Biology subjects

Xie, L.

Publications and source records attributed to Xie, L..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Cardiac oxidative stress monitoring enabled by hierarchical mechanical adaptation

Soft bioelectronics have advanced cardiac monitoring through electrophysiological tracking, yet this alone cannot resolve the metabolic pathology essential to surgical decision-making. However, real-time molecular sensing on beating hearts remains unresolved due to deformation-induced sensor failure and stress-induced metabolite artifacts. This challenge is exemplified by ischemia-reperfusion injury (IRI), a major cardiac surgery complication characterized by reactive oxidative species (ROS) bursts, where true pathological ROS signals being confounded by mechanotransduction-induced ROS artifacts. Herein, we propose an enzymatic cardiac oxidative stress biosensor (E-cardiac) with hierarchical mechanical adaptation: macro-scale biofluid-mediated contact, micro-scale fiber reorganization, and nano-scale enzymatic confinement within gold nanoarches dissipate interfacial stress. This produces ultrathin ([~]460 nm), soft (0.79 kPa) E-cardiac with robust electrochemical stability (100% strain), low detection limit (380 nM), rapid adhesion (<3 s), stable biosensing on beating heart, as well as minimal invasive deployment capability. Mechanical analysis and cellular studies confirm mitigated stress-induced ROS and absent PIEZO channel activation. Validated across cardiomyocytes, ex vivo tissues, multi-species ischemia models (mouse, rat, rabbit, pig), rat ischemia-reperfusion injury, and Langendorff hearts simulating graded perfusion deficits, E-cardiac quantitatively differentiates IRI severity (sham < ischemia < reperfusion) as well as detecting the "ECG blind window". The E-cardiac platform provides real-time metabolic feedback for surgical guidance during cardiac procedures, enabling timely intervention before irreversible damage.

bioengineering↗

bioRxiv2026

Highly Accurate Estimation of the Fold Accuracy of Protein Structural Models

BackgroundThe function of a protein is intrinsically linked to its three-dimensional fold, and deep learning has revolutionized the field by enabling high-accuracy structure prediction at an unprecedented scale. Nevertheless, the growing deployment of these predictive pipelines in drug discovery and structural biology reveals a critical bottleneck that lies in the lack of independent and rigorous estimation of model accuracy (EMA) methodologies. ResultsHere we present DeepUMQA-Global, a single-model deep learning framework for estimating accuracy of protein structure models. Our method employs a structure-sequence cross-consistency mechanism to evaluate the bidirectional compatibility between the predicted structure and the input sequence, enabling comprehensive characterization of fold accuracy. DeepUMQA-Global outperforms the self-assessment confidence scores of AlphaFold3, achieving improvements of 57.8% in Pearson correlation and 49.0% in Spearman correlation. With respect to the CASP16 retrospective benchmark, DeepUMQA-Global outperforms all single-model accuracy estimation methods that participated in CASP16 and achieves performance comparable to that of the top consensusObased methods. A lightweight consensus strategy built upon DeepUMQA-Global ranks first among all CASP16 participants, surpassing all other methods, including consensus approaches, and highlighting the strength of our method. Remarkably, DeepUMQA-Global demonstrates a strong ability to discriminate between alternative conformational states of proteins, as evidenced in the CASP unique alternative conformation protein complex target and the CoDNaS benchmark. ConclusionsOur results indicate that DeepUMQA-Global can be extended to broader protein modeling tasks, moving beyond static evaluation to offer a foundation for dynamic conformation EMA, where it accurately discriminates alternative conformational states and demonstrates reliable predictive fidelity in model accuracy estimation.

bioinformatics↗

bioRxiv2026

π-MSNet: A billion-scale, AI-ready living proteomics data portal

Artificial intelligence (AI) is reshaping proteomics workflows, delivering remarkable gains in both peptide identification sensitivity and quantitative performance. However, the potential of deep learning models in proteomics has not been fully exploited due to the scarcity of large-scale, high-quality and consistently labeled datasets. Here, we present {pi}-MSNet, a billion-scale, AI-ready living mass spectrometry (MS) data portal. Using a uniform identification and quality control workflow, it comprises over 1.66 billion MS/MS spectra, 501 million peptide-spectrum matches (PSMs), and 9 million precursors from 36,356 LC-MS/MS runs across ten instrument types and 55 diverse species. Through community collaboration, the data are shared via international, interactive, and living web resources. Enabled by the built-in MSNetLoader Python API for seamless and scalable data access--with native support for PyTorch and TensorFlow--{pi}-MSNet provides an AI-ready data framework for efficient training and systematic benchmarking of multiple models across three representative tasks (e.g., MS/MS spectrum prediction, retention time prediction, and de novo peptide sequencing). In particular, by retraining multiple models on {pi}-MSNet, we achieved consistent performance improvements over their original versions. These improved models were subsequently integrated into the {pi}-MSNet agent to enable interactive, deployment-free use. Through SDRF (Sample and Data Relationship Format) metadata, an open-source cloud analysis workflow, and a community-driven interactive data portal that supports continuous data submission, {pi}-MSNet serves as a living, AI-ready resource for reproducible benchmarking, robust model training, and accelerated AI innovation in proteomics.

bioinformatics↗

bioRxiv2026

Molecular Architecture of Cryptococcus Cell Walls Reveals Species-Specific Chitosan-Dependent Remodeling

Cryptococcus neoformans and Cryptococcus gattii are fungal pathogens that cause life-threatening infections, including cryptococcal meningitis. A distinctive feature of the cryptococcal cell wall is the extensive deacetylation of chitin to chitosan, a modification that is essential for virulence but whose structural role in cell-wall organization remains poorly understood. Here, we analyzed the cell walls of wild-type strains of both species and their avirulent chitosan-deficient mutants, which serve as vaccine candidates. Loss of chitosan disrupted cell morphology and altered cell-wall ultrastructure, with more pronounced defects in C. neoformans. Solid-state NMR revealed that aggregated -1,3-glucans form the principal rigid domain of the cell wall in both species and are closely associated with chitin microfibrils, whereas surrounding {beta}-glucans and mannoproteins constitute a more dynamic matrix. Chitosan modulates hydration and flexibility, and its loss increases chitin exposure and triggers species-specific remodeling of the polysaccharide network. In C. neoformans, chitosan depletion increased -1,3-glucan content and reduced {beta}-glucan levels, whereas C. gattii selectively lost one -1,3-glucan subtype while maintaining {beta}-glucan levels. Although capsule production remained intact, chitosan deficiency altered glucuronoxylomannan linkage patterns and mannoprotein composition. These findings reveal how chitosan organizes cryptococcal cell-wall architecture and highlight distinct structural adaptation strategies among pathogenic Cryptococcus species.

biochemistry↗

bioRxiv2026

Terazosin drives sex-dependent adrenergic-bioenergetic reprogramming to restore network function in Alzheimer's disease

Alzheimers disease (AD) has long been defined by amyloid-{beta} plaques and hyperphosphorylated tau, yet disease-modifying therapies remain critically limited. Growing evidence reframes AD as a system-level failure driven by early dysregulation of synaptic, metabolic, and neuroimmune pathways, preceding overt protein aggregation and originating in selectively vulnerable circuits, including the locus coeruleus (LC)-hippocampal noradrenergic axis. This complexity underscores the need for therapeutic strategies that engage the disease at a network level, early in its trajectory. To this end, using a machine learning-based systems pharmacology framework for drug repurposing applied to human AD transcriptomic datasets, we identified terazosin (TZ) as a candidate predicted to reverse AD-associated molecular signatures. TZ is an FDA-approved -adrenergic receptor antagonist and phosphoglycerate kinase-1 activator. It was administered chronically via the diet (0.5 mg/kg bw/day) to male and female TgF344-AD rats and wild-type littermates from 5 to 11 months of age, preceding overt pathology. Bulk hippocampal RNA sequencing revealed sex-specific transcriptional remodeling in transgenic rats, strongly conserved with human AD datasets. Male TgF344-AD rats exhibited suppression of synaptic and transcriptional maintenance pathways with concurrent activation of metabolic, proteostatic, extracellular matrix, and vascular stress responses; females showed suppression of survival and vascular structural signaling alongside heightened DAM-like immune activation, amyloid-associated stress, and cell death programs. TZ reversed these signatures in a sex-dependent manner: males showed enhanced immune surveillance and reduced proteostasis burden, while females showed reinforcement of synaptic, survival, and metabolic pathways. TgF344-AD rats displayed selective LC-derived hippocampal noradrenergic axonopathy without global neuronal loss. TZ preserved fiber integrity preferentially in females and partially reversed LC vulnerability-associated transcriptional signatures in both sexes. TZ also reduced amyloid-{beta} plaque burden in both sexes, attenuated hyperphosphorylated tau exclusively in females, and induced microglial morphological shifts in males. Finally, TZ restored wild-type spatial learning in transgenic animals, with females appearing to derive the greater cognitive benefit. Together, these findings demonstrate that TZ induced systems-level reprogramming of AD-relevant molecular pathways and preserved vulnerable noradrenergic circuitry in a sex-dependent manner. Moreover, TZ rescued spatial cognition in transgenic rats, with cognitive gains seemingly more pronounced in females. These results support adrenergic-bioenergetic modulation as a translational strategy for early-stage AD and underscore the necessity of sex as a biological variable in disease-modifying treatment development.

neuroscience↗

bioRxiv2026

ChiMER: Integrating chromatin architecture into splicing graphs for chimeric enhancer RNAs detection

MotivationEnhancer-derived RNAs (eRNAs) and their fusion with protein-coding genes represent a crucial yet understudied layer of transcriptional regulation. eRNAs are typically expressed at low levels, which makes fusion events difficult to detect with conventional fusion detection tools. In addition, these tools are not designed to capture fusion transcripts arising from spatial proximity between distal regulatory elements and gene loci. Reads spanning such regions are also frequently filtered as mapping artifacts. As a result, computational approaches for systematically identifying spatially mediated enhancer-exon fusion transcripts remain lacking. MethodsWe developed ChiMER, a graph-based framework for detecting ChiMeric Enhancer RNAs from short-read RNA-seq data. ChiMER constructs splice graphs with chromatin contact information to introduce enhancer-exon edges and uses graph alignment to search for potential transcriptional paths. A ranking-based scoring module then prioritizes high-confidence events. Evaluations on simulated and real RNA-seq datasets show that ChiMER achieves higher sensitivity than conventional linear fusion detection methods while maintaining low false-positive rates. ResultsApplied to cancer cell line RNA-seq datasets, ChiMER identified multiple enhancer-exon chimeric transcripts, several associated with super-enhancer regions. Multi-omics analysis further shows that fusion transcripts occur in transcriptionally active regulatory environments and frequently coincide with strong R-loop signals, suggesting a potential role of RNA-DNA hybrid structures in facilitating long-range transcriptional joining events. Availabilityhttps://github.com/Candlelight-XYJ/ChiMER Contactyujia.xiang@outlook.com, xielinhai@ncpsb.org.cn

bioinformatics↗

bioRxiv2026

EmbryoTempoFormer: clip-based developmental tempo inference from zebrafish brightfield time-lapse microscopy

Nominal hours post fertilization (hpf) are widely used to index zebrafish embryogenesis, yet under condition shifts--such as temperature change, genetic perturbation, or environmental stress--nominal time can decouple from true developmental progression. In such settings, biologically meaningful variation is better described as a systematic change in developmental tempo rather than a simple temporal offset. Here we introduce an embryo-resolved framework that treats developmental tempo as the primary quantity of interest in brightfield time-lapse imaging. We present EmbryoTempoFormer (ETF), a clip-based CNN-Transformer that predicts developmental progression from short time-lapse clips and is trained with a within-embryo temporal-difference consistency regularizer to promote temporally coherent trajectories. Crucially, we couple model predictions with an embryo-level inference and statistical workflow: temporally correlated clip-level outputs are aggregated into interpretable embryo-level tempo and stability readouts, and cross-condition effects are quantified using embryo-bootstrap confidence intervals with embryos--rather than frames or clips--as independent units, avoiding pseudo-replication. Using temperature perturbation as a representative domain shift, we robustly quantify condition-induced changes in global developmental dynamics and show that developmental delay predominantly manifests as reduced developmental tempo. This framework enables statistically principled, high-throughput phenotyping for perturbation screens, drug assays, and environmental stress studies. HIGHLIGHTSO_LIClip-based CNN-Transformer predicts developmental time from brightfield time-lapse microscopy. C_LIO_LIWithin-embryo temporal-difference consistency improves trajectory self-consistency. C_LIO_LIEmbryo-level anchored tempo slopes enable interpretable cross-condition comparisons. C_LIO_LIReproducible pipeline via code, scripts, and a Zenodo bundle with embryo-level inference C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/710433v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@ddbf01org.highwire.dtl.DTLVardef@26e975org.highwire.dtl.DTLVardef@a911d7org.highwire.dtl.DTLVardef@1d24f1b_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2026

A foundation AI model enhances electron microscopy image analysis

Electron microscopy (EM) and advanced volume EM have extensive applications in deciphering cellular ultrastructures for life sciences. However, quality issues of EM images significantly impede the precise analysis and discovery of nanoscale biological structures. Here, we present an unsupervised Foundation Model for Five Tasks (DF5T) model for image enhancement through denoising, deblurring, super-resolution, two-dimensional (2D) inpainting, and three-dimensional (3D) isotropic restoration. DF5T was trained on an extensive dataset of multi-source membrane-bound organelle EM images, consisting of a total of over 2.25 million images. On unseen data, DF5T outperformed the existing state-of-the-art models in all five tasks. Furthermore, DF5T substantially compensates for missing three-dimensional structural information and reveals significant ultrastructural alterations in organelle geometry following chemical treatment. We demonstrate that DF5T significantly enhances electron microscopy images quality by improving the accuracy of downstream organelle segmentation and 3D structural restoration, thereby contributing to future advances in biological research.

cell biology↗

bioRxiv2026

SPCoral: diagonal integration of spatial multi-omics across diverse modalities and technologies

Spatial multi-omics is indispensable for decoding the comprehensive molecular landscape of biological systems. However, the integration of multi-omics remains largely unresolved due to inherent disparities in molecular features, spatial morphology, and resolution. Here we developed SPCoral for diagonal integration of spatial multiomics across adjacent slices. SPCoral extracts spatial covariation patterns via graph attention networks, followed by the use of optimal transport to identify high-confidence anchors in an unsupervised, feature-independent manner. SPCoral utilizes a crossmodality attention network to enable seamless cross-resolution feature integration alongside robust cross-omics prediction. Comprehensive benchmarking demonstrates SPCorals superior performance across different technologies, modalities and varied resolutions. The integrated multi-omics representation further improves spatial domain identification, effectively augments experimental data, enables cross-modal association analysis, and facilitates cell-cell communication. SPCoral exhibits good scalability with data size, reveals biological insights that are not attainable using a single modality. In summary, SPCoral offers a powerful framework for spatial multi-omics integration across various technologies and biological scenarios.

bioinformatics↗

bioRxiv2026

Benchmarking generative scaffold design methods for peptide engineering in TCR-MHC complexes

De novo peptide design at T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) interfaces is a central challenge in computational immunology, with direct implications for vaccine development, cancer immunotherapy, and autoimmune disease. Despite rapid advances in generative protein modeling, there is currently no systematic benchmark evaluating these methods in the highly constrained and immunologically relevant setting of peptide-MHC presentation and TCR recognition. Here, we present two complementary contributions. First, we introduce a multi-stage computational pipeline for peptide design in predefined TCR-pMHC contexts, integrating generative modeling with sequence optimization and structure-based filtering. Second, we establish a benchmark for evaluating generative peptide design methods in TCR-pMHC complexes. Using a curated dataset of high-quality crystal structures deposited after the AlphaFold3 training cutoff, we assess state-of-the-art generative approaches for peptide backbone generation, sequence design, and the enrichment of near-native solutions. We explicitly examine whether different backbone generation strategies respect the geometric constraints of the MHC binding groove and recover native-like peptide conformations. Our results reveal substantial method-dependent differences: some generative strategies fail systematically in the groove-bound peptide setting, whereas others generate physically plausible backbones with varying accuracy and conformational diversity. We further show that enforcing anchor constraints strongly influences peptide conformations at non-anchor positions, highlighting a trade-off between structural accuracy and conformational sampling. To enable fair and reproducible comparison, we introduce a standardized, multi-stage scoring protocol that integrates MHC binding prediction, physics-based energy evaluation, and independent structure prediction confidence metrics to enrich near-native designs from large candidate pools. Together, this work establishes the first comprehensive pipeline and benchmark for generative peptide design at TCR-pMHC interfaces and provides practical guidelines for developing peptide design workflows and evaluating generative models in immunologically constrained protein design settings.

bioinformatics↗

bioRxiv2026

Agomelatine drives sex-specific neuroprotection and reduced pathology in rat and human Alzheimer's models

Alzheimers disease (AD) remains without effective disease-modifying therapies, underscoring the need for interventions that target interconnected molecular and cellular processes driving cognitive decline. Leveraging a cross-species translational framework integrating a progressive rat model of Alzheimers disease with human iPSC-derived neurons carrying familial AD mutations, we identify agomelatine (AGO) as a disease-modifying candidate. A clinically used melatonergic agonist and 5-HT2C serotonergic antagonist, we found that AGO acts as a sex-selective modulator of AD-related neuronal and microglial dysfunction with therapeutic relevance across species. TgF344-AD rats and their wildtype littermates received chronic dietary AGO ([~]10 mg/kg/day) from 5 to 11 months of age and underwent hippocampal-dependent spatial learning assessment, quantitative hippocampal histopathology, and bulk RNA sequencing to evaluate the therapeutic effect on cognition, pathology, and molecular mechanisms. Human isogenic iPSC-derived cortical neurons carrying PSEN2N141I or APPV717I mutations were treated with 20 {micro}M AGO followed by bulk RNA sequencing, to define AGO-driven transcriptional pathway modulation in AD neurons In TgF344-AD rats, AGO produced robust female-specific benefits. AGO selectively restored hippocampal-dependant cognitive performance in female but not male transgenic rats. These improvements were independent of amyloid burden and instead aligned with reductions in microgliosis and pathogenic AT8-positive tau phosphorylation. Additionally, AGO normalized reactive and amoeboid microglial states exclusively in females and enhanced doublecortin-defined neurogenesis without altering mature NeuN neuronal density. This coordinated hippocampal stabilization highlights AGOs capacity to restore plasticity rather than simply suppress pathology. Transcriptomic analyses revealed sex-divergent mechanisms underlying these effects. In females, AGO activated metabolic, oxygen-handling, lipid-processing, neuroimmune, and CREB/IGF-1 signaling pathways while suppressing ER-stress, epigenetic, and ion-channel transcripts, changes consistent with resilience-promoting cellular reprogramming. In males, AGO preferentially modulated mitochondrial redox biology, transcriptional regulators, and extracellular matrix components. Despite these differences, both sexes showed AGO-induced engagement of conserved AD-relevant pathways, including shared induction of synaptic plasticity and hemoglobin/oxygen-transport related genes, suggesting a convergent neuroprotective molecular signature. To translate these findings to a human system, we examined AGOs effects in PSEN2N141I and APPV717I iPSC-derived cortical neurons. Both mutations produced convergent deficits in synaptic integrity, neuronal maturity, trophic signaling, proteostasis, metabolism, and excitability, alongside dysregulated developmental and ECM-remodeling programs. AGO partially reversed these pathogenic transcriptional changes, up-regulating synaptic, metabolic, vesicle-trafficking, and redox-stress resilience genes while suppressing pathological developmental and inflammatory pathways, demonstrating conserved engagement of neuronal recovery programs. Together, these results identify AGO as a promising non-amyloid therapeutic candidate capable of modulating AD-relevant pathways in rodents and human models. The sex-selective efficacy observed in vivo, combined with conserved transcriptional responses across species, underscores the translational relevance of AGO-driven molecular reprogramming in AD.

neuroscience↗

bioRxiv2026

quantms-rescoring enables deep proteome coverage across protein quantification, immunopeptidomics, and post-translational modifications experiments.

The growing volume of public proteomics datasets and the advent of novel machine learning (ML)-based methods create unprecedented opportunities for discovery through large-scale reanalysis. However, traditional desktop tools are increasingly insufficient for processing and integrating data at this scale. To address this challenge, we present a novel package, quantms-rescoring, that extends the cloud-native quantms workflow with a machine learning-based rescoring module. Unlike prior tools that rescore single-engine outputs, quantms-rescoring seamlessly integrates multiple search engines (SAGE, COMET, and MSGF+), performs automatic model selection, model fine-tuning, and scales reproducibly on cloud infrastructures. In quantms-rescoring, we rely on multiple fragment-ion intensity (AlphaPeptDeep and MS2PIP) and retention-time prediction (DeepLC) methods to improve results from multiple peptide database search engines. It features automatic model selection, fine-tuning, and retraining for MS/MS intensity and retention time prediction to select the best model for a given dataset. We applied the novel workflow to five representative datasets spanning DDA label-free quantification, TMT 10-plex isobaric labelling of tumor proteomics data, immunopeptidomics, phospho-proteomics, and unseen lysine malonylation experiments. We achieved a 16-22.8% increase in identified spectra, along with the quantification of 2191 additional phosphorylated peptides and 1337 phosphosites. In the tandem mass tag (TMT)-labeled clear cell renal cell carcinoma dataset, 76 novel differentially expressed multiple search engines identified proteins with quantms-rescoring. Additionally, novel 11,688 HLA-II potential binders were detected in the immunopeptidomics dataset by multiple search engines with quantms-rescoring. For unseen malonylation data, we reported more than 58.8% malonylation PSMs and 30.5% modification sites than COMET alone. Together, these results show that integrating multi-engine searches with machine learning-derived features can be combined in a scalable workflow that enhances identification, PTM localization, and quantification performance.

bioinformatics↗

bioRxiv2026

Unifying constraints linking protein folding and native dynamics decoded from AlphaFold

The interplay between protein folding and native dynamics remains a central question in biophysics. Analyzing an extensive set of AlphaFold-predicted structures, we uncover a robust relationship between folding topology (contact order) and native dynamics (fluctuation entropy), showing that long-range contacts that slow folding also restrict conformational flexibility across protein sizes and taxonomic groups. Scaling analysis reveals that this relationship, together with its chain-length dependence, is consistent with power-law-like trends, reflecting common organizing constraints of protein architecture. Across species, increasing organismal complexity is associated with proteome-wide shifts toward lower contact order and higher fluctuation entropy. Together, evidence from folding, stability, and functional dynamics converges on unifying constraints, revealing an intrinsic physical organizing principle captured by AI models.

bioinformatics↗

bioRxiv2026

Can AI Conduct Autonomous Scientific Research? Case Studies on Two Real-World Tasks

Recent advances in artificial intelligence (AI) have prompted claims about autonomous "AI scientists," yet systematic evaluations of these capabilities remain scarce. This exploratory study investigates whether current AI frameworks can execute scientific research tasks beyond isolated demonstrations. We tested eight open-source AI frameworks (Agent Laboratory, AutoGen, BabyAGI, GPT Researcher, MOOSE-Chem2, SciAgents, SciMON, and Virtual Lab) on two tasks that aimed to reproduce research on algorithm development from recent papers in uncertainty quantification and protein interaction discovery. In our evaluation, no framework completed a full research cycle from literature understanding through computational execution to validated results and scientific paper writing. While all systems showed competence in conceptual tasks such as planning and summarization, they consistently failed at robust implementation. Every framework produced sophisticated hallucinations. Deployment proved demanding, requiring substantial debugging and technical expertise, which undermines common claims about the democratization of science with AI. Despite these limitations, the frameworks showed promise as research assistants for methodological planning and ideation under careful human supervision. Our findings suggest that the explored AI systems cannot yet autonomously conduct scientific research, but may provide real value for specific subtasks within the research workflow. We offer preliminary observations to help researchers and developers better understand the gap between advertised and actual capabilities of AI in science.

bioinformatics↗

bioRxiv2025

Visualization of photoreceptor outer segment renewal using AAV-delivered Dendra2-tagged rhodopsin

Visualization of photoreceptor outer segment (OS) renewal dynamics is essential for vision research but has proved difficult in mice due to the small size and dense packing of their photoreceptors. Lack of effective protein "trackers" and the time-consuming generation of transgenic reporter lines add to these challenges. In this study, we evaluated AAV-mediated delivery of photoconvertible Rhodopsin/Dendra2 and Peripherin2/Dendra2 fusion proteins as a means to track OS renewal in mouse photoreceptors. OS renewal was assessed by two approaches: (1) comparing the lengths of native (green) Dendra2 domains at two post-infection time points, and (2) using photoconversion of Dendra2 to distinguish newly synthesized discs from preexisting ones within the same cell. We validated this method in both wild type mice and a Tmem138-deficient ciliopathy mutant, in which a reduced OS renewal rate was observed. Taken together, this method offers a rapid genetic tool for "real-time" evaluation of OS renewal dynamics in mice, overcoming limitations posed by the compact and densely organized photoreceptor architecture.

cell biology↗

bioRxiv2025

Accurate and robust 3D genome feature discovery from multiplexed DNA FISH

Multiplexed DNA FISH (also known as FISH Omics) is a powerful imaging technology to map three-dimensional (3D) genome organization in thousands of single cells at kilobase resolution. However, existing computational approaches to identify 3D genome features, such as A/B compartments, topologically associating domains (TADs) and chromatin loops, have been mainly adapted from Hi-C and do not account for the distinct measurement errors across the three XYZ physical axes. Here we present ArcFISH (accurate and robust chromatin analysis from FISH data), which explicitly models axis-specific measurement errors in a unified statistical framework to detect multi-level 3D genome features. Comprehensive evaluations using multiple simulated as well as experimental datasets show that ArcFISH reliably identifies chromatin loops under heterogeneous measurement errors, detects TAD boundaries highly enriched for active histone marks H3K4me3 and H3K36me3, and requires as few as 30 chromatin traces to faithfully assign A/B compartments. Applying ArcFISH to multiplexed DNA FISH data from mouse embryonic stem cells and mouse excitatory neurons reveals cell-type-specific chromatin loops and TADs, overlapping with differentially expressed genes. ArcFISH is freely available at https://hyuyu104.github.io/ArcFISH.

genomics↗

bioRxiv2025

Characterization of hippocampal subfields using histology-based annotated postmortem MRI: Lessons for in vivo segmentation II

High-resolution in vivo magnetic resonance imaging (MRI) of hippocampal subfields is a rapidly advancing field due to their implication in cognition, disorder, and disease. Hippocampal subfield segmentation on in vivo MRI is generally guided by postmortem reference material, which has been limited by small sample sizes that preclude comprehensive characterization of subfield border locations and their variability. Addressing this, we characterized hippocampal subfield border variability in two ultra-high-resolution postmortem MRI datasets with combined annotated histological sections, including cases with and without dementia. We examined: 1) the order of appearance and disappearance of subfields along the long axis of the hippocampus; 2) the order of appearance and disappearance of subicular subregions; 3) the medial-lateral position of subicular subregional boundaries along the hippocampal body; 4) the location of the CA3 relative to hippocampal head digitations; 5) the subfield borders in the hippocampal body relative to a volume proportion of the dark band; and 6) the association of hippocampal length and subiculum-CA1 border location with diagnosis, demographic factors, and factors related to postmortem imaging. Our findings reaffirmed that there is a consistent order of appearance and disappearance of subfields in the hippocampal head and tail, respectively. The subicular subregions exhibited a first in, last out order of appearance and disappearance, and pre/parasubiculum consistently occupied half of the subicular complex in coronal slices throughout the hippocampal body. Hippocampal head digitations were not a reliable landmark for CA3 appearance, but SRLM proportionality did offer a potentially consistent approach for estimating CA2 and CA3 subfield borders in relation to the hippocampal border. No clear relationship was found between the anatomical features and diagnosis, demographic factors, and factors related to postmortem imaging. These findings have implications for the development and harmonization of hippocampal subfield segmentation protocols and interpretation of high-resolution functional MRI studies of the human hippocampus.

neuroscience↗

bioRxiv2025

Deconvolution of Sparse-count RNA Sequencing Data for Tumor Cells Using Embedded Negative Binomial Distributions

Estimating tumor-specific transcript proportions from mixed bulk samples has potential to inform novel biology. However, estimation accuracy using existing methods in sparse-count data such as microRNA-seq and spatial transcriptomics has yet to be established. We generated a mixed small RNA benchmark dataset to demonstrate analytical challenges. To resolve them, we developed DeMixNB, a semi-reference-based deconvolution model assuming a sum of negative binomial distributions. Applications to miRNA-seq from 856 patients with breast cancer and 3,755 spatial spots from lung cancer generated either clinical or mechanistic insights into tumor cell plasticity. This supports the important utility of DeMixNB to investigate cancer RNomes.

bioinformatics↗