Prokaryotic gene expression datasets, including single-cell transcriptomics and community meta-transcriptomics, are growing rapidly, opening discovery opportunities while creating new analytical challenges. Module detection methods, such as independent component analysis, are powerful tools for expression analysis focused on identifying coordinated gene programs that consistently appear within gene expression compendia. Here, we present iModulonMiner 2.0, an end-to-end expression module detection workflow that adds five main capabilities to address emerging multi-dataset, multi-strain, and multi-modality challenges. First, it expands from bulk RNA-seq to support single-cell RNA-seq, community meta-transcriptomics, and multi-omics datasets, along with a standardized data pipeline. Second, it adapts multi-view learning to identify signals that are shared versus context-specific across strains, species, and modalities, supporting cross-context comparison and integration. Third, it provides new robust and prior-guided single-compendium inference methods. Fourth, it adds post-processing diagnostics for interpretability. Fifth, it supports multi-scale transfer of regulatory knowledge from bulk compendia to other modalities. Available as open-source software, iModulonMiner 2.0 provides a unified, module-based workflow for interpreting diverse prokaryotic expression compendia across modalities and biological scales.

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.

The prediction of enzyme kinetic parameters, particularly kcat and Km, is rapidly evolving from physics-based modeling toward datadriven artificial intelligence (AI) approaches, driven by expanding biochemical databases and advances in machine learning (ML) and deep learning (DL). We focus on AI-based enzyme kinetic parameter prediction, with an emphasis on kcat and Km, while also considering related parameters, such as Ki and kcat/Km. The key findings reveal that: (i) Representation learning increasingly relies on pretrained protein language models, while structural embeddings often show limited additional performance gains over sequence-based approaches; (ii) Model performance is highly data-dependent: classical ML regressors can outperform DL architectures on small datasets when using comparable feature representations, though multimodal feature integration can improve generalization; and (iii) Multitask frameworks for the joint prediction of kcat and Km may provide synergistic benefits, although the magnitude of the improvement may be limited. Future progress will require standardized benchmarks, mechanistically informed model architectures, uncertainty-aware prediction, and robust out-of-distribution validation to improve biochemical relevance, interpretability, and real-world utility

Agrobacterium-mediated transformation (AMT) is a critical method for genetic manipulation of non-model fungi, yet it remains a laborious and inefficient technique. When co-cultured in acetosyringone-supplemented induction medium, Agrobacterium transfers DNA directly into yeast cells using its virulence machinery. Membrane filters are commonly used to support the co-culture of yeast and Agrobacterium on agar plates, however some reports demonstrate that these filters are unnecessary for specific yeast species. Here we confirm across diverse budding yeasts that membrane filters are not necessary for effective AMT. Concentrating the cells via centrifugation and “spotting” the cell pellet directly onto the induction medium proved effective. This reduces hands-on time to 15 minutes and eliminates filter cost. In the oleaginous basidiomycete yeast, Rhodotorula toruloides, this simplified method increases transformation efficiency by 66% to 2,500 transformants per 106 recipient cells. We further optimized the Agrobacterium:Rhodotorula cell ratio and culture resuspension volume to achieve more than 200,000 CFU per transformation representing a 2–3 fold improvement over previously implemented protocols. This spot-plating method was successfully applied to seven yeast species of a distantly related phylum, Ascomycota, including one for which genetic transformation has not previously been reported, Botryozyma nematodophila. This approach highlights the broad applicability of the spot-plating methods across diverse yeast systems. Furthermore, this method could facilitate high-throughput transformation workflows that are critical for genome-scale functional studies.

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.