Biology subjects

Seal, S.

Publications and source records attributed to Seal, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Model Validation Protocols for Machine Learning in Small Molecule Drug Discovery

Machine learning (ML) models for molecular property prediction are increasingly deployed in drug discovery, yet their adoption in real-world scenarios requires an understanding of the conditions in which a model succeeds or fails. While standardized benchmarks are powerful instruments to measure and unlock progress in ML research, they should not be blindly treated as the end goal. Especially static and retrospective benchmarks, in which no true unknown test set is employed, limit our ability to robustly validate a model's performance. Building on the collective expertise of a cross-industry consortium, we present a model validation framework consisting of five recommendations that would enable the community to move beyond aggregate metrics toward understanding where and why molecular property prediction models fail. We connect evaluation choices to real-world applications and case studies encountered in pharmaceutical research. The framework proposes splitting strategies that mimic realistic distribution shifts and expose common failure modes. We apply the recommended framework to a recently released dataset of absorption, distribution, metabolism, and excretion (ADME) properties. Across two complementary model algorithms, our case studies reveal four distinct failure modes (extrapolation, interpolation, representation, and evaluation), showing that model errors arise not only from distribution shift but also from limitations in molecular representations. Our results show that commonly used evaluation protocols can significantly overestimate performance and may not detect important model failure modes. All software and data are released via https://github.com/srijitseal/polaris.

bioinformatics↗

bioRxiv2026

MSGPCA: Multi-Slice Graph PCA for replicate-aware Spatial Omics analysis

As spatial transcriptomics (ST) and spatial proteomics (SP) technologies mature, experimental designs are increasingly moving beyond single-slice analyses toward multi-slice studies involving one or more donors and experimental conditions. Although these designs enable the identification of reproducible spatial signals, they also introduce substantial biological heterogeneity, particularly when integrating non-serial slices or anatomically distinct regions. If not modeled carefully, such variation can blur slice-specific tissue structure, mask conserved molecular patterns, and limit the discovery of biologically relevant latent structure. Although dimension reduction is essential for representing high-dimensional molecular data in a lower-dimensional space, existing multi-slice methods typically enforce a globally shared representation that inadequately accommodates slice-level heterogeneity. To address this limitation, we propose Multi-Slice Graph Principal Component Analysis (MSGPCA), which decomposes molecular variation into shared spatial factors conserved across slices and slice-specific factors that capture local tissue microarchitecture. In downstream analyses, MSGPCA-derived representations recover spatial tissue structure, denoise molecular profiles, and reveal biologically interpretable metafeatures associated with shared and slice-specific biology. In a mass spectrometry imaging dataset comprising nonserial slices of ductal carcinoma in situ (DCIS) and invasive breast cancer (IBC), the shared factors captured broad biological differences across tissue regions, whereas the slice-specific factors revealed intratumoral spatial variation within the IBC microenvironment. In human dorsolateral prefrontal cortex ST data, MSGPCA recovered laminar cortical architecture across adjacent slices, closely aligning with expert pathologist annotations. Together, these findings demonstrate that MSGPCA resolves shared tissue architecture while preserving local microenvironmental variation in complex multi-slice spatial omics datasets.

bioinformatics↗

bioRxiv2026

RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data

High-throughput spatial omics technologies enable molecular profiling within intact tissue architecture, yet identifying concise, predictive, and biologically interpretable marker panels for cell types, tissue domains, and disease-associated tissue classes remains challenging. This limitation hinders the development of actionable panels for targeted validation and downstream translation. Existing pipelines rely largely on univariate differential-expression analyses, which ignore joint molecular structure and provide limited predictive insight. Multivariate machine-learning methods, including random forest,XGBoost, elastic net, and specialized single-cell panel-selection approaches, can capture predictive patterns but typically lack explicit spatial modeling and probabilistic feature selection, relying instead on model-specific importance scores or user-specified panel sizes. We develop rapid kernel machine regression (RKMR), a scalable framework for spatial-omics marker discovery that integrates nonlinear kernel modeling, spike-and-slab variable selection, and spatial dependence. RKMR uses automatic relevance determination (ARD) kernels and sparsity-inducing priors to capture nonlinear marker-outcome relationships and implicit feature interactions while producing approximate posterior inclusion probabilities (PIPs) that quantify model-based uncertainty in feature inclusion. To scale inference to large spatial datasets, RKMR combines low-rank kernel approximations with stochastic variational optimization. In simulations, RKMR consistently achieves higher AUPRC than competing methods across a range of molecular-signal and spatial-effect settings. Across spatial transcriptomics and scRNA-seq datasets, RKMR identifies parsimonious marker sets that recover reported cell-type signatures and reproducible tissue-layer markers. These results establish RKMR as a scalable and uncertainty-aware framework for translating high-dimensional spatial omics data into robust, experimentally actionable marker panels.

bioinformatics↗

bioRxiv2026

FusedFCR: A Fused Forward Continuation-Ratio model for marker selection along cell-fate trajectories

Time-course single-cell RNA sequencing (scRNA-seq) data collected across ordered stages provide population-level snapshots of differentiation, disease progression, and aging. Supervised pseudotime methods use observed stage labels to reconstruct continuous progression but generally do not identify marker genes associated with changes from one stage to the next. Unsupervised pseudotime-based marker selection methods infer latent trajectories directly from expression data and identify trajectory-associated genes, but do not explicitly link these associations to the observed stages. We propose FusedFCR, a regularized forward continuation-ratio model that represents cellular progression through a sequence of conditional transitions across ordered stages. FusedFCR combines a lasso penalty for gene selection with a fusion penalty that encourages similar effects across adjacent transitions while allowing transient and direction-changing associations. The resulting transition-specific coefficients support interpretable gene selection and a continuous pseudotime-like projection anchored to the observed developmental stages. In simulations, FusedFCR accurately recovers gene-effect trajectories and improved predictive performance relative to alternative methods. Applied to one mouse and three human datasets (mouse pancreatic beta-cells, human extravillous trophoblast, human induced pluripotent stem cell derived astrocytes, and human endometrial cells during the secretory phase), FusedFCR identifies biologically interpretable genes associated with distinct developmental transitions. Gene set enrichment analysis further reveals stage-specific pathway activity consistent with known developmental biology, while held-out stage-classification accuracy was competitive or superior across both datasets. Together, these results show that FusedFCR complements pseudotemporal ordering by identifying which molecular programs change and when those changes emerge along the developmental trajectory. An accompanying R package is available on GitHub.

bioinformatics↗

bioRxiv2026

An optimized workflow for spatial transcriptomics across early development in Xenopus

How gene expression patterns change spatially as the embryo transitions from simple to complex structures remains a major developmental biology question. Recently developed imaging-based spatial transcriptomics (ST) enable mapping expression of multiple gene at a single-cell resolution. Although Xenopus is a key model in embryology there is no established ST pipeline, and commercially available techniques face many challenges (sample preparation, probe design, cell segmentation). Furthermore, the highly diverse cell shapes and sizes across developmental stages and between different tissues represent major hurdles to accurately defining cells. Here, we describe an optimized workflow for ST in blastula-to-tailbud-stage frog embryos using Merscope, commercial MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization) originally designed for standard mammalian tissues. With stringent quality control and tailored computational pipelines, we optimize this technology for robust, semi-quantitative profiling of spatial transcriptomic landscapes in non-mammalian embryos. Reliable tissue preservation and cell-segmentation enable high-resolution mapping of gene expression during the development of a complex multi-tissue organization. This versatile strategy applies broadly to various dynamic systems, from embryos of various model organisms to complex and heterogeneous organs in mammals. Summary statementThis Single-cell Spatial Transcriptomics pipeline and reference atlas in Xenopus - a model organism in embryology - overcome technical challenges and resolve dynamic changes in patterning during development.

developmental biology↗

bioRxiv2026

Human-supervised Agentic AI for Hypothesis Generation and Experimental Assistance in Drug Repurposing

Computational drug repurposing has largely been focused on rapid hypothesis generation, yet real-world applications span a far broader lifecycle, from drug candidate suggestion to designing experiments, analyzing assay data, and iteratively refining candidates. Here, we demonstrate that agentic AI can operate throughout this lifecycle. To this end, we developed RepurAgent, a hierarchical multi-agent AI system comprising a supervisor agent and a planning agent that coordinate four specialized sub-agents (research, prediction, data, and report), through a human-in-the-loop design, with episodic memory and retrieval-augmented generation. The system is grounded in data, tools, and standard operating procedures specific for drug repurposing, developed within the REMEDi4ALL consortium. We validated the agentic system across three scenarios spanning the various stages within the repurposing lifecycle: in Acute Myeloid Leukemia, a blinded expert evaluation indicated that RepurAgent produced substantially more novel and mechanistically credible candidates compared to a vanilla LLM baseline; in a retrospective COVID-19 antiviral screen, RepurAgent acted as an adaptive experimental collaborator, prioritizing compounds with AUC-ROC up to 0.99 without predefined thresholds and flagging confounders missed in manual review; and for Multiple Sulfatase Deficiency, it prioritized 81 high-confidence candidates from 5000 compounds, which were further corroborated by domain experts. These results demonstrate that agentic AI can support across the drug repurposing lifecycle, from hypothesis generation to experimental analysis. RepurAgent is open source and deployed at https://repuragent.serve.scilifelab.se/.

bioinformatics↗

bioRxiv2026

SHOT-CCR: Biologically guided adversarial training for test-time adaptation in cellular morphology

AO_SCPLOWBSTRACTC_SCPLOWPervasive batch effects are a common issue, especially in recent large-scale Cell Painting datasets, which have been produced to aid AI-enhanced drug discovery efforts. Technical differences arising from experiments carried out in different batches can cause models to fail to generalize to unseen batches, despite good predictive performance "within batch". We propose a biologically grounded test-time adaptation framework, SHOT-CCR, which uses cell-invariant gradient reversal to decouple morphological signal from experimental confounders. Our approach performs 4.5% better than the current RxRx1 benchmark, classifying 1,139 classes of siRNA genetic perturbations with 91.6% accuracy. We deliver consistent results over four distinct cell types and two prominent Cell Painting datasets - RxRx1 and a subset of JUMP-CP. Across 484 classes of CRISPR perturbations in JUMP-CP our method improves accuracy by 15.7%.

cell biology↗

bioRxiv2026

Metabolic vulnerability is a target of the antineoplastic effect of breastfeeding.

Lactation is associated with a protective effect against breast and ovarian cancer as well as against cardiovascular diseases suggesting a systemic effect. Here, we show that the serum of lactating mice and breastfeeding mothers have targeted antineoplastic effects, while serum from virgin mice and matched post-partum but non-lactating women do not. The effect is specific to cancer cells expressing Pappalysin-A (PAPP-A), a target that is shared among diseases affected by breastfeeding. RNAseq revealed that lactating serum inhibits mitochondrial function and we found that PAPP-A alone lowers mitochondrial function, suggesting that lactation serum acts by exploiting the metabolic vulnerability of these cancer cells. Using serum proteomics, we identified corticotropin release factor (CRF) as being unique to serum of lactating women and we show that CRF alone mimics the mitochondrial and anti-tumorigenic effect of lactating serum. Blocking the CRF receptor, inhibits the protective effect of lactating serum. Since CRF has shown efficacy in the clinic in other settings, our findings raise the possibility to extend its use to mimic or enhance the protective effect of breastfeeding. SummaryWe show that the serum from lactating women has anti-cancer activity and used multi-omics approaches to identify corticotropin release factor as a peptide able to mimic the effect of lactating serum by targeting cells with low mitochondrial activity.

cancer biology↗

bioRxiv2026

TRACKING EVOLUTIONARY COSTS OF IMMUNE ADAPTATION AGAINST SINGLE VERSUS COINFECTING PATHOGENS

Immunity against pathogens is costly and can adversely affect fitness through resource allocation or physiological trade-offs. Infection by multiple pathogens may further worsen the effects if hosts require the activation of multiple immune responses, which in turn can elevate the overall immunological costs. Poor body condition and stressful environments can also exacerbate these trade-offs. In this study, we experimentally tested these possibilities using Tribolium castaneum populations that were evolving against either a single or a set of coinfecting bacterial pathogens. Contrary to our expectations, none of these evolved beetles showed any measurable trade-offs between pathogen resistance vs major fitness traits, such as reproduction or lifespan. Instead, they appeared to increase reproductive success and resistance to starvation, suggesting improved body condition that could mask underlying fitness costs. However, evolved beetles showed reduced quinone production, an externally secreted antimicrobial defence, indicating trade-offs between internal vs external immunity. Finally, we identified reproductive costs only under limited resource availability, but not under suboptimal resource quality, suggesting that trade-offs can be highly condition-dependent. Overall, this study provides a unique comparison across pathogens and infection types, highlighting the importance of analysing variation in life-history traits within relevant ecological contexts to understand fitness costs of evolving immunity.

evolutionary biology↗

bioRxiv2026

Differences in T-cell counts and neighborhood patterns in human colorectal adenomas and sessile serrated lesions

BackgroundT-cell responses influence recurrence and survival in colorectal cancer. T-cell subset distributions vary by molecular phenotype and location, shaping cytotoxic or immune-cold tumor immune microenvironments (TiMEs). However, the T-cell contexture and their spatial proximities within preinvasive lesions are not well characterized. MethodsWe analyzed sessile serrated lesions (SSLs), tubulovillous/villous adenomas (TVs), and tubular adenomas (TAs) from three completed studies (N=120). Whole-slide multiplex immunofluorescence was used to quantify eight T-cell subsets (CD4, CD8, Th1, Th17, Treg, Tc1, Tc17, TcTreg). Counts were compared by histology using a generalized linear mixed model with a negative binomial distribution, including an offset for total cell counts and adjusting for age, sex, anatomic location, and lesion size. Nearest-neighbor (NN) analyses assessed proximities of T-cell pairs across lesion types. ResultsTAs and SSLs had higher CD4 and CD8 T-cell counts compared with TVs (q<0.05). SSLs had lower Th17 counts than TAs (q<0.05) and compared with TVs leaned toward fewer Tregs (q=0.07). NN analysis showed that TVs, compared with SSLs and TAs, had increased Treg clustering. In contrast, TA versus SSL comparisons revealed predominant CD4 clustering with Th17, Th1, and CD8 subsets. ConclusionTVs are characterized by lower T-cell infiltration and a greater tendency for regulatory T-cell clustering, consistent with an immune-cold TiME relative to TAs. SSLs and TAs were both more immune-infiltrated than TVs, but SSLs appeared less inflamed and less dominated by regulatory subsets. In contrast, CD4dominant clustering in TAs suggested stronger helper coordination. Preinvasive lesions therefore demonstrate immune and spatial heterogeneity by lesion types.

pathology↗

bioRxiv2025

CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data

High-throughput bulk and single-cell omics technologies enable comprehensive molecular profiling, yet identifying compact, biologically interpretable marker sets that distinguish cell types, conditions, or disease states remains challenging. Standard pipelines rely on univariate differential expression tests, which ignore gene-gene dependencies and nonlinear effects, while multivariate machine-learning (ML) methods often lack principled feature selection and uncertainty quantification. The Bayesian kernel machine regression (BKMR) framework offers an appealing alternative because it (a) captures non-linear gene-outcome relationships and higher-order interactions, and (b) enables automatic relevance determination (ARD) through sparsity-inducing priors. However, we show that the traditional latent Gaussian process (GP) formulation of BKMR is inadequate for discrete outcomes (e.g., cell-type labels), leading to biased inference and unstable variable selection. We propose a copula-based Bayesian kernel machine regression (CBKMR) model that uses outcome-appropriate discrete marginals while a Gaussian copula captures kernel-induced dependence across observations. To ensure scalability to modern single-cell datasets, we further introduce a nearest-neighbor GP-based variant, NNCBKMR, which reduces computational complexity from O(N 3) to nearly linear in N. Simulation studies show that CBKMR more accurately captures nonlinear effects and yields stronger marker-selection performance than BKMR and top ensemble ML methods (e.g., random forests, XGBoost). Applications to multiple scRNA-seq datasets demonstrate that CBKMR identifies concise marker panels that align closely with expert-annotated gene signatures while providingposterior uncertainty for principled decision-making.

bioinformatics↗

bioRxiv2025

GRASS-NB: Group-structured variable selection for spatial negative binomial data with applications to cancer registry and spatial omics

Spatially structured, overdispersed count data with high-dimensional predictors are increasingly observed across studies from population-level epidemiology to cellular-level spatial omics. Feature selection is critical to identify influential predictors, such as key risk factors or biomarkers. Few Bayesian studies have assessed negative binomial regression (NBR) models with standard variable selection priors, like the mixture spike-and-slab (SS) or continuous horseshoe (HS), but mostly under aspatial settings. Features often form groups; for instance, in population surveys, caloric intake and physical activity may fall under "Diet & Exercise", while cigarette use and smoking laws belong to "Smoking". We propose a flexible NBR model that accommodates spatial autocorrelation and introduces a novel group-structured prior by hybridizing SS and HS shrinkage. The models performance with different priors is evaluated in terms of specificity, precision, and computational cost under challenging scenarios, including "large p, small n" cases. We further apply the model to CDC state-level cancer data, comprising demographic, screening, and behavioral covariates, to identify key drivers and population-level risk factors, and to a melanoma spatial omics dataset for predictive modeling expression of gene. An efficient R package is provided on GitHub.

bioinformatics↗

bioRxiv2025

Transfer learning enables discovery of sub-micromolar antibacterials for ESKAPE pathogens from ultra-large chemical spaces

The rise of antimicrobial resistance, especially among gram-negative ESKAPE pathogens, presents an urgent global health threat. However, the discovery of new antibiotics is hampered by sparse publicly available antibacterial data, complex bacterial defenses, and weak economic incentives. Here, we introduce a transfer learning framework using deep graph neural networks (DGNNs) to identify antibacterials from ultra-large chemical libraries. DGNNs were first pre-trained on large molecular datasets of protein-ligand simulations, binding affinities, and physicochemical properties to learn generalizable chemical features, and then fine-tuned on limited antibacterial screening data. Compared to classical methods, transfer learning significantly improved enrichment factors and predictive performance in cross-dataset benchmarks. Applying this strategy to the ChemDiv and Enamine libraries, we virtually screened over a billion compounds and prioritized 156 candidates. Experimental testing against Escherichia coli revealed that 54% of compounds exhibited antibacterial activity (MIC [≤]64 {micro}g/ml), with several demonstrating sub-micromolar potency and broad-spectrum efficacy against gram-positive and gram-negative pathogens, including three ESKAPE species. Of 18 broad-spectrum candidates, 15 showed minimal cytotoxicity and no hemolytic activity. These results validate our approach for navigating underexplored chemical space and identifying potent, low-toxicity compounds with antibiotic activity. We release open-source models and a scalable workflow to accelerate antibacterial discovery in the face of data scarcity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/669197v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@d1e793org.highwire.dtl.DTLVardef@ba6c85org.highwire.dtl.DTLVardef@12ab303org.highwire.dtl.DTLVardef@4ea019_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

In vivo anti-tumor activity of high-dose parenteral ascorbic acid is mediated primarily via cofactor activity, not via oxidative stress.

The anti-tumor effect of high-dose ascorbic acid (AA) has been demonstrated in multiple in vitro and in vivo cancer models with the postulation of two primary categories of mechanisms: antioxidant/cofactor activity and H2O2-mediated oxidative damage. Both mechanisms have been conclusively demonstrated in vitro. However, while parenteral high-dose AA-induced cofactor activity (TET-mediated DNA demethylation and prolyl/asparaginyl hydroxylase-mediated HIF activity inhibition via reduction of enzymatic Fe3+ to Fe2+) has been demonstrated intratumorally in vivo in multiple models, the cumulative data on parenteral high-dose AA-induced intratumoral oxidative damage in vivo has been inconclusive. Furthermore, the relative contribution of the seemingly opposing mechanisms towards in vivo anti-cancer activity has not been studied concurrently. We therefore sought to definitively delineate the roles of both antioxidant/cofactor activity and prooxidant functions of high-dose AA in the in vivo anti-tumor response. Using two syngeneic mouse tumor models, the AA-sensitive A20 model and the AA-resistant Renca model, we assessed markers of DNA and lipid oxidative damage as well as the specific roles of TET2 and AA transporter SLC23A2 in the anti-tumor response to parenteral high-dose AA. In the sensitive A20 model, loss of either Tet2 or Slc23a2 fully reversed anti-tumor activity. Similarly, overexpression of Tet2 in the resistant Renca model (which expresses high baseline levels of AA transporters SLC23A1 and SLC23A2, but does not express TET2), resulted in increased CD8+T cell infiltration and dramatic reduction in tumor growth overall. In both A20 and Renca models, high-dose parenteral AA increased total intratumoral antioxidant capacity, and this was attenuated by Slc23a2 knockdown in A20. High-dose AA treatment also resulted in a Tet2- and Slc23a2-dependent increase in intratumoral 5-hydroxymethylcytosine. Intracellular oxidative damage markers, 8-OHdG and 4-HNE, were not induced in tumors by high-dose AA in either model. In contrast, these markers were robustly induced in vitro by high-dose AA in A20 and Renca cells. Using dynamic real-time extracellular H2O2 measurements with high-dose AA, difference in molecular oxygen concentration between standard in vitro and hypoxic in vivo conditions was identified as an important factor underlying the marked discrepancy between the abundant in vitro and absent in vivo intratumoral oxidative stress with high-dose AA. Furthermore, using additional syngeneic models resistant (MB49) and sensitive (MC38) to AA-induced potentiation of anti-PD1 checkpoint inhibition, we demonstrate that very low catalase expression does not confer sensitivity to high-dose AA in vivo (further arguing against the H2O2 mechanism in vivo), that TET2 expression alone is not sufficient to drive an AA-induced anti-tumor response (either as a single agent or in combination with immunotherapy), and that high-dose AA can significantly enhance the efficacy of anti-PD1 immunotherapy even in the absence of single-agent activity. Our data strongly indicate that the in vivo anti-tumor effect of high-dose parenteral AA-including potentiation of immunotherapy-is mediated primarily by its specific antioxidant/cofactor activity (with TET2 expression likely being necessary but certainly not sufficient), and not via oxidative stress. Collectively, the study represents a paradigm shift in our understanding of the cumulative mechanisms of in vivo anti-cancer activity of high-dose AA, with critical implications not just for the clinical translation of AA as an anti-cancer agent (including in enhancing immunotherapy efficacy) but also the field of free radical biology. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/655101v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@efc305org.highwire.dtl.DTLVardef@ecb41corg.highwire.dtl.DTLVardef@24ac2dorg.highwire.dtl.DTLVardef@1a3f0b1_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Small molecule bioactivity benchmarks are often well-predicted by counting cells

Phenotypic profiling methods, such as Cell Painting and gene expression, have been widely used to predict compound bioactivity, often showing improvement over predictive models based on chemical structures alone. We discovered that a large subset of assays in widely-used benchmark datasets either directly relate to cell health and cytotoxicity or are assays intending to capture a more specific phenotype but whose active compounds impact cell count, while inactives do not. As a result, counting cells can achieve similar predictive performance as Cell Painting or gene expression data. Filtering benchmarks to include only assays relating to protein targets reveals that Cell Painting can capture information that cannot be predicted by mere cell counting. We re-evaluated three benchmark datasets used with Cell Painting data and observed that, in many cases, cell count models produced an AUC comparable to models using the full Cell Painting profiles. However, in protein-target-specific benchmarks across 17 distinct protein targets, Cell Painting features demonstrated unique predictive power, outperforming mean balanced accuracy from cell count models with a relative improvement of 19.6%. We propose five practical recommendations for benchmarking machine learning models for predicting bioactivity, including using cell count as a baseline feature. Although multi-class classification applications (such as matching samples based on their morphological profile) are less likely to be predictable by cell count than bioactivity benchmarks, these recommendations are broadly applicable to machine learning for drug discovery.

bioinformatics↗

bioRxiv2025

SpaceBF: Spatial coexpression analysis using Bayesian Fused approaches in spatial omics datasets

Advances in spatial omics enable measurement of genes (spatial transcriptomics) and peptides, lipids, or N-glycans (mass spectrometry imaging) across thousands of locations within a tissue. While detecting spatially variable molecules is a well-studied problem, robust methods for identifying spatially varying co-expression between molecule pairs remain limited. We introduce SpaceBF, a Bayesian fused modeling framework that estimates co-expression at both local (location-specific) and global (tissue-wide) levels. SpaceBF enforces spatial smoothness via a fused horseshoe prior on the edges of a predefined spatial adjacency graph, allowing large, edge-specific differences to escape shrinkage while preserving overall structure. In extensive simulations, SpaceBF achieves higher specificity and power than commonly used methods that leverage geospatial metrics, including bivariate Morans I and Lees L. We also benchmark the proposed prior against standard alternatives, such as intrinsic conditional autoregressive (ICAR) and Matern priors. Applied to spatial transcriptomics and proteomics datasets, SpaceBF reveals cancer-relevant molecular interactions and patterns of cell-cell communication (e.g., ligand-receptor signaling), demonstrating its utility for principled, uncertainty-aware co-expression analysis of spatial omics data.

bioinformatics↗

bioRxiv2025

Cell Painting for cytotoxicity and mode-of-action analysis in primary human hepatocytes

High-throughput, human-relevant approaches for predicting chemical toxicity are urgently needed for better decision-making in human health. Here, we apply image-based profiling (the Cell Painting assay) and two cytotoxicity assays (metabolic and membrane damage readouts) to primary human hepatocytes after exposure to eight concentrations of 1085 compounds that include pharmaceuticals, pesticides, and industrial chemicals with known liver toxicity-related outcomes. Three computational methods (CellProfiler, a Cell Painting-specific convolutional neural network, and a pretrained vision transformer) were compared to extract morphology features from single cells or entire images. We used these morphology features to predict activity in the measured cytotoxicity assays, as well as in 412 curated ToxCast assays that span cytotoxicity, cell-based, and cell-free categories. We found that the morphological profiles detect compound bioactivity at lower concentrations than standard cytotoxicity assays. In supervised analyses, they predict cytotoxicity and targeted cell-based assay readouts, but not cell-free assay readouts. We also found that the various feature extraction methods performed relatively similarly and that filtering out non-bioactive or cytotoxic concentrations did not boost supervised assay prediction performance for any assay endpoint category, although it did have a large influence on unsupervised cluster analysis. We envision that image-based profiling could serve as a key component of modern safety assessment.

pharmacology and toxicology↗

bioRxiv2025

BORC segregates synaptic vesicle and lysosomal proteins through motors UNC-104/KIF1A and UNC-116/KIF5

While synaptic vesicle proteins (SVPs) and lysosomal proteins can be present together in axons, the origin of these compartments is unclear. These SV-lysosomes are however thought to segregate further to SVPs and lysosomal proteins. In this study, we identify genes and characterize a genetic pathway involved in the segregation of SVPs and lysosomal proteins in the neuronal cell body. We identify a novel role for BORC (BLOC-1-related complex) in segregating SVPs and lysosomal proteins in the cell body. BORC subunit SAM-4/Myrlysin acts through ARL-8 and Kinesin motor proteins UNC-116/KIF5 and UNC-104/KIF1A in segregating SVPs and lysosomal proteins. Additionally, we also show that LRK-1/LRRK2 and APB-3/AP-3 ({beta}3), involved in pre-SV biogenesis, regulate the segregation of SVPs and lysosomal proteins in the neuronal cell body. LRK-1 recruits SAM-4 that in turn governs the localisation of APB-3 suggesting a hierarchical pathway of LRK-1-SAM-4-APB-3 for the segregation of SVPs and lysosomal proteins. Additionally, we also observe that the size of lysosomal protein-containing compartments (LPCCs) is smaller in SAM-4 and LRK-1. This size regulation depends on UNC-116. Together, we show that BORC recruited by LRK-1 and in turn via motors and along with AP-3 mediates the segregation of SVPs and lysosomal proteins in the neuronal cell body.

cell biology↗