High-throughput Omics Technologies Research Articles

Sepsis Research Using Omics Technology in the European Union and the United Kingdom: Maps, Trends, and Future Implications.

High-throughput omics technologies have become valuable tools for systems science research and clinical management of sepsis. This article analyzes sepsis research using omics technologies in the European Union (EU) and the United Kingdom from 1990 to May 2023 using bibliometric data from the Web of Science database. Using VOSviewer for network analysis, we examined the distribution patterns, funding characteristics, and collaborations among the states, noting trends of convergence and divergence. The analysis included 2078 articles, revealing an increasing rate of publications on sepsis research using omics approaches. The United Kingdom's research output is notably high, contributing 28.3% of the total research from the EU and United Kingdom combined. Germany, France, the Netherlands, and Italy together account for 56.9% of the publications from the EU member states. The United States is the leading international collaborator, particularly with the United Kingdom, followed by Germany and France. The EU-15 countries have significantly more publication outputs in this domain with growing but limited inclusion of the newer members of the EU. We suggest that the role of EU member states and the United Kingdom in sepsis research using omics technologies can be advanced by facilitating high-value, technology-driven health research, fostering collaboration, convergence, and equity in global health and biomedical research.

Integrative Multi-Omics Analysis for Etiology Classification and Biomarker Discovery in Stroke: Advancing towards Precision Medicine.

Recent advancements in high-throughput omics technologies have opened new avenues for investigating stroke at the molecular level and elucidating the intricate interactions among various molecular components. We present a novel approach for multi-omics data integration on knowledge graphs and have applied it to a stroke etiology classification task of 30 stroke patients through the integrative analysis of DNA methylation and mRNA, miRNA, and circRNA. This approach has demonstrated promising performance as compared to other existing single technology approaches.

Broadening the horizon of immune thrombocytopenia through Omics approaches.

Immune thrombocytopenia (ITP) is a highly heterogeneous autoimmune bleeding disorder characterized by low platelet counts due to an immune-mediated platelet destruction and impaired platelet production. The pathophysiology is multifactorial and remains to be fully unravelled. Consequently, disease trajectories and responses to therapeutics, despite the availability of multiple agents, can be unpredictable and differing between patients. There is an urgent need for the identification of diagnostic and therapeutic biomarkers, but this has proven to be challenging to achieve. To shed light on this, two studies in this issue of the British Journal of Haematology have recognized the opportunity of using high-throughput Omics technologies in ITP. Sun etal. performed proteomics, and Li etal. metabolomics, on bone marrow biopsy samples of patients with ITP. This was conducted using mass spectrometry and, due to the generation of large datasets, in combination with machine learning. These studies set the stage for further investigations exploring the high potential of multi-omics technologies in order to shed light on the heterogeneity in ITP, accelerating the path towards a much needed personalized medicine approach. Commentary on: Li etal. Metabolomics profile and machine learning prediction of treatment responses in immune thrombocytopenia: A prospective cohort study. Br J Haematol 2024;204:2405-2417. Commentary on: Sun etal. Proteomics landscape and machine learning prediction of long-term response to splenectomy in primary immune thrombocytopenia. Br J Haematol 2024;204:2418-2428.

Abstract 6812: Durable control of brain tumors by glioma inhibitory macrophages and IL33

Abstract Glioblastoma is the most common and deadly form of brain cancer. Even with aggressive treatment including surgery, chemotherapy, and radiotherapy, survival outcomes for newly diagnosed glioblastoma patients remains less than two years. Novel high throughput omics technologies have expanded our understanding of the role of innate immune system in brain tumors, that are generally believed to drive glioma progression and enable evasion of the adaptive immune system. However, targeting of this axis in the clinic remains an unmet opportunity. Previously, we discovered that the dual-function (secreted and nuclear) cytokine IL-33 is a crucial regulator of the inflammatory microenvironment that promotes glioma tumorigenesis through phenotypic and functional changes in the innate immune cell repertoire. Strikingly, when IL-33 is prevented from entering the nucleus, by deletion of its nuclear localization sequence (ΔNLS IL-33), but is still secreted, in vivo tumor growth is dramatically inhibited resulting in prolonged long-term survival. Using multiplex immunohistochemistry and spatial transcriptomics with temporal resolution across different stages of tumor progression, we identified a population of glioma-inhibitory macrophages (GIMs) unique to this suppressive environment. Assessment of GIMs in xenografts generated from patient brain tumor initiating cells found an enriched presence of these cells in xenografts with long-term survival (greater than 300 days) versus short-term survival (less than 100 days). The ability of GIMs to inhibit glioma progression was demonstrated when tumors established using a combination of ΔNLS IL-33 expressing cancer cells together with highly tumorigenic cells resulted in a growth inhibitory environment that significantly extended survival. A deeper molecular characterization of this phenotype and development of clinical strategies are currently underway. Citation Format: Shyam V. Menon, Xueqing Lun, Peipei Zeng, Jianbo Zhang, Bo Young Ahn, Henry Yu, Alisha Poole, Ngoc Ha Dang, Katalin Osz, Jennifer A. Chan, Daniela F. Quail, Stephen M. Robbins, Donna L. Senger. Durable control of brain tumors by glioma inhibitory macrophages and IL33 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6812.

Biodiversity Medicine: New Horizon and New Opportunity for Cancer.

Accessibility to standard of care remains a challenge to patients in low- and middle-income countries (LMIC), hampering efforts to alleviate the burden of cancer and to improve overall health outcomes. In response to this pressing global health care issue, we propose here a new strategy to create affordable, easily accessible, and effective therapeutic solutions to address this inequity in cancer treatment: the use of science-based biodiversity medicine as an alternative to modern drug therapy, in which we will leverage and combine high-throughput omics technologies with artificial intelligence, to study local biodiversity, their potential anticancer properties, and short- and long-term clinical response and outcomes.

A Look into Ocular Diseases: The Pivotal Role of Omics Sciences in Ophthalmology Research.

Precision medicine is a new medical approach which considers both population characteristics and individual variability to provide customized healthcare. The transition from traditional reactive medicine to personalized medicine is based on a biomarker-driven process and a deep knowledge of biological mechanisms according to which the development of diseases occurs. In this context, the advancements in high-throughput omics technologies represent a unique opportunity to discover novel biomarkers and to provide an unbiased picture of the biological system. One of the medical fields in which omics science has started to be recently applied is that of ophthalmology. Ocular diseases are very common, and some of them could be highly disabling, thus leading to vision loss and blindness. The pathogenic mechanism of most ocular diseases may be dependent on various genetic and environmental factors, whose effect has not been yet completely understood. In this context, large-scale omics approaches are fundamental to have a comprehensive evaluation of the whole system and represent an essential tool for the development of novel therapies. This Review summarizes the recent advancements in omics science applied to ophthalmology in the last ten years, in particular by focusing on proteomics, metabolomics and lipidomics applications from an analytical perspective. The role of high-efficiency separation techniques coupled to (high-resolution) mass spectrometry ((HR)MS) is also discussed, as well as the impact of sampling, sample preparation and data analysis as integrating parts of the analytical workflow.

Omics Studies of Tumor Cells under Microgravity Conditions.

Cancer is defined as a group of diseases characterized by abnormal cell growth, expansion, and progression with metastasis. Various signaling pathways are involved in its development. Malignant tumors exhibit a high morbidity and mortality. Cancer research increased our knowledge about some of the underlying mechanisms, but to this day, our understanding of this disease is unclear. High throughput omics technology and bioinformatics were successful in detecting some of the unknown cancer mechanisms. However, novel groundbreaking research and ideas are necessary. A stay in orbit causes biochemical and molecular biological changes in human cancer cells which are first, and above all, due to microgravity (µg). The µg-environment provides conditions that are not reachable on Earth, which allow researchers to focus on signaling pathways controlling cell growth and metastasis. Cancer research in space already demonstrated how cancer cell-exposure to µg influenced several biological processes being involved in cancer. This novel approach has the potential to fight cancer and to develop future cancer strategies. Space research has been shown to impact biological processes in cancer cells like proliferation, apoptosis, cell survival, adhesion, migration, the cytoskeleton, the extracellular matrix, focal adhesion, and growth factors, among others. This concise review focuses on publications related to genetic, transcriptional, epigenetic, proteomic, and metabolomic studies on tumor cells exposed to real space conditions or to simulated µg using simulation devices. We discuss all omics studies investigating different tumor cell types from the brain and hematological system, sarcomas, as well as thyroid, prostate, breast, gynecologic, gastrointestinal, and lung cancers, in order to gain new and innovative ideas for understanding the basic biology of cancer.

Biomarkers of heart failure: advances in omics studies.

Heart failure is a complex syndrome characterized by progressive circulatory dysfunction, manifesting clinically as pulmonary and systemic venous congestion, alongside inadequate tissue perfusion. The early identification of HF, particularly at the mild and moderate stages (stages B and C), presents a clinical challenge due to the overlap of signs, symptoms, and natriuretic peptide levels with other cardiorespiratory pathologies. Nonetheless, early detection coupled with timely pharmacological intervention is imperative for enhancing patient outcomes. Advances in high-throughput omics technologies have enabled researchers to analyze patient-derived biofluids and tissues, discovering biomarkers that are sensitive and specific for HF diagnosis. Due to the diversity of HF etiology, it is insufficient to study the diagnostic data of early HF using a single omics technology. This study reviewed the latest progress in genomics, transcriptomics, proteomics, and metabolomics for the identification of HF biomarkers, offering novel insights into the early clinical diagnosis of HF. However, the validity of biomarkers depends on the disease status, intervention time, genetic diversity and comorbidities of the subjects. Moreover, biomarkers lack generalizability in different clinical settings. Hence, it is imperative to conduct multi-center, large-scale and standardized clinical trials to enhance the diagnostic accuracy and utility of HF biomarkers.

A journey from omics to clinicomics in solid cancers: Success stories and challenges.

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

DataColor: unveiling biological data relationships through distinctive color mapping

Abstract In the era of rapid advancements in high-throughput omics technologies, the visualization of diverse data types with varying orders of magnitude presents a pressing challenge. To bridge this gap, we introduce DataColor, an all-encompassing software solution meticulously crafted to address this challenge. Our aim is to empower users with the ability to handle a wide array of data types through an assortment of tools, while simultaneously streamlining parameter selection for rapid insights and detailed enhancements. DataColor stands as a robust toolkit, encompassing 23 distinct tools coupled with over 600 parameters. The defining characteristic of this toolkit is its adept utilization of the color spectrum, allowing for the representation of data spanning diverse types and magnitudes. Through the integration of advanced algorithms encompassing data clustering, normalization, squarified layouts, and customizable parameters, DataColor unveils an abundance of insights that lay hidden within the intricate relationships embedded in the data. Whether you find yourself navigating the analysis of expansive datasets or embarking on the quest to visualize intricate patterns, DataColor stands as the comprehensive and potent solution. We extend the availability of DataColor to all users at no cost, accessible through the following link: https://github.com/frankgenome/DataColor.

Application of omics in the diagnosis of metabolic dysfunction-associated fatty liver disease

Metabolic dysfunction-associated fatty liver disease (MAFLD) is the most common chronic liver disease worldwide, and the risk of all-cause and liver-related mortality significantly increases with the degree of fibrosis. Early diagnosis of MAFLD and its degree of liver fibrosis are of great significance, so it is particularly important to find an accurate and simple, non-invasive diagnostic method. In recent years, high-throughput omics technology has developed rapidly and played an important role in the non-invasive diagnosis and prediction of fibrosis degree in MAFLD. This article summarizes the application progress of genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, radiomics, and the combination of multi-omics for the diagnosis of MAFLD disease.

RedRibbon: A new rank-rank hypergeometric overlap for gene and transcript expression signatures.

High-throughput omics technologies have generated a wealth of large protein, gene, and transcript datasets that have exacerbated the need for new methods to analyse and compare big datasets. Rank-rank hypergeometric overlap is an important threshold-free method to combine and visualize two ranked lists of P-values or fold-changes, usually from differential gene expression analyses. Here, we introduce a new rank-rank hypergeometric overlap-based method aimed at gene level and alternative splicing analyses at transcript or exon level, hitherto unreachable as transcript numbers are an order of magnitude larger than gene numbers. We tested the tool on synthetic and real datasets at gene and transcript levels to detect correlation and anticorrelation patterns and found it to be fast and accurate, even on very large datasets thanks to an evolutionary algorithm-based minimal P-value search. The tool comes with a ready-to-use permutation scheme allowing the computation of adjusted P-values at low time cost. The package compatibility mode is a drop-in replacement to previous packages. RedRibbon holds the promise to accurately extricate detailed information from large comparative analyses.

Multi-omics reveals aging-related pathway in natural aging mouse liver

Aging is associated with gradual changes in liver structure, altered metabolites and other physiological/pathological functions in hepatic cells. However, its characterized phenotypes based on altered metabolites and the underlying biological mechanism are unclear. Advancements in high-throughput omics technology provide new opportunities to understand the pathological process of aging. Here, in our present study, both metabolomics and phosphoproteomics were applied to identify the altered metabolites and phosphorylated proteins in liver of young (the WTY group) and naturally aged (the WTA group) mice, to find novel biomarkers and pathways, and uncover the biological mechanism. Analysis showed that the body weights, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) increased in the WTA group. The grips decreased with age, while the triglyceride (TG) and cholesterol (TC) did not change significantly. The increase of fibrosis, accumulation of inflammatory cells, hepatocytes degeneration, the deposition of lipid droplets and glycogen, the damaged mitochondria, and deduction of endoplasmic reticulum were observed in the aging liver under optical and electron microscopes. In addition, a network of metabolites and phosphorylated proteomes of the aging liver was established. Metabolomics detected 970 metabolites in the positive ion mode and 778 metabolites in the negative ion mode. A total of 150 pathways were pooled. Phosphoproteomics identified 2618 proteins which contained 16621 phosphosites. A total of 164 pathways were detected. 65 common pathways were detected in two omics. Phosphorylated protein heat shock protein HSP 90-alpha (HSP90A) and v-raf murine viral oncogene homolog B1(BRAF), related to cancer pathway, were significantly upregulated in aged mice liver. Western blot verified that protein expression of MEK and ERK, downstream of BRAF pathway were elevated in the liver of aging mice. However, the protein expression of BRAF was not a significant difference. Overall, these findings revealed a close link between aging and cancer and contributed to our understanding of the multi-omics changes in natural aging.

COVID-19-Omics Report: From Individual Omics Approaches to Precision Medicine

During the COVID-19 pandemic, it became apparent that precision medicine relies heavily on biological multi-omics discoveries. High throughput omics technologies, such as host genomics, transcriptomics, proteomics, epigenomics, metabolomics/lipidomics, and microbiomics, have become an integral part of precision diagnostics. The large number of data generated by omics technologies allows for the identification of vulnerable demographic populations that are susceptible to poor disease outcomes. Additionally, these data help to pinpoint the omics-based biomarkers that are currently driving advancements in precision and preventive medicine, such as early diagnosis and disease prognosis, individualized treatments, and vaccination. This report summarizes COVID-19-omic studies, highlights the results of completed and ongoing omics investigations in individuals who have experienced severe disease outcomes, and examines the impact that repurposed/novel antiviral drugs, targeted immunotherapeutics, and vaccines have had on individual and public health.

Generative models for protein sequence modeling: recent advances and future directions.

The widespread adoption of high-throughput omics technologies has exponentially increased the amount of protein sequence data involved in many salient disease pathways and their respective therapeutics and diagnostics. Despite the availability of large-scale sequence data, the lack of experimental fitness annotations underpins the need for self-supervised and unsupervised machine learning (ML) methods. These techniques leverage the meaningful features encoded in abundant unlabeled sequences to accomplish complex protein engineering tasks. Proficiency in the rapidly evolving fields of protein engineering and generative AI is required to realize the full potential of ML models as a tool for protein fitness landscape navigation. Here, we support this work by (i) providing an overview of the architecture and mathematical details of the most successful ML models applicable to sequence data (e.g. variational autoencoders, autoregressive models, generative adversarial neural networks, and diffusion models), (ii) guiding how to effectively implement these models on protein sequence data to predict fitness or generate high-fitness sequences and (iii) highlighting several successful studies that implement these techniques in protein engineering (from paratope regions and subcellular localization prediction to high-fitness sequences and protein design rules generation). By providing a comprehensive survey of model details, novel architecture developments, comparisons of model applications, and current challenges, this study intends to provide structured guidance and robust framework for delivering a prospective outlook in the ML-driven protein engineering field.

Principles and challenges of modeling temporal and spatial omics data.

Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.

Data-driven approaches to improve water-use efficiency and drought resistance in crop plants

With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.

A multi-omics dataset of human transcriptome and proteome stable reference

The development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits their application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. The transcriptome and proteome of most cell lines shift during culturing, which limits their applicability as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (r = 0.983~0.997) and proteome (r = 0.966~0.988 for data-dependent acquisition, r = 0.970~0.994 for data-independent acquisition) after 9 subculturing generations, which allows this steady standard sample to be consistently produced on an industrial scale in long term. Moreover, this stability was maintained across labs and platforms. In sum, our study provides omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.

Skeletal muscle omics signatures in cancer cachexia: perspectives and opportunities.

Cachexia is a life-threatening complication of cancer that occurs in up to 80% of patients with advanced cancer. Cachexia reflects the systemic consequences of cancer and prominently features unintended weight loss and skeletal muscle wasting. Cachexia impairs cancer treatment tolerance, lowers quality of life, and contributes to cancer-related mortality. Effective treatments for cancer cachexia are lacking despite decades of research. High-throughput omics technologies are increasingly implemented in many fields including cancer cachexia to stimulate discovery of disease biology and inform therapy choice. In this paper, we present selected applications of omics technologies as tools to study skeletal muscle alterations in cancer cachexia. We discuss how comprehensive, omics-derived molecular profiles were used to discern muscle loss in cancer cachexia compared with other muscle-wasting conditions, to distinguish cancer cachexia from treatment-related muscle alterations, and to reveal severity-specific mechanisms during the progression of cancer cachexia from early toward severe disease.

Traditional Chinese medicine in human diseases treatment: New insights of their potential mechanisms.

For millennia, traditional Chinese medicine (TCM) has relieved the pain of countless patients with its unique theory and treatment method, which has provoked researchers' interest for exploring the biological and molecular mechanisms. This special issue highlights recent advances of this ancient and mysterious medical system in the basic science research field. The authors in this volume explored the molecular characteristics of TCM syndromes and the disease-resistant mechanisms of acupuncture and Chinese herbs in the diseases effecting the human motor system, digestive system, nervous system, and other organ systems by applying high-throughput omics technologies, molecular biology experiments, animal models and other methods. Alongside enhancing their perception of TCM from these latest findings, readers can also understand how to cross the systematic theory of TCM with modern molecular biology techniques. These studies advance our understanding of the potential mechanisms of TCM in treating human diseases, and also provide inspiration for the development of novel TCM-based therapeutic strategies. We hope these efforts will promote extensive development in TCM research.