Amyloidosis is a shared name for several rare, complex and serious diseases caused by extra-cellular deposits of different misfolded proteins. Accurate characterization of the amyloid protein is essential for patient care. Immunoelectron microscopy (IEM) and laser microdissection followed by tandem mass spectrometry (LMD-MS) are new gold standards for molecular subtyping. Both methods perform superiorly to immunohistochemistry, but their complementarities, strengths and weaknesses across amyloid subtypes and organ biopsy origin remain undefined. Therefore, we performed a retrospective study of 106 Congo Red positive biopsies from different involved organs; heart, kidney, lung, gut mucosa, skin and bone marrow. IEM, performed with gold-labelled antibodies against kappa light chains, lambda light chains, transthyretin and amyloid A, identified specific staining of amyloid fibrils in 91.6%; in six biopsies amyloid fibrils were not identified, and in two, the fibril subtype could not be established. LMD-MS identified amyloid protein signature in 98.1%, but in nine the amyloid protein could not be clearly identified. MS identified protein subtype in 89.6%. Corresponding specificities ranged at organ level from 94-100%. Concordance was 89.6-100% for different amyloid subtypes. Importantly, combined use of both methods increased the diagnostic classification to 100%. Some variety in performances at organ level was observed.

In the present study, we evaluated four small molecule affinity-based probes based on agarose-immobilized benzamidine (ABA), O-Phospho-L-Tyrosine (pTYR), 8-Amino-hexyl-cAMP (cAMP), or 8-Amino-hexyl-ATP (ATP) for their ability to remove high-abundant proteins such as serum albumin from plasma samples thereby enabling the detection of medium-to-low abundant proteins in plasma samples by mass spectrometry-based proteomics. We compared their performance with the most commonly used immunodepletion method, the Multi Affinity Removal System Human 14 (MARS14) targeting the top 14 most abundant plasma proteins and also the ProteoMiner protein equalization method by label-free quantitative liquid chromatography tandem mass spectrometry (LC-MSMS) analysis. The affinity-based probes demonstrated a high reproducibility for low-abundant plasma proteins, down to picomol per mL levels, compared to the Multi Affinity Removal System (MARS) 14 and the Proteominer methods, and also demonstrated superior removal of the majority of the high-abundant plasma proteins. The ABA-based affinity probe and the Proteominer protein equalization method performed better compared to all other methods in terms of the number of analyzed proteins. All the tested methods were highly reproducible for both high-abundant plasma proteins and low-abundant proteins as measured by correlation analyses of six replicate experiments. In conclusion, our results demonstrated that small-molecule based affinity-based probes are excellent alternatives to the commonly used immune-depletion methods for proteomic biomarker discovery studies in plasma. Data are available via ProteomeXchange with identifier PXD020727.

The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.

Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically throughout the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fibril-forming protein, and its accurate identification is essential to the choice of treatment. Mass spectrometry-based proteomics has become the method of choice for the identification of the amyloidogenic protein. Regrettably, this identification relies on manual and subjective interpretation of mass spectrometry data by an expert, which is undesirable and may bias diagnosis. To circumvent this, we developed a statistical model-assisted method for the unbiased identification of amyloid-containing biopsies and amyloidosis subtyping. Based on data from mass spectrometric analysis of amyloid-containing biopsies and corresponding controls. A Boruta method applied on a random forest classifier was applied to proteomics data obtained from the mass spectrometric analysis of 75 laser dissected Congo Red positive amyloid-containing biopsies and 78 Congo Red negative biopsies to identify novel “amyloid signature” proteins that included clusterin, fibulin-1, vitronectin complement component C9 and also three collagen proteins, as well as the well-known amyloid signature proteins apolipoprotein E, apolipoprotein A4, and serum amyloid P. A SVM learning algorithm were trained on the mass spectrometry data from the analysis of the 75 amyloid-containing biopsies and 78 amyloid-negative control biopsies. The trained algorithm performed superior in the discrimination of amyloid-containing biopsies from controls, with an accuracy of 1.0 when applied to a blinded mass spectrometry validation data set of 103 prospectively collected amyloid-containing biopsies. Moreover, our method successfully classified amyloidosis patients according to the subtype in 102 out of 103 blinded cases. Collectively, our model-assisted approach identified novel amyloid-associated proteins and demonstrated the use of mass spectrometry-based data in clinical diagnostics of disease by the unbiased and reliable model-assisted classification of amyloid deposits and of the specific amyloid subtype.

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ObjectiveThe pathogenesis of abdominal aortic aneurysms (AAA) involves a disturbed balance of breakdown and buildup of arterial proteins. We envision that individuals with AAA carry generalized arterial protein alterations either because of effects of genetically or environmental AAA risk factors or because of compensatory changes due to signaling molecules released from the affected aneurysmal tissue.ApproachProtein extraction and quantitative proteome analysis by LC-MS/MS (liquid chromatography-mass spectrometry) was done on individual samples from the internal mammary artery from 11 individuals with AAA and 33 sex- and age-matched controls without AAA. Samples were selected from a biobank of leftover internal mammary arterial tissue gathered at coronary by-pass operations.ResultsWe identified and quantitated 877 proteins, of which 44 were differentially expressed between the two groups (nominal p-values without correction for multiple testing). Some proteins related to the extracellular matrix displayed altered concentrations in the AAA group, particularly among elastin-related molecules [elastin, microfibrillar-associated protein 4 (MFAP4), lysyl oxidase]. In addition, several histones e.g. (e.g. HIST1H1E, HIST1H2BB) and other vascular cell proteins (e.g. versican, type VI collagen) were altered.ConclusionsOur results support the notion that generalized alterations occur in the arterial tree in patients with AAA. Elastin-related proteins and histones seem to be part of such changes, however these preliminary results require replication in an independent set of specimens and validation by functional studies.

Acute myocardial infarction (AMI) remains a major cause of mortality and morbidity, and cardiogenic shock (CS) a major cause of hospital mortality after AMI. Especially for ST elevation myocardial infarction (STEMI) patients, fast intervention is essential. Few proteins have proven clinically applicable for AMI. Most proposed biomarkers are based on a priori hypothesis-driven studies of single proteins, not enabling identification of novel candidates. For clinical use, the ability to predict AMI is important; however, studies of proteins in prediction models are surprisingly scarce. Consequently, we applied proteome data for identifying proteins associated with definitive STEMI, CS, and all-cause mortality after admission, and examined the ability of the proteins to predict these outcomes. Methods and Results: Proteome-wide data of 497 patients with suspected STEMI were investigated; 381 patients were diagnosed with STEMI, 35 with CS, and 51 died during the first year. Data analysis was conducted by logistic and Cox regression modeling for association analysis, and by multivariable LASSO regression models for prediction modeling. Association studies identified 4 and 29 proteins associated with definitive STEMI or mortality, respectively. Prediction models for CS and mortality (holding two and five proteins, respectively) improved the prediction ability as compared with protein-free prediction models; AUC of 0.92 and 0.89, respectively. Conclusion: The association analyses propose individual proteins as putative protein biomarkers for definitive STEMI and survival after suspected STEMI, while the prediction models put forward sets of proteins with putative predicting ability of CS and survival. These proteins may be verified as biomarkers of potential clinical relevance. KEYWORDS-Acute myocardial infarction, area under the curve, association analysis, LASSO regression modeling, prediction modeling, proteome-wide analysis, ST elevation myocardial infarction (STEMI), tandem mass spectrometry ABBREVIATIONS-AMI-acute myocardial infarction; AUC-area under the curve; CS-cardiogenic shock; FDR-false discovery rate; HR-hazard ratio; lasso-least absolute shrinkage and selection operator; nano-LC-MS-MS-Nanoscale liquid chromatography coupled to tandem mass spectrometry; OR-odds ratio; PREDICT-CS study-the prediction and risk assessment in patients admitted to acute coronary angiography and development of cardiogenic shock study; STEMI-ST elevation myocardial infarction

Specific plasma proteins serve as valuable markers for various diseases and are in many cases routinely measured in clinical laboratories by fully automated systems. For safe diagnostics and monitoring using these markers, it is important to ensure an analytical quality in line with clinical needs. For this purpose, information on the analytical and the biological variation of the measured plasma protein, also in the context of the discovery and validation of novel, disease protein biomarkers, is important, particularly in relation to for sample size calculations in clinical studies. Nevertheless, information on the biological variation of the majority of medium-to-high abundant plasma proteins is largely absent. In this study, we hypothesized that it is possible to generate data on inter-individual biological variation in combination with analytical variation of several hundred abundant plasma proteins, by applying LC-MS/MS in combination with relative quantification using isobaric tagging (10-plex TMT-labeling) to plasma samples. Using this analytical proteomic approach, we analyzed 42 plasma samples prepared in doublets, and estimated the technical, inter-individual biological, and total variation of 265 of the most abundant proteins present in human plasma thereby creating the prerequisites for power analysis and sample size determination in future clinical proteomics studies. Our results demonstrated that only five samples per group may provide sufficient statistical power for most of the analyzed proteins if relative changes in abundances >1.5-fold are expected. Seventeen of the measured proteins are present in the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database, and demonstrated remarkably similar biological CV’s to the corresponding CV’s listed in the EFLM database suggesting that the generated proteomic determined variation knowledge is useful for large-scale determination of plasma protein variations.