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Nature Reviews. Genetics Dec 2022Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the... (Review)
Review
Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.
Topics: Humans; Aged; Proteomics; Aging; Biomarkers; Epigenomics; Metabolomics
PubMed: 35715611
DOI: 10.1038/s41576-022-00511-7 -
Molecular & Cellular Proteomics : MCP Jun 2023The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease.... (Review)
Review
The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.
Topics: Humans; Genomics; Proteomics; Multiomics; Metabolomics; Neoplasms
PubMed: 37119971
DOI: 10.1016/j.mcpro.2023.100561 -
Briefings in Bioinformatics Nov 2019Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and... (Review)
Review
Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.
Topics: Computational Biology; Humans; Metabolomics; Precision Medicine; Tissue Banks
PubMed: 29304189
DOI: 10.1093/bib/bbx170 -
Journal of Molecular and Cellular... Oct 2021Organ-on-a-chip (OOC) platforms involve the miniaturization of cell culture systems and enable a variety of novel experimental approaches. These range from modeling the... (Review)
Review
Organ-on-a-chip (OOC) platforms involve the miniaturization of cell culture systems and enable a variety of novel experimental approaches. These range from modeling the independent effects of biophysical forces on cells to screening novel drugs in multi-organ microphysiological systems, all within microscale devices. As in living systems, the incorporation of vascular structure is a key feature common to almost all organ-on-a-chip systems. In this review we highlight recent advances in organ-on-a-chip technologies with a focus on the vasculature. We first present the developmental process of the blood vessels through which vascular cells assemble into networks and remodel to form complex vascular beds under flow. We then review self-assembled vascular models and flow systems for the study of vascular development and biology as well as pre-patterned vascular models for the generation of perfusable microvessels for modeling vascular and tissue function. We finally conclude with a perspective on developing future OOC approaches for studying different aspects of vascular biology. We highlight the fit for purpose selection of OOC models towards either simple but powerful testbeds for therapeutic development, or complex vasculature to accurately replicate human physiology for specific disease modeling and tissue regeneration.
Topics: Animals; Biology; Blood Vessels; Guided Tissue Regeneration; Humans; Lab-On-A-Chip Devices
PubMed: 34118217
DOI: 10.1016/j.yjmcc.2021.06.002 -
Experimental & Molecular Medicine Sep 2020Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently,... (Review)
Review
Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently, bulk multiomics analyses, such as multidimensional genomic and proteogenomic analyses, have proven beneficial for obtaining a comprehensive understanding of cellular events. This benefit has facilitated the development of single-cell multiomics analysis, which enables cell type-specific gene regulation to be examined. The cardinal features of single-cell multiomics analysis include (1) technologies for single-cell isolation, barcoding, and sequencing to measure multiple types of molecules from individual cells and (2) the integrative analysis of molecules to characterize cell types and their functions regarding pathophysiological processes based on molecular signatures. Here, we summarize the technologies for single-cell multiomics analyses (mRNA-genome, mRNA-DNA methylation, mRNA-chromatin accessibility, and mRNA-protein) as well as the methods for the integrative analysis of single-cell multiomics data.
Topics: Animals; Biotechnology; Computational Biology; Epigenomics; Gene Expression Profiling; Genomics; Humans; Organ Specificity; Proteomics; Single-Cell Analysis; Transcriptome
PubMed: 32929225
DOI: 10.1038/s12276-020-0420-2 -
International Journal of Molecular... Sep 2019Recent advances in omics technologies have led to unprecedented efforts characterizing the molecular changes that underlie the development and progression of a wide... (Review)
Review
Recent advances in omics technologies have led to unprecedented efforts characterizing the molecular changes that underlie the development and progression of a wide array of complex human diseases, including cancer. As a result, multi-omics analyses-which take advantage of these technologies in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics areas-have been proposed and heralded as the key to advancing precision medicine in the clinic. In the field of precision oncology, genomics approaches, and, more recently, other omics analyses have helped reveal several key mechanisms in cancer development, treatment resistance, and recurrence risk, and several of these findings have been implemented in clinical oncology to help guide treatment decisions. However, truly integrated multi-omics analyses have not been applied widely, preventing further advances in precision medicine. Additional efforts are needed to develop the analytical infrastructure necessary to generate, analyze, and annotate multi-omics data effectively to inform precision medicine-based decision-making.
Topics: Biomarkers; Computational Biology; Epigenomics; Genomics; Humans; Metabolomics; Neoplasms; Precision Medicine; Proteomics
PubMed: 31561483
DOI: 10.3390/ijms20194781 -
PLoS Biology Mar 2021Why would a computational biologist with 40 years of research experience say bioinformatics is dead? The short answer is, in being the Founding Dean of a new School of...
Why would a computational biologist with 40 years of research experience say bioinformatics is dead? The short answer is, in being the Founding Dean of a new School of Data Science, what we do suddenly looks different.
Topics: Computational Biology; Curriculum; Data Science; Humans; Information Dissemination; Schools; Students
PubMed: 33735179
DOI: 10.1371/journal.pbio.3001165 -
Analytica Chimica Acta Jan 2021Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA... (Review)
Review
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
Topics: Biomedical Research; Computational Biology; Genomics; Metabolomics; Phenotype
PubMed: 33248648
DOI: 10.1016/j.aca.2020.10.038 -
Journal of Translational Medicine Nov 2021
Topics: Proteomics; Signal Transduction; Systems Biology; Translational Research, Biomedical
PubMed: 34847911
DOI: 10.1186/s12967-021-03148-y -
The Plant Cell Sep 2019
Topics: Cell Biology; Genomics; History, 21st Century; Humans; Plant Cells; Publications; Synthetic Biology
PubMed: 31311835
DOI: 10.1105/tpc.19.00547