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Abstract Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC’s accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis. 

Intestinal microbiota confers susceptibility to diet-induced obesity yet many probiotic species that synthesize tryptophan (trp) actually attenuate this effect, however the underlying mechanisms are unclear. We monocolonized germ-free (GF) mice with a widely consumed probiotic Lacticaseibacillus rhamnosus GG (LGG) under trp-free or -sufficient dietary conditions. We obtained untargeted metabolomics from the mouse feces and serum using liquid chromatography-mass spectrometry and obtained intestinal transcriptomic profiles via bulk-RNA sequencing. When comparing LGG-monocolonized mice with GF mice, we found a synergy between LGG and dietary trp in markedly promoting the transcriptome of fatty acid metabolism and -oxidation. Upregulation was specific and was not observed in transcriptomes of trp-fed conventional mice and mice monocolonized with Ruminococcus gnavus. Metabolomics showed that fecal and serum metabolites were also modified by LGG-host-trp interaction. We developed an R-Script based MEtabolome-TRanscriptome Correlation Analysis (METRCA) algorithm and uncovered LGG- and trp-dependent metabolites that were positively or negatively correlated with fatty acid metabolism and -oxidation gene networks. This high throughput metabolome-transcriptome correlation strategy can be used in similar investigations to reveal potential interactions between specific metabolites and functional or disease-related transcriptomic networks. 

BACKGROUND & AIMS: Lacticaseibacillus rhamnosus GG (LGG) is the world’s most consumed probiotic species but its mechanism of action on intestinal permeability and differentiation as well as its interactions with an essential source of signaling metabolites, dietary tryptophan, are incompletely studied. METHODS: Untargeted metabolomic and transcriptomic analysis were performed for LGG mono-colonized germ-free (GF) mice fed with tryptophan (trp)-free or -sufficient diets. LGG-derived metabolites were profiled in vitro under anaerobic and aerobic conditions. Multiomic correlations were performed using a newly developed metabolome-transcriptome correlating bioinformatic algorism. Newly uncovered gut barrier-modulating metabolites whose abundances are regulated by LGG and dietary trp were functionally tested in Trans-Epithelial Electrical Resistance (TEER) assay, mouse enteroid, and dextran sulfate sodium (DSS) experimental colitis. The contribution of trp-methylnicotinamide (MNA) pathway to barrier protection is delineated at specific tight junction (TJ) proteins and enterocyte-promoting factors with gain and loss of function approaches. RESULTS: LGG, strictly in the presence of dietary trp, promotes the enterocyte program and the expression of multiple TJ genes, particularly Ocln. Fecal and serum metabolites that are synergistically stimulated by LGG and dietary trp are identified. Functional evaluations revealed a novel LGG-stimulated trp-dependent Vitamin B3 metabolism pathway, with MNA unexpectedly being the most robust barrier-protective metabolite in vitro and in vivo. Reduced serum MNA is significantly associated with increased disease activity in IBD patients. Exogenous MNA enhances gut barrier in homeostasis and robustly promotes colonic healing in DSS colitis. MNA is sufficient to promote intestinal epithelial Ocln and RNF43, a master inhibitor of Wnt pathway. Blocking trp or Vitamin B3 absorption abolishes barrier recovery in vivo. CONCLUSIONS: Our study uncovers a novel LGG-regulated dietary trp-dependent production of MNA that protects gut barrier against colitis. 

Abstract Spatially resolved transcriptomics technologies enable the measurement of transcriptome information while retaining the spatial context at the regional, cellular or sub-cellular level. While previous computational methods have relied on gene expression information alone for clustering single-cell populations, more recent methods have begun to leverage spatial location and histology information to improve cell clustering and cell-type identification. In this study, using seven semi-synthetic datasets with real spatial locations, simulated gene expression and histology images as well as ground truth cell-type labels, we evaluate 15 clustering methods based on clustering accuracy, robustness to data variation and input parameters, computational efficiency, and software usability. Our analysis demonstrates that even though incorporating the additional spatial and histology information leads to increased accuracy in some datasets, it does not consistently improve clustering compared with using only gene expression data. Our results indicate that for the clustering of spatial transcriptomics data, there are still opportunities to enhance the overall accuracy and robustness by improving information extraction and feature selection from spatial and histology data. 

Abstract A pressing challenge in single-cell transcriptomics is to benchmark experimental protocols and computational methods. A solution is to use computational simulators, but existing simulators cannot simultaneously achieve three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill this gap, we propose scDesign2, a transparent simulator that achieves all three goals and generates high-fidelity synthetic data for multiple single-cell gene expression count-based technologies. In particular, scDesign2 is advantageous in its transparent use of probabilistic models and its ability to capture gene correlations via copulas. 

Abstract MotivationSingle-cell RNA sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within individual cells. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information. ResultsHere we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and 6 different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design. In addition, scDesign demonstrates reproducibility across biological replicates and independent studies. We also discuss the performance of multiple differential expression and dimension reduction methods based on the protocol-dependent scRNA-seq data generated by scDesign. scDesign is expected to be an effective bioinformatic tool that assists rational scRNA-seq experimental design and comparison of scRNA–seq computational methods based on specific research goals. Availability and implementationWe have implemented our method in the R package scDesign, which is freely available at https://github.com/Vivianstats/scDesign. Supplementary informationSupplementary data are available at Bioinformatics online. 

Abstract Paneth cells (PCs), a specialized secretory cell type in the small intestine, are increasingly recognized as having an essential role in host responses to microbiome and environmental stresses. Whether and how commensal and pathogenic microbes modify PC composition to modulate inflammation remain unclear. Using newly developed PC‐reporter mice under conventional and gnotobiotic conditions, we determined PC transcriptomic heterogeneity in response to commensal and invasive microbes at single cell level. Infection expands the pool of CD74⁺PCs, whose number correlates with auto or allogeneic inflammatory disease progressions in mice. Similar correlation was found in human inflammatory disease tissues. Infection‐stimulated cytokines increase production of reactive oxygen species (ROS) and expression of a PC‐specific mucosal pentraxin (Mptx2) in activated PCs. A PC‐specific ablation ofMyD88reduced CD74⁺PC population, thus ameliorating pathogen‐induced systemic disease. A similar phenotype was also observed in mice lacking Mptx2. Thus, infection stimulates expansion of a PC subset that influences disease progression.