Biological Insights Research Articles

Deciphering the virome of Chunkung (Cnidium officinale) showing dwarfism-like symptoms via a high-throughput sequencing analysis.

Viruses have notable effects on agroecosystems, wherein they can adversely affect plant health and cause problems (e.g., increased biosecurity risks and economic losses). However, our knowledge of their diversity and interactions with specific host plants in ecosystems remains limited. To enhance our understanding of the roles that viruses play in agroecosystems, comprehensive analyses of the viromes of a wide range of plants are essential. High-throughput sequencing (HTS) techniques are useful for conducting impartial and unbiased investigations of plant viromes, ultimately forming a basis for generating further biological and ecological insights. This study was conducted to thoroughly characterize the viral community dynamics in individual plants. An HTS-based virome analysis in conjunction with proximity sampling and a tripartite network analysis were performed to investigate the viral diversity in chunkung (Cnidium officinale) plants. We identified 61 distinct chunkung plant-associated viruses (27 DNA and 34 RNA viruses) from 21 known genera and 6 unclassified genera in 14 known viral families. Notably, 12 persistent viruses (7 DNA and 5 RNA viruses) were exclusive to dwarfed chunkung plants. The detection of viruses from the families Partitiviridae, Picobirnaviridae, and Spinareoviridae only in the dwarfed plants suggested that they may contribute to the observed dwarfism. The co-infection of chunkung by multiple viruses is indicative of a dynamic and interactive viral ecosystem with significant sequence variability and evidence of recombination. We revealed the viral community involved in chunkung. Our findings suggest that chunkung serves as a significant reservoir for a variety of plant viruses. Moreover, the co-infection rate of individual plants was unexpectedly high. Future research will need to elucidate the mechanisms enabling several dozen viruses to co-exist in chunkung. Nevertheless, the important insights into the chunkung virome generated in this study may be relevant to developing effective plant viral disease management and control strategies.

Colorectal Cancer Stem Cell Biomarkers: Biological Traits and Prognostic Insights.

Due to self-renewal, differentiation, and limitless proliferation properties, Cancer Stem Cells (CSCs) increase the probability of tumor development. These cells are identified by using CSC markers, which are highly expressed proteins on the cell surface of CSCs. Recently, the therapeutic application of CSCs as novel biomarkers improved both the prognosis and diagnosis outcome of colorectal Cancer. In the present review, we focused on a specific panel of colorectal CSC markers, including LGR5, ALDH, CD166, CD133, and CD44, which offers a targeted and comprehensive analysis of their functions. The selection criteria for these markersCancer were based on their established significance in Colorectal Cancer (CRC) pathogenesis and clinical outcomes, providing novel insights into the CSC biology of CRC. Through this approach, we aim to elevate understanding and stimulate further research for developing effective diagnostic and therapeutic strategies in CRC.

From Blur to Brilliance: The Ascendance of Advanced Microscopy in Neuronal Cell Biology.

The intricate network of the brain's neurons and synapses poses unparalleled challenges for research, distinct from other biological studies. This is particularly true when dissecting how neurons and their functional units work at a cell biological level. While traditional microscopy has been foundational, it was unable to reveal the deeper complexities of neural interactions. However, an imaging renaissance has transformed our capabilities. Advancements in light and electron microscopy, combined with correlative imaging, now achieve unprecedented resolutions, uncovering the most nuanced neural structures. Maximizing these tools requires more than just technical proficiency. It is crucial to align research aims, allocate resources wisely, and analyze data effectively. At the heart of this evolution is interdisciplinary collaboration, where various experts come together to translate detailed imagery into significant biological insights. This review navigates the latest developments in microscopy, underscoring both the promise of and prerequisites for bending this powerful tool set to understanding neuronal cell biology.

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders.

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

Proteomics study of primary and recurrent adamantinomatous craniopharyngiomas

BackgroundAdamantinomatous craniopharyngiomas (ACPs) are rare benign epithelial tumours with high recurrence and poor prognosis. Biological differences between recurrent and primary ACPs that may be associated with disease recurrence and treatment have yet to be evaluated at the proteomic level. In this study, we aimed to determine the proteomic profiles of paired recurrent and primary ACP, gain biological insight into ACP recurrence, and identify potential targets for ACP treatment.MethodPatients with ACP (n = 15) or Rathke’s cleft cyst (RCC; n = 7) who underwent surgery at Sanbo Brain Hospital, Capital Medical University, Beijing, China and received pathological confirmation of ACP or RCC were enrolled in this study. We conducted a proteomic analysis to investigate the characteristics of primary ACP, paired recurrent ACP, and RCC. Western blotting was used to validate our proteomic results and assess the expression of key tumour-associated proteins in recurrent and primary ACPs. Flow cytometry was performed to evaluate the exhaustion of tumour-infiltrating lymphocytes (TILs) in primary and recurrent ACP tissue samples. Immunohistochemical staining for CD3 and PD-L1 was conducted to determine differences in T-cell infiltration and the expression of immunosuppressive molecules between paired primary and recurrent ACP samples.ResultsThe bioinformatics analysis showed that proteins differentially expressed between recurrent and primary ACPs were significantly associated with extracellular matrix organisation and interleukin signalling. Cathepsin K, which was upregulated in recurrent ACP compared with that in primary ACP, may play a role in ACP recurrence. High infiltration of T cells and exhaustion of TILs were revealed by the flow cytometry analysis of ACP.ConclusionsThis study provides a preliminary description of the proteomic differences between primary ACP, recurrent ACP, and RCC. Our findings serve as a resource for craniopharyngioma researchers and may ultimately expand existing knowledge of recurrent ACP and benefit clinical practice.

Scaling approaches and macroecology provide a foundation for assessing ecological resilience in the Anthropocene.

In the Anthropocene, intensifying ecological disturbances pose significant challenges to our predictive capabilities for ecosystem responses. Macroecology-which focuses on emergent statistical patterns in ecological systems-unveils consistent regularities in the organization of biodiversity and ecosystems. These regularities appear in terms of abundance, body size, geographical range, species interaction networks, or the flux of matter and energy. This paper argues for moving beyond qualitative resilience metaphors, such as the 'ball and cup', towards a more quantitative macroecological framework. We suggest a conceptual and theoretical basis for ecological resilience that integrates macroecology with a stochastic diffusion approximation constrained by principles of biological symmetry. This approach provides an alternative novel framework for studying ecological resilience in the Anthropocene. We demonstrate how our framework can effectively quantify the impacts of major disturbances and their extensive ecological ramifications. We further show how biological scaling insights can help quantify the consequences of major disturbances, emphasizing their cascading ecological impacts. The nature of these impacts prompts a re-evaluation of our understanding of resilience. Emphasis on regularities of ecological assemblages can help illuminate resilience dynamics and offer a novel basis to predict and manage the impacts of disturbance in the Anthropocene more efficiently. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Metabolic plasticity, essentiality and therapeutic potential of ribose-5-phosphate synthesis in Toxoplasma gondii

Ribose-5-phosphate (R5P) is a precursor for nucleic acid biogenesis; however, the importance and homeostasis of R5P in the intracellular parasite Toxoplasma gondii remain enigmatic. Here, we show that the cytoplasmic sedoheptulose-1,7-bisphosphatase (SBPase) is dispensable. Still, its co-deletion with transaldolase (TAL) impairs the double mutant’s growth and increases 13C-glucose-derived flux into pentose sugars via the transketolase (TKT) enzyme. Deletion of the latter protein affects the parasite’s fitness but is not lethal and is correlated with an increased carbon flux via the oxidative pentose phosphate pathway. Further, loss of TKT leads to a decline in 13C incorporation into glycolysis and the TCA cycle, resulting in a decrease in ATP levels and the inability of phosphoribosyl-pyrophosphate synthetase (PRPS) to convert R5P into 5′-phosphoribosyl-pyrophosphate and thereby contribute to the production of AMP and IMP. Likewise, PRPS is essential for the lytic cycle. Not least, we show that RuPE-mediated metabolic compensation is imperative for the survival of the ΔsbpaseΔtal strain. In conclusion, we demonstrate that multiple routes can flexibly supply R5P to enable parasite growth and identify catalysis by TKT and PRPS as critical enzymatic steps. Our work provides novel biological and therapeutic insights into the network design principles of intracellular parasitism in a clinically-relevant pathogen.

Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.

Supervised deep learning is used to model the complex relationship between genomic sequence and regulatory function. Understanding how these models make predictions can provide biological insight into regulatory functions. Given the complexity of the sequence to regulatory function mapping (the cis-regulatory code), it has been suggested that the genome contains insufficient sequence variation to train models with suitable complexity. Data augmentation is a widely used approach to increase the data variation available for model training, however current data augmentation methods for genomic sequence data are limited. Inspired by the success of comparative genomics, we show that augmenting genomic sequences with evolutionarily related sequences from other species, which we term phylogenetic augmentation, improves the performance of deep learning models trained on regulatory genomic sequences to predict high-throughput functional assay measurements. Additionally, we show that phylogenetic augmentation can rescue model performance when the training set is down-sampled and permits deep learning on a real-world small dataset, demonstrating that this approach improves data efficiency. Overall, this data augmentation method represents a solution for improving model performance that is applicable to many supervised deep learning problems in genomics. The open-source GitHub repository agduncan94/phylogenetic_augmentation_paper includes the code for rerunning the analyses here and recreating the figures. Supplementary data are available at Bioinformatics online.

ScButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders

Recent advancements for simultaneously profiling multi-omics modalities within individual cells have enabled the interrogation of cellular heterogeneity and molecular hierarchy. However, technical limitations lead to highly noisy multi-modal data and substantial costs. Although computational methods have been proposed to translate single-cell data across modalities, broad applications of the methods still remain impeded by formidable challenges. Here, we propose scButterfly, a versatile single-cell cross-modality translation method based on dual-aligned variational autoencoders and data augmentation schemes. With comprehensive experiments on multiple datasets, we provide compelling evidence of scButterfly’s superiority over baseline methods in preserving cellular heterogeneity while translating datasets of various contexts and in revealing cell type-specific biological insights. Besides, we demonstrate the extensive applications of scButterfly for integrative multi-omics analysis of single-modality data, data enhancement of poor-quality single-cell multi-omics, and automatic cell type annotation of scATAC-seq data. Moreover, scButterfly can be generalized to unpaired data training, perturbation-response analysis, and consecutive translation.

Abstract LB333: Improved spatially resolved single-cell transcriptomic imaging in archival tissues with MERSCOPE

Abstract The advent of spatial transcriptomics has enabled a revolution in how complex tissues are studied. However, samples with lower quality RNA due to degradation, protein crosslinking, or high RNase content remain challenging for spatial transcriptomic measurement. In particular, formalin fixed, paraffin embedded (FFPE) tissues are the most widely used sample types in clinical and molecular diagnosis, yet they are notoriously difficult for single-cell transcriptomic analysis. To accurately profile the gene expression in FFPE samples in situ, a spatial transcriptomics technique with high detection efficiency and single molecule resolution is required. The Vizgen® MERSCOPE® Platform for spatial genomics is built on Multiplexed Error Robust in situ Hybridization (MERFISH) technology and directly profiles the transcriptome of intact tissues with high sensitivity in high-quality samples. Here we present an updated workflow to perform MERFISH in low and high-quality samples. We demonstrated its application in more than 5 FFPE sample types from mouse and human, including archival samples. For each tissue type, hundreds of thousands of cells were captured using the updated MERSCOPE Platform workflow, generating 100s million counts and their spatial information for profiled genes in each sample. The updated workflow involves streamlined sample preparation and chemistry optimization to improve sensitivity. MERSCOPE accurately profiled gene expression in situ and mapped cell types in archival human samples across a range of low and high RNA qualities. We compared the performance of MERSCOPE imaging using the updated protocol to the previous version and observed a significant increase in gene counts per 100 micron2 of tissue. We also demonstrated increased reproducibility between replicates with the streamlined workflow and chemistry. Furthermore, we demonstrated the updated workflow is compatible with simultaneous protein-based cell boundary staining. Finally, we constructed a spatially resolved single-cell atlas across low-quality archival breast and lung tumor types, mapped and cataloged different cell types within the tumor microenvironment, and systematically characterized the gene expression among cells. Spatially resolved transcriptomic profiling of low-quality samples at single-cell level provides enormous opportunities in cancer research. These improvements will enable new genomic inquiries into previously intractable tissues like FFPE, leading to new biological insights into cancer progression. Citation Format: Jiang He, Bin Wang, Justin He, Renchao Chen, Benjamin Patterson, Sudhir Tattikota, Timothy Wiggin, Lizi Maziashvili, Peter Reinhold, Manisha Ray, George Emanuel. Improved spatially resolved single-cell transcriptomic imaging in archival tissues with MERSCOPE [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB333.

Analysis of COVID-19 Disease Model: Backward Bifurcation and Impact of Pharmaceutical and Nonpharmaceutical Interventions

The SEIQHR model, introduced in this study, serves as a valuable tool for anticipating the emergence of various infectious diseases, such as COVID-19 and illnesses transmitted by insects. An analysis of the model’s qualitative features was conducted, encompassing the computation of the fundamental reproduction number, R0. It was observed that the disease-free equilibrium point remains singular and locally asymptotically stable when R0<1, while the endemic equilibrium point exhibits uniqueness when R0>1. Additionally, specific conditions were outlined to guarantee the local asymptotic stability of both equilibrium points. Employing numerical simulations, the graphical representation illustrated the influence of model parameters on disease dynamics and the potential for its eradication across different noninteger orders of the Caputo derivative. In essence, the adoption of a fractional epidemic model contributes to a deeper comprehension and enhanced biological insights into the dynamics of diseases.

Abstract LB247: TCPAplus: An LLM-empowered chatbot for analyzing a large protein expression atlas of human cancers

Abstract We present a comprehensive functional proteomics resource comprising nearly 9,000 samples from The Cancer Genome Atlas and Cancer Cell Line Encyclopedia, using ~500 high-quality antibodies through reverse phase protein arrays. To enhance its utility, we have developed an analytical chatbot harnessing advanced large language models. This intuitive platform empowers researchers to effortlessly query protein-centered cancer omics data, conduct related analyses, and visualize results using natural language, significantly easing the translation from intricate proteogenomic data to valuable biological insights. Citation Format: Wei Liu, Jun Liu, Yitao Tang, Chaozhong Liu, Meiyi Song, Zhenlin Ju, Shweth V. Kumar, Yiling Lu, Rehan Akbani, Gordon Mills, Han Liang. TCPAplus: An LLM-empowered chatbot for analyzing a large protein expression atlas of human cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB247.

Abstract LB252: iCellSight: An interactive tool for analyzing and visualizing in-situ high-plex cellular protein data

Abstract Introduction The increased use of tissue-based multiplex immunohistochemistry (mIHC) in cancer research has significantly enhanced the generation of spatial hyper-plex images, crucial for understanding complex tumor-immune microenvironments. This advancement has led to a growing demand for spatial data analysis tools that enable quick and insightful evaluation. While existing spatial analysis software like Steinbock and SPIAT offer foundational support, effectively managing the high-dimensional and voluminous cellular data to derive meaningful biological insights remains challenging. This is especially true for researchers lacking bioinformatics expertise who seek to conduct quick preliminary evaluations. To address this gap, we introduce iCellSight: a user-friendly, code-free interactive analysis and visualization tool tailored to empower researchers with rapid and intuitive interpretation of in-situ cellular data. Material and methods A formalin-fixed, paraffin-embedded colorectal carcinoma (CRC) patient tissue section was subjected to staining using COMET (Lunaphore, Switzerland), which automates sequential immunofluorescence staining and imaging of up to 40 protein markers. The stacked image file produced was analyzed with HALO (Indica Labs, USA), which generated cell-level data like cell coordinates, stain intensity values and user-defined cell types. We developed a Shiny App tool, using the Shiny R package, with key features for cell data interpretation: (1) user-input cell number subsampling; (2) dimensional reduction plots with PhenoGraph for unsupervised clustering, allowing a choice of UMAP or t-SNE, and neighbor number selection of 30, 50 or 150; (3) 2D tissue space mapping of PhenoGraph clusters; and (4) heatmap visualizations displaying cluster-wise biomarker expression (with customizable color schemes and scaling), biomarker co-expression, and cluster pair-wise distance. Results Using iCellSight, 297,000 cells were detected from the CRC sample, where 27 clusters were identified. We observed 3 distinct clusters from the t-SNE plot that consist of macrophages, granulocytes, and NK cells and monocytes respectively. From the cluster projection in 2D tissue space, the cluster of NK cells and monocytes was found to be densely populated in a particular area of the tumor nest. Conclusion We presented iCellSight, a code-free visualization and analysis tool which streamlines the semi-automated analysis of spatial hyper-plex protein data. Designed with an intuitive user interface, this app enables researchers to efficiently navigate its features without requiring any prior coding expertise. We anticipate that the visualizations and analysis provided by this tool will significantly assist researchers in their data analyses and facilitate the validation of experimental hypotheses. Citation Format: Yancun Zhu, Felicia Wee, Menaka Priyadharsani Rajapakse, Esther Gek Teo, Nicholas Ang, Solomonraj Wilson, Zheng Yi Ho, Willa Wen-You Yim, Li Yen Chong, Craig Ryan Joseph, Jeffrey Chun Lim, Zhen Wei Neo, Chwee Ming Lim, Bernett Lee, Olaf Rotzschke, Joe Yeong, Mai Chan Lau. iCellSight: An interactive tool for analyzing and visualizing in-situ high-plex cellular protein data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB252.

A high heterozygosity genome assembly of Aedes albopictus enables the discovery of the association of PGANT3 with blood-feeding behavior

The Asian tiger mosquito, Aedes albopictus, is a global invasive species, notorious for its role in transmitting dangerous human arboviruses such as dengue and Chikungunya. Although hematophagous behavior is repulsive, it is an effective strategy for mosquitoes like Aedes albopictus to transmit viruses, posing a significant risk to human health. However, the fragmented nature of the Ae. albopictus genome assembly has been a significant challenge, hindering in-depth biological and genetic studies of this mosquito. In this research, we have harnessed a variety of technologies and implemented a novel strategy to create a significantly improved genome assembly for Ae. albopictus, designated as AealbF3. This assembly boasts a completeness rate of up to 98.1%, and the duplication rate has been minimized to 1.2%. Furthermore, the fragmented contigs or scaffolds of AealbF3 have been organized into three distinct chromosomes, an arrangement corroborated through syntenic plot analysis, which compared the genetic structure of Ae. albopictus with that of Ae. aegypti. Additionally, the study has revealed a phylogenetic relationship suggesting that the PGANT3 gene is implicated in the hematophagous behavior of Ae. albopictus. This involvement was preliminarily substantiated through RNA interference (RNAi) techniques and behavioral experiment. In summary, the AealbF3 genome assembly will facilitate new biological insights and intervention strategies for combating this formidable vector of disease. The innovative assembly process employed in this study could also serve as a valuable template for the assembly of genomes in other insects characterized by high levels of heterozygosity.

Stem cell dynamics and cellular heterogeneity across lineage subtypes of castrate resistant prostate cancer.

To resist lineage-dependent therapies such as androgen receptor inhibition, prostate luminal epithelial adenocarcinoma cells often adopt a stem-like state resulting in lineage-plasticity and phenotypic heterogeneity. Castrate resistant prostate adenocarcinoma can transition to neuroendocrine and occasionally to amphicrine, co-expressed luminal and neuroendocrine, phenotypes. We developed CRPC patient-derived organoid models that preserve heterogeneity of the originating tumor, including an amphicrine model displaying a range of luminal and neuroendocrine phenotypes. To gain biological insight and to identify potential treatment targets within heterogeneous tumor cell populations, we assessed the lineage hierarchy and molecular characteristics of various CRPC tumor subpopulations. Transcriptionally similar stem/progenitor cells were identified for all lineage populations. Lineage tracing in amphicrine CRPC showed that heterogeneity originated from distinct subclones of infrequent stem/progenitor cells that produced mainly quiescent differentiated amphicrine progeny. By contrast, adenocarcinoma CRPC progeny originated from stem/progenitor cells and self-renewing differentiated luminal cells. NEPC was composed almost exclusively of self-renewing stem/progenitor cells. Amphicrine subpopulations were enriched for secretory luminal, mesenchymal, and enzalutamide treatment persistent signatures that characterize clinical progression. Finally, the amphicrine stem/progenitor subpopulation was specifically depleted with an AURKA inhibitor, which blocked tumor growth. These data illuminate distinct stem cell characteristics for subtype-specific CRPC in addition to demonstrating a context for targeting differentiation-competent prostate stem cells.

Native-state proteomics of Parvalbumin interneurons identifies unique molecular signatures and vulnerabilities to early Alzheimer’s pathology

Dysfunction in fast-spiking parvalbumin interneurons (PV-INs) may represent an early pathophysiological perturbation in Alzheimer’s Disease (AD). Defining early proteomic alterations in PV-INs can provide key biological and translationally-relevant insights. We used cell-type-specific in-vivo biotinylation of proteins (CIBOP) coupled with mass spectrometry to obtain native-state PV-IN proteomes. PV-IN proteomic signatures include high metabolic and translational activity, with over-representation of AD-risk and cognitive resilience-related proteins. In bulk proteomes, PV-IN proteins were associated with cognitive decline in humans, and with progressive neuropathology in humans and the 5xFAD mouse model of Aβ pathology. PV-IN CIBOP in early stages of Aβ pathology revealed signatures of increased mitochondria and metabolism, synaptic and cytoskeletal disruption and decreased mTOR signaling, not apparent in whole-brain proteomes. Furthermore, we demonstrated pre-synaptic defects in PV-to-excitatory neurotransmission, validating our proteomic findings. Overall, in this study we present native-state proteomes of PV-INs, revealing molecular insights into their unique roles in cognitive resiliency and AD pathogenesis.

Enhancing mass spectrometry data analysis: A novel framework for calibration, outlier detection, and classification

Mass spectrometry (MS) is a powerful analytical technique in metabolomics, enabling the identification and quantification of metabolites. However, analyzing MS data poses challenges such as batch effects, outliers, and high-dimensional data. In this paper, we propose a comprehensive framework for MS data analysis. The framework integrates data calibration, outlier detection, and automatic classification modules. Data calibration is performed using a deep autoencoder to remove batch effects. Outlier detection combines multiple algorithms through ensemble learning to identify and remove outliers. Automatic classification utilizes a transformer model to handle high-dimensional data and capture global feature relationships. Experimental results on myocardial infarction (MI) and coronary heart disease (CHD) datasets demonstrate the effectiveness of the framework. It outperforms traditional classification models and achieves higher accuracy. The proposed framework provides a robust solution for MS data analysis, facilitating more accurate classification and enabling reliable biological insights in metabolomics research.

Research autopsy programmes in oncology: shared experience from 14 centres across the world.

While there is a great clinical need to understand the biology of metastatic cancer in order to treat it more effectively, research is hampered by limited sample availability. Research autopsy programmes can crucially advance the field through synchronous, extensive, and high-volume sample collection. However, it remains an underused strategy in translational research. Via an extensive questionnaire, we collected information on the study design, enrolment strategy, study conduct, sample and data management, and challenges and opportunities of research autopsy programmes in oncology worldwide. Fourteen programmes participated in this study. Eight programmes operated 24 h/7 days, resulting in a lower median postmortem interval (time between death and start of the autopsy, 4 h) compared with those operating during working hours (9 h). Most programmes (n = 10) succeeded in collecting all samples within a median of 12 h after death. A large number of tumour sites were sampled during each autopsy (median 15.5 per patient). The median number of samples collected per patient was 58, including different processing methods for tumour samples but also non-tumour tissues and liquid biopsies. Unique biological insights derived from these samples included metastatic progression, treatment resistance, disease heterogeneity, tumour dormancy, interactions with the tumour micro-environment, and tumour representation in liquid biopsies. Tumour patient-derived xenograft (PDX) or organoid (PDO) models were additionally established, allowing for drug discovery and treatment sensitivity assays. Apart from the opportunities and achievements, we also present the challenges related with postmortem sample collections and strategies to overcome them, based on the shared experience of these 14 programmes. Through this work, we hope to increase the transparency of postmortem tissue donation, to encourage and aid the creation of new programmes, and to foster collaborations on these unique sample collections. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Genomic regions and biological pathways associated with sex-limited reproductive traits in bovine species.

Many animal species exhibit sex-limited traits, where certain phenotypes are exclusively expressed in one sex. Yet, the genomic regions that contribute to these sex-limited traits in males and females remain a subject of debate. Reproductive traits are ideal phenotypes to study sexual differences since they are mostly expressed in a sex-limited way. Therefore, this study aims to use local correlation analyses to identify genomic regions and biological pathways significantly associated with male and female sex-limited traits in two distinct cattle breeds (Brahman (BB) and Tropical Composite (TC)). We used the Correlation Scan method to perform local correlation analysis on 42 trait pairs consisting of six female and seven male reproductive traits recorded on ~1000 animals for each sex in each breed. To pinpoint a specific region associated these sex-limited reproductive traits, we investigated the genomic region(s) consistently identified as significant across the 42 trait pairs in each breed. The genes found in the identified regions were subjected to Quantitative Trait Loci (QTL) colocalization, QTL enrichment analyses, and functional analyses to gain biological insight into sexual differences. We found that the genomic regions associated with the sex-limited reproductive phenotypes are widely distributed across all the chromosomes. However, no single region across the genome was associated all the 42 reproductive trait pairs in the two breeds. Nevertheless, we found a region on the X-chromosome to be most significant for 80-90% (BB; 33 and TC; 38) of the total 42 trait pairs. A considerable number of the genes in this region were regulatory genes. By considering only genomic regions that were significant for at least 50% of the 42 trait pairs, we observed more regions spread across the autosomes and the X-chromosome. All genomic regions identified were highly enriched for trait-specific QTL linked to sex-limited traits (percentage normal sperm, metabolic weight, average daily gain, carcass weight, age at puberty, etc.). The gene list created from these identified regions were enriched for biological pathways that contribute to the observed differences between sexes. Our results demonstrate that genomic regions associated with male and female sex-limited reproductive traits are distributed across the genome. Yet, chromosome X seems to exert a relatively larger effect on the phenotypic variation observed between the sexes.

Generalized reporter score-based enrichment analysis for omics data.

Enrichment analysis contextualizes biological features in pathways to facilitate a systematic understanding of high-dimensional data and is widely used in biomedical research. The emerging reporter score-based analysis (RSA) method shows more promising sensitivity, as it relies on P-values instead of raw values of features. However, RSA cannot be directly applied to multi-group and longitudinal experimental designs and is often misused due to the lack of a proper tool. Here, we propose the Generalized Reporter Score-based Analysis (GRSA) method for multi-group and longitudinal omics data. A comparison with other popular enrichment analysis methods demonstrated that GRSA had increased sensitivity across multiple benchmark datasets. We applied GRSA to microbiome, transcriptome and metabolome data and discovered new biological insights in omics studies. Finally, we demonstrated the application of GRSA beyond functional enrichment using a taxonomy database. We implemented GRSA in an R package, ReporterScore, integrating with a powerful visualization module and updatable pathway databases, which is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/ReporterScore). We believe that the ReporterScore package will be a valuable asset for broad biomedical research fields.