Spatial omics technologies provide new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organized cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalize. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.

Alternative splicing (AS) is a major driver of transcriptomic diversity, yet its computational analysis remains challenged by inconsistent event definitions across tools and by the sensitivity of quantification methods to annotation quality. Here, we present a comprehensive benchmark of seven AS detection tools—replicate Multivariate Analysis of Transcript Splicing (rMATS), Super-fast Pipeline for alternative splicing analysis, version 2 (SUPPA2), Modeling Alternative Junction Inclusion Quantification + Visualization Of Inferred Local splicing Alternatives (MAJIQ+VOILA), EventPointerST (EPST), Shiba, LeafCutter, and the newly introduced EventPointer BAM (EPBAM)—evaluated across three complementary datasets: reverse transcription PCR (RT-PCR) validated experiments, simulated data, and Lexogen Spike-In RNA Variant (SIRV). The inclusion of SIRV, which provide experimentally derived sequencing data with fully controlled ground truth, is central to our evaluation framework, enabling rigorous assessment of quantification accuracy under real technical conditions. We assess tool performance under three annotation scenarios—complete, incomplete, and overloaded—reflecting the annotation uncertainty commonly encountered in practice. EPBAM extends the EventPointer framework by enabling de novo event detection coupled with a coverage-corrected Ψ quantification strategy. Our results demonstrate that annotation quality substantially drives tool performance: annotation-dependent tools achieve peak accuracy under complete annotations, whereas EPBAM exhibits the most robust and consistent behaviour across annotation conditions, recovering unannotated events with low false discovery rates. No single tool dominates across all evaluation metrics, highlighting the importance of dataset diversity and standardized benchmarking practices for meaningful tool comparison in the AS field.

Identifying sustainable alternatives to animal proteins is a central challenge for global food system transformation. Replacing animal proteins requires preserving food functionalities—such as gelation, foaming, and emulsification—that arise from the collective behavior of heterogeneous protein mixtures within food matrices. While these properties are assessed experimentally at the ingredient level, systematically comparing the vast diversity of natural proteins remains difficult, and most artificial intelligence (AI)-based protein models focus on individual proteins rather than proteome-level behavior. Here, we present AlterProtX, an AI-enabled framework that integrates molecular- and proteome-scale features to guide alternative protein discovery. AlterProtX predicts protein thermal stability, a processing-relevant property, and integrates it with six intermolecular interaction attributes into distribution-based proteome representations. This multiscale approach enables mechanistic comparison between animal and non-animal proteomes, revealing molecular features underlying functional similarity and divergence. By integrating allergenic potential and nutritional adequacy, AlterProtX supports early-stage prioritization of candidate protein sources and provides an interpretable platform for proteome-level evaluation of food proteins.

The activation of many chromosomally integrated mobile genetic elements by RecA-DNA nucleoprotein filaments may represent an adaptation to evading deletion through homologous recombination. Consistent with bacterial transformation facilitating such eliminations of inserted viral DNA, cross-taxa comparisons found isolates’ prophage content was negatively associated with homologous recombination rates. Assaying the effect of the composition of the extracellular DNA pool on transformation of a lysogen demonstrated that DNA from conspecific donors drove both prophage activation and deletion, whereas DNA originating from different species triggered only prophage activation. Accordingly, rates of horizontal DNA transfer mechanisms varied across bacterial ecologies, with homologous recombination common relative to prophage accumulation when DNA originated from fewer donor species.

Small molecule antiviral drugs have transformed the management of infections such as those caused by HIV, hepatitis B virus, hepatitis C virus, herpesviruses and influenza viruses. Effective antiviral therapeutics remain unavailable for many other often life-threatening viral infections, many of which are caused by RNA viruses. The development of potent, safe and orally available antivirals is therefore essential both to address current unmet medical needs and to strengthen epidemic and pandemic preparedness. In this Review, we discuss the rationale for building a diversified portfolio of broader-spectrum antivirals against RNA viruses with activity spanning multiple genera, entire families or even crossing viral families. We provide a non-exhaustive overview of approved and investigational antiviral agents active against RNA viruses and discuss the biological determinants that underlie their antiviral spectrum. Finally, we outline the clinical development frameworks required to accelerate the evaluation of such drugs, in particular in the context of outbreaks. In this Review, Neyts and colleagues discuss the rationale for developing broader-spectrum antivirals against RNA viruses, survey promising antiviral targets and agents, and outline strategies to accelerate clinical development and strengthen outbreak preparedness.

Microbial biomass is a central functional trait governing nutrient turnover and trophic transfer in aquatic ecosystems, yet how chemical stress reshapes biomass at the level of individual cells remains poorly resolved. Conventional toxicity assays primarily detect population-level growth inhibition and overlook sublethal physiological responses and heterogeneity within microbial populations. Here, we quantify single-cell dry-mass, a label-free proxy for cellular biomass and biosynthetic state, in the fast-growing marine bacterium Vibrio natriegens. Across thousands of cells, we assessed how exposure to copper, zinc, diclofenac, bisphenol A, bisphenol E, and bisphenol Z alters cellular biomass. Chemical stress elicited contrasting responses: copper, zinc, and diclofenac reduced median dry-mass by up to 36%, whereas bisphenols increased median dry-mass by up to 24%. These changes were not simply proportional to population-level growth inhibition. Beyond shifts in central tendency, stress altered the shape and variability of biomass distributions, revealing additional distribution-level responses that are not detectable in bulk measurements. These results identify single-cell dry-mass as an integrative trait of microbial stress physiology and show that chemical perturbations can drive opposing cellular biomass responses that are not fully captured by population growth measurements. By linking chemical stress to microbial biomass traits, this work provides a controlled framework for testing cellular-scale responses in more complex ecological settings.

Bacterial extracellular vesicles (BEVs) are nanosized membrane-bound nanoparticles naturally secreted by Gram-negative and Gram-positive bacteria. These vesicles mediate intercellular communication through delivery of diverse bioactive cargo, including proteins, lipids, nucleic acids, metabolites, and signaling molecules. Initially recognized as virulence determinants in pathogenic bacteria, BEVs are increasingly recognized as multifunctional platforms with potential applications in sustainable agriculture. This review examines BEV biogenesis, cargo composition, plant uptake, and emerging roles in plant growth promotion, stress adaptation, and disease resistance. Emphasis is placed on BEV-mediated modulation of plant immunity through pattern recognition receptors (PRRs), pattern-triggered immunity (PTI), and induced systemic resistance (ISR). Potential roles of vesicle-associated phytohormones, ACC deaminase, siderophores, extracellular enzymes, and regulatory RNAs in nutrient acquisition, phytohormone homeostasis, and stress responses are critically evaluated. Emerging evidence linking BEVs to drought, salinity, heat, heavy metal, and nutrient-deficiency responses is examined, with emphasis on antioxidant defense, osmotic adjustment, ion homeostasis, and stress-responsive signaling. The review also discusses engineered BEVs as potential biostimulants, nano-biofertilizers, biocontrol agents, and precision delivery systems, including CRISPR-based engineering, synthetic vesicles, AI-guided cargo optimization, and smart nanoformulations. Major challenges include scalable production, standardization, biosafety, regulation, and field validation. However, critical knowledge gaps remain regarding BEV perception, cellular uptake, cargo delivery, and the causal basis of plant responses, together with uncertainties surrounding biosafety, reproducibility, and performance under field conditions. Collectively, BEVs represent promising biological nanocarriers for climate-resilient and sustainable agriculture, but further mechanistic, methodological, and field-based research is required to establish their reliability and practical agricultural potential. Graphical abstract

The failure of antibiotic treatment is a growing concern, driven in large part by the rising incidence of antimicrobial resistance (AMR). Increasing evidence points to the importance of microbial competition in preventing the colonization of disease-causing pathogens and the spread of AMR. Crucially, the pathogens we target with antibiotics do not exist in isolation but instead compete and interact with the microbial communities that colonize the human body, microbiomes. Here, we therefore argue that the key to approaching the challenge of antibiotic treatment failure is understanding the strategies and mechanisms that microbes use to succeed in microbiomes. We discuss how microbial competition influences the colonization of pathogens and the spread of resistance via horizontal gene transfer. We then discuss how microbial competition may be used to synergize with antibiotic treatment or even replace it. Overall, we argue that studying microbial interactions offers a powerful approach to understanding and even mitigating antibiotic treatment failure.

The gut microbiota plays a pivotal part in human health, yet the molecular mechanisms that underlie its effects are largely unexplored. Bacteroides, a dominant genus in the human gut microbiota, is depleted in patients with atherosclerosis, but its causal relationship with disease remains unclear. Here, using a mouse model, we show that administration of Bacteroides uniformis alleviates atherosclerosis through the upregulation of hepatic low-density lipoprotein receptor expression. Bioactivity-guided screening revealed pentadecanoic acid (PA, C15:0), a saturated odd-chain fatty acid, as a principal bioactive metabolite. PA supplementation reduced  atherosclerotic plaque burden by around 50% and significantly improved plasma lipid profiles, a result that underscores its therapeutic potential. Mechanistically, PA enhances cholesterol clearance by directly inhibiting HMG-CoA reductase, suppressing hepatic cholesterol biosynthesis and promoting plasma low-density lipoprotein cholesterol removal. Analyses of 100 gut bacterial strains revealed that PA production occurs across multiple Bacteroidota genera. Notably, PA is markedly depleted in patients with dyslipidaemia. In summary, a Bacteroidota-derived odd-chain fatty acid regulates gut–liver crosstalk, and modulation of the microbial–metabolic axis has atheroprotective potential. The administration of Bacteroides uniformis in mice can mitigate atherosclerosis via pentadecanoic acid, which directly inhibits HMG-CoA reductase to promote hepatic cholesterol clearance.

Antibiotic resistance has emerged as a major bottleneck in antibiotic development, frequently undermining promising drug candidates and negating years of research and pharmaceutical investment. Despite growing recognition of this issue, discovery pipelines still emphasize potency and target specificity whereas resistance potential is often considered later in the development process. Predicting resistance remains challenging owing to the diversity of genetic mechanisms, species-specific adaptive pathways and potential side effects of resistance on bacterial viability. However, technological advances now enable systematic mapping of resistance evolution, the dissemination of resistance genes and prediction of the clinical impact. To improve early identification of resistance-prone antibiotic candidates, it is crucial to evaluate five key parameters, including de novo resistance evolvability across pathogens, resistance–fitness–virulence trade-offs, resistance stability, cross-resistance potential and health risk of resistance genes. Integrating these factors provides a systematic, resistance-based framework for classifying new antibiotic candidates and guiding compound prioritization and refinement. Utilizing this framework within the antibiotic development pipeline shifts resistance prediction from retrospective observation to a prospective guiding principle in drug design. Resistance emergence is a major challenge to antibiotic development, often undermining promising drug candidates and negating years of research and pharmaceutical investment. In this Review, Pál and colleagues propose a resistance-based classification system for antibiotic development that integrates early resistance predictions into the prospective development of new antibiotics.