While many computational tools exist for designing CRISPR-Cas experiments, there is a need for a centralized resource that combines individual tools to predict the most efficient genome editing strategy for a given application. To fill this gap, we develop EditABLE (EditABLE-app.stanford.edu), an online resource that provides optimal CRISPR editors and guide RNAs based on user provided sequence data with functionalities for base editing, prime editing, and integrase-mediated editing. We demonstrate the utility of EditABLE by applying it to one of the most common monogenic disorders, autosomal dominant polycystic kidney disease (ADPKD), identifying specific editing tools across the ADPKD mutation landscape.

Single-cell multi-omics sequencing technologies enable profiling various cellular aspects, offering valuable biological insights. Integrating unpaired multi-omics data, which involves profiling different modalities from distinct cells within the same overall population, remains challenging yet crucial for a comprehensive understanding of cell states and molecular dynamics. Although numerous computational methods for integrating such unpaired data exist, a systematic evaluation of the choices at each step is lacking. The recent emergence of technologies simultaneously profiling multiple modalities within the same cell provides paired datasets, allows for the systematic evaluation of unpaired pipelines. We leverage paired scRNA-seq with scATAC-seq and histone modifications(ChIP-seq) to systematically evaluate methods for unpaired scRNA and peak-based epigenomic (ATAC-seq and ChIP-seq) data integration to establish a robust, general pipeline. We benchmark individual steps, including feature linking, dimension reduction, and clustering, and evaluate their combinatorial effects on pipeline performance by testing various choices at each stage. Our findings reveal that while gene activity scores show limited correlation with gene expression, they effectively preserve cellular neighborhoods for clustering. Dimension reduction emerges as the most critical step, with non-linear methods generally offering better performance and linear methods providing robustness. Optimal transport (OT)-based label transfer consistently outperforms other strategies across various embeddings. This benchmark of unpaired integration provides valuable insights for developing methods suited for increasingly complex multi-omics study designs.

BackgroundCell-cell communication via ligand–receptor signaling is a fundamental mechanism shaping multicellular organization and functional heterogeneity within tissue microenvironments. Recent advances in spatial transcriptomics (ST) have enabled unprecedented opportunities to systematically infer such interactions under the native spatial context. While prior studies have summarized or compared existing cell–cell interaction (CCI) inference methods, comprehensive benchmarking of tools specifically developed for ST applications remains limited.ResultsHere, we present a comprehensive evaluation of nine computational CCI inference methods on a series of realistic simulation settings and nine real ST datasets from three independent studies, spanning Visium, Stereo-seq, and Xenium platforms. Method performance was assessed based on ligand-receptor prediction accuracy, spatial coherence of interactions, biological relevance via pathway enrichment, and computational efficiency.ConclusionsOur results demonstrate substantial variability in tool performance across spatial resolutions, tissue contexts, and platforms, offering practical guidance for tool selection. This study also highlights key challenges in applying existing tools to real ST data and provides insights to inform future advances in spatially resolved cell–cell interaction analysis.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13059-026-04063-5.

Understanding how genetic variation shapes tissue structure is crucial for disease biology, yet scalable, general-purpose frameworks for genetic analysis of histology traits are lacking. We present HistoGWAS, a framework for genome-wide association studies of histology data that leverages foundation models for automated trait definition, variance component models for efficient association testing, and generative models for variant effect interpretation. Applied to 11 tissues from the Genotype-Tissue Expression project, HistoGWAS identifies four genome-wide significant loci associated with tissue histology-tissue quantitative trait loci (tissueQTLs)-which we link to molecular changes and complex traits. Power analyses demonstrate scalability to population-scale histology cohorts.

The rice node is a critical hub for the distribution of mineral nutrients, mediated by transporters. Manganese (Mn) is an essential micronutrient for plant growth. However, the precise cell types and the cell-type-enriched transporter genes in rice node, and the molecular mechanisms underlying the translocation and distribution of Mn in rice remain poorly understood. We characterize 11 distinct cell types using multiple cluster-enriched genes in rice node I through single-nucleus RNA sequencing (snRNA-seq), systematically profile the expression patterns of putative 1,144 transporter genes within 11 cell types, and identify six candidate transporter genes linked to the tissue-specific deposition of six elements through combining spatial ionomics in node, respectively. Furthermore, we functionally characterize OsMTP7 that is highly expressed in phloem cells in node, as well as in root stele cells and anther. OsMTP7 is localized to plasma membrane in rice and shows efflux activity for Mn. OsMTP7 knockout inhibites Mn uptake and xylem-mediated Mn translocation in root and Mn distribution in node, leading to decreased Mn concentration in various organs and root xylem sap, resulting in reduced biomass and yield. We reveal that OsMTP7 knockout alters expression of genes for multiple biological processes in spikelets using bulk RNA-seq, resulting in increased oxidative stress in anther and low fertility. Our study reveals the precise cell types and the cell-type-enriched transporter genes in node, identifies candidate transporters for elements deposition in node, and demonstrates a novel and critical Mn efflux transporter mediating Mn uptake, translocation and distribution for improving growth and yield in rice.

Cell-cell communication regulates complex biological processes in multicellular systems. Existing scRNA-seq-based methods typically aggregate gene expression by clusters, overlooking within-cluster heterogeneity. We present scComm, a computational framework that infers cell-cell communications between individual cells using supervised contrastive learning. In simulations, scComm outperforms other methods and achieves up to 95% accuracy. Applied to colorectal cancer, it reveals cell-cell communications linked to PD-1 blockade response and tertiary lymphoid structures. In liver cancer, it identifies three novel tumor subtypes and angiogenesis-promoting neutrophil subtypes that have unique tumor microenvironments. scComm enables high-resolution cell-cell communication analysis, uncovering biological insights missed by existing approaches.

BackgroundHyperosmotic shock and the resulting cell volume compression are commonly experienced by organs such as the kidneys, causing rapid formation of hyperosmotic phase separation (HOPS) condensates in the cytoplasm and nucleoplasm. Although the causal relationship between hyperosmotic shock and condensation has been characterized, the diffusion dynamics of biomolecular condensates in hyperosmotically compressed cells and their underlying mechanisms remain largely unknown.ResultsWe systematically characterize the dynamics of HOPS condensates formed by model protein mRNA decapping enzyme 1A (DCP1A) through live-cell fluorescent single-particle tracking (SPT) across timescales. We find that HOPS condensates predominantly exhibit sub-diffusion rather than free diffusion, while a small fraction undergo bursts of super-diffusion. Using imaging to measure spatial accessibility inside cells and fluorescence labels for specific cellular organelles, we show that sub-diffusion arises from endoplasmic reticulum (ER) attachment, whereas super-diffusion reflects microtubule-dependent active transport. We further reconstruct spatial accessibility within hyperosmotically compressed cells using trajectories of genetically encoded multimeric nanoparticles (GEMs) and find that, despite compression, the cytoplasm remains accessible via diffusion and does not exhibit physical corralling. This indicates that restricted condensate mobility arises primarily from specific molecular interactions rather than from physical barriers.ConclusionsOur findings challenge the view that the cytosol becomes static and constrained during hyperosmotic compression. Instead, it remains dynamic, while condensates are spatially organized through docking to membrane structures with intermittent episodes of long-range transport. This model reshapes our understanding of the physical environment within stressed cells and provides a framework for how condensates achieve spatiotemporal organization through interactions with cellular structures and active processes.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13059-026-04042-w.

The floral transition in maize represents a pivotal developmental switch that determines flowering time, environmental adaptation, and yield-related traits. However, the molecular mechanisms governing shoot apical meristem reprogramming and cell identity changes during this process remain poorly understood. By integrating time-course bulk RNA-seq, single-cell transcriptomics, chromatin accessibility, and transcription factor binding profiles, we construct a spatiotemporal molecular framework of the maize shoot apical meristem floral transition. Our analyses reveal global transcriptional reprogramming accompanied by pronounced cell type-specific regulation dynamics. At a global level, our transcriptional-level inference suggests that pathways associated with chromatin remodeling, environmental response, and reproductive development are sequentially activated. We further identify a ZmMADS69-ZmRap2.7-ZMM4 regulatory module that fine-tunes the floral transition within the shoot apical meristem. At single-cell resolution, we find that the floral transition is not driven by a uniform transcriptional switch, but instead emerges from the coordinated action of spatially distinct shoot apical meristem domains. Through differential expression, trajectory, and co-expression module analyses, we further identify previously unrecognized roles for the inflorescence regulators UNBRANCHED2 and UNBRANCHED3 in promoting the floral transition, suggesting that they coordinate floral induction with subsequent inflorescence development. Our study establishes a comprehensive spatiotemporal regulatory framework for themaize floral transition, providing mechanistic insights into shoot apical meristem reprogramming and offering a foundation for identifying new regulators to improve maize adaptation and yield.

BackgroundThe integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. Using the kidney as an emblematic example of a complex organ, we perform a systematic evaluation of multimodal single-cell integration strategies, with heart tissue used for additional methodological validation.ResultsWe generate a benchmarking dataset for the renal cortex by integrating 3' and 5' scRNA-seq with joint snRNA-seq and snATAC-seq, profiling 119,744 high-quality nuclei/cells from 19 donors. To align cell identities and enable consistent comparisons, we develop the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assess integration strategies. "Horizontal" integration of scRNA and snRNA-seq improves cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq has an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration is especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases.ConclusionsOur work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13059-026-04002-4.