Type V CRISPR-Cas12 systems evolved from transposon-encoded TnpB proteins, with type V-U4 nucleases (Cas12n) as evolutionary intermediates linking TnpB to larger type V effectors. Despite their compact size, most Cas12n orthologs, including Corynebacterium glutamicum Cas12n (CgCas12n), exhibit low genome-editing activity in mammalian cells. Here, we engineered CgCas12n using an arginine enrichment strategy, generating a variant with approximately 60-fold enhanced editing efficiency. Concurrently, we optimized the sgRNA scaffold to reduce its size without compromising activity. These improvements were combined to establish an enhanced CgCas12n system (eCgCas12n) that enables efficient mammalian genome editing. We further adapted this system for base editing by constructing a cytosine base editor (eCgCas12n-CBE) that mediates efficient C-to-T conversion. As proof of concept, eCgCas12n-CBE introduced a premature stop codon into the Dmd gene, reducing dystrophin expression and impairing myogenic differentiation. AAV9-mediated delivery of eCgCas12n-CBE into mouse skeletal muscle demonstrated its in vivo editing capability. Collectively, this work establishes a robust engineering strategy to enhance compact Cas12n nucleases and provides an efficient genome- and base-editing platform for functional genetic studies.

Cancer-associated fibroblasts (CAFs) promote tumorigenesis and represent potential therapeutic targets, highlighting the need for precise understanding of CAF heterogeneity. In colorectal cancer (CRC), CAF subsets and nomenclature vary widely, and many studies overlook the diversity of normal colon fibroblasts that shapes tumor mesenchyme. Here, we combine large-scale single-cell RNA-sequencing analysis with spatial validation and functional assays to construct a comprehensive reference map of human normal colon fibroblasts and mesenchymal subsets, guided by the well-characterized mouse colon. We define three major fibroblast populations, subepithelial myofibroblasts (SEMFs), mucosa-associated fibroblasts (MAFs), and submucosa-associated fibroblasts (SAFs), and characterize a previously underexplored muscle-embedded interstitial fibroblast (MIF) population. This reference enabled mapping of CRC-specific changes, revealing four cancer-specific CAF subsets, including inflammatory CAFs (iCAFs), matrix CAFs (mCAFs), and two precursor populations (pre-CAFs). We also identify robust CRC CAF markers including CTHRC1, infer transcriptional regulators, and define distinct developmental trajectories driving iCAF and mCAF activation. Together, our study provides a spatially and transcriptionally resolved reference map of fibroblasts in the normal colon and CRC, enabling mechanistic studies and informing CAF-targeted therapies.

The cell division cycle is characterised by oscillatory dynamics in regulatory mechanisms and biosynthesis, coordinated with genome replication and segregation. To understand these dynamics, quantitative cell cycle-dependent protein concentration data are essential. Unfortunately, accurately resolving cell cycle-dependent protein dynamics is challenging because single-cell proteomics is currently infeasible and bulk proteomics requires - inherently imperfect - cell synchronisation. Here, we developed a computational method to deconvolve cell cycle-dependent protein concentration dynamics and applied it to new budding yeast bulk proteome data. Key to this method was a yeast population model, parameterised with experimental cell cycle progression and volume growth data, for quantifying the desynchronisation in sampled populations. We performed deconvolution on 3272 proteins, using cross-validation to determine regularisation parameters, and identified 539 proteins with cell cycle-dependent dynamics. Many of these dynamics were consistent with known yeast biology and dynamic proteins were enriched for several metabolic process, extending previous observations and supporting the emerging picture of metabolic activity as varying substantially over cell cycle phases. We consider the generated cell cycle-resolved budding yeast proteome data a key resource.

Pancreatic ductal adenocarcinoma (PDA) is profoundly immunosuppressive. To help define this behavior, we present integrated experimental and computational frameworks to elucidate therapeutic T cell dynamics. Through the development of TME-CARTographer (TME-CART), a computational pipeline integrating high-dimensional data, graph theory, behavior analysis, and deep learning (DL), we present quantitative insights on 4D T cell-TME interactions in live PDA tumors. Mapping physical immunosuppression demonstrates that collagen fiber architectures direct migration while concomitantly limiting off-axis movement, creating immune exclusion zones. Expanding these findings, we establish that the collagen matrix harbors and spatially organizes immunosuppressive myeloid cells to serve as cooperative co-modulators of T cell behaviors, including migration, sampling, repulsion, and sequestration. Consistent with these findings, DL defines both linear and nonlinear collagen matrix and cellular neighborhood interactions as drivers of T cell behavior. The TME-CART DL framework also accurately predicts shifts in immunosuppression following depletion of myeloid cells. Overall, we identify synergistic barriers impeding anti-tumor T cell behaviors and present TME-CART as a discovery platform for interpreting complex 4D data to enhance the understanding and design of immunotherapies.

A recent study reported sequence biases in transcription factor (TF) footprinting data, and expressed concern about the accuracy in TF footprint discovery using V-plot analysis. Here we define two graphical parameters of V-plot as important criteria to implement into future analysis. Guaranteed by the criteria, V-plot still serves as a powerful and straightforward tool for TF footprint discovery from various chromatin accessibility sequencing datasets.

Integrating multi-omics data is essential for microbiome research, as microbial communities are shaped by and respond to interdependent processes, including taxonomic composition, metabolite production and utilization, and gene expression. However, accurately capturing ecosystem-wide patterns across these modalities is statistically challenging due to differences in scale, sparsity, and compositionality. While a growing number of multi-omics methods have emerged, they differ in their mathematical objectives and modeling assumptions, which in turn shape how biological patterns are represented and interpreted. This underscores the need for tools that explicitly account for the statistical properties of microbial ecosystems. Here, we present Joint Robust Principal Component Analysis (Joint-RPCA), a method designed with these statistical properties in mind and broadly applicable to multi-omics settings with similar challenges. Built on the OptSpace matrix completion framework, Joint-RPCA assumes an underlying shared low-rank structured component across modalities to identify shared variation and cross-modal associations from matched samples. Within this setting and under these statistical assumptions, Joint-RPCA showed stronger performance than the benchmarked general-purpose methods in phenotype separation and feature association tasks, achieving up to sixfold improvement in classification accuracy and over 100-fold faster runtimes. Applied to real-world datasets, including the Integrative Human Microbiome Project (iHMP), mammalian gut microbiomes, and decomposition studies, Joint-RPCA reveals replicable and interpretable multi-omic patterns, offering a scalable and domain-aware solution for systems-level microbiome analysis. Joint-RPCA is available in both Python (https://github.com/biocore/gemelli) and R (https://bioconductor.org/packages/mia).

The lack of standardised workflows and ambiguous metabolite annotations hampers metabolomics integration with prior knowledge, thus limiting the extraction of meaningful biological insights. We present MetaProViz (Metabolomics Processing, functional analysis and Visualization), an open-source Bioconductor R package for metabolomics data analysis that integrates prior knowledge to generate mechanistic hypotheses (https://saezlab.github.io/MetaProViz/). MetaProViz operates on annotated intensity values and offers a flexible framework consisting of five modules: processing, differential analysis, prior knowledge integration, functional analysis and visualisation, applicable to intracellular and exometabolomics experiments. To improve functional analysis, we created the Metabolism Signature Database (MetSigDB), a collection of annotated metabolite sets. MetSigDB includes pathway-metabolite, metabolite-receptor, metabolite-transporter sets, and chemical class-metabolite sets. MetaProViz enables the conversion of gene sets to metabolite sets, metabolite identifier expansion and analyses mapping ambiguities. The MetaProViz functional analysis toolkit includes sample metadata analysis, enrichment analysis and biologically informed clustering. By applying MetaProViz to kidney cancer metabolomics data, we identified increased methionine usage in line with decreased methionine levels in tumour samples. In summary, MetaProViz facilitates and improves the analysis and interpretation of metabolomics data.

Transcription factors (TFs) are central to gene regulation and play critical roles in development, cellular homeostasis and disease. The ability to accurately measure TF activity is essential to understanding how TFs respond to signals and regulate target genes. In one commonly used approach, activities of TFs are computationally inferred from genome-wide chromatin accessibility data (ATAC-seq). However, it has remained unclear how well these inferences reflect actual regulatory activity of TFs. An alternative approach employs a collection of synthetic reporters that are designed to each probe the regulatory activity of a single TF. In this study, we systematically compared TF activities as inferred by ATAC-seq with those measured by multiplexed reporters, across diverse perturbations known to alter specific TF activities. We observed considerable overlap between the two methods, but also notable discrepancies. Our findings suggest that reporter assays and chromatin-based inference capture distinct aspects of TF function: reporter assays are more sensitive to signal-responsive TFs, while ATAC-seq better detects chromatin-modifying TFs.

Transcription is an inherently dynamic and stochastic process that often occurs in bursts, governed by gene-gene regulatory interactions and thereby driving cell-to-cell heterogeneity. However, a genome-wide, mechanistic understanding of how regulatory networks globally shape transcriptional bursting dynamics remains lacking. Here, we present BurstLink, an interpretable and tractable statistical-mechanistic framework that simultaneously infers coupled regulatory interactions and transcriptional bursting kinetics at the genome-wide scale from single-cell data. BurstLink introduces reweighted mutual information to quantify regulatory strength as network edge weights, while jointly inferring regulatory directionality and interaction type for each gene pair within a unified mechanistic model of transcriptional bursting. Applied to mouse embryonic fibroblasts data, BurstLink reveals several genome-wide regulatory mechanisms on transcriptional bursting: downstream target genes exhibit higher burst frequency and gene-expression variability than upstream transcription factor genes; stronger transcription factor binding affinity is associated with lower burst frequency and higher burst size of target genes. Notably, positive regulation primarily enhances the burst frequency and gene-expression variability in target genes, in contrast to negative regulation. In summary, BurstLink deciphers multiple general principles of global transcriptional dynamics, providing novel biological insights into cell fate decisions.

Coronaviruses employ discontinuous transcription to produce canonical subgenomic RNAs (sgRNAs) essential for gene expression. Although TRS-dependent template switching mechanism has been proposed, its structural basis remains poorly defined, and the functional significance of abundant non-canonical sgRNAs persists as a critical gap since the discovery of discontinuous RNA synthesis. Here, we help bridge this gap through the first cross-genus integrated analysis of coronavirus transcriptomes and RNA interactomes. We show that canonical sgRNA formation is associated with same-direction RNA-RNA interactions. In contrast, non-canonical sgRNAs form through distinct architectural mechanisms: short-range junctions mediated by stem-loop structures overlapping genomic deletion hotspots, and conserved long-range ORF1a-N interactions generating sgRNAs encoding immune-modulatory ORFs - a function not previously attributed to non-canonical transcription. These findings suggest architecturally programmed discontinuous RNA synthesis and highlight a potential link between non-canonical sgRNAs, genomic plasticity, and immune modulation, which may have implications for coronavirus adaptation.

Macrophages function as immune sentinels that distinguish diverse threats and mount appropriate responses. This stimulus-response specificity (SRS) is partly encoded in the dynamics of the NFκB transcription factor. While most studies examine single ligands, physiological exposures involve complex multi-ligand mixtures. Using a mathematical model that captures heterogeneous single-cell NFκB responses, we generated simulation datasets for mixtures of up to five ligands and validated key predictions with live-cell microscopy. Following iterative refinement of the model, we quantified SRS with Wasserstein distance and machine learning classification and found that NFκB temporal coding can partially convey the presence of specific ligands within mixtures. By generating simulation datasets across all ligand pairs at doses spanning the full responsiveness range, we found several cases of synergy and antagonism between stimuli. Antagonism was for example the result of a limited supply of stimulus-cofactor CD14 or endosomal transport capacity. Synergy depended on ultra-sensitive IKK activation in cells with low receptor expression. While synergy does not enhance SRS, antagonism between TLR9 and TLR3 signaling pathways due to endosomal transport competition may enhance the distinguishability of CpG-pIC from ligand mixtures. These studies therefore identified antagonism mechanisms in signaling pathways as key to maintaining immune specificity under complex conditions.

Large-scale genomic language models (gLMs) hold promise for modeling gene regulation, yet their ability to capture personal gene expression variations remains unresolved. We developed xDecoder, a unified decoding framework that utilizes gLMs and sequence-to-function (S2F) embeddings to learn how personal genetic variation shapes gene expression from paired genome-transcriptome data. Compared to the pretrained genomic models, xDecoder with personalized DNA-RNA training makes cross-individual prediction tractable for seen genes in a few-shot setting. However, zero-shot prediction at unseen loci remains unreliable and gene-dependent, revealing a cross-locus transfer bottleneck of current sequence models. Experiments incorporating individual-level chromatin accessibility suggested that regulatory-state information important for unseen-locus prediction is not fully captured by current DNA-only models. Overall, these results highlight the potential utility of the few-shot setting, the limitations of DNA-only models, and point toward multi-omic, variant-aware frameworks as a promising direction for building personalized regulatory models.

The state of a cell depends not only on protein abundance, but also on the biochemical and cellular activities of proteins, which are largely invisible to abundance profiling alone. Here, we introduce a multi-omics framework that infers context-specific protein activities from transcriptomic, phosphoproteomic, and protein correlation-based protein-protein interaction data, integrating modality-specific algorithms via network diffusion. Applying it to a panel of phenotypically diverse HeLa cell lines, whose genetic drift provides a natural perturbation system, we make three findings. First, physical separation of monomeric and assembled protein fractions by protein correlation profiling provides direct evidence that complex assembly buffers variation in gene copy number and transcription, a mechanism previously only inferred from bulk measurements. Second, using Let7 perturbation data, CRISPR gene dependency scores, and subcellular localization, we orthogonally validate that inferred protein activities capture functional regulation linked to cellular phenotypes inaccessible from abundance data alone. Third, differential analysis of context-specific activity profiles identifies molecular mechanisms underlying phenotypic divergence, including a WIPF1/WIPF2--Arp2/3 axis governing invadopodium formation and infection susceptibility, and an immunoproteasome switch linked to immune adaptation.

Noncanonical small RNAs, such as tRNA-derived (tsRNAs) and rRNA-derived (rsRNAs) fragments, are more abundant than microRNAs and arise from selective cleavage events rather than random degradation. While fragmentation of parental RNAs produces functionally diverse small RNAs, current analytical approaches are limited to abundance measures and cannot systematically quantify differential cleavage signals. Here, we present qMAP, a computational framework profiling differential fragmentation of parental RNAs from small RNA sequencing data. qMAP integrates two complementary models to identify condition-specific fragmentation patterns and includes a dedicated module to pinpoint the small RNA species driving these differences. Using qMAP, we uncover dynamic tRNA and rRNA fragmentation during mouse cell reprogramming, demonstrate the classification power of RNA fragmentation in human ulcerative colitis, develop and validate a blood-based RNA fragmentation signature of recurrent implantation failure, and identify aging-associated RNA fragmentation in sperm, which supports RNA fragmentation as a distinct regulatory dimension beyond expression/abundance information. qMAP enables systematic exploration of the regulatory "RNA fragmentome", providing a foundational tool for both mechanistic discovery and translational applications of noncanonical small RNAs.