Multi-OMICS: a critical technical perspective on integrative lipidomics approaches

Kopczynski, Dominik; Coman, Cristina; Zahedi, René P.; Lorenz, Kristina; Sickmann, Albert; Ahrends, Robert

doi:10.1016/j.bbalip.2017.02.003

Cited by 30 publications

(16 citation statements)

References 42 publications

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“…In addition, if limited amounts of patient samples are available, it may not be feasible to perform multiple omic analyses using a single sample. Translation of integrated omics approaches to the clinic will require streamlining processes for sample collection and storage, reducing technical variation, improving reproducibility, standardizing analytical methods, reducing costs and reducing time for sample analyses (Kopczynski et al 2017, Wilson et al 2017. Given that genetic testing is now beginning to be routinely used in the clinic, as well as many examples of small molecule assays, it is only a matter of time before these challenges are addressed for newer and more comprehensive omics platforms to contribute essential clinical data that will provide insights in prognosis and diagnosis of diseases.…”

Section: Hurdles In Implementing Multi-omics Approaches In the Clinicmentioning

confidence: 99%

Integrated omics: tools, advances and future approaches

Misra

Langefeld

Olivier

et al. 2019

Journal of Molecular Endocrinology

371

240

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With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

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Section: Hurdles In Implementing Multi-omics Approaches In the Clinicmentioning

confidence: 99%

Integrated omics: tools, advances and future approaches

Misra

Langefeld

Olivier

et al. 2019

Journal of Molecular Endocrinology

371

240

View full text Add to dashboard Cite

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“…Unfortunately, lipidome analysis strategies involving characterisation of the changes in bacterial lipid composition and structure are not necessarily sufficient to fully describe the adaptations that occur in lipid metabolism in response to a particular environmental stress, as changes in the lipidome may result from alterations in the expression or activity of a specific enzyme, or a collection of enzymes, involved in a particular metabolic pathway. Thus, 'multi-omics' analysis approaches (e.g., lipidomics and proteomics), which enable the identification, characterisation and quantification of both lipids and proteins extracted from a single sample [35][36][37][38][39], may provide a more holistic understanding of bacterial lipid membrane adaptation mechanisms, including elucidation of the relationship between a specific stress and alterations in individual lipids and specific lipid metabolism enzymes [40,41].…”

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confidence: 99%

Multi-Omic Analysis to Characterize Metabolic Adaptation of the E. coli Lipidome in Response to Environmental Stress

et al. 2022

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As an adaptive survival response to exogenous stress, bacteria undergo dynamic remodelling of their lipid metabolism pathways to alter the composition of their cellular membranes. Here, using Escherichia coli as a well characterised model system, we report the development and application of a ‘multi-omics’ strategy for comprehensive quantitative analysis of the temporal changes in the lipidome and proteome profiles that occur under exponential growth phase versus stationary growth phase conditions i.e., nutrient depletion stress. Lipidome analysis performed using ‘shotgun’ direct infusion-based ultra-high resolution accurate mass spectrometry revealed a quantitative decrease in total lipid content under stationary growth phase conditions, along with a significant increase in the mol% composition of total cardiolipin, and an increase in ‘odd-numbered’ acyl-chain length containing glycerophospholipids. The inclusion of field asymmetry ion mobility spectrometry was shown to enable the enrichment and improved depth of coverage of low-abundance cardiolipins, while ultraviolet photodissociation-tandem mass spectrometry facilitated more complete lipid structural characterisation compared with conventional collision-induced dissociation, including unambiguous assignment of the odd-numbered acyl-chains as containing cyclopropyl modifications. Proteome analysis using data-dependent acquisition nano-liquid chromatography mass spectrometry and tandem mass spectrometry analysis identified 83% of the predicted E. coli lipid metabolism enzymes, which enabled the temporal dependence associated with the expression of key enzymes responsible for the observed adaptive lipid metabolism to be determined, including those involved in phospholipid metabolism (e.g., ClsB and Cfa), fatty acid synthesis (e.g., FabH) and degradation (e.g., FadA/B,D,E,I,J and M), and proteins involved in the oxidative stress response resulting from the generation of reactive oxygen species during β-oxidation or lipid degradation.

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“…Large multinational consortium-driven proteogenomic studies will increasingly exploit the unique possibilities of synergizing genomic, transcriptomic, proteomic, metabolomic, and lipidomic datasets [236]. Thus, the 'cancer moonshot' program initiated by former US vice-president Joe Biden [237,238] led to the formation of the International Cancer Proteogenomics Consortium (ICPC), bringing together 'some of the world's leading cancer and proteogenomic research centers' in order to improve our understanding of cancer.…”

Section: Five-year Perspectivementioning

confidence: 99%

Detecting post-translational modification signatures as potential biomarkers in clinical mass spectrometry

Mnatsakanyan

Shema

Basik

et al. 2018

Expert Review of Proteomics

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Numerous diseases are caused by changes in post-translational modifications (PTMs). Therefore, the number of clinical proteomics studies that include the analysis of PTMs is increasing. Combining complementary information-for example changes in protein abundance, PTM levels, with the genome and transcriptome (proteogenomics)-holds great promise for discovering important drivers and markers of disease, as variations in copy number, expression levels, or mutations without spatial/functional/isoform information is often insufficient or even misleading. Areas covered: We discuss general considerations, requirements, pitfalls, and future perspectives in applying PTM-centric proteomics to clinical samples. This includes samples obtained from a human subject, for instance (i) bodily fluids such as plasma, urine, or cerebrospinal fluid, (ii) primary cells such as reproductive cells, blood cells, and (iii) tissue samples/biopsies. Expert commentary: PTM-centric discovery proteomics can substantially contribute to the understanding of disease mechanisms by identifying signatures with potential diagnostic or even therapeutic relevance but may require coordinated efforts of interdisciplinary and eventually multi-national consortia, such as initiated in the cancer moonshot program. Additionally, robust and standardized mass spectrometry (MS) assays-particularly targeted MS, MALDI imaging, and immuno-MALDI-may be transferred to the clinic to improve patient stratification for precision medicine, and guide therapies.

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Multi-OMICS: a critical technical perspective on integrative lipidomics approaches

Cited by 30 publications

References 42 publications

Integrated omics: tools, advances and future approaches

Integrated omics: tools, advances and future approaches

Multi-Omic Analysis to Characterize Metabolic Adaptation of the E. coli Lipidome in Response to Environmental Stress

Detecting post-translational modification signatures as potential biomarkers in clinical mass spectrometry

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