Extracellular ATP (eATP) acts as a damage-associated molecular pattern in both animals and plants. In plants, eATP is recognized by the purinergic receptors P2K1 and P2K2, which mediate responses to various environmental stresses. From a p2k1 suppressor screen in Arabidopsis thaliana, we identified two allelic mutants of the cutin biosynthesis gene, BODYGUARD (BDG). The BDG mutations specifically suppress p2k1 and p2k1p2k2 mutant defects in response to eATP. The suppression of p2k1 by bdg modifies plant defense against the necrotrophic fungal pathogen, Botrytis cinerea. In addition, the results show that the defective cuticle in bdg mutants generates cutin breakdown products, such as cutin monomers, which serve as signaling molecules to activate eATP response and plant immunity through an alternative eATP signaling pathway. Our data provide new insights into the signaling role of cutin and reveal the first link between the cuticle and eATP signaling.

Soybean (Glycine max) root nodulation is a symbiotic process that requires complex molecular and cellular coordination. The phloem plays a crucial role not only in nutrient transport but also in long-distance signaling that regulates nodulation. However, the molecular mechanisms underlying phloem-specific regulation during nodulation remain poorly characterized. Here, we developed a phloem-specific Translating Ribosome Affinity Purification sequencing system to investigate the translational dynamics of phloem-associated genes during nodulation. Using a phloem-specific promoter (Glyma.01G040700) combined with the GAL4-UAS amplification system, we successfully captured the translatome of soybean root phloem at early (72 h postinoculation, hpi) and late (21 d postinoculation, dpi) nodulation stages. Differential expression analysis revealed dynamic translational reprogramming, with 2,636 differentially expressed genes (DEGs) at 72 hpi and 8,422 DEGs at 21 dpi. Gene ontology and pathway enrichment analyses showed stage-specific regulatory shifts, including early activation of ethylene and defense pathways and late-stage enhancement of nutrient transport and vascular development. Transcription factor analysis identified GmbHLH121 as a key phloem-specific regulator of nodulation. Functional validation using RNA interference knockdown and overexpression experiments demonstrated that GmbHLH121 negatively regulates nodule formation, likely acting downstream of or independently from early nodulation signaling pathways. Additionally, we uncovered dynamic regulation of cell wall-modifying enzymes (pectin methylesterase [PME] and PME inhibitors) in the phloem, implicating their role in modulating plasmodesmata permeability and facilitating symplastic connectivity during nodulation. Our findings highlight the critical role of phloem-mediated translational regulation in coordinating root nodulation, emphasizing the phloem as an active regulatory hub for long-distance signaling and symbiotic efficiency.

Hepatocellular carcinoma (HCC), a type of hepatic malignancy, is strongly linked to chronic hepatitis B virus (HBV) infection. HBV infection commonly advances asymptomatically for months; symptom onset may include nausea, fever, abdominal discomfort, jaundice, and anorexia. Existing therapies aim to alleviate symptoms, but no definitive cure exists. Prolonged exposure can lead to chronic infection, increasing the risk of HCC. Due to these risks, the infectivity of HBV-positive blood presents biosafety challenges for laboratory research on the virus. To address this, we seek to establish a refined HBV pseudovirus system using lentiviral backbone and utilizing an HBV surface antigen (HBsAg) plasmid, and the development of a NTCP-HepG2 cell line. The infectivity was measured using flow cytometry. This pseudo virus provides a non-infectious cell line for investigating HBV cellular uptake, surface receptor interactions, and prospective antiviral targets. By eliminating uncontrolled replication, our model enhances both research feasibility and laboratory safety.

Motivation Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in 2D and 3D space. However, the limited sequencing depth, increasing spatial resolution, and growing spatial spots in spatial omics technologies present significant computational challenges in identifying biologically meaningful molecules with variable spatial distributions across various omics modalities.Results We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in large-scale spatial omics data. scBSP demonstrates significantly enhanced computational efficiency, processing high-resolution spatial omics data within seconds, and exhibits robust cross-platform performance by consistently identifying spatially variable features with high reproducibility across various sequencing platforms.Availability and implementation scBSP is available for download from R CRAN at https://cran.r-project.org/web/packages/scBSP/index.html and PyPI at https://pypi.org/project/scbsp/.

Macrophage polarization into inflammatory (M1) and repairing/healing (M2) functional phenotypes are fundamental mechanisms in immune defensive responses, tissue repair, and disease control. Conventional phenotyping approaches based on molecular biomarkers are limited by destructive protocols, static endpoint analyses, and a disregard for the biomechanical attributes of cells. In this study, an integrated artificial intelligence (AI)-atomic force microscopy (AFM) platform is introduced that enables label-free, mechanophenotyping of macrophages at single-cell resolution. Using nanoscale force mapping, morphological and nanomechanical profiles are captured details, such as Young's modulus, adhesion, and sphericity, across diverse macrophage activation states. These profiles are interpreted through a deep neural network (DNN) trained with pixel-wise data enhancement and a meta-confidence estimator for dynamic, robust classification. The system accurately distinguishes naïve (M0), M1, and M2 functional phenotypes of human macrophages, even across donor heterogeneity, in the absence of conventional immunolabeling. The method reveals mixed macrophage polarization states and correlates cytoskeletal remodeling with mechanical biomarkers, establishing a direct link between cellular mechanics and immune function. This platform introduces a dynamic, non-destructive strategy for immune monitoring, redefining cellular mechanics as a critical dimension in diagnostic and therapeutic contexts, and laying the groundwork for the emerging field of mechanoimmunology.

Single-cell large language models (scLLMs) capture essential biological insights from vast single-cell atlases but struggle in out-of-context applications, where zero-shot predictions can be unreliable. To address this, we introduce a single-cell parameter-efficient fine-tuning (scPEFT) framework that integrates learnable, low-dimensional adapters into scLLMs. By freezing the backbone model and updating only the adapter parameters, scPEFT efficiently adapts to specific tasks using limited custom data. This approach mitigates catastrophic forgetting, reduces parameter tuning by over 96%, and decreases GPU memory usage by more than half, significantly enhancing scLLMs's accessibility for resource-constrained researchers. Validated across diverse datasets, scPEFT outperformed zero-shot models and traditional fine-tuning in disease-specific, cross-species, and under-characterized cell population tasks. Its attention-mechanism analysis identified COVID-related genes associated with specific cell states and uncovered unique blood cell subpopulations, demonstrating scPEFT's capacity for condition-specific interpretations. These findings position scPEFT as an efficient solution for improving scLLMs' utilities in general single-cell analyses.

The immunogenic binding interactions of antigens are complex and interconnected. A new transformer-based model can simultaneously predict the bindings of antigens to two main receptors.

Spatially resolved transcriptomics have enabled the inference of gene expression patterns within two and three-dimensional space, while introducing computational challenges due to growing spatial resolutions and sparse expressions. Here, we introduce scBSP, an open-source, versatile, and user-friendly package designed for identifying spatially variable genes in large-scale spatial transcriptomics. scBSP implements sparse matrix operation to significantly increase the computational efficiency in both computational time and memory usage, processing the high-definition spatial transcriptomics data for 19,950 genes on 181,367 spots within 10 seconds. Applied to diverse sequencing data and simulations, scBSP efficiently identifies spatially variable genes, demonstrating fast computational speed and consistency across various sequencing techniques and spatial resolutions for both two and three-dimensional data with up to millions of cells. On a sample with hundreds of thousands of sports, scBSP identifies SVGs accurately in seconds to on a typical desktop computer.

Single-cell sequencing transformed biology and medicine, providing an unprecedented high-resolution view at the cellular level. However, the vast variability inherent in single-cell sequencing data impedes its utility for in-depth downstream analysis. Inspired by the foundation models in natural language processing, recent advancements have led to the development of single-cell Large Language Models (scLLMs). These models are designed to discern universal patterns across diverse single-cell datasets, thereby enhancing the signal-to-noise ratio. Despite their potential, multiple studies indicate existing scLLMs do not perform well in zero-short settings, highlighting a pressing need for more effective adaptation techniques. This research proposes several adaptation techniques for scLLMs by preserving the original model parameters while selectively updating newly introduced tensors. This approach aims to overcome the limitations associated with traditional fine-tuning practices, such as catastrophic forgetting and computational inefficiencies. We introduce two Parameter-Efficient Fine-Tuning (PEFT) strategies specifically tailored to refine scLLMs for cell type identification. Our investigations utilizing scGPT demonstrate that PEFT can enhance performance, with the added benefit of up to a 90% reduction in parameter training compared to conventional fine-tuning methodologies. This work paves the way for a new direction in leveraging single-cell models with greater efficiency and efficacy in single-cell biology.

Human embryonic stem cells (hESCs) cultured in media containing bone morphogenic protein 4 (BMP4; B) differentiate into trophoblast-like cells. Supplementing media with inhibitors of activin/nodal signaling (A83–01) and of fibroblast growth factor 2 (PD173074) suppresses mesoderm and endoderm formation and improves specification of trophoblast-like lineages, but with variable effectiveness. We compared differentiation in four BMP4-containing media: mTeSR1-BMP4 only, mTeSR1-BAP, basal medium with BAP (basal-BAP), and a newly defined medium, E7-BAP. These media variably drive early differentiation towards trophoblast-like lineages with upregulation of early trophoblast markers CDX2 and KRT7 and downregulation of pluripotency markers (OCT4 and NANOG). As expected, based on differences between media in FGF2 and its inhibitors, downregulation of mesendoderm marker EOMES was variable between media. By day 7, only hESCs grown in E7-BAP or basal-BAP expressed HLA-G protein, indicating the presence of cells with extravillous trophoblast characteristics. Expression of HLA-G and other differentiation markers (hCG, KRT7, and GCM1) was highest in basal-BAP, suggesting a faster differentiation in this medium, but those cultures were more inhomogeneous and still expressed some endodermal and pluripotency markers. In E7-BAP, HLA-G expression increased later and was lower. There was also a low but maintained expression of some C19MC miRNAs, with more CpG hypomethylation of the ELF5 promoter, suggesting that E7-BAP cultures differentiate slower along the trophoblast lineage. We conclude that while all protocols drive differentiation into trophoblast lineages with varying efficiency, they have advantages and disadvantages that must be considered when selecting a protocol for specific experiments.

Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.

Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a spatial granularity-guided and non-parametric model to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This new method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.

The Shangri-La region, located in southwest China, is known for its dry climate and intense UltraViolet (UV) radiation due to its high altitude. Research conducted in the past has demonstrated that UV radiation, particularly UV-B radiation, enhances plants' ability to withstand cold temperatures, diseases, and arid conditions. Indeed, the Cabernet Sauvignon grape variety, predominantly cultivated in Shangri-La where the UV intensity reaches 21-22w/m2, has displayed remarkable resilience in recent decades. However, the specific impact of UVB radiation on Cabernet Sauvignon grape leaves has yet to be fully understood. Consequently, our objective is to investigate the influence of UV radiation on grapes grown at high altitudes. To gain insights into the short-term effects on grape leaves, we conducted a comprehensive investigation involving anatomical and photosynthetic changes. Additionally, we analyzed the transcriptomic and proteomic regulation associated with these changes following exposure to UV-B radiation at intensities ranging from 15-20w/m2 for durations of 3 and 7 days. The findings of our study demonstrated that exposure to UV-B radiation, particularly at intensities surpassing 10w/m2, led to noticeable changes in the morphology of epidermal and palisade tissue cells. Furthermore, after a 3-day exposure to UV-B, we observed an upregulation of proteins responsible for cytoskeletal organization and cell wall synthesis. Conversely, following a 7-day treatment, proteins associated with disease resistance and antioxidants showed increased expression. Transcriptomic analysis revealed a significant enrichment of proteins and genes linked to pathogen infection, defense mechanisms, and antioxidant processes. Overall, our research highlighted the positive impact of intense UV radiation at high altitudes on enhancing disease resistance and environmental adaptability in Cabernet Sauvignon grape plants.

The subcortical maternal complex (SCMC) is a multiprotein complex in oocytes and preimplantation embryos that is encoded by maternal effect genes. The SCMC is essential for zygote-to-embryo transition, early embryogenesis, and critical zygotic cellular processes, including spindle positioning and symmetric division. Maternal deletion of Nlrp2 , which encodes an SCMC protein, results in increased early embryonic loss and abnormal DNA methylation in embryos. We performed RNA sequencing on pools of meiosis II (MII) oocytes from wild-type and Nlrp2 -null female mice that were isolated from cumulus-oocyte complexes (COCs) after ovarian stimulation. Using a mouse reference genome-based analysis, we found 231 differentially expressed genes (DEGs) in Nlrp2 -null compared to WT oocytes (123 up- and 108 downregulated; adjusted p < 0.05). The upregulated genes include Kdm1b , a H3K4 histone demethylase required during oocyte development for the establishment of DNA methylation marks at CpG islands, including those at imprinted genes. The identified DEGs are enriched for processes involved in neurogenesis, gland morphogenesis, and protein metabolism and for post-translationally methylated proteins. When we compared our RNA sequencing data to an oocyte-specific reference transcriptome that contains many previously unannotated transcripts, we found 228 DEGs, including genes not identified with the first analysis. Interestingly, 68% and 56% of DEGs from the first and second analyses, respectively, overlap with oocyte-specific hyper- and hypomethylated domains. This study shows that there are substantial changes in the transcriptome of mouse MII oocytes from female mice with loss of function of Nlrp2 , a maternal effect gene that encodes a member of the SCMC.

Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta.

Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs such as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from spatial transcriptomics. The embedding is then visualized by mapping into color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of 10x Genomics Visium spatial transcriptomics datasets on the human and mouse cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on pre-defined region- or cell-type-enriched genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer's disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.

Voltage-dependent anion channels (VDACs) are the most abundant proteins in the mitochondrial outer membranes of all eukaryotic cells. They participate in mitochondrial energy metabolism, mitochondria-mediated apoptosis, and cell growth and reproduction. Here, the chromosomal localizations, gene structure, conserved domains, and phylogenetic relationships were analyzed. The amino acid sequences of VDACs were found to be highly conserved. The tissue-specific transcript analysis from transcriptome data and qRT-PCR demonstrated that grapevine VDACs might play an important role in plant growth and development. It was also speculated that VDAC3 might be a regulator of modulated leaf and berry development as the expression patterns during these developmental stages are up-regulated. Further, we screened the role of all grape VDACs ’ response to pathogen stress and found that VDAC3 from downy mildew Plasmopara viticola -resistant Chinese wild grapevine species Vitis piasezkii “Liuba-8” had a higher expression than the downy mildew susceptible species Vitis vinifera cv. “Thompson Seedless” after inoculation with P. viticola. Overexpression of VpVDAC3 resulted in increased resistance to pathogens, which was found to prevent VpVDAC3 protein accumulation through protein post-transcriptional regulation. Taken together, these data indicate that VpVDAC3 plays a role in P. viticola defense and provides the evidence with which to understand the mechanism of grape response to pathogen stress.

We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics by reconstructing and segmenting a transcriptome mapped RGB image. RESEPT can identify the tissue architecture, and represent corresponding marker genes and biological functions accurately. RESEPT also provides critical insights into the underlying mechanisms driving the complex tissue heterogeneities in Alzheimer’s disease and glioblastoma.

Background: Drought stress is a major abiotic factor that affects rapeseed (Brassica napus L.) productivity. Though previous studies indicated that long non-coding RNAs (lncRNAs) play a key role in response to drought stress, a scheme for genome-wide identification and characterization of lncRNAs’ response to drought stress is still lacking, especially in the case of B. napus. In order to further understand the molecular mechanism of the response of B. napus to drought stress, we compared changes in the transcriptome between Q2 (a drought-tolerant genotype) and Qinyou8 (a drought-sensitive genotype) in response to drought stress and rehydration treatment at the seedling stage. Results: A total of 5,546 down-regulated and 6,997 up-regulated mRNAs were detected in Q2 compared with 7,824 and 10,251 in Qinyou8, respectively; 369 down-regulated and 108 up-regulated lncRNAs were detected in Q2 compared with 449 and 257 in Qinyou8, respectively. LncRNA- mRNA interaction network analysis indicated that the co-expression network of Q2 was composed of 145 network nodes and 5,175 connections, while the co-expression network of Qinyou8 was composed of 305 network nodes and 22,327 connections. We further identified 34 TFs corresponding to 126 differentially expressed lncRNAs in Q2, and 45 TFs corresponding to 359 differentially expressed lncRNAs in Qinyou8. Differential expression analysis of lncRNAs indicated that up- and down-regulated mRNAs co-expressed with lncRNAs participated in different metabolic pathways and were involved in different regulatory mechanisms in the two genotypes. Notably, some lncRNAs were co-expressed with BnaC07g44670D, which are associated with plant hormone signal transduction. Additionally, some mRNAs which were co-located with XLOC_052298, XLOC_094954 and XLOC_012868 were mainly categorized as signal transport and defense/stress response. Conclusions: The results of this study increased our understanding of expression characterization of rapeseed lncRNAs in response to drought stress and re-watering, which would be useful to provide a reference for the further study of the function and action mechanisms of lncRNAs under drought stress and re-watering.