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.
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Abstract Free, publicly-accessible full text available December 1, 2025 -
Abstract Aluminum‐dependent stoppage of root growth requires the DNA damage response (DDR) pathway including the p53‐like transcription factor SUPPRESSOR OF GAMMA RADIATION 1 (SOG1), which promotes terminal differentiation of the root tip in response to Al dependent cell death. Transcriptomic analyses identified Al‐induced SOG1‐regulated targets as candidate mediators of this growth arrest. Analysis of these factors either as loss‐of‐function mutants or by overexpression in the
als3‐1 background shows ERF115, which is a key transcription factor that in other scenarios is rate‐limiting for damaged stem cell replenishment, instead participates in transition from an actively growing root to one that has terminally differentiated in response to Al toxicity. This is supported by a loss‐of‐functionerf115 mutant raising the threshold of Al required to promote terminal differentiation of Al hypersensitiveals3‐1 . Consistent with its key role in stoppage of root growth, a putativeERF115 barley ortholog is also upregulated following Al exposure, suggesting a conserved role for this ATR‐dependent pathway in Al response. In contrast to other DNA damage agents, these results show that ERF115 and likely related family members are important determinants of terminal differentiation of the root tip following Al exposure and central outputs of the SOG1‐mediated pathway in Al response.Free, publicly-accessible full text available July 15, 2025 -
Abstract Summary Here, we presented the scHiCDiff software tool that provides both nonparametric tests and parametirc models to detect differential chromatin interactions (DCIs) from single-cell Hi-C data. We thoroughly evaluated the scHiCDiff methods on both simulated and real data. Our results demonstrated that scHiCDiff, especially the zero-inflated negative binomial model option, can effectively detect reliable and consistent single-cell DCIs between two conditions, thereby facilitating the study of cell type-specific variations of chromatin structures at the single-cell level.
Availability and implementation scHiCDiff is implemented in R and freely available at GitHub (https://github.com/wmalab/scHiCDiff).
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Abstract We present new cosmological parameter constraints from the eBOSS Lyman-α forest survey. We use a new theoretical model and likelihood based on the PRIYA simulation suite. PRIYA is the first suite to resolve the Lyman-
α forest in a (120 Mpc/h)3volume, using a multi-fidelity emulation technique. We use PRIYA to predict Lyman-α forest observables with ≲ 1% interpolation error over an 11 dimensional (9 simulated, 2 in post-processing) parameter space. We identify an internal tension within the flux power spectrum data. Once the discrepant data is removed, we find the primeval scalar spectral index measured at a pivot scale ofk 0= 0.78 Mpc-1to benP = 1.009+0.027-0.018at 68% confidence. This measurement from the Lyman-α forest flux power spectrum alone is in reasonable agreement with Planck, and in tension with earlier eBOSS analyses. The amplitude of matter fluctuations isσ 8= 0.733+0.026-0.029at 68% confidence, in agreement with Dark Energy Survey weak lensing measurements and other small-scale structure probes and in tension with CMB measurements from Planck and ACT. The effective optical depth to Lyman-α photons from our pipeline is in good agreement with earlier high resolution measurements. We find a linear power atz = 3 andk = 0.009 s/km of Δ2L = 0.302+0.024-0.027with a slopen eff= -2.264+0.026-0.018. Our flux power spectrum only chains prefer a low level of heating during helium reionization. When we add IGM temperature data we findnP = 0.983 ± 0.020 andσ 8= 0.703+0.023-0.027. Our chains prefer an early and long helium reionization event, as suggested by measurements from the helium Lyman-α forest. In the near future we will use our pipeline to infer cosmological parameters from the DESI Lyman-α data.Free, publicly-accessible full text available July 1, 2025 -
Abstract How a developing organ robustly coordinates the cellular mechanics and growth to reach a final size and shape remains poorly understood. Through iterations between experiments and model simulations that include a mechanistic description of interkinetic nuclear migration, we show that the local curvature, height, and nuclear positioning of cells in the
Drosophila wing imaginal disc are defined by the concurrent patterning of actomyosin contractility, cell-ECM adhesion, ECM stiffness, and interfacial membrane tension. We show that increasing cell proliferation via different growth-promoting pathways results in two distinct phenotypes. Triggering proliferation through insulin signaling increases basal curvature, but an increase in growth through Dpp signaling and Myc causes tissue flattening. These distinct phenotypic outcomes arise from differences in how each growth pathway regulates the cellular cytoskeleton, including contractility and cell-ECM adhesion. The coupled regulation of proliferation and cytoskeletal regulators is a general strategy to meet the multiple context-dependent criteria defining tissue morphogenesis. -
Abstract Background Rock-dwelling microorganisms are key players in ecosystem functioning of Antarctic ice free-areas. Yet, little is known about their diversity and ecology, and further still, viruses in these communities have been largely unexplored despite important roles related to host metabolism and nutrient cycling. To begin to address this, we present a large-scale viral catalog from Antarctic rock microbial communities.
Results We performed metagenomic analyses on rocks from across Antarctica representing a broad range of environmental and spatial conditions, and which resulted in a predicted viral catalog comprising > 75,000 viral operational taxonomic units (vOTUS). We found largely undescribed, highly diverse and spatially structured virus communities which had predicted auxiliary metabolic genes (AMGs) with functions indicating that they may be potentially influencing bacterial adaptation and biogeochemistry.
Conclusion This catalog lays the foundation for expanding knowledge of virosphere diversity, function, spatial ecology, and dynamics in extreme environments. This work serves as a step towards exploring adaptability of microbial communities in the face of a changing climate.
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Abstract Visualizing spatial assay data in anatomical images is vital for understanding biological processes in cell, tissue, and organ organizations. Technologies requiring this functionality include traditional one-at-a-time assays, and bulk and single-cell omics experiments, including RNA-seq and proteomics. The spatialHeatmap software provides a series of powerful new methods for these needs, and allows users to work with adequately formatted anatomical images from public collections or custom images. It colors the spatial features (e.g. tissues) annotated in the images according to the measured or predicted abundance levels of biomolecules (e.g. mRNAs) using a color key. This core functionality of the package is called a spatial heatmap plot. Single-cell data can be co-visualized in composite plots that combine spatial heatmaps with embedding plots of high-dimensional data. The resulting spatial context information is essential for gaining insights into the tissue-level organization of single-cell data, or vice versa. Additional core functionalities include the automated identification of biomolecules with spatially selective abundance patterns and clusters of biomolecules sharing similar abundance profiles. To appeal to both non-expert and computational users, spatialHeatmap provides a graphical and a command-line interface, respectively. It is distributed as a free, open-source Bioconductor package (https://bioconductor.org/packages/spatialHeatmap) that users can install on personal computers, shared servers, or cloud systems.
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Abstract High fat diets (HFDs) have been linked to several diseases including obesity, diabetes, fatty liver, inflammatory bowel disease (IBD) and colon cancer. In this study, we examined the impact on intestinal gene expression of three isocaloric HFDs that differed only in their fatty acid composition—coconut oil (saturated fats), conventional soybean oil (polyunsaturated fats) and a genetically modified soybean oil (monounsaturated fats). Four functionally distinct segments of the mouse intestinal tract were analyzed using RNA-seq—duodenum, jejunum, terminal ileum and proximal colon. We found considerable dysregulation of genes in multiple tissues with the different diets, including those encoding nuclear receptors and genes involved in xenobiotic and drug metabolism, epithelial barrier function, IBD and colon cancer as well as genes associated with the microbiome and COVID-19. Network analysis shows that genes involved in metabolism tend to be upregulated by the HFDs while genes related to the immune system are downregulated; neurotransmitter signaling was also dysregulated by the HFDs. Genomic sequencing also revealed a microbiome altered by the HFDs. This study highlights the potential impact of different HFDs on gut health with implications for the organism as a whole and will serve as a reference for gene expression along the length of the intestines.
Free, publicly-accessible full text available December 1, 2024 -
ABSTRACT We introduce MF-Box, an extended version of MFEmulator, designed as a fast surrogate for power spectra, trained using N-body simulation suites from various box sizes and particle loads. To demonstrate MF-Box’s effectiveness, we design simulation suites that include low-fidelity (LF) suites (L1 and L2) at 256 and $100 \, \rm {Mpc\, ~}h^{-1}$, each with 1283 particles, and a high-fidelity (HF) suite with 5123 particles at $256 \, \rm {Mpc\, ~}h^{-1}$, representing a higher particle load compared to the LF suites. MF-Box acts as a probabilistic resolution correction function, learning most of the cosmological dependencies from L1 and L2 simulations and rectifying resolution differences with just three HF simulations using a Gaussian process. MF-Box successfully emulates power spectra from our HF testing set with a relative error of $\lt 3~{{\ \rm per\ cent}}$ up to $k \simeq 7 \, h\rm {Mpc}{^{-1}}$ at z ∈ [0, 3], while maintaining a cost similar to our previous multifidelity approach, which was accurate only up to z = 1. The addition of an extra LF node in a smaller box significantly improves emulation accuracy for MF-Box at $k \gt 2 \, h\rm {Mpc}{^{-1}}$, increasing it by a factor of 10. We conduct an error analysis of MF-Box based on computational budget, providing guidance for optimizing budget allocation per fidelity node. Our proposed MF-Box enables future surveys to efficiently combine simulation suites of varying quality, effectively expanding the range of emulation capabilities while ensuring cost efficiency.
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ABSTRACT We assemble the largest C iv absorption line catalogue to date, leveraging machine learning, specifically Gaussian processes, to remove the need for visual inspection for detecting C iv absorbers. The catalogue contains probabilities classifying the reliability of the absorption system within a quasar spectrum. Our training set was a sub-sample of DR7 spectra that had no detectable C iv absorption in a large visually inspected catalogue. We used Bayesian model selection to decide between our continuum model and our absorption-line models. Using a random hold-out sample of 1301 spectra from all of the 26 030 investigated spectra in DR7 C iv catalogue, we validated our pipeline and obtained an 87 per cent classification performance score. We found good purity and completeness values, both $\sim 80{{\ \rm per\ cent}}$, when a probability of $\sim 95{{\ \rm per\ cent}}$ is used as the threshold. Our pipeline obtained similar C iv redshifts and rest equivalent widths to our training set. Applying our algorithm to 185 425 selected quasar spectra from SDSS DR12, we produce a catalogue of 113 775 C iv doublets with at least 95 per cent confidence. Our catalogue provides maximum a posteriori values and credible intervals for C iv redshift, column density, and Doppler velocity dispersion. We detect C iv absorption systems with a redshift range of 1.37–5.1, including 33 systems with a redshift larger than 5 and 549 absorbers systems with a rest equivalent width greater than 2 Å at more than 95 per cent confidence. Our catalogue can be used to investigate the physical properties of the circumgalactic and intergalactic media.