GigaScience最新文献

Current tools for spatial omics analysis often face challenges in performing integrated transcriptomics and metabolomics analysis, in-depth biological interpretation, and user-friendly operation. To address this, we developed SMIntegration, the first web-based graphical platform designed specifically for integrated spatial metabolomics and transcriptomics analysis. Built with R/Shiny and deployed using Docker containerization, the platform provides a complete integration workflow, starting from pre-processed spatial features through to functional annotation. Its core functions include: (1) automated and interactive spatial registration; (2) cross-modal spatial pattern recognition; (3) flexible differential analysis of genes and mass features based on clustering results, user-defined regions, or cell type annotations; and (4) group-specific gene-metabolite network construction and interactive visualization. Using adjacent mouse brain coronal sections (Stereo-seq transcriptomics and AFADESI-MS metabolomics) as an example, SMIntegration successfully identified both the periaqueductal gray and subcommissural organ, which were missed by single-modality clustering. Cell type analysis revealed an association between astrocyte-enriched GABA metabolism and Slc6a11, while a comparison between the cornu ammonis region and the midbrain periaqueductal gray dissected glutamatergic and endogenous cannabinoid signaling pathway modules. With a zero-code interface, SMIntegration enables a wide range of researchers to deeply explore gene-metabolite interaction mechanisms within microenvironments during development, homeostasis, and disease.

Background: Hybridisation between divergent species can result in meiotic aberrations and the emergence of asexual reproduction. Yet, it remains poorly understood to what extent such outcomes arise from genome-wide incompatibilities versus more specific conflicts among individual chromosomes inherited from parental species, including their ability to pair during meiosis in hybrids. It is also unclear how interspecific hybrids cope with differences in sex determination systems, particularly in the context of increased ploidy. Addressing these questions requires high-quality, chromosome-level reference genomes of the parental species involved in hybrid formation.

Findings: Here, we present the first chromosome-level genome assemblies for three hybridising Cobitis species (C. elongatoides, C. taenia, and C. tanaitica), providing a comprehensive framework for investigating the genomic and cytogenetic basis of hybrid sterility and the transition to asexuality. By integrating genome scaffolding, male/female pooled sequencing (Pool-Seq), and molecular cytogenetics, we uncover extensive structural variation among homologous chromosomes of the three species, despite overall karyotype conservation. Population-level analyses revealed that each species possesses distinct, non-homologous sex chromosomes, highlighting rapid sex chromosome turnover in this recently diverged lineage. Finally, the design of chromosome-specific painting probes, which we applied to meiotic metaphase I spreads of diploid hybrids. This approach revealed striking differences in the pairing success of orthologous chromosomes.

Conclusions: Our results demonstrate that individual orthologous chromosomes differ markedly in their ability to form bivalents during meiosis in hybrids, indicating that hybrid meiotic behaviour is shaped by chromosome-specific incompatibilities rather than uniform genome-wide failure. We also found that even closely related parental species possess distinct, non-homologous sex chromosomes, highlighting rapid turnover of sex determination systems in hybridising lineages. Together, these findings provide a high-resolution genomic and cytogenetic framework to explore how the architecture of inherited parental genomes influences sex-specific reproductive outcomes in hybrids-ranging from male sterility to the establishment of fertile, clonally reproducing female lineages-and how such asymmetries may contribute to the emergence of asexuality in vertebrates.

Strain-level metagenomic classification is essential for understanding microbial diversity and functional potential, yet remains challenging, particularly when sample composition is unknown and reference databases are large and redundant. Here we present MADRe, a modular and scalable pipeline for long-read strain-level metagenomic classification based on Metagenome Assembly-Driven Database Reduction. Beyond system-level integration, MADRe introduces statistical strategies that leverage assembly-derived genomic context to guide database reduction and probabilistic read reassignment. Specifically, it combines long-read metagenome assembly, contig-to-reference reassignment using an expectation-maximization framework for reference reduction, and probabilistic read mapping reassignment on a reduced database to achieve sensitive and precise strain-level classification. We extensively evaluated MADRe on simulated datasets, mock communities, and a real anaerobic digester sludge metagenome. Across diverse similarity and coverage conditions, MADRe consistently improves precision by reducing false-positive strain detections. MADRe's design allows users to apply either the database reduction or read classification step individually. Using only the read classification step shows results on par with other tested tools. MADRe is open source and publicly available at https://github.com/lbcb-sci/MADRe.

Background: West Africa has high biodiversity that is relatively understudied, especially for insects. Studies of West African arthropod diversity can therefore help address important questions regarding conservation, ecosystem services, and insecticide use and other species-control interventions in agriculture and disease management. We intensively sampled arthropods in Ghana using complementary trapping methods, generated DNA barcodes, and classified sequences by Barcode Index Numbers (BINs, a species proxy). Using this dataset, we investigate assemblage composition, temporal activity patterns, and the state of regional biodiversity sampling.

Results: Sequencing DNA from 95,996 individuals captured using Malaise, yellow pan, pitfall, Heath and Centre for Disease Control (CDC) traps, we identified 10,120 unique BINs. The rate of species accumulation did not approach an asymptote for any taxonomic group or trap type, indicating high biodiversity. The different trap types sampled different subsets of the local community, with greatest similarity between yellow pan and pitfall traps. More insects and species (BINs) were trapped during the day than at night. Our dataset shared more BINs in the Barcode of Life Database with South Africa than with any other country, although this predominantly reflects the limited sampling and DNA sequencing campaigns in Africa.

Conclusions: This study more than doubles the published BINs for West Africa, offering insights into the biodiversity of an ecologically important but understudied taxon and region. Using multiple trap types allowed a more complete assessment of the local arthropod assemblage. The public release of these data will support and stimulate further taxonomic and ecological work in the region.

Background: Structural variants (SVs) are increasingly recognised as key contributors to human diseases. However, our understanding of SVs in health and disease is limited, mainly due to their structural complexity and variable length in individuals as well as limitations inherent to the available genomic technologies and reference genome used.

Results: To systematically evaluate SVs across human whole-genome samples using hg38/GRCh38 and gapless T2T-CHM13 references, we introduced an innovative multiplatform approach, LongReadChecker (LoReC), which advances SV comparison and annotation based on distance variance, intersection, gene overlap and the closest SV in the clinical database. Comparison of the performance in detecting SVs from public and our own whole-genome datasets from short-read sequencing (SRS), available long-read sequencing (LRS) platforms and optical genome mapping (OGM) revealed that most SVs detected by SRS were confirmed by LRS, but LRS can identify twice as many SVs (25,000 SVs/genome) with greater read mapping accuracy. Our LoReC analysis further highlights the utility of the T2T-CHM13 reference in SV detection, as 20% more deletions and 20% less insertions were detected compared with hg38/GRCh38, which was particularly evident in long-read datasets. Since 80% of the SVs detected by LRS/SRS are smaller than 0.5 kbp, OGM did not detect them.

Conclusions: Our study revealed that introducing distance variance, intersection, gene overlap and the closest SV in the clinical database may help compare and annotate SVs in diagnostics. Our data showed that LRS together with T2T-CHM13 gapless sequences can improve the diagnostics of patients with human diseases when SRS fails to identify the cause.

Herbarium collections are a vast but underutilized resource for ancient DNA research, containing over 400 million specimens with detailed metadata and spanning centuries of global biodiversity. Understanding patterns of DNA preservation in natural collections is crucial for optimizing ancient DNA studies and informing future curation practices. We analysed genomic data for 573 herbarium specimens from six plant species from the genera Hordeum and Oryza collected from the Americas and Eurasia over 220 years. Using standardized laboratory protocols and shotgun sequencing, we quantified DNA degradation and elucidated factors that accelerate it. We find significant age-dependent DNA fragmentation rates, indicating temporal degradation processes not detected in prehistoric samples. In our analysis, DNA decay rates in herbarium specimens were almost eight times faster than in moa bones, reflecting fundamental differences in tissue composition and preservation environments. Environmental conditions at the time of specimen collection emerged as the major determinants of post-mortem damage rates, with the interaction term between temperature and genus being the dominant driver of cytosine deamination. We find no effect of sample storage on DNA damage and degradation. These findings provide insights into how climatic origin, preservation environment, taxonomic identity and age influence DNA preservation while highlighting opportunities for improving institutional preservation practices. Due to standardised preservation conditions, museum collections can provide better insights into DNA damage and degradation over time than archaeological and paleontological samples.

Background: Zygotic genome activation (ZGA) is a pivotal process during early embryogenesis, marking the maternal-to-zygotic transition. ZGA is regulated by a variety of epigenetic and transcriptional factors. However, the transcriptional regulatory mechanisms underlying ZGA in livestock species remain largely unclear.

Results: By integrating ATAC-seq and RNA-seq, we characterized chromatin accessibility and transcriptional dynamics in goat embryos. Transcriptional inhibition with α-amanitin markedly reduced promoter accessibility and disrupted RNA polymerase II (Pol II)-mediated transcription. Motif enrichment analysis identified ZNF331 as a potential regulator with specific upregulation at the 8-cell stage. Functional knockdown of ZNF331 resulted in impaired embryonic development, reduced blastocyst formation, and widespread transcriptome alterations. Mechanistically, ZNF331 depletion caused abnormal elevation of Pol II Ser5 phosphorylation, excessive transcriptional activity, maternal mRNA retention, and excessive activation of zygotic genes.

Conclusion: Our study identifies ZNF331 as a critical regulator of goat ZGA, functioning through fine-tuning Pol II Ser5 phosphorylation to balance maternal transcript clearance and zygotic gene activation. These findings highlight the essential role of the ZNF331-Pol II axis in goat embryogenesis and suggest a potentially conserved mechanism across mammals.

The integration of high-throughput single-cell profiling technologies with RNA velocity analysis has enabled the reconstruction of dynamic cellular differentiation trajectories at unprecedented resolution. Despite these advances, current visualization techniques for RNA velocity are predominantly confined to two-dimensional representations, typically employing arrows or streamlines. While effective for depicting simple cellular trajectories, these approaches are insufficient for capturing the complex topologies of multipartite cellular transitions. This limitation highlights the need for advanced three-dimensional visualization tools that can more accurately convey the structure and dynamics of velocity-inferred transitions in single-cell data. Here, we present Cell Journey, an interactive visualization platform specifically developed for three-dimensional analysis and representation of RNA velocity trajectories derived from single-cell datasets. The platform features an intuitive graphical interface supporting both unimodal and multimodal data, accommodates multiple input formats, and provides extensive customization capabilities for trajectory visualization. Cell Journey computes RNA velocity vector fields on a user-defined three-dimensional grid and constructs velocity trajectories using either Euler integration or the fourth-order Runge-Kutta method. The platform enables dynamic exploration of cellular dynamics through interactive visual elements, including streamlines, streamlets, cones, and volumetric plots. Furthermore, it allows users to investigate changes in feature activity along selected paths, facilitating deeper insights into cellular state transitions within complex multimodal single-cell datasets.

Scientific metadata often suffer from incompleteness, inconsistency, and formatting errors, which hinder effective discovery and reuse of the associated datasets. We present a method that combines Generative Pre-trained Transformer 4 (GPT-4) with structured metadata templates from the Center for Expanded Data Annotation and Retrieval (CEDAR) knowledge base to automatically standardize metadata and to ensure compliance with established standards. A CEDAR template specifies the expected fields of a metadata submission and their permissible values. Our standardization process involves using CEDAR templates to guide the GPT-4 in accurately correcting and refining metadata entries in bulk, resulting in significant improvements in metadata retrieval performance, especially in recall-the proportion of relevant datasets retrieved from the total relevant datasets available. Using the BioSample and Gene Expression Omnibus (GEO) repositories maintained by the National Center for Biotechnology Information (NCBI), we demonstrate that retrieval of datasets whose metadata are altered by GPT-4 when provided with CEDAR templates (GPT-4+CEDAR) is substantially better than retrieval of datasets whose metadata are in their original state and that of datasets whose metadata are altered using GPT-4 with only data-dictionary guidance (GPT-4+DD). The average recall increases dramatically, from 17.65% with baseline raw metadata to 62.87% with GPT-4+CEDAR. Furthermore, we evaluate the robustness of our approach by comparing GPT-4 against other large language models, including LLaMA-3 and MedLLaMA2, demonstrating consistent performance advantages for GPT-4+CEDAR. These results underscore the transformative potential of combining advanced language models with symbolic models of standardized metadata structures for more effective and reliable data retrieval, thus accelerating scientific discoveries and data-driven research.

A characteristic feature of the liver is the presence of numerous polyploid hepatocytes. However, the functional distinctions among diploid, tetraploid, and octoploid hepatocytes remain poorly understood. In this study, we employed the spatially resolved single-cell sequencing technology, Stereo-cell, to dissect the transcriptomic and functional heterogeneity across hepatocyte ploidy subtypes. We detail the development of Stereo-cell Imaging-based ploidy Identification (SCIPI), a technical pipeline that integrates bright-field cell contour recognition, DAPI-based nuclear area and number quantification, and UMI-barcoded single-cell transcriptomics. This approach enables precise identification of six core hepatocyte subtypes: mononucleated diploid (2n×1), mononucleated tetraploid (4n×1), binucleated tetraploid (2n×2), mononucleated octoploid (8n×1), binucleated octoploid (4n×2), and binucleated hexadecaploid (8n×2) hepatocytes. Single-cell transcriptomic analysis based on ploidy annotation revealed that gene expression levels scale positively with increasing ploidy and nuclear number. Metabolic pathway-associated genes were significantly upregulated in polyploid cells, suggesting that cellular polyploidization enhances the metabolic activity of hepatocytes. Furthermore, this SCIPI strategy is broadly applicable to the study of various polyploid tissues, offering a novel and versatile framework for innovative ploidy-resolved research across diverse biological researches.