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PubMed2026

Base-pair resolution conservation data improves cell type specific sequence-to-expression prediction.

MOTIVATION: Genomic sequence-to-activity models can decipher gene regulatory mechanisms and predict the functional impact of regulatory variants. However, current models struggle to integrate information from sequences outside promoters, especially information from cell type specific regulatory elements. RESULTS: Here, we propose incorporating base-pair resolution evolutionary conservation data into genomic sequence-to-expression predictors. We explore two training strategies-training from scratch or fine-tuning an existing sequence-only model with additional conservation input. We find that in both cases, base-pair resolution conservation data improves cell type specific sequence-to-expression prediction, with training from scratch yielding the greatest benefit. The improvement in cell type specific expression prediction can be attributed in part to the fact that models trained on sequence and conservation data learn to better recognize cell type specific regulatory elements than models trained on sequence alone. AVAILABILITY: Code is available at https://github.com/ni-lab/basenji-phyloP.

Conserved Sequence↗

PubMed2026

Towards time-resolved MicroED grid preparation using mix-and-inject gas dynamic virtual nozzles.

Recent progress in gas dynamic virtual nozzle (GDVN) technologies in combination with high-brilliance synchrotron and X-ray free-electron lasers (XFELs) has allowed the visualization of protein dynamics in crystallo by mixing macromolecular protein crystals with a substrate using tunable mixing times on the order of milliseconds to seconds prior to serial X-ray diffraction data collection. This has become the method of choice for high-resolution structure determination of intermediate states. However, such experiments require large counts of crystals of proper sizes for high-resolution data collection, and premium beam times for screening efforts. Cryogenic microcrystal electron diffraction (MicroED) represents a complementary technique that may be a more accessible avenue for time-resolved nanocrystallography compared with serial X-ray diffraction experiments. MicroED can produce full diffraction datasets from just a few submicrometre-thick crystals, and the approach is more readily accessible, requiring standard cryogenic transmission electron microscopy (TEM) equipment available at many universities and institutes. Cryogenic MicroED, like other forms of cryo-EM, begins with rapidly freezing biological material on electron microscopy grids. In the case of MicroED, micro- to nano-crystals (<500 nm thick) are deposited onto electron microscopy grids and plunge-frozen for subsequent electron diffraction data collection. Here, we have incorporated GDVN technology developed originally for XFEL experiments into the freezing process as a first step towards time-resolved studies. We describe the limited deposition efficiency of the model MicroED protein proteinase K on TEM grids using GDVNs, preceding sample vitrification and successful MicroED data collection. We discuss both the initial results from such experiments and the methodological challenges in developing this approach into a reliable workflow for millisecond-to-second time-resolved structural studies of macromolecules. Our results promise a strategy to deposit crystals on grids using GDVNs and determine high-resolution structures by MicroED, constituting a first step towards development of time-resolved MicroED experiments.

MicroED↗

PubMed2026

HXMS: a standardized file format for HX-MS data.

MOTIVATION: Hydrogen/deuterium exchange-mass spectrometry (HX-MS) is a rapidly expanding technique used to investigate protein conformational ensembles. The growing popularity and utility of HX-MS has driven the development of diverse instrumentation and software, resulting in inconsistent, non-standardized data analysis and representation. Most HX-MS data formats also employ only mean deuteration representations of the data rather than full isotopic mass spectra, which reduces the information content of the data and limits downstream quantitative analysis. RESULTS: Inspired by reliable protein structure and genomics data formats, we present HXMS, a unified, lightweight, scalable, and human-readable file format for HX-MS data. The HXMS format preserves the isotopic mass envelopes for all peptides, captures the full experimental time-course including fully deuterated control samples, and contains all other key information. It supports multimodal distributions, post-translational modifications (PTMs), and experimental replicates. To promote compatibility with existing HX-MS workflows, we also developed PFLink, a Python package that converts exported data files from commonly used HX-MS software to the HXMS format. PFLink and the HXMS format will enable quantitative, higher-resolution data processing, improved data sharing and storage among HX-MS practitioners, future machine learning applications, and further developments in HX-MS analysis. AVAILABILITY AND IMPLEMENTATION: PFLink is publicly available to install locally on HuggingFace, alongside documentation, or use online at HuggingFace (https://huggingface.co/spaces/glasgow-lab/PFlink). The supplementary information includes sample input files, sample HXMS files, and a generic unfilled PFlink custom CSV file that users may populate with key experimental conditions and results, which can then be read and converted into the HXMS format.

Software↗

PubMed2026

Multiscale Modeling Primer: Focus on Chromatin and Epigenetics.

A central challenge in modern biology is to understand how molecular interactions produce cellular and organismal functions across vast spatiotemporal scales. Nowhere is this challenge more apparent than in the study of chromatin, where meters of DNA compact into a micron-sized nucleus. How this polymer folds is a dynamic process, regulated by epigenetic modifications-chemical changes to DNA and histones that involve only a handful of atoms. These small changes cooperate to produce emergent, higher-order structures that define cellular identity and function. To explain this system, we must integrate static, high-resolution snapshots from techniques like cryo-EM with dynamic, lower-resolution data from microscopy and genomics. Multiscale computational models are essential tools that bridge these experimental gaps and reveal the mechanisms of emergent behavior. However, the communication divide between experimental biologists and quantitative modelers often hampers progress. This primer addresses that gap. It first introduces the fundamental biology of chromatin and epigenetics at an introductory level for non-biologists audiences. We then survey the landscape of computational approaches, from atomistic to systems-level models, and connect them to the experimental data that inform and validate them at an introductory level for non-computationalists. We argue that the next frontier will require us to build integrative models that can predict how molecular perturbations mechanistically alter cellular phenotypes, which will open a new era of chromatin-targeted therapeutics.

Chromatin Dynamics↗

PubMed2026

Digital pathology, image analysis, and artificial intelligence in liver disease.

Advances in digital pathology, image analysis, and artificial intelligence (AI) are rapidly transforming how pathologists and researchers interact with tissue samples and enable the development of diagnostic tools that harness high-resolution whole-slide images; these advances are in turn creating new opportunities for research, education, and routine clinical care globally. Liver disease is no exception, and digital pathology and AI have many applications in the diagnosis of liver cancer and liver diseases and in the assessment and management of transplantation. Although quantitative image analysis techniques have been applied to liver disease in research settings for over 50 years, recent improvements in image resolution, data storage, and the availability of advanced AI methods such as deep learning have driven multiple exciting developments. In this Review, we summarise the advancements in digital pathology, image analysis, and AI in liver disease. Key challenges such as access to and the logistics of using digital solutions, quality issues, and appropriate guidance in research and clinical use are reviewed, along with potential solutions to these challenges in the context of liver pathology and liver disease. Digital technologies are well established in liver pathology research, and access in clinical practice is increasing, with potential to address current laboratory challenges. Further evaluation is required to assess real-world effectiveness, clinical safety, and implementation of AI tools in liver pathology.

Journal Article↗

PubMed2026

Ancient DNA and Human Physiology.

Ancient DNA (aDNA) enables the reconstruction of chronologically sampled genomes from ancient humans, animals, plants, pathogens, and microorganisms, as well as environmental DNA, providing a record of biological changes through time. Improvements in short and degraded DNA extraction methods and low-cost sequencing now enable the generation of broad, cross-regional datasets that expand evolutionary analyses from past population demography to biological mechanisms. By tracking temporal shifts of allele frequencies, integrating functional genomics resources (e.g., gene expression, chromatin structure variation), modeling population demography to separate selection from genetic drift, and aligning genetic changes with archaeological, cultural, and climatic data, aDNA has the potential to link sequence variation to physiological function within their temporal and environmental contexts. In this review, we summarize illustrative case studies from aDNA research spanning complex traits, dietary adaptations, and responses to pathogens and other environmental changes, showing how human biology has evolved under multiple selective pressures through time. These dated signals help triage experimental work and expose mechanisms that are rare or absent in living cohorts. Although some challenges remain, such as geographic and temporal sampling disparities, limitations in data resolution and variant detection, and genotype-phenotype uncertainties, rapid methodological progress and stronger ethical frameworks are expanding what can be inferred, making aDNA a promising tool for refining physiological pathways, their timing, and their drivers.

Humans↗

PubMed2026

Contrasting signals of selection at the EDAR gene in global and Latin American populations.

The EDAR gene is a classic target of positive selection in humans, mainly through the nonsynonymous variant rs3827760 (EDARV370A) associated with ectodermal traits. Using high-resolution data from the 1000 Genomes Project, we combined sliding-window F_ST, BayeScan, and extended haplotype homozygosity (EHH) analyses to examine global and Latin American patterns of differentiation. Globally, a strong signal of positive selection was confirmed at EDAR, dominated by the rs3827760 haplotype background and its extended linkage disequilibrium structure. In contrast, within Latin America, differentiation reflected admixture-driven haplotype persistence rather than contemporary selection. A genome-wide FST scan comparing individuals from the upper and lower quartiles of Native American ancestry showed that EDAR lies among the most highly differentiated regions in this contrast, consistent with ancestry-driven haplotype structure rather than post-admixture adaptive evolution. These results indicate that EDAR retains its evolutionary signature globally but not within recently admixed populations, where demographic history rather than selection shapes its genetic landscape.

Humans↗

PubMed2025

scBSP: a fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data.

MOTIVATION: Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in 2D and 3D space. However, the limited sequencing depth, increasing spatial resolution, and growing spatial spots in spatial omics technologies present significant computational challenges in identifying biologically meaningful molecules with variable spatial distributions across various omics modalities. RESULTS: We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in large-scale spatial omics data. scBSP demonstrates significantly enhanced computational efficiency, processing high-resolution spatial omics data within seconds, and exhibits robust cross-platform performance by consistently identifying spatially variable features with high reproducibility across various sequencing platforms. AVAILABILITY AND IMPLEMENTATION: scBSP is available for download from R CRAN at https://cran.r-project.org/web/packages/scBSP/index.html and PyPI at https://pypi.org/project/scbsp/.

Software↗

PubMed2025

A hierarchical, count-based model highlights challenges in scATAC-seq data analysis and points to opportunities to extract finer-resolution information.

BACKGROUND: Data from Single-cell Assay for Transposase Accessible Chromatin with Sequencing (scATAC-seq) is highly sparse. While current computational methods feature a range of transformation procedures to extract meaningful information, major challenges remain. RESULTS: Here, we discuss the major scATAC-seq data analysis challenges such as sequencing depth normalization and region-specific biases. We present a hierarchical count model that is motivated by the data generating process of scATAC-seq data. Our simulations show that current scATAC-seq data, while clearly containing physical single-cell resolution, are too sparse to infer true informational-level single-cell, single-region of chromatin accessibility states. CONCLUSIONS: While the broad utility of scATAC-seq at a cell type level is undeniable, describing it as fully resolving chromatin accessibility at single-cell resolution, particularly at individual locus level, may overstate the level of detail currently achievable. We conclude that chromatin accessibility profiling at true single-cell, single-region resolution is challenging with current data sensitivity, but that it may be achieved with promising developments in optimizing the efficiency of scATAC-seq assays.

Single-Cell Analysis↗

PubMed2026

Overcoming Artificial Structures in Resolution-Enhanced Hi-C Data by Signal Decomposition and Multi-Scale Attention.

Computational enhancement is an important strategy for inferring high-resolution features from genome-wide chromosome conformation capture (Hi-C) data, which typically have limited resolution. Deep learning has been highly successful in this task but we show that it creates prevalent artificial structures in the enhanced data due to the need to divide the large contact matrix into small patches. In addition, previous deep learning methods largely focus on local patterns, which cannot fully capture the complexity of Hi-C data. Here we propose Smooth, High-resolution, and Accurate Reconstruction of Patterns (SHARP) for enhancing Hi-C data. It uses the novel approach of decomposing the data into three types of signals, due to one-dimensional proximity, contiguous domains, and other fine structures, respectively, and applies deep learning only to the third type of signals, such that enhancement of the first two is unaffected by the patches. For the deep learning part, SHARP uses both local and global attention mechanisms to capture multi-scale contextual information. We compare SHARP with state-of-the-art methods extensively, including application to data from new samples and another species, and show that SHARP has superior performance in terms of resolution enhancement accuracy, avoiding creation of artificial structures, identifying significant interactions, and enrichment in chromatin states.

Hi‐C↗

PubMed2025

Unicorn: enhancing single-cell Hi-C data with blind super-resolution for 3D genome structure reconstruction.

MOTIVATION: Single-cell Hi-C (scHi-C) data provide critical insights into chromatin interactions at individual cell levels, uncovering unique genomic 3D structures. However, scHi-C datasets are characterized by sparsity and noise, complicating efforts to accurately reconstruct high-resolution chromosomal structures. In this study, we present ScUnicorn, a novel blind super-resolution framework for scHi-C data enhancement. ScUnicorn uses an iterative degradation kernel optimization process, unlike traditional super-resolution approaches, which rely on downsampling, predefined degradation ratios, or constant assumptions about the input data to reconstruct high-resolution interaction matrices. Hence, our approach more reliably preserves critical biological patterns and minimizes noise. Additionally, we propose 3DUnicorn, a maximum likelihood algorithm that leverages the enhanced scHi-C data to infer precise 3D chromosomal structures. RESULTS: Our evaluation demonstrates that ScUnicorn achieves superior performance over the state-of-the-art methods in terms of Peak Signal-to-Noise Ratio, Structural Similarity Index Measure, and GenomeDisco scores. Moreover, 3DUnicorn's reconstructed structures align closely with experimental 3D-FISH data, underscoring its biological relevance. Together, ScUnicorn and 3DUnicorn provide a robust framework for advancing genomic research by enhancing scHi-C data fidelity and enabling accurate 3D genome structure reconstruction. AVAILABILITY AND IMPLEMENTATION: Unicorn implementation is publicly accessible at https://github.com/OluwadareLab/Unicorn.

Single-Cell Analysis↗

PubMed2026

Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.

Understanding the geographic spread of emerging respiratory viruses is critical for pandemic preparedness, yet the early spatiotemporal dynamics of the 2009 H1N1 pandemic influenza and severe acute respiratory syndrome coronavirus 2 in the United States remain unclear. While mobility and genomic data have revealed important aspects of pandemic spatial spread, several key questions remain: Did the two pandemics follow similar spatial transmission routes? How rapidly did they spread across the United States? What role did stochastic processes play in early spatial transmission? To address these questions, we integrated high-resolution disease data with a robust, data-efficient inference framework combining air travel, commuting flows, and pathogen superspreading potentials to reconstruct their spatial spread across US metropolitan areas. The two pandemics exhibited distinct transmission pathways across locations; however, both pandemics established local circulation in most metropolitan areas within weeks, driven by several shared transmission hubs. Early spatial spread was more strongly associated with air travel than with commuting, though stochastic dynamics introduced substantial uncertainty in transmission routes, creating challenges for timely detection and control. Simulations indicate that broad wastewater surveillance coverage beyond top transmission hubs coupled with effective infection control may slow initial spatial expansion. Our findings highlight the rapid, stochastic spread of pandemic respiratory pathogens and the difficulties of early outbreak containment.

Humans↗

PubMed2026

Beyond data and technology: the need for new thinking to enable the era of precision prevention.

BACKGROUND: Global flagship initiatives increasingly advocate for proactive health maintenance to alleviate the growing burden on reactive, disease-focused healthcare systems. Precision prevention is conceived as the targeted modulation of causal pathways across the disease continuum, from latent risk and pre-disease states to clinical manifestation, surpassing conventional public health prevention strategies that prioritise managing population-level risk factors. Traditional discovery and implementation models, however, remain poorly aligned with the pace and breadth of scientific and technological advances. This review outlines key barriers to scaling precision prevention and argues for the integration of conceptual, methodological, and policy perspectives into a single implementation‑oriented framework. MAIN: Individualised risk stratification lies at the core of precision prevention. Genomics serves as a stable substrate for lifetime susceptibility assessment, while meaningful prediction in multifactorial chronic disease requires additional risk monitoring using dynamic intermediate molecular markers and high-resolution exposomic data. Machine learning and other artificial intelligence (AI) methods are increasingly helpful tools for integrating large, heterogeneous and temporally structured real-world data to generate personalised predictions of health trajectories. Trustworthy AI-enabled risk prediction or decision-support systems are expected to provide transparency about model logic, assumptions and performance. In discovery, existing diagnostic classifications and conventional case-control designs can obscure mechanistic heterogeneity. Shifting toward precision phenotyping and biologically grounded disease redefinition could reveal a new layer of molecular understanding. Evidence generation strategies that reflect the temporal change of disease, including high‑risk enrichment, surrogate endpoints, and adaptive, trajectory-based monitoring, are particularly important for common conditions with prolonged latency periods (e.g., cancer, cardiovascular disease). Features often dismissed as "noise", such as stochastic molecular variation and minimal exposures, may in fact encode meaningful individual-level signals and thus merit investigation. CONCLUSION: To shift healthcare from reactive treatment toward proactive health maintenance requires coordinated action from stakeholders to reshape the pillars of discovery, reform outcome assessments and modernise implementation strategies.

Humans↗

PubMed2026

From Peaks to Power: Systematic Evaluation of Chromatographic Sampling Reveals Determinants of Quantification and Biological Discovery in DIA Proteomics.

Modern DIA proteomics increasingly emphasizes throughput and depth for large-cohort studies, but methods are often optimized using proxy metrics that can mask losses in quantifiable signal and statistical power. Here, we evaluate how data points per peak and other chromatographic features jointly contribute to quantification and downstream biological discovery. Using a matrix-matched calibration curve dataset, we checked how the number of data points per peak (DPPP) affects the limits of detection and quantification (LOD/LOQ). Reduced DPPP minimally affected LOD but substantially degraded LOQ. Feature modeling and nonparametric association analyses identified precursor peak area as the strongest feature-level predictor of LOQ, whereas DPPP showed weaker and context-dependent effects. Simulations of chromatographic peak integration recapitulated these trends, showing that increased sampling primarily improves integration precision, while quantitative accuracy is strongly governed by peak height and peak shape. Finally, when comparing 20 cancer vs 20 control plasma samples processed with Seer Proteograph, the decrease in DPPP led to a loss of statistical significance for proteins with low-abundance precursors. These findings argue that DIA optimization should prioritize LOQ and statistical power metrics─not identifications alone─by balancing sampling density with chromatographic peak height and quality to maximize useful biological signal.

Proteomics↗

PubMed2026

Genomic Characterisation of Carbapenem-Resistant Klebsiella pneumoniae and Enterobacter hormaechei Clinical Isolates from Nigeria: Evidence of Resistance, Virulence, and Putative Plasmid-Mediated Gene Sharing.

The global proliferation of carbapenem-resistant Enterobacterales (CRE) constitutes one of the most urgent public health threats, yet high-resolution genomic data from sub-Saharan Africa remain critically scarce. We applied whole-genome sequencing (WGS) and comparative phylogenomics to characterise antimicrobial resistance determinants, virulence genes, and mobile genetic elements (MGEs) in three carbapenem-resistant clinical isolates originating from three tertiary hospitals (selected from a broader surveillance collection spanning four facilities) in Osun State, southwestern Nigeria. We purposively selected three isolates, two Klebsiella pneumoniae subsp. pneumoniae (K22, ST411; K31, ST17) and one Enterobacter hormaechei subsp. steigerwaltii (K32, ST45) from a broader surveillance collection of 27 carbapenem-non-susceptible Enterobacterales, to represent phenotypically and genotypically divergent lineages. Resistome analysis revealed extensive plasmid-associated β-lactam and aminoglycoside resistance in K31 (including blaCTX-M-15, blaOXA-1, and blaTEM-1). K32 harboured an intrinsic chromosomal blaACT-17 AmpC gene, while IS26 and ISEcp1 insertion sequences, consistent with transposon-mediated mobilisation, flanked its acquired aminoglycoside and sulfonamide resistance cassettes. K22 lacked detected acquired carbapenemase, ESBL, or plasmid-mediated AmpC genes, indicating that its carbapenem-resistant phenotype may involve non-carbapenemase mechanisms such as porin alteration or efflux-mediated reduced susceptibility; however, this mechanism requires confirmation by direct ompK35/ompK36 sequence analysis and/or phenotypic outer membrane protein profiling. Virulome profiling identified a broader repertoire of siderophore, adhesion, and biofilm genes in both K. pneumoniae isolates than in E. hormaechei. Phylogenomic analysis demonstrated that K22 and K31 cluster within the broader K. pneumoniae population framework but represent distinct high-risk lineages (ST411 and ST17) rather than a single clonal outbreak. Analysis also identified a shared plasmid backbone between K31 and K32, supporting interspecies horizontal gene transfer. These descriptive genomic findings identify clinically relevant resistance and virulence determinants in three purposively selected carbapenem-resistant Enterobacterales from Nigerian tertiary-care hospitals. The detection of shared resistance elements between K. pneumoniae and E. hormaechei suggests possible plasmid-mediated gene sharing. Still, larger WGS studies with long-read sequencing and patient-level epidemiological data are required to define transmission and dissemination patterns.

Nigeria↗

PubMed2016

Gene co-expression analysis identifies brain regions and cell types involved in migraine pathophysiology: a GWAS-based study using the Allen Human Brain Atlas.

Migraine is a common disabling neurovascular brain disorder typically characterised by attacks of severe headache and associated with autonomic and neurological symptoms. Migraine is caused by an interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified over a dozen genetic loci associated with migraine. Here, we integrated migraine GWAS data with high-resolution spatial gene expression data of normal adult brains from the Allen Human Brain Atlas to identify specific brain regions and molecular pathways that are possibly involved in migraine pathophysiology. To this end, we used two complementary methods. In GWAS data from 23,285 migraine cases and 95,425 controls, we first studied modules of co-expressed genes that were calculated based on human brain expression data for enrichment of genes that showed association with migraine. Enrichment of a migraine GWAS signal was found for five modules that suggest involvement in migraine pathophysiology of: (i) neurotransmission, protein catabolism and mitochondria in the cortex; (ii) transcription regulation in the cortex and cerebellum; and (iii) oligodendrocytes and mitochondria in subcortical areas. Second, we used the high-confidence genes from the migraine GWAS as a basis to construct local migraine-related co-expression gene networks. Signatures of all brain regions and pathways that were prominent in the first method also surfaced in the second method, thus providing support that these brain regions and pathways are indeed involved in migraine pathophysiology.

Atlases as Topic↗

PubMed2026

highSpaClone enables copy number alteration inference and tumor subclone analysis for high-resolution spatial transcriptomics.

High-resolution spatially resolved transcriptomics (SRT) offers unprecedented opportunities to investigate tumor heterogeneity but poses substantial computational and analytical challenges. Here, we present highSpaClone, a computational framework for copy number alteration (CNA) inference and tumor subclone identification from high-resolution SRT data across multiple spatial scales. By integrating spatial constraints into CNA estimation and clonal clustering, highSpaClone enables neighboring spatial locations to share information, thereby improving the robustness of genomic signals and the accuracy of subclone delineation. Across multiple Xenium and Visium HD datasets, highSpaClone revealed unique transcriptional programs, clonal evolutionary trajectories, and distinct tumor-microenvironment interactions. Furthermore, in human colorectal cancer samples, highSpaClone detected CNA events in histologically normal epithelial regions, highlighting early genomic alterations associated with field cancerization. These findings establish highSpaClone as a scalable framework for studying clonal architecture and tumor evolution.

CP: cancer biology↗

PubMed2026

Human and mouse adrenal glands are characterized by species-specific steroidogenic states and tissue turnover.

The adult adrenal cortex undergoes constant renewal, yet underlying human-specific mechanisms remain poorly understood. Here we generated single-cell and spatial transcriptomic atlases of adult human and mouse adrenal glands, leveraging single-cell-resolution spatial data and a rare clonal mosaic case for lineage inference. In humans, we identified age-associated zona glomerulosa (ZG) cell states with direct cortisol synthesis capacity and sex-specific differences in inferred cholesterol balance. Cross-species comparison revealed conserved aldosterone-producing ZG but notable divergence in zona fasciculata markers, absence of zona reticularis homologs in mice and differential SHH-WNT4 signaling in proliferating cells. We uncovered human WT1- capsule-to-ZG transition and vascular smooth muscle cell-to-steroidogenic transitions supported by mosaic lineage evidence. We revealed dispersed proliferating cortical SF1+EZH2+ cells throughout the human cortex in contrast with ZG restriction in mice. Taken together, our data expand the centripetal renewal model and establish a comparative framework for human adrenocortical biology.

Animals↗