization of the inferred landscapes consistently revealed high-fitness ridges that link core motifs at different distances from the start codon. In summary, gpmap-tools provides a flexible, interpretable framework for studying complex genotype-phenotype maps, opening new avenues for understanding the architecture of genetic interactions and their evolutionary consequences.

evolutionary biology↗

bioRxiv2025

Intratumoral Resident Microbes Directly Participate in Resistance to EGFR-TKI Targeted Drug Therapy through Metabolism in NSCLC

EGFR-TKIs targeted therapy represents a cornerstone in the treatment of non-small cell lung cancer (NSCLC). Nonetheless, a subset of patients inevitably develops acquired resistance to EGFR-TKIs, posing a significant clinical challenge. An increasing number of evidence has documented the presence of microbial communities within tumor microenvironments, but the potential interplay between tumor-resident microbiota and resistance to EGFR-TKIs remains poorly understood. Here, we report that prolonged EGFR-TKIs treatment markedly alters the diversity of tumor-resident microbiota, characterized by a notable enrichment of Bacillus mycoides and Pseudomonas veronii. In vitro experiments revealed that co-culture of gefitinib with either B. mycoides or P. veronii significantly attenuated its tumor-suppressive efficacy, both in vitro and in vivo. Mechanistically, gefitinib underwent extensive biotransformation in the presence of these bacteria, yielding redox derivatives and conjugation products, which collectively diminished its antitumor activity. Specifically, B. mycoides metabolized gefitinib into B.mPM9-C33 H45 Cl F N3 O4, while P. veronii generated P.vPM2-C24 H29 Cl N4 O3, both of which were consistently detected in in vivo and in vitro models. Furthermore, heterologous expression of maltose acetyltransferase from B. mycoides in E. coli BL21 conferred the capacity to metabolize gefitinib, underscoring the enzymatic basis of this metabolic conversion. Collectively, these findings demonstrate that B. mycoides and P. veronii drive tumor resistance to EGFR-TKIs through metabolic inactivation of the drug, unveiling a previously unrecognized role of tumor-resident microbiota in modulating therapeutic responses and providing novel insights into the mechanisms underlying EGFR-TKIs resistance in NSCLC.

microbiology↗

bioRxiv2025

PELSA-Decipher: a software tool for the processing and interpretation of ligand protein interaction dataset acquired by PELSA

Knowledge of protein-ligand interactions is essential for comprehending various aspects of life science, including drug mechanisms of action, regulatory processes in cellular metabolism and signaling, etc. Recently, a robust ligand modification-free method, termed as peptide-centric local stability assay (PELSA), is developed to identify the protein targets and binding regions of diverse ligands at proteomics scale. This method has unprecedented sensitivity and can be broadly applied to ligands including drugs, metabolites, metal ions, antibodies tec. However, the extraction of key information on ligand-protein interaction, including the binding protein, binding site, and binding affinity, is not a trivial task, which hampers the wide utilization of PELSA strategy by the research community. To address this, we developed a software tool, PELSA-Decipher, to facilitate the efficient processing of dataset obtained in PELSA experiment, including raw data processing, result visualization, and the generation of reports and high-quality images, thereby will greatly promote the broader applications of PELSA strategy. The PELSA-Decipher software can be downloaded free of charge from the website https://github.com/DICP-1809.

bioinformatics↗

bioRxiv2025

Multimodal optical imaging and modulation through Smart Dura in non-human primates

Multimodal neural interfaces that integrate electrical and optical functionalities are promising tools for neuroscientific and clinical applications that involve recording and manipulating neuronal activity. However, most technologies for multimodal implementation are largely restricted to small animal models and lack the ability to translate to the larger brains of non-human primates (NHPs). Smart Dura, a recently developed large-scale neural interface for NHPs, enables high-density electrophysiological recordings and broad optical accessibility, providing multiscale information with enhanced spatiotemporal resolution. In this paper, the multimodal capabilities of Smart Dura are demonstrated through integration with multiphoton imaging, optical coherence tomography angiography (OCTA), and intrinsic signal optical imaging (ISOI), as well as optical manipulations such as photothrombotic lesioning and optogenetics. Through the transparent Smart Dura, in vivo fluorescence vascular imaging is achieved down to depths of 200 and 550 m using two-photon and three-photon microscopy, respectively. When combined with simultaneous electrophysiology, Smart Dura also enables assessment of vascular and neural dynamics via OCTA and ISOI, the induction of ischemic stroke, and the application of optogenetic neuromodulation across a wide cortical area of 20 mm in diameter. These capabilities support comprehensive investigations of brain dynamics in NHPs, advancing translational neurotechnology for human applications.

neuroscience↗

bioRxiv2025

Smart Dura: a functional artificial dura for multimodal neural recording and modulation

A multi-modal neural interface capable of long-term recording and stimulation is essential for advancing brain monitoring and developing targeted therapeutics. Among the traditional electrophysiological methods, micro-electrocorticography (ECoG) is appealing for chronic applications because it provides a good compromise between invasiveness and high-resolution neural recording. When combining ECoG with optical technologies, such as calcium imaging and optogenetics, this multi-modal approach enables the simultaneous collection of neural activity from individual neurons and the ability to perform cell-specific manipulation. While previous efforts have focused on multi-modal interfaces for small animal models, scaling these technologies to larger brains, of primates, remains challenging. In this paper, we present a multi-modal neural interface, named Smart Dura, a functional version of the commonly used artificial dura with integrated electrophysiological electrodes for large cortical area coverage for the NHP brain. The Smart Dura is fabricated using a thin-film microfabrication process to monolithically integrate a micron-scale electrode array into a soft, flexible, and transparent substrate with high-density electrodes (up to 256 electrodes) while providing matched mechanical compliance with the native tissue and achieving high optical transparency (exceeding 97%). Our in vivo experiments demonstrate electrophysiological recording capabilities combined with neuromodulation, as well as optical transparency via multiphoton imaging. This work paves the way toward a chronic neural interface that can provide large-scale, bidirectional interfacing for multimodal and closed-loop neuromodulation capabilities to study cortical brain activity in non-human primates, with the potential for translation to humans.

neuroscience↗

bioRxiv2025

Fast, accurate parsing of antibody structures and antibody-antigen interactions enables a comprehensive structural antibody database

Antibody (Ab) structures and antibody-antigen interactions (AAIs) are essential for understanding immune recognition and designing Ab therapeutics. While existing structural Ab databases provide valuable insights, they still have notable limitations. Here, we present SAAINT-parser, an computational workflow for rapid, accurate, and robust extraction of Ab and AAI information from the Protein Data Bank (PDB). SAAINT-parser features precise detection of Ab chains, accurate pairing of Ab chains, and reliable identification of AAIs. The resulting SAAINT-DB contains 18,031 data entries from 9,373 PDB structures, offering a comprehensive and up-to-date resource. Detailed analyses of SAAINT-DB reveals its advantages over the most widely used SAbDab database, particularly in terms of data accuracy and completeness. Furthermore, SAAINT-DB provides nearly twice as many nonredundant, manually curated Ab-Ag binding affinity data compared to SAbDab. Both SAAINT-parser and SAAINT-DB are available as open-source resources, aimed at advancing Ab-related research and benefiting the broader scientific community.

bioinformatics↗