Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure

Matrix-assisted laser desorption ionisation (MALDI) mass spectrometry (MS): basics and clinical applications

Background Matrix-assisted laser desorption ionisation (MALDI) mass spectrometry (MS) has been used for more than 30 years. Compared with other analytical techniques, it offers ease of use, high throughput, robustness, cost-effectiveness, rapid analysis and sensitivity. As advantages, current clinical techniques (e.g. immunoassays) are unable to directly measure the biomarker; rather, they measure secondary signals. MALDI-MS has been extensively researched for clinical applications, and it is set for a breakthrough as a routine tool for clinical diagnostics. Content This review reports on the principles of MALDI-MS and discusses current clinical applications and the future clinical prospects for MALDI-MS. Furthermore, the review assesses the limitations currently experienced in clinical assays, the advantages and the impact of MALDI-MS to transform clinical laboratories. Summary MALDI-MS is widely used in clinical microbiology for the screening of microbial isolates; however, there is scope to apply MALDI-MS in the diagnosis, prognosis, therapeutic drug monitoring and biopsy imaging in many diseases. Outlook There is considerable potential for MALDI-MS in clinic as a tool for screening, profiling and imaging because of its high sensitivity and specificity over alternative techniques.

Finding a Needle in a Haystack: Proteomics in Heart Failure

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Proteomics has aided HF biomarker discovery, which allows for greater disease insights.

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Experiment design can be tailored to HF research to discover novel biomarkers.

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Primary methods include MS, protein microarray, aptamer, and PEA-based technologies.

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Proteomics can detect unique low abundance proteins and detect protein modifications.

Circulating protein biomarkers provide information regarding pathways in heart failure (HF) and can add important value to clinicians. Advancements in proteomics allow researchers to measure a multitude of proteins simultaneously with excellent sensitivity and selectivity to detect low abundance proteins. This helps identify previously unrecognized pathways in HF and discover biomarkers and potential targets for HF therapies. Although several proteomic methods exist, including mass spectrometry, protein microarray, aptamer, and proximity extension assay−based techniques, each have their unique advantages. This paper provides an overview of the various proteomic methods, with examples of how each has contributed to understanding the pathways in HF.

Multiomic approaches to delineate the pathogenesis of cardiac disease

PURPOSE OF REVIEW

Comprehensive analyses of the genome, transcriptome, proteome and metabolome are instrumental in identifying biomarkers of disease, to gain insight into mechanisms underlying the development of cardiovascular disease, and show promise for better stratifying patients according to disease subtypes. This review highlights recent 'omics' studies, including integration of multiple 'omics' that have advanced mechanistic understanding and diagnosis in humans and animal models.

RECENT FINDINGS

Transcriptome-based discovery continues to be a primary method to obtain data for hypothesis generation and the understanding of disease pathogenesis has been enhanced by single cell-based methods capable of revealing heterogeneity in cellular responses. Advances in proteome coverage and quantitation of individual protein species, together with enhanced methods for detecting posttranslational modifications, have improved discovery of protein-based biomarkers.

SUMMARY

High-throughput assays capable of quantitating the vast majority of any particular type of biomolecule within a tissue sample, isolated cells or plasma are now available. In order to make best use of the large amount of data that can be generated on given molecule types, as well as their interrelationships in disease, continued development of pattern-recognition algorithms ('machine learning') will be required and the subclassification of disease that is made possible by such algorithms will be likely to inform clinical practice, and vice versa.

Plasma proteomic approach in patients with heart failure: insights into pathogenesis of disease progression and potential novel treatment targets

AIMS

To provide insights into pathogenesis of disease progression and potential novel treatment targets for patients with heart failure by investigation of the plasma proteome using network analysis.

METHODS AND RESULTS

The plasma proteome of 50 patients with heart failure who died or were rehospitalised were compared with 50 patients with heart failure, matched for age and sex, who did not have an event. Peptides were analysed on two-dimensional liquid chromatography coupled to tandem mass spectrometry (2D LC ESI-MS/MS) in high definition mode (HDMSE). We identified and quantified 3001 proteins, of which 51 were significantly up-regulated and 46 down-regulated with more than two-fold expression changes in those who experienced death or rehospitalisation. Gene ontology enrichment analysis and protein-protein interaction networks of significant differentially expressed proteins discovered the central role of metabolic processes in clinical outcomes of patients with heart failure. The findings revealed that a cluster of proteins related to glutathione metabolism, arginine and proline metabolism, and pyruvate metabolism in the pathogenesis of poor outcome in patients with heart failure who died or were rehospitalised.

CONCLUSIONS

Our findings show that in patients with heart failure who died or were rehospitalised, the glutathione, arginine and proline, and pyruvate pathways were activated. These pathways might be potential targets for therapies to improve poor outcomes in patients with heart failure.

Circulating proteomic signature of early death in heart failure patients with reduced ejection fraction

Heart failure (HF) remains a main cause of mortality worldwide. Risk stratification of patients with systolic chronic HF is critical to identify those who may benefit from advanced HF therapies. The aim of this study is to identify plasmatic proteins that could predict the early death (within 3 years) of HF patients with reduced ejection fraction hospitalized in CHRU de Lille. The subproteome targeted by an aptamer-based technology, the Slow Off-rate Modified Aptamer (SOMA) scan assay of 1310 proteins, was profiled in blood samples from 168 HF patients, and 203 proteins were significantly modulated between patients who died of cardiovascular death and patients who were alive after 3 years of HF evaluation (Wilcoxon test, FDR 5%). A molecular network was built using these 203 proteins, and the resulting network contained 2281 molecules assigned to 34 clusters annotated to biological pathways by Gene Ontology. This network model highlighted extracellular matrix organization as the main mechanism involved in early death in HF patients. In parallel, an adaptive Least Absolute Shrinkage and Selection Operator (LASSO) was performed on these 203 proteins, and six proteins were selected as candidates to predict early death in HF patients: complement C3, cathepsin S and F107B were decreased and MAPK5, MMP1 and MMP7 increased in patients who died of cardiovascular causes compared with patients living 3 years after HF evaluation. This proteomic signature of 6 circulating plasma proteins allows the identification of systolic HF patients with a risk of early death.