State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing

Transcriptomics and metabolomics: Challenges of studying obesity in osteoarthritis

Objective

Obesity is a leading risk factor for both the incidence and progression of osteoarthritis (OA). Omic technologies, including transcriptomics and metabolomics are capable of identifying RNA and metabolite profiles in tissues and biofluids of OA patients. The objective of this review is to highlight studies using transcriptomics and metabolomics that contribute to our understanding of OA pathology in relation to obesity.

Design

We conducted a targeted search of PUBMED for articles, and GEO for datasets, published up to February 13, 2024, screening for those using high-throughput transcriptomic and metabolomic techniques to study human or pre-clinical animal model tissues or biofluids related to obesity-associated OA. We describe relevant studies and discuss challenges studying obesity as a disease-related factor in OA.

Results

Of the 107 publications identified by our search criteria, only 15 specifically used transcriptomics or metabolomics to study joint tissues or biofluids in obesity-related OA. Specific transcriptomic and metabolomic signatures associated with obesity-related OA have been defined in select local joint tissues, biofluids and other biological material. However, considerable challenges exist in understanding contributions of obesity-associated modifications of transcriptomes and metabolomes related to OA, including sociodemographic, anthropometric, dietary and molecular redundancy-related factors.

Conclusions

A number of additional transcriptomic and metabolomic studies are needed to comprehensively understand how obesity affects OA incidence, progression and outcomes. Integration of transcriptome and metabolome signatures from multiple tissues and biofluids, using network-based approaches will likely help to better define putative therapeutic targets that could enable precision medicine approaches to obese OA patients.

Insights into cyanobacterial blooms through the lens of omics

Cyanobacteria are oxygen-producing photosynthetic bacteria that convert carbon dioxide into biomass upon exposure to sunlight. However, favorable conditions cause harmful cyanobacterial blooms (HCBs), which are the dense accumulation of biomass at the water surface or subsurface, posing threats to freshwater ecosystems and human health. Understanding the mechanisms underlying cyanobacterial bloom formation is crucial for effective management. In this regard, recent advancements in omics technologies have provided valuable insights into HCBs, which have raised expectations to develop more effective control methods in the near future. This literature review aims to present the genomic architecture, adaptive mechanisms, microbial interactions, and ecological impacts of HCBs through the lens of omics. Genomic analysis indicates that the genome plasticity of cyanobacteria has enabled their resilience and effective adaptation to environmental changes. Transcriptomic investigations have revealed that cyanobacteria use various strategies for adapting to environmental stress. Additionally, metagenomic and metatranscriptomic analyses have emphasized the significant role of the microbial community in regulating HCBs. Finally, we offer perspectives on potential opportunities for further research in this field.

Systems-level computational modeling in ischemic stroke: from cells to patients

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development.

Best practices for data management and sharing in experimental biomedical research

Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

Collection of biospecimens from the inspiration4 mission establishes the standards for the space omics and medical atlas (SOMA)

The SpaceX Inspiration4 mission provided a unique opportunity to study the impact of spaceflight on the human body. Biospecimen samples were collected from four crew members longitudinally before (Launch: L-92, L-44, L-3 days), during (Flight Day: FD1, FD2, FD3), and after (Return: R + 1, R + 45, R + 82, R + 194 days) spaceflight, spanning a total of 289 days across 2021-2022. The collection process included venous whole blood, capillary dried blood spot cards, saliva, urine, stool, body swabs, capsule swabs, SpaceX Dragon capsule HEPA filter, and skin biopsies. Venous whole blood was further processed to obtain aliquots of serum, plasma, extracellular vesicles and particles, and peripheral blood mononuclear cells. In total, 2,911 sample aliquots were shipped to our central lab at Weill Cornell Medicine for downstream assays and biobanking. This paper provides an overview of the extensive biospecimen collection and highlights their processing procedures and long-term biobanking techniques, facilitating future molecular tests and evaluations.As such, this study details a robust framework for obtaining and preserving high-quality human, microbial, and environmental samples for aerospace medicine in the Space Omics and Medical Atlas (SOMA) initiative, which can aid future human spaceflight and space biology experiments.

The Gut Microbiome and Symptom Burden After Kidney Transplantation: An Overview and Research Opportunities

Many kidney transplant recipients continue to experience high symptom burden despite restoration of kidney function. High symptom burden is a significant driver of quality of life. In the post-transplant setting, high symptom burden has been linked to negative outcomes including medication non-adherence, allograft rejection, graft loss, and even mortality. Symbiotic bacteria (microbiota) in the human gastrointestinal tract critically interact with the immune, endocrine, and neurological systems to maintain homeostasis of the host. The gut microbiome has been proposed as an underlying mechanism mediating symptoms in several chronic medical conditions including irritable bowel syndrome, chronic fatigue syndrome, fibromyalgia, and psychoneurological disorders via the gut-brain-microbiota axis, a bidirectional signaling pathway between the enteric and central nervous system. Post-transplant exposure to antibiotics, antivirals, and immunosuppressant medications results in significant alterations in gut microbiota community composition and function, which in turn alter these commensal microorganisms' protective effects. This overview will discuss the current state of the science on the effects of the gut microbiome on symptom burden in kidney transplantation and future directions to guide this field of study.

Comparative analysis of integrative classification methods for multi-omics data

Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple 'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications.

Nanomedomics

Nanomaterials have attractive physicochemical properties. A variety of nanomaterials such as inorganic, lipid, polymers, and protein nanoparticles have been widely developed for nanomedicine via chemical conjugation or physical encapsulation of bioactive molecules. Superior to traditional drugs, nanomedicines offer high biocompatibility, good water solubility, long blood circulation times, and tumor-targeting properties. Capitalizing on this, several nanoformulations have already been clinically approved and many others are currently being studied in clinical trials. Despite their undoubtful success, the molecular mechanism of action of the vast majority of nanomedicines remains poorly understood. To tackle this limitation, herein, this review critically discusses the strategy of applying multiomics analysis to study the mechanism of action of nanomedicines, named nanomedomics, including advantages, applications, and future directions. A comprehensive understanding of the molecular mechanism could provide valuable insight and therefore foster the development and clinical translation of nanomedicines.

Spatial genomics: mapping human steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.

Multiomics analyses reveal the central role of the nucleolus and its machinery during heat stress acclimation in Pinus radiata

Global warming is causing rapid changes in mean annual temperature and more severe drought periods. These are major contributors of forest dieback, which is becoming more frequent and widespread. In this work, we investigated how the transcriptome of Pinus radiata changed during initial heat stress response and acclimation. To this end, we generated a high-density dataset employing Illumina technology. This approach allowed us to reconstruct a needle transcriptome, defining 12 164 and 13 590 transcripts as down- and up-regulated, respectively, during a time course stress acclimation experiment. Additionally, the combination of transcriptome data with other available omics layers allowed us to determine the complex inter-related processes involved in the heat stress response from the molecular to the physiological level. Nucleolus and nucleoid activities seem to be a central core in the acclimating process, producing specific RNA isoforms and other essential elements for anterograde-retrograde stress signaling such as NAC proteins (Pra_vml_051671_1 and Pra_vml_055001_5) or helicase RVB. These mechanisms are connected by elements already known in heat stress response (redox, heat-shock proteins, or abscisic acid-related) and with others whose involvement is not so well defined such as shikimate-related, brassinosteriods, or proline proteases together with their potential regulatory elements. This work provides a first in-depth overview about molecular mechanisms underlying the heat stress response and acclimation in P. radiata.

Omics-based biomarkers as useful tools in metabolic dysfunction-associated steatotic liver disease clinical practice: How far are we?

Unmet needs exist in metabolic dysfunction-associated steatotic liver disease (MASLD) risk stratification. Our ability to identify patients with MASLD with advanced fibrosis and at higher risk for adverse outcomes is still limited. Incorporating novel biomarkers could represent a meaningful improvement to current risk predictors. With this aim, omics technologies have revolutionized the process of MASLD biomarker discovery over the past decades. While the research in this field is thriving, much of the publication has been haphazard, often using single-omics data and specimen sets of convenience, with many identified candidate biomarkers but lacking clinical validation and utility. If we incorporate these biomarkers to direct patients' management, it should be considered that the roadmap for translating a newly discovered omics-based signature to an actual, analytically valid test useful in MASLD clinical practice is rigorous and, therefore, not easily accomplished. This article presents an overview of this area's current state, the conceivable opportunities and challenges of omics-based laboratory diagnostics, and a roadmap for improving MASLD biomarker research.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

The omics era: a nexus of untapped potential for Mendelian chromatinopathies

The OMICs cascade describes the hierarchical flow of information through biological systems. The epigenome sits at the apex of the cascade, thereby regulating the RNA and protein expression of the human genome and governs cellular identity and function. Genes that regulate the epigenome, termed epigenes, orchestrate complex biological signaling programs that drive human development. The broad expression patterns of epigenes during human development mean that pathogenic germline mutations in epigenes can lead to clinically significant multi-system malformations, developmental delay, intellectual disabilities, and stem cell dysfunction. In this review, we refer to germline developmental disorders caused by epigene mutation as "chromatinopathies". We curated the largest number of human chromatinopathies to date and our expanded approach more than doubled the number of established chromatinopathies to 179 disorders caused by 148 epigenes. Our study revealed that 20.6% (148/720) of epigenes cause at least one chromatinopathy. In this review, we highlight key examples in which OMICs approaches have been applied to chromatinopathy patient biospecimens to identify underlying disease pathogenesis. The rapidly evolving OMICs technologies that couple molecular biology with high-throughput sequencing or proteomics allow us to dissect out the causal mechanisms driving temporal-, cellular-, and tissue-specific expression. Using the full repertoire of data generated by the OMICs cascade to study chromatinopathies will provide invaluable insight into the developmental impact of these epigenes and point toward future precision targets for these rare disorders.

Computational frameworks integrating deep learning and statistical models in mining multimodal omics data

BACKGROUND

In health research, multimodal omics data analysis is widely used to address important clinical and biological questions. Traditional statistical methods rely on the strong assumptions of distribution. Statistical methods such as testing and differential expression are commonly used in omics analysis. Deep learning, on the other hand, is an advanced computer science technique that is powerful in mining high-dimensional omics data for prediction tasks. Recently, integrative frameworks or methods have been developed for omics studies that combine statistical models and deep learning algorithms.

METHODS AND RESULTS

The aim of these integrative frameworks is to combine the strengths of both statistical methods and deep learning algorithms to improve prediction accuracy while also providing interpretability and explainability. This review report discusses the current state-of-the-art integrative frameworks, their limitations, and potential future directions in survival and time-to-event longitudinal analysis, dimension reduction and clustering, regression and classification, feature selection, and causal and transfer learning.

Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies

OBJECTIVE

Osteoarthritis (OA) is a complex disease involving contributions from both local joint tissues and systemic sources. Patient characteristics, encompassing sociodemographic and clinical variables, are intricately linked with OA rendering its understanding challenging. Technological advancements have allowed for a comprehensive analysis of transcripts, proteomes and metabolomes in OA tissues/fluids through omic analyses. The objective of this review is to highlight the advancements achieved by omic studies in enhancing our understanding of OA pathogenesis over the last three decades.

DESIGN

We conducted an extensive literature search focusing on transcriptomics, proteomics and metabolomics within the context of OA. Specifically, we explore how these technologies have identified individual transcripts, proteins, and metabolites, as well as distinctive endotype signatures from various body tissues or fluids of OA patients, including insights at the single-cell level, to advance our understanding of this highly complex disease.

RESULTS

Omic studies reveal the description of numerous individual molecules and molecular patterns within OA-associated tissues and fluids. This includes the identification of specific cell (sub)types and associated pathways that contribute to disease mechanisms. However, there remains a necessity to further advance these technologies to delineate the spatial organization of cellular subtypes and molecular patterns within OA-afflicted tissues.

CONCLUSIONS

Leveraging a multi-omics approach that integrates datasets from diverse molecular detection technologies, combined with patients' clinical and sociodemographic features, and molecular and regulatory networks, holds promise for identifying unique patient endophenotypes. This holistic approach can illuminate the heterogeneity among OA patients and, in turn, facilitate the development of tailored therapeutic interventions.

Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in African American and European American patients with TNBC

Racial disparities in triple-negative breast cancer (TNBC) outcomes have been reported. However, the biological mechanisms underlying these disparities remain unclear. We integrated imaging mass cytometry and spatial transcriptomics, to characterize the tumor microenvironment (TME) of African American (AA) and European American (EA) patients with TNBC. The TME in AA patients was characterized by interactions between endothelial cells, macrophages, and mesenchymal-like cells, which were associated with poor patient survival. In contrast, the EA TNBC-associated niche is enriched in T-cells and neutrophils suggestive of an exhaustion and suppression of otherwise active T cell responses. Ligand-receptor and pathway analyses of race-associated niches found AA TNBC to be immune cold and hence immunotherapy resistant tumors, and EA TNBC as inflamed tumors that evolved a distinctive immunosuppressive mechanism. Our study revealed the presence of racially distinct tumor-promoting and immunosuppressive microenvironments in AA and EA patients with TNBC, which may explain the poor clinical outcomes.

HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification

MOTIVATION

The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information.

RESULTS

This article presents HyperTMO, a Trusted Multi-Omics integration framework based on Hypergraph convolutional network for patient classification. HyperTMO constructs hypergraph structures to represent the association between samples in single-omics data, then evidence extraction is performed by hypergraph convolutional network, and multi-omics information is integrated at an evidence level. Last, we experimentally demonstrate that HyperTMO outperforms other state-of-the-art methods in breast cancer subtype classification and Alzheimer's disease classification tasks using multi-omics data from TCGA (BRCA) and ROSMAP datasets. Importantly, HyperTMO is the first attempt to integrate hypergraph structure, evidence theory, and multi-omics integration for patient classification. Its accurate and robust properties bring great potential for applications in clinical diagnosis.

AVAILABILITY AND IMPLEMENTATION

HyperTMO and datasets are publicly available at https://github.com/ippousyuga/HyperTMO.

Applications of transcriptomics in ischemia reperfusion research in lung transplantation

Ischemia-reperfusion (IR) injury contributes to primary graft dysfunction, a major cause of early mortality after lung transplantation. Transcriptomics uses high-throughput techniques to profile the RNA transcripts within a sample and provides a unique view of the mechanisms underlying various biological processes. This review aims to highlight the applications of transcriptomics in lung IR injury studies, which have thus far revealed inflammatory responses to be the major event activated by IR, identified potential biomarkers and therapeutic targets, and investigated the mechanisms of therapeutic interventions. Ex vivo lung perfusion, together with advanced bioinformatic and transcriptomic techniques, including single-cell RNA-sequencing, microRNA profiling, and multi-omics, continue to expand the capabilities of transcriptomics. In the future, the construction of biospecimen banks and the promotion of international collaborations among clinicians and researchers have the potential to advance our understanding of IR injury and improve the management of lung transplant recipients.

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.

An integrated Bayesian framework for multi-omics prediction and classification

With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner.

TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction

Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus presenting significant challenges in the integration of multi-omics data. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation module and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks; then, it applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we designed a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined-beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks.

Genomic data integration tutorial, a plant case study

BACKGROUND

The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts.

RESULTS

To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration.

CONCLUSION

The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.

Optimizing MS-Based Multi-Omics: Comparative Analysis of Protein, Metabolite, and Lipid Extraction Techniques

Multi-omics integrates diverse types of biological information from genomic, proteomic, and metabolomics experiments to achieve a comprehensive understanding of complex cellular mechanisms. However, this approach is also challenging due to technical issues such as limited sample quantities, the complexity of data pre-processing, and reproducibility concerns. Furthermore, existing studies have primarily focused on technical performance assessment and the presentation of modified protocols through quantitative comparisons of the identified protein counts. Nevertheless, the specific differences in these comparisons have been minimally investigated. Here, findings obtained from various omics approaches were profiled using various extraction methods (methanol extraction, the Folch method, and Matyash methods for metabolites and lipids) and two digestion methods (filter-aided sample preparation (FASP) and suspension traps (S-Trap)) for resuspended proteins. FASP was found to be more effective for the identification of membrane-related proteins, whereas S-Trap excelled in isolating nuclear-related and RNA-processing proteins. Thus, FASP may be suitable for investigating the immune response and bacterial infection pathways, whereas S-Trap may be more effective for studies focused on the mechanisms of neurodegenerative diseases. Moreover, regarding the choice of extraction method, the single-phase method identified organic compounds and compounds related to fatty acids, whereas the two-phase extraction method identified more hydrophilic compounds such as nucleotides. Lipids with strong hydrophobicity, such as ChE and TG, were identified in the two-phase extraction results. These findings highlight that significant differences among small molecules are primarily identified due to the varying polarities of extraction solvents. These results, obtained by considering variables such as human error and batch effects in the sample preparation step, offer comprehensive and detailed results not previously provided by existing studies, thereby aiding in the selection of the most suitable pre-processing approach.

A multi-omics data analysis workflow packaged as a FAIR Digital Object

BACKGROUND

Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object.

FINDINGS

We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub.

CONCLUSIONS

Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

Advancing Precision Medicine with Gene and Cell Therapy in Malaysia: Ethical, Legal, and Social Implications

A new era of gene and cell therapy for treating human diseases has been envisioned for several decades. However, given that the technology can alter any DNA/cell in human beings, it poses specific ethical, legal, and social difficulties in its application. In Malaysia, current bioethics and medical ethics guidelines tackle clinical trials and biomedical research, medical genetic services, and stem cell research/therapy. However, no comprehensive framework and policy is available to cater to ethical gene and cell therapy in the country. Incorporating ethical, legal, and social implications (ELSI) would be crucial to guide the appropriate use of human gene and cell therapy in conjunction with precision medicine. Policy experts, scientists, bioethicists, and public members must debate the associated ELSI and the professional code of conduct while preserving human rights.

Spatial pharmacology using mass spectrometry imaging

The emerging and powerful field of spatial pharmacology can map the spatial distribution of drugs and their metabolites, as well as their effects on endogenous biomolecules including metabolites, lipids, proteins, peptides, and glycans, without the need for labeling. This is enabled by mass spectrometry imaging (MSI) that provides previously inaccessible information in diverse phases of drug discovery and development. We provide a perspective on how MSI technologies and computational tools can be implemented to reveal quantitative spatial drug pharmacokinetics and toxicology, tissue subtyping, and associated biomarkers. We also highlight the emerging potential of comprehensive spatial pharmacology through integration of multimodal MSI data with other spatial technologies. Finally, we describe how to overcome challenges including improving reproducibility and compound annotation to generate robust conclusions that will improve drug discovery and development processes.

Advances in steroid research from the pioneering neurosteroid concept to metabolomics: New insights into pregnenolone function

Advances in neuroendocrinology have led to major discoveries since the 19th century, identifying adaptive loops for maintaining homeostasis. One of the most remarkable discoveries was the concept of neurosteroids, according to which the brain is not only a target but also a source of steroid production. The identification of new membrane steroid targets now underpins the neuromodulatory effects of neurosteroids such as pregnenolone, which is involved in functions mediated by the GPCR CB1 receptor. Structural analysis of steroids is a key feature of their interactions with the phospholipid membrane, receptors and resulting activity. Therefore, mass spectrometry-based methods have been developed to elucidate the metabolic pathways of steroids, the ultimate approach being metabolomics, which allows the identification of a large number of metabolites in a single sample. This approach should enable us to make progress in understanding the role of neurosteroids in the functioning of physiological and pathological processes.

How spatial omics approaches can be used to map the biological impacts of stress in psychiatric disorders: a perspective, overview and technical guide

Exposure to significant levels of stress and trauma throughout life is a leading risk factor for the development of major psychiatric disorders. Despite this, we do not have a comprehensive understanding of the mechanisms that explain how stress raises psychiatric disorder risk. Stress in humans is complex and produces variable molecular outcomes depending on the stress type, timing, and duration. Deciphering how stress increases disorder risk has consequently been challenging to address with the traditional single-target experimental approaches primarily utilized to date. Importantly, the molecular processes that occur following stress are not fully understood but are needed to find novel treatment targets. Sequencing-based omics technologies, allowing for an unbiased investigation of physiological changes induced by stress, are rapidly accelerating our knowledge of the molecular sequelae of stress at a single-cell resolution. Spatial multi-omics technologies are now also emerging, allowing for simultaneous analysis of functional molecular layers, from epigenome to proteome, with anatomical context. The technology has immense potential to transform our understanding of how disorders develop, which we believe will significantly propel our understanding of how specific risk factors, such as stress, contribute to disease course. Here, we provide our perspective of how we believe these technologies will transform our understanding of the neurobiology of stress, and also provided a technical guide to assist molecular psychiatry and stress researchers who wish to implement spatial omics approaches in their own research. Finally, we identify potential future directions using multi-omics technology in stress research.

A High-Throughput PIXUL-Matrix-Based Toolbox to Profile Frozen and Formalin-Fixed Paraffin-Embedded Tissues Multiomes

Large-scale high-dimensional multiomics studies are essential to unravel molecular complexity in health and disease. We developed an integrated system for tissue sampling (CryoGrid), analytes preparation (PIXUL), and downstream multiomic analysis in a 96-well plate format (Matrix), MultiomicsTracks96, which we used to interrogate matched frozen and formalin-fixed paraffin-embedded (FFPE) mouse organs. Using this system, we generated 8-dimensional omics data sets encompassing 4 molecular layers of intracellular organization: epigenome (H3K27Ac, H3K4m3, RNA polymerase II, and 5mC levels), transcriptome (messenger RNA levels), epitranscriptome (m6A levels), and proteome (protein levels) in brain, heart, kidney, and liver. There was a high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles confirmed known organ-specific superenhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic profiles, known to be poorly correlated with transcriptomic data, can be more accurately predicted by the full suite of multiomics data, compared with using epigenomic, transcriptomic, or epitranscriptomic measurements individually.

Evo-Devo Mechanobiology: The Missing Link

While the modern framework of evolutionary development (evo-devo) has been decidedly genetic, historic analyses have also considered the importance of mechanics in the evolution of form. With the aid of recent technological advancements in both quantifying and perturbing changes in the molecular and mechanical effectors of organismal shape, how molecular and genetic cues regulate the biophysical aspects of morphogenesis is becoming increasingly well studied. As a result, this is an opportune time to consider how the tissue-scale mechanics that underlie morphogenesis are acted upon through evolution to establish morphological diversity. Such a focus will enable a field of evo-devo mechanobiology that will serve to better elucidate the opaque relations between genes and forms by articulating intermediary physical mechanisms. Here, we review how the evolution of shape is measured and related to genetics, how recent strides have been made in the dissection of developmental tissue mechanics, and how we expect these areas to coalesce in evo-devo studies in the future.

BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation

One of the challenges in multi-omics data analysis for precision medicine is the efficient exploration of undiscovered molecular interactions in disease processes. We present BioMOBS, a workflow consisting of two data visualization tools integrated with an open-source molecular information database to perform clinically relevant analyses (https://github.com/driesheylen123/BioMOBS). We performed exploratory pathway analysis with BioMOBS and demonstrate its ability to generate relevant molecular hypotheses, by reproducing recent findings in type 2 diabetes UK biobank data. The central visualisation tool, where data-driven and literature-based findings can be integrated, is available within the github link as well. BioMOBS is a workflow that leverages information from multiple data-driven interactive analyses and visually integrates it with established pathway knowledge. The demonstrated use cases place trust in the usage of BioMOBS as a procedure to offer clinically relevant insights in disease pathway analyses on various types of omics data.

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

Exploring the role of transcriptional and post-transcriptional processes in mRNA co-expression

Co-expression of two or more genes at the single-cell level is usually associated with functional co-regulation. While mRNA co-expression-measured as the correlation in mRNA levels-can be influenced by both transcriptional and post-transcriptional events, transcriptional regulation is typically considered dominant. We review and connect the literature describing transcriptional and post-transcriptional regulation of co-expression. To enhance our understanding, we integrate four datasets spanning single-cell gene expression data, single-cell promoter activity data and individual transcript half-lives. Confirming expectations, we find that positive co-expression necessitates promoter coordination and similar mRNA half-lives. Surprisingly, negative co-expression is favored by differences in mRNA half-lives, contrary to initial predictions from stochastic simulations. Notably, this association manifests specifically within clusters of genes. We further observe a striking compensation between promoter coordination and mRNA half-lives, which additional stochastic simulations suggest might give rise to the observed co-expression patterns. These findings raise intriguing questions about the functional advantages conferred by this compensation between distal kinetic steps.

Multi-omics analysis in developmental bone biology

Single-cell omics and multi-omics have revolutionized our understanding of molecular and cellular biological processes at a single-cell level. In bone biology, the combination of single-cell RNA-sequencing analyses and in vivo lineage-tracing approaches has successfully identified multi-cellular diversity and dynamics of skeletal cells. This established a new concept that bone growth and regeneration are regulated by concerted actions of multiple types of skeletal stem cells, which reside in spatiotemporally distinct niches. One important subtype is endosteal stem cells that are particularly abundant in young bone marrow. The discovery of this new skeletal stem cell type has been facilitated by single-cell multi-omics, which simultaneously measures gene expression and chromatin accessibility. Using single-cell omics, it is now possible to computationally predict the immediate future state of individual cells and their differentiation potential. In vivo validation using histological approaches is the key to interpret the computational prediction. The emerging spatial omics, such as spatial transcriptomics and epigenomics, have major advantage in retaining the location of individual cells within highly complex tissue architecture. Spatial omics can be integrated with other omics to further obtain in-depth insights. Single-cell multi-omics are now becoming an essential tool to unravel intricate multicellular dynamics and intercellular interactions of skeletal cells.

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics

Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.

An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics

Multi-omic analyses are necessary to understand the complex biological processes taking place at the tissue and cell level, but also to make reliable predictions about, for example, disease outcome. Several linear methods exist that create a joint embedding using paired information per sample, but recently there has been a rise in the popularity of neural architectures that embed paired -omics into the same non-linear manifold. This work describes a head-to-head comparison of linear and non-linear joint embedding methods using both bulk and single-cell multi-modal datasets. We found that non-linear methods have a clear advantage with respect to linear ones for missing modality imputation. Performance comparisons in the downstream tasks of survival analysis for bulk tumor data and cell type classification for single-cell data lead to the following insights: First, concatenating the principal components of each modality is a competitive baseline and hard to beat if all modalities are available at test time. However, if we only have one modality available at test time, training a predictive model on the joint space of that modality can lead to performance improvements with respect to just using the unimodal principal components. Second, -omic profiles imputed by neural joint embedding methods are realistic enough to be used by a classifier trained on real data with limited performance drops. Taken together, our comparisons give hints to which joint embedding to use for which downstream task. Overall, product-of-experts performed well in most tasks and was reasonably fast, while early integration (concatenation) of modalities did quite poorly.

Multi-omic analyses and network biology in cardiovascular disease

Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.

The Quartet Data Portal: integration of community-wide resources for multiomics quality control

The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.

The role of stress in the biological embedding of experience

Exposure to early adversity is one of the most important and pervasive risk factors for the development of nearly all major mental disorders across the lifespan. In the search for the mediating mechanisms and processes that underlie long-term stability of these effects, changes to stress-associated hormonal and cellular signalling have emerged as prime candidates. This review summarises evidence showing that experience of early adversity in the form of childhood abuse or neglect and exposure to severe institutional deprivation influences multiple interconnected bio-behavioural, physiological and cellular processes. This paper focusses on dysregulations of hormonal stress regulation, altered DNA methylation pattern, changes to transcriptomic profiles in the context of stress-immune interplay, and mitochondrial biology. Consistent findings that have emerged include a relative cortisol hypoactivity and hyporeactivity in response to challenge, increased activity of pro-inflammatory genes, and altered mitochondrial function. The majority of investigations have focussed on single outcomes, but there is a clear rationale of conceiving the implicated physiological processes as interconnected parts of a wider stress-associated regulatory network, which in turn is connected to behaviour and mental disorders. This calls for integrated and longitudinal investigations to come to a more comprehensive understanding of the role of stress in the biological embedding of experience. The review concludes with considerations of how stress research can contribute to translational efforts through characterising subtypes of mental disorders which arise as a function of early adversity, and have distinct features of behavioral and biological stress processing.

Integrated multi-omics analyses of microbial communities: a review of the current state and future directions

Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions.

NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures

Precision medicine relies on the identification of robust disease and risk factor signatures from omics data. However, current knowledge-driven approaches may overlook novel or unexpected phenomena due to the inherent biases in biological knowledge. In this study, we present a data-driven signature discovery workflow for DNA methylation analysis utilizing network-coherent autoencoders (NCAEs) with biologically relevant latent embeddings. First, we explored the architecture space of autoencoders trained on a large-scale pan-tissue compendium (n = 75 272) of human epigenome-wide association studies. We observed the emergence of co-localized patterns in the deep autoencoder latent space representations that corresponded to biological network modules. We determined the NCAE configuration with the strongest co-localization and centrality signals in the human protein interactome. Leveraging the NCAE embeddings, we then trained interpretable deep neural networks for risk factor (aging, smoking) and disease (systemic lupus erythematosus) prediction and classification tasks. Remarkably, our NCAE embedding-based models outperformed existing predictors, revealing novel DNA methylation signatures enriched in gene sets and pathways associated with the studied condition in each case. Our data-driven biomarker discovery workflow provides a generally applicable pipeline to capture relevant risk factor and disease information. By surpassing the limitations of knowledge-driven methods, our approach enhances the understanding of complex epigenetic processes, facilitating the development of more effective diagnostic and therapeutic strategies.

Computational immunohistochemical mapping adds immune context to histological phenotypes in mouse models of colitis

Inflammatory bowel disease (IBD) is characterized by chronic, dysregulated inflammation in the gastrointestinal tract. The heterogeneity of IBD is reflected through two major subtypes, Crohn's Disease (CD) and Ulcerative Colitis (UC). CD and UC differ across symptomatic presentation, histology, immune responses, and treatment. While colitis mouse models have been influential in deciphering IBD pathogenesis, no single model captures the full heterogeneity of clinical disease. The translational capacity of mouse models may be augmented by shifting to multi-mouse model studies that aggregate analysis across various well-controlled phenotypes. Here, we evaluate the value of histology in multi-mouse model characterizations by building upon a previous pipeline that detects histological disease classes in hematoxylin and eosin (H&E)-stained murine colons. Specifically, we map immune marker positivity across serially-sectioned slides to H&E histological classes across the dextran sodium sulfate (DSS) chemical induction model and the intestinal epithelium-specific, inducible Villin-CreERT2;Klf5fl/fl (Klf5ΔIND) genetic model. In this study, we construct the beginning frameworks to define H&E-patch-based immunophenotypes based on IHC-H&E mappings.

Touching Base with Some Mediterranean Diseases of Interest from Paradigmatic Cases at the "Magna Graecia" University Unit of Infectious Diseases: A Didascalic Review

Among infectious diseases, zoonoses are increasing in importance worldwide, especially in the Mediterranean region. We report herein some clinical cases from a third-level hospital in Calabria region (Southern Italy) and provide a narrative review of the most relevant features of these diseases from epidemiological and clinical perspectives. Further, the pathogenic mechanisms involved in zoonotic diseases are reviewed, focusing on the mechanisms used by pathogens to elude the immune system of the host. These topics are of particular concern for individuals with primary or acquired immunodeficiency (e.g., people living with HIV, transplant recipients, patients taking immunosuppressive drugs). From the present review, it appears that diagnostic innovations and the availability of more accurate methods, together with better monitoring of the incidence and prevalence of these infections, are urgently needed to improve interventions for better preparedness and response.

Organoids as complex (bio)systems

Organoids are three-dimensional structures derived from stem cells that mimic the organization and function of specific organs, making them valuable tools for studying complex systems in biology. This paper explores the application of complex systems theory to understand and characterize organoids as exemplars of intricate biological systems. By identifying and analyzing common design principles observed across diverse natural, technological, and social complex systems, we can gain insights into the underlying mechanisms governing organoid behavior and function. This review outlines general design principles found in complex systems and demonstrates how these principles manifest within organoids. By acknowledging organoids as representations of complex systems, we can illuminate our understanding of their normal physiological behavior and gain valuable insights into the alterations that can lead to disease. Therefore, incorporating complex systems theory into the study of organoids may foster novel perspectives in biology and pave the way for new avenues of research and therapeutic interventions to improve human health and wellbeing.

Multiset correlation and factor analysis enables exploration of multi-omics data

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

Integrated transcriptomics and proteomics assay identifies the role of FCGR1A in maintaining sperm fertilization capacity during semen cryopreservation in sheep

Semen cryopreservation is a promising technology employed in preserving high-quality varieties in animal husbandry and is also widely applied in the human sperm bank. However, the compromised qualities, such as decreased sperm motility, damaged membrane structure, and reduced fertilization competency, have significantly hampered the efficient application of this technique. Therefore, it is imperative to depict various molecular changes found in cryopreserved sperm and identify the regulatory network in response to the cryopreservation stress. In this study, semen was collected from three Chinese Merino rams and divided into untreated (fresh semen, FS) and programmed freezing (programmed freezing semen, PS) groups. After measuring different quality parameters, the ultra-low RNA-seq and tandem mass tag-based (TMT) proteome were conducted in both the groups. The results indicated that the motility (82.63% ± 3.55% vs. 34.10% ± 2.90%, p < 0.05) and viability (89.46% ± 2.53% vs. 44.78% ± 2.29%, p < 0.05) of the sperm in the FS group were significantly higher compared to those in the PS group. In addition, 45 upregulated and 291 downregulated genes, as well as 30 upregulated and 48 downregulated proteins, were found in transcriptomics and proteomics data separately. Moreover, three integrated methods, namely, functional annotation and enrichment analysis, Pearson's correlation analysis, and two-way orthogonal partial least squares (O2PLS) analysis, were used for further analysis. The results suggested that various differentially expressed genes and proteins (DEGs and DEPs) were mainly enriched in leishmaniasis and hematopoietic cell lineage, and Fc gamma receptor Ia (FCGR1A) was significantly downregulated in cryopreserved sperm both at mRNA and protein levels in comparison with the fresh counterpart. In addition, top five genes (FCGR1A, HCK, SLX4, ITGA3, and BET1) and 22 proteins could form a distinct network in which genes and proteins were significantly correlated (p < 0.05). Interestingly, FCGR1A also appeared in the top 25 correlation list based on O2PLS analysis. Hence, FCGR1A was selected as the most potential differentially expressed candidate for screening by the three integrated multi-omics analysis methods. In addition, Pearson's correlation analysis indicated that the expression level of FCGR1A was positively correlated with sperm motility and viability. A subsequent experiment was conducted to identify the biological role of FCGR1A in sperm function. The results showed that both the sperm viability (fresh group: 87.65% ± 4.17% vs. 75.8% ± 1.15%, cryopreserved group: 48.15% ± 0.63% vs. 42.45% ± 2.61%, p < 0.05) and motility (fresh group: 83.27% ± 4.15% vs. 70.41% ± 1.07%, cryopreserved group: 45.31% ± 3.28% vs. 35.13% ± 2.82%, p < 0.05) were significantly reduced in fresh and frozen sperm when FCGR1A was blocked. Moreover, the cleavage rate of embryos fertilized by FCGR1A-blocked sperm was noted to be significantly lower in both fresh (95.28% ± 1.16% vs. 90.44% ± 1.56%, p < 0.05) and frozen groups (89.8% ± 1.50% vs. 82.53% ± 1.53%, p < 0.05). In conclusion, our results revealed that the downregulated membrane protein FCGR1A can potentially contribute to the reduced sperm fertility competency in the cryopreserved sheep sperm.