Biology subjects

Hou, J.

Publications and source records attributed to Hou, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

A Large-Scale Cryo-EM RNA Motif Dataset and Benchmark for Machine Learning-Based Structure Modeling

MotivationRNA molecules play critical roles in gene regulation, viral replication, and cellular control, with their functions tightly coupled to three-dimensional structure. Advances in cryogenic electron microscopy (cryo-EM) now enable RNA structure characterization across a broad resolution range. RNA secondary structural motifs, including hairpins, internal loops, and bulges, act as fundamental building blocks of RNA tertiary architecture and are key targets in RNA-focused therapeutic design. Despite this, most computational approaches for RNA structure prediction from cryo-EM density maps do not explicitly utilize secondary structural motifs as intermediate representations, largely due to the absence of large-scale, high-quality, and motif-resolved datasets suitable for machine learning. ResultsHere, we present a large, open-source dataset containing over 125,000 motif-resolved cryo-EM density maps paired with corresponding atomic structures, spanning 25 classes of RNA secondary structural motifs. The dataset covers resolutions from 1.5 [A] to 34.0 [A], encompassing both near-atomic and low-resolution density maps relevant to RNA modeling. Each motif instance includes a segmented cryo-EM density map represented as a standardized 3D voxel grid, with atomic-level motif annotations propagated to voxel-level labels for RNA backbone, ribose sugar, and nucleobase components. Segmentation quality is validated via cross-correlation analysis, demonstrating strong agreement between motif-level density maps and atomic reference models. To illustrate the datasets utility, high-resolution maps (1.5-2.8 [A]) were used to train a machine learning classifier that distinguished five motif classes with a specificity of 0.948. Availability and ImplementationSource code, implementation of the fully automated pipeline, and the benchmark datasets are publicly available at GitHubhttps://github.com/DrDongSi/3DEM-RNA-Motif-Dataset Zenodohttps://zenodo.org/communities/3dem-rna-motif-dataset Contacthoujie@msu.edu, dongsi@uw.edu

bioinformatics↗

bioRxiv2026

Multifaceted Functional Complexity of SARS-CoV-2 Helicase Nsp13 Underlies Its Integrated Motor and Remodeling Activities

SARS-CoV-2 nonstructural protein 13 (Nsp13) is a superfamily 1 helicase essential for viral replication. Although its canonical ATP-dependent unwinding activity is well established, the broader functional repertoire of Nsp13 remains unclear. Here, we show that Nsp13 encodes a high degree of complexity by acting as a tunable nucleic acid remodeler that integrates motor and non-motor activities within a single protein. Nsp13 operates in multiple mechanistically distinct regimes, including a canonical ATP-dependent helicase mode and a Mg{superscript 2}-primed, ATP-independent remodeling state capable of destabilizing short duplexes, hairpins, and G-quadruplexes. Mg{superscript 2} binding allosterically stabilizes a compact RecA1-RecA2 configuration, priming the enzyme for ATP-independent remodeling. Unwinding polarity is substrate-dependent, with duplexes supporting bidirectional remodeling, whereas G-quadruplexes are preferentially resolved in the 5'[->]3' direction. Beyond strand separation, Nsp13 also exhibits robust strand annealing and nucleic acid chaperone activities. Cofactors, substrate topology, and enzyme concentration dynamically regulate these activities. Together, our findings establish Nsp13 as a highly integrated nucleic acid remodeling system and reveal how a single viral helicase switches between motor-driven and remodeling-dominated states to meet the structural demands of replication and transcription.

biochemistry↗

bioRxiv2026

Exploring DNA methylation profiles in the pathogenesis of human osteoporosis via whole-genome bisulfite sequencing

BackgroundOsteoporosis is a prevalent bone metabolic disorder characterized by reduced bone mass, disruption of bone microarchitecture, and increased bone fragility, leading to a heightened risk of fracture. This condition significantly impairs patients quality of life and increases mortality risk. Emerging evidence suggests that DNA methylation may play a crucial role in regulating the expression of genes related to bone metabolism, thereby influencing the development of osteoporosis. However, the precise relationship between DNA methylation and osteoporosis remains unclear and warrants further investigation. ResultsOur study revealed significant differences in both the quantity and ratio of DNA methylation between individuals with osteoporosis and non-osteoporosis controls, with differences predominantly occurring in CpG islands. GO/KEGG enrichment analyses highlighted distinct osteoporosis-related gene pathways. Notably, we identified six genes, MSX1, HOXD4, AXIN2, WNT5A, TGFB1, and STAT3, respectively, that are potentially involved in the pathogenesis of osteoporosis and are broadly involved in various diseases and biological processes. ConclusionsThese findings indicate distinct methylation patterns between osteoporosis patients and healthy individuals, with differential methylation levels in genes associated with osteoporosis. This research offers new insights into the epigenetic mechanisms underlying osteoporosis.

genetics↗

bioRxiv2025

Rapid ecosystem collapse and biofilter formation following seabed methane leakage

Globally, vast amounts of methane are trapped within subseafloor gas hydrates. Recent evidence suggests that climate- and human-induced perturbations can destabilize gas hydrates and trigger methane release, yet the response of deep-sea ecosystems to these changes remains poorly understood. Here we show multi-year in situ monitoring of seabed ecosystem following methane leakage induced by hydrate exploration activities. Within just two years, benthic microbial and eukaryotic diversity in the affected areas declined significantly, while microbial and macrofaunal abundance increased. Integrated geochemical and omics analyses reveal the rapid successional shift of seabed ecosystem to a novel chemosynthetic system. Aerobic methanotrophs (Methyloprofundus) co-established with unexpectedly fast growing anaerobic methanotrophic archaea (ANME-3), accompanied by a rapid recruitment of opportunistic polychaetes that bioirrigated the seabed to >50 cm depths. The active animal-microbe interactions sustained exceptionally high rates of methane oxidation that utilized diverse electron acceptors. We demonstrate that methane hydrate destabilization can trigger rapid collapse of native seabed ecosystem while driving the formation of an effective methane biofilter consuming this potent greenhouse gas much faster than previously estimated. Understanding seabed ecosystem response and feedback is critical for predicting benthic methane cycling under ongoing global change and for developing sustainable strategies for methane hydrate resource management.

ecology↗

bioRxiv2025

Population-scale chemical response revealed by a barcoded yeast collection

Natural genetic variation shapes how microbial populations adapt to environmental and chemical challenges, but scalable approaches to map genotype-phenotype relationships across diverse genetic backgrounds remain limited. Here, we developed a systematically barcoded collection of 520 Saccharomyces cerevisiae natural isolates that captures the ecological, geographical and genetic diversity of the species. Using pooled barcode sequencing, we profiled fitness responses to over 600 bioactive and natural compounds, revealing broader and more polarized bioactivity than the standard yeast gene-deletion collection. Fitness-based clustering defined six major compound groups with reproducible, population-structured sensitivity patterns. Genome-wide association analysis identified significant genetic variants across 107 compounds, linking natural polymorphisms to chemical responses and involving genes in genome maintenance, ribosome biogenesis, vesicular trafficking and stress tolerance. Together, our barcoded natural population provides a scalable framework for chemical-genetic screening, enabling systematic dissection of how genetic diversity shapes microbial fitness and adaptation.

evolutionary biology↗

bioRxiv2025

Loss of CFIm activates YAP/TAZ and connects mRNA cleavage and polyadenylation inhibition to BRCAness

The YAP/TAZ transcription program plays a crucial role in development, regeneration, and cancer. CFIm is a master regulator of mRNA alternative polyadenylation (APA). Loss of CFIm function was shown in human cancer but its impact on the gene expression program is not well characterized. Here we report the discovery that loss of CFIm in cancer cells activates YAP/TAZ and promotes therapeutic resistance. We identified a CFIm-NEDD4L-LATS1 regulatory axis that suppressed YAP/TAZ activation. Furthermore, we found that CFIm loss in the presence of mRNA cleavage and polyadenylation (CPA) inhibition repressed key DNA repair genes in the Fanconi anemia (FA) and homology-directed repair (HDR) pathways, which induced the BRCAness phenotype and aggravated DNA damage. Our study reveals a hidden link between mRNA 3' end processing and the YAP/TAZ transcription program in addition to illustrating how CFIm function impacts the selection of therapeutic agents in cancer.

molecular biology↗

bioRxiv2025

Ginkgolic acid attenuates e chinococcus granulosus infection-induced hepatic fibrosis by inhibiting Smad4 SUMOylation

BackgroundThe SUMOylation modification is closely linked to the progression of fibrotic diseases, yet its role in hepatic fibrosis associated with cystic echinococcosis (CE) remains unclear. This study aimed to investigate the function of SUMOylation in CE-related hepatic fibrosis and evaluate the anti-fibrotic effects and mechanisms of ginkgolic acid (GA) via regulation of the SUMOylation pathway. MethodologyPeri-lesional (PL) and adjacent normal (AN) liver tissues from CE patients were collected to examine histopathology and SUMO pathway proteins. A CE-infected mouse model was established and treated with GA to assess cyst burden, serum TGF-{beta}1 levels, hepatic fibrosis markers, and SUMO-related proteins. In vitro, macrophages and hepatic stellate cells (HSCs, LX-2 line) were stimulated with Echinococcus granulosus cyst fluid (EgCF) or TGF-{beta}1 to evaluate GAs effects on macrophage polarization (CD206/CD86), HSC activation (-SMA/PCNA), Smad4 SUMOylation, and nuclear translocation. Macrophage-HSC crosstalk was investigated via conditioned medium co-culture assays. ResultFibrosis was exacerbated in peri-lesional liver tissues of CE patients, accompanied by SUMO pathway activation. GA significantly alleviated hepatic fibrosis in CE mice and reversed SUMO pathway dysregulation. Mechanistically, GA inhibited EgCF-induced pro-fibrotic M2 macrophage polarization and blocked Smad4 SUMOylation and nuclear translocation by modulating SUMOylation. Furthermore, GA directly suppressed HSC activation and bidirectionally disrupted the pro-fibrotic crosstalk between macrophages and HSCs under EgCF stimulation, ultimately alleviating fibrosis. ConclusionThis study reveals the critical role of SUMOylation modification in CE-associated hepatic fibrosis and elucidates a novel anti-fibrotic mechanism whereby GA targets the SUMOylation-Smad4 axis to regulate the immune microenvironment. Author summaryThis study demonstrates aberrant activation of the SUMOylation pathway during hepatic fibrosis progression in cystic echinococcosis (CE), characterized by upregulated SUMO1/Ubc9 expression and downregulated SENP1 in peri-cystic liver tissue. Ginkgolic acid (GA) intervention significantly attenuated CE-associated liver fibrosis in mice, evidenced by reduced cyst volume, decreased TGF-{beta}1 levels, and suppressed expression of fibrotic markers (-SMA/COL1A1). GA concurrently reversed dysregulated SUMO pathway protein expression. Mechanistically, GA upregulates the deSUMOylating enzyme SENP1, thereby inhibiting Echinococcus granulosus cyst fluid (EgCF)-induced SUMOylation and nuclear translocation of Smad4. This blockade impedes macrophage polarization toward the pro-fibrotic M2 phenotype (CD206{downarrow}) and suppresses hepatic stellate cell (HSC) activation (-SMA/PCNA{downarrow}). Furthermore, GA disrupts the pro-fibrotic bidirectional crosstalk between HSCs and macrophages. Collectively, these findings indicate that GA ameliorates CE-induced hepatic fibrosis by targeting the SUMO-Smad4 axis to modulate the immune microenvironment, providing a novel therapeutic strategy.

molecular biology↗

bioRxiv2025

Tripleknock: predicting lethal effect of three-gene knockout in bacteria by deep learning

Investigating the lethal effect of multi-gene knockout is essential for discovering novel antibiotics targets and metabolic engineering. Unlike single genes or gene pairs, three-gene combinations involve more intricate interactions, making experimental screening time-consuming. Computational methods, particularly Genome-scale metabolic Model (GEM)-based Flux Balance Analysis (FBA), requires constructing new GEMs from experimental data, limiting its use for new species. Moreover, using FBA for three-gene knockout screening could take several years. Therefore, a faster and GEMs-independent approach is needed to facilitate genome-wide three-gene knockout screening. Here, we introduce Tripleknock, for predicting the lethal effects of three-gene knockouts. Tripleknock was trained using whole-genome data from Escherichia coli K-12 MG1655, and three-gene knockout simulations using FBA. The model uses a threshold of 90% reduction in cell growth to define lethal effect as the prediction output. Compared to FBA, Tripleknock achieves predictions approximately 20 times faster, reaching an average cross-species F1 score of 0.77 on six pathogenic species within the Enterobacteriaceae family. For closely related species such as pathogenic E. coli and Shigella, Tripleknock reaches F1 scores exceeding 0.83. To our knowledge, Tripleknock is the first end-to-end model for predicting lethal effects of three-gene knockout in bacteria. Data availabilityTripleknock is publicly available at: https://github.com/Peneapple/Tripleknock

bioinformatics↗

bioRxiv2025

ContrastQA: A Label-guided Graph Contrastive Learning-based approach for protein complex structure quality assessment

Despite recent progress, the Estimation of Model Accuracy (EMA) for protein complexes remains less advanced compared to that for protein monomers. A key challenge lies in effectively integrating both interface-specific and global structural information to accurately assess the quality of protein complexes. Here, we introduce ContrastQA, the first EMA framework for protein complexes that incorporates the proposed label-guided graph contrastive learning based on interface quality. By integrating a geometric graph neural network to model global structural features, ContrastQA effectively captures both local (interface-level) and global (structure-level) information for accurate model quality estimation. ContrastQA achieved ranking losses of 0.123 and 0.116 on the TMscore and GDT-TS metrics on the CASP16 dataset, which are 0.015 (10.9%) and 0.012 (8.7%) lower than the second-best EMA method with ranking losses of 0.138 and 0.128. Furthermore, for the more challenging CASP16 hard targets, our method achieved a TMscore ranking loss of 0.131, which is 0.023 (14.9%) lower than that of the second-best EMA method with a ranking loss of 0.154. Our study demonstrates the strong effectiveness of the label-guided graph contrastive learning module. These findings suggest that our graph contrastive learning framework serves as a valuable pre-training strategy for learning protein structure representations. The source code is available at https://github.com/Cao-Labs/ContrastQA. Significance StatementHigh-quality proteins generated by high-precision structural prediction methods for protein complexes helping biologists prioritize models for drug discovery and understanding disease mechanisms. Recent advances in predicting protein complex structures (like AlphaFold3) still struggle to match experimental precision. A critical bottleneck lies in accurately estimating the quality of predicted models-known as Estimation of Model Accuracy (EMA). Here, we introduce ContrastQA, implementing label-guided graph contrastive learning framework to explore the generalization of high-quality proteins. Our method demonstrates outstanding performance, provides an effective solution for assessing the structural quality of protein complexes.

bioinformatics↗

bioRxiv2025

Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data

During cell-cell communication (CCC), pathways activated by different ligand-receptor pairs may have crosstalk with each other. While multiple methods have been developed to infer CCC networks and their downstream response using single-cell RNA-seq data (scRNA-seq), the potential crosstalk between pathways connecting CCC with its downstream targets has been ignored. Here we introduce a machine learning-based method SigXTalk to analyze the crosstalk using scRNA-seq data by quantifying signal fidelity and specificity, two critical quantities measuring the effect of crosstalk. Specifically, a hypergraph learning method is used to encode the higher-order relations among receptors, transcription factors and target genes within regulatory pathways. Benchmarking of SigXTalk using simulation data shows the effectiveness, robustness, and accuracy in identifying key shared molecules among crosstalk pathways and their roles in transferring shared CCC information. Analysis of disease data shows SigXTalks capability in identifying crucial signals, targets, regulatory networks, and CCC patterns that distinguish different disease conditions. Applications to the data with multiple time points reveals SigXTalks capability in tracking the evolution of crosstalk pathways over time. Together our studies provide a systematic analysis of CCC-induced regulatory networks from the perspective of crosstalk between pathways.

systems biology↗

bioRxiv2025

Mechanism of SK2 channel gating and its modulation by the bee toxin apamin and small molecules

Small-conductance calcium-activated potassium channel 2 (SK2) serves a variety of biological functions by coupling intracellular calcium dynamics with membrane potential. SK2 modulators are in development for the treatment of neurological and cardiovascular diseases, though the mechanisms of pharmacological modulation remain incompletely understood. We determined structures of an SK2-4 chimeric channel in Ca2+-bound and Ca2+-free conformations and in complex with the bee toxin apamin, a small molecule inhibitor, and a small molecule activator. The structures revealed that the S3-S4 linker forms a hydrophobic constriction at the extracellular opening of the pore. Apamin binds to this extracellular constriction and blocks the exit of potassium ions. Furthermore, we identified a structurally related SK2 inhibitor and activator that bind to the transmembrane domains. The compounds exert opposing effects on gating by differentially modulating the conformation of the S6 helices. These results provide important mechanistic insights to facilitate the development of targeted SK2 channel therapeutics.

pharmacology and toxicology↗

bioRxiv2025

Cloche/Npas4l is a pro-regenerative platelet factor during zebrafish heart regeneration

Zebrafish has full capacity of heart regeneration, but little is known about how blood cells, especially platelets, are involved in this regenerative process. Here we report that cloche/npas4l is a pro-regenerative platelet factor for heart regeneration. We found that haploinsufficiency of npas4l disrupted cardiomyocyte (CM) and endothelial cell (EC) proliferation and heart regeneration after injury. A single-cell transcriptomic atlas revealed that npas4l was dynamically expressed in platelets after heart injury and regulated robust interactions between platelets-CMs or -ECs. Decreasing platelets by either nitroreductase-mediated platelet depletion or platelet-deficient mutant mpl impaired CM and EC proliferation, and over-expression of npas4l in platelets sufficiently induced CM and EC proliferation in uninjured hearts, as well as rescued CM and EC proliferation defects in cloche mutants. Mechanistically, Npas4l positively regulated a panel of ligands expression including bmp6 in platelets to fine-tune CM proliferation and heart regeneration. Therefore, this work unveils a novel platelet Npas4l signaling and presents mechanisms on how platelets modulate CM/EC proliferation via ligand-receptor network during zebrafish heart regeneration.

cell biology↗

bioRxiv2025

Developing Foundation Models for Predicting Viral Animal Host Range in Intelligent Surveillance

Emerging human infectious viruses originating from animals continue to pose a persistent threat to global public health. Understanding the host range of animal viruses is crucial for identifying potential spillover pathways and mitigating the risk of future pandemics. Here, we present VirHRanger, a prediction method that integrates foundation models trained on viral genome and protein sequences, alongside genomic and protein compositional traits, viral phylogeny, and protein-protein interactions. To systematically predict the animal host range, VirHRanger incorporates host taxonomy-aware neural networks trained on a comprehensive collection of animal-virus associations spanning mammals, birds, and arthropods. Within a dataset of 4,006 virus species spanning 99 viral families, our model achieved robust performance with a micro-averaged AUROC of 0.938 across all host categories, demonstrating its effectiveness in capturing generalizable host signals from viral genetic data. On a dataset of 315 novel viruses, which are associated with key reservoir animal hosts and insect vectors, VirHRanger notably outperformed the homology-based method, exhibiting a strong generalizability to novel viruses. Furthermore, VirHRanger identified host range variations among closely related viruses within the Coronaviridae family and successfully predicted the ability of SARS-CoV-2 to infect humans and other animal hosts. These findings highlight the potential of VirHRanger to transform sequencing data into timely insights for disease control during the early stages of zoonotic outbreaks.

bioinformatics↗

bioRxiv2024

Retrotransposon LINE1 trans-regulatory differentiation process maintain embryonic stem cell identity

Long Interspersed Nuclear Elements-1s (LINE1s) are one of the >17% most abundant Retrotransposons in mammalian genomes. LINE1 RNA is high expression during early embryonic development, meanwhile is under tight epigenetic control. Some studies have confirmed LINE1 play a critical role in early embryo development and ESC identity. Previous studies focused on the role of LINE1 in cis-regulation, however, the function of L1 RNA in tans-regulation remains largely unknown. In this study, we performed a high-throughput proximity MARGI (pxMARGI) experiment found transposon LINE1 RNA trans-regulated prefer to young LINE1 subfamily, and in hybrid and non-hybrid two different manner repress ESC to dual progress of 2C-like cells and differentiation. In differentiation process, LINE1 RNA as a scaffold recruit polycomb core subunits combine three core pluripotent factors maintain ESC identity. In 2C process, LINE1 RNA in a sequence-specific manner recruit Kap1 to old L1 subfamily, and recruit ELL3 to RE 5 UTR maintain ESC self-renewal. Our data point to LINE1 transcriptions more detail trans-regulation mechanism to orchestrate developmental progression for the self-renewal of embryonic stem cells (ESCs). LINE1 RNA may be potential biomarker for noninvasive embryo selection.

genomics↗

bioRxiv2024

Scaling Dense Representations for Single Cell with Transcriptome-Scale Context

Developing a unified model of cellular systems is a canonical challenge in biology. Recently, a wealth of public single-cell RNA sequencing data as well as rapid scaling of self-supervised learning methods have provided new avenues to address this longstanding challenge. However, rapid parameter scaling has been essential to the success of large language models in text and images, while similar scaling has not been attempted with Transformer architectures for cellular modeling. To produce accurate, transferable, and biologically meaningful representations of cellular systems, we develop AIDO.Cell, a pretrained module for representing gene expression and cellular systems in an AI-driven Digital Organism [1]. AIDO.Cell contains a series of 3M, 10M, 100M, and 650M parameter encoder-only dense Transformer models pre-trained on 50 million human cells from diverse tissues using a read-depth-aware masked gene expression pretraining objective. Unlike previous models, AIDO.Cell is capable of handling the entire human transcriptome as input without truncation or sampling tricks, thus learning accurate and general representations of the human cells entire transcriptional context. This pretraining with a longer context was enabled through FlashAttention-2, mixed precision, and large-scale distributed systems training. AIDO.Cell (100M) achieves state-of-the-art results in tasks such as zero-shot clustering, cell-type classification, and perturbation modeling. Our findings reveal interesting loss scaling behaviors as we increase AIDO.Cells parameters from 3M to 650M, providing insights for future directions in single-cell modeling. Models and code are available through ModelGenerator in https://github.com/genbio-ai/AIDO and on Hugging Face.

bioinformatics↗

bioRxiv2024

GOBoost: Leveraging Long-Tail Gene Ontology Terms for Accurate Protein Function Prediction

MotivationWith the advancement of deep learning, researchers have increasingly proposed computational methods based on deep learning techniques to predict protein function. However, many of these methods treat protein function prediction as a multi-label classification problem, often overlooking the long-tail distribution of functional labels (i.e., Gene Ontology Terms) in datasets. To address this issue, we propose the GOBoost method, which incorporates the proposed long-tail optimization ensemble strategy. Besides, GOBoost introduces the proposed global-local label graph module and multi-granularity focal loss function to enhance long-tail functional information, mitigate the long-tail phenomenon, and improve overall prediction accuracy. ResultsWe evaluate GOBoost and other state-of-the-art (SOTA) protein function prediction methods on the PDB and AF2 datasets. The GOBoost outperformed SOTA methods across all evaluation metrics on both datasets. Notably, in the AUPR evaluation on the PDB test set, GOBoost improved by 10.71%, 35.91%, and 22.71% compared to the SOTA HEAL method in the MF, BP, and CC functions. The experimental results demonstrate the necessity and superiority of designing models from the label long-tail distribution perspective. Availabilityhttps://github.com/Cao-Labs/GOBoost Contactcaora@plu.edu

bioinformatics↗

bioRxiv2024

Natural variations of TaMYB7-A1 regulate PHS resistance and specify wheat geographic adaptation

Crop domestication tended to select against seed dormancy for uniform germination--raising risks of undesirable pre-harvest sprouting (PHS) -- but regulation of seed dormancy and PHS in wheat are grossly under-characterized. Here, we identified wheat PHS resistance loci by GWAS. TaMYB7-A1 confers PHS resistance by elevating ABA signaling and seed dormancy in grains. Three TaMYB7-A1 haplotypes (Hap-1/2/3) contrast in PHS resistance, with Gly23 and Gly92 crucial for binding to and activating TaABI5 in Hap-1/3. A MITE transposon in the TaMYB7-A1Hap-1 promoter likely recruited the TaAZF1-TaABI4 module to boost expression, resulting in strong PHS resistance. TaMYB7-A1Hap-1 originated from wild einkorn and was integrated into hexaploid wheat through introgression into wild emmer. Different haplotypes of TaMYB7-A1, in conjunction with alleles of other major PHS resistance genes, is pivotal in shaping wheats adaptability to rainfall conditions in China, US and Europe during the harvest season. Introduction of TaMYB7-A1Hap-1 into elite cultivars confers PHS resistance without yield defects. Thus, artificial and natural selection across diverse climates regions have collectively shaped wheat adaptation and enabled rational delivery of improved lines tailored to local cropping needs.

plant biology↗

bioRxiv2024

Single-cell spatial transcriptomics of fixed, paraffin-embedded biopsies reveals colitis-associated cell networks

Background & AimsImaging-based, single-cell spatial transcriptomics (iSCST) using formalin-fixed, paraffin-embedded (FFPE) tissue could transform translational research by retaining all tissue cell subsets and spatial locations while enabling the analysis of archived specimens. We aimed to develop a robust framework for applying iSCST to archived clinical FFPE mucosal biopsies from patients with inflammatory bowel disease (IBD). MethodsWe performed a comprehensive benchmarking comparison of three iSCST platforms capable of analyzing FFPE specimens. We analyzed FFPE mucosal biopsies (n=57) up to 5 years old from non-IBD controls (HC; n=9) and patients with ulcerative colitis (UC;n=11). After platform-specific cell segmentation, we applied a uniform data processing pipeline to all datasets, including transcript detection, cell annotation, differential gene expression, and neighborhood enrichment. Transcriptomic signatures identified with iSCST were validated using external, publicly available bulk transcriptomic datasets. ResultsA custom 290-plex Xenium gene panel exhibited the highest sensitivity and specificity for transcript detection, enabling precise identification and quantification of diverse cell subsets and differentially expressed genes across cell types and disease states. We mapped transcriptionally distinct fibroblast subsets to discrete spatial locations and identified inflammation-associated fibroblasts (IAFs) and monocytes as a colitis-associated cellular neighborhood. We also identified signatures associated with Vedolizumab (VDZ) responsiveness. VDZ non-responders were characterized by an IAF-monocyte transcriptional signature, while responders exhibited enrichment of epithelial gene sets. ConclusionsOur optimized iSCST framework for archived FFPE biopsies provides unique advantages for assessing the role of colitis-associated cellular networks in routinely collected clinical samples. FFPE-based biomarkers could integrate with existing clinical workflows and potentially aid in risk-stratifying patients.

immunology↗