A probabilistic framework for peptide and protein quantification from data-dependent and data-independent LC-MS proteomics experiments

Paper Spray Ionization Ion Mobility Mass Spectrometry of Sebum Classifies Biomarker Classes for the Diagnosis of Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder, and identification of robust biomarkers to complement clinical diagnosis will accelerate treatment options. Here, we demonstrate the use of direct infusion of sebum from skin swabs using paper spray ionization coupled with ion mobility mass spectrometry (PS-IM-MS) to determine the regulation of molecular classes of lipids in sebum that are diagnostic of PD. A PS-IM-MS method for sebum samples that takes 3 min per swab was developed and optimized. The method was applied to skin swabs collected from 150 people and elucidates ∼4200 features from each subject, which were independently analyzed. The data included high molecular weight lipids (>600 Da) that differ significantly in the sebum of people with PD. Putative metabolite annotations of several lipid classes, predominantly triglycerides and larger acyl glycerides, were obtained using accurate mass, tandem mass spectrometry, and collision cross section measurements.

A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics

In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735).

Measurement(s)	Digital Data Repository
Technology Type(s)	Digital Data Repository

Enhanced cardiac hypoxic injury in atherogenic dyslipidaemia results from alterations in the energy metabolism pattern

OBJECTIVE

Dyslipidaemia is a major risk factor for myocardial infarction that is known to correlate with atherosclerosis in the coronary arteries. We sought to clarify whether metabolic alterations induced by dyslipidaemia in cardiomyocytes collectively constitute an alternative pathway that escalates myocardial injury.

METHODS

Dyslipidaemic apolipoprotein E and low-density lipoprotein receptor (ApoE/LDLR) double knockout (ApoE^-/-/LDLR^-/-) and wild-type C57BL/6 (WT) mice aged six months old were studied. Cardiac injury under reduced oxygen supply was evaluated by 5 min exposure to 5% oxygen in the breathing air under electrocardiogram (ECG) recording and with the assessment of troponin I release. To address the mechanisms LC/MS was used to analyse the cardiac proteome pattern or in vivo metabolism of stable isotope-labelled substrates and HPLC was applied to measure concentrations of cardiac high-energy phosphates. Furthermore, the effect of blocking fatty acid use with ranolazine on the substrate preference and cardiac hypoxic damage was studied in ApoE^-/-/LDLR^-/- mice.

RESULTS

Hypoxia induced profound changes in ECG ST-segment and troponin I leakage in ApoE^-/-/LDLR^-/- mice but not in WT mice. The evaluation of the cardiac proteomic pattern revealed that ApoE^-/-/LDLR^-/- as compared with WT mice were characterised by coordinated increased expression of mitochondrial proteins, including enzymes of fatty acids' and branched-chain amino acids' oxidation, accompanied by decreased expression levels of glycolytic enzymes. These findings correlated with in vivo analysis, revealing a reduction in the entry of glucose and enhanced entry of leucine into the cardiac Krebs cycle, with the cardiac high-energy phosphates pool maintained. These changes were accompanied by the activation of molecular targets controlling mitochondrial metabolism. Ranolazine reversed the oxidative metabolic shift in ApoE^-/-/LDLR^-/- mice and reduced cardiac damage induced by hypoxia.

CONCLUSIONS

We suggest a novel mechanism for myocardial injury in dyslipidaemia that is consequent to an increased reliance on oxidative metabolism in the heart. The alterations in the metabolic pattern that we identified constitute an adaptive mechanism that facilitates maintenance of metabolic equilibrium and cardiac function under normoxia. However, this adaptation could account for myocardial injury even in a mild reduction of oxygen supply.

Peptide filtering differently affects the performances of XIC-based quantification methods

In bottom-up proteomics, data are acquired on peptides resulting from proteolysis. In XIC-based quantification, the quality of the estimation of protein abundance depends on how peptide data are filtered and on which quantification method is used to express peptide intensity as protein abundance. So far, these two questions have been addressed independently. Here, we studied to what extent the relative performances of the quantification methods depend on the filters applied to peptide intensity data. To this end, we performed a spike-in experiment using Universal Protein Standard to evaluate the performances of five quantification methods in five datasets obtained after application of four peptide filters. Estimated protein abundances were not equally affected by filters depending on the computation mode and the type of data for quantification. Furthermore, we found that filters could have contrasting effects depending on the quantification objective. Intensity modeling proved to be the most robust method, providing the best results in the absence of any filter. However, the different quantification methods can achieve similar performances when appropriate peptide filters are used. Altogether, our findings provide insights into how best to handle intensity data according to the quantification objective and the experimental design. SIGNIFICANCE: We believe that our results are of major importance because they address, as far as we know for the first time, the crossed-effects of peptide intensity data filtering and XIC-based quantification methods on protein quantification. While previous papers have dealt with peptide filtering independently of the quantification method, here we combined four peptide filters (based on peptide sharing between proteins, retention time variability, peptides occurrence and peptide intensity profiles) with five XIC-based quantification methods representing different modes of calculating protein abundances from peptide intensities. For these different combinations, we analyzed the quality of protein quantification in terms of precision, accuracy and linearity of response to increasing protein concentration using a spike-in experiment. We showed that not only filters effect on the estimation of protein abundances depend on the quantification methods but also that quantification methods can reach similar performances when appropriate peptide filters are used. Also, depending on the quantification objective, i.e. absolute or relative, filters can have contrasting effects and we demonstrated that protein quantification by the peptide intensity modeling was the most robust method.

Specificity of the osmotic stress response in Candida albicans highlighted by quantitative proteomics

Stress adaptation is critical for the survival of microbes in dynamic environments, and in particular, for fungal pathogens to survive in and colonise host niches. Proteomic analyses have the potential to significantly enhance our understanding of these adaptive responses by providing insight into post-transcriptional regulatory mechanisms that contribute to the outputs, as well as testing presumptions about the regulation of protein levels based on transcript profiling. Here, we used label-free, quantitative mass spectrometry to re-examine the response of the major fungal pathogen of humans, Candida albicans, to osmotic stress. Of the 1,262 proteins that were identified, 84 were down-regulated in response to 1M NaCl, reflecting the decrease in ribosome biogenesis and translation that often accompanies stress. The 64 up-regulated proteins included central metabolic enzymes required for glycerol synthesis, a key osmolyte for this yeast, as well as proteins with functions during stress. These data reinforce the view that adaptation to salt stress involves a transient reduction in ribosome biogenesis and translation together with the accumulation of the osmolyte, glycerol. The specificity of the response to salt stress is highlighted by the small proportion of quantified C. albicans proteins (5%) whose relative elevated abundances were statistically significant.

Scanning Quadrupole Data-Independent Acquisition, Part A: Qualitative and Quantitative Characterization

A novel data-independent acquisition (DIA) method incorporating a scanning quadrupole in front of a collision cell and orthogonal acceleration time-of-flight mass analyzer is described. The method has been characterized for the qualitative and quantitative label-free proteomic analysis of complex biological samples. The principle of the scanning quadrupole DIA method is discussed, and analytical instrument characteristics, such as the quadrupole transmission width, scan/integration time, and chromatographic separation, have been optimized in relation to sample complexity for a number of different model proteomes of varying complexity and dynamic range including human plasma, cell lines, and bacteria. In addition, the technological merits over existing DIA approaches are described and contrasted. The qualitative and semiquantitative performance of the method is illustrated for the analysis of relatively simple protein digest mixtures and a well-characterized human cell line sample using untargeted and targeted search strategies. Finally, the results from a human cell line were compared against publicly available data that used similar chromatographic conditions but were acquired with DDA technology and alternative mass analyzer systems. Qualitative comparison showed excellent concordance of results with >90% overlap of the detected proteins.

OLFM4, KNG1 and Sec24C identified by proteomics and immunohistochemistry as potential markers of early colorectal cancer stages

Background

Despite recent advances in colorectal cancer (CRC) diagnosis and population screening programs, the identification of patients with preneoplastic lesions or with early CRC stages remains challenging and is important for reducing CRC incidence and increasing patient’s survival.

Methods

We analysed 76 colorectal tissue samples originated from early CRC stages, normal or inflamed mucosa by label-free proteomics. The characterisation of three selected biomarker candidates was performed by immunohistochemistry on an independent set of precancerous and cancerous lesions harbouring increasing CRC stages.

Results

Out of 5258 proteins identified, we obtained 561 proteins with a significant differential distribution among groups of patients and controls. KNG1, OLFM4 and Sec24C distributions were validated in tissues and showed different expression levels especially in the two early CRC stages compared to normal and preneoplastic tissues.

Conclusion

We highlighted three proteins that require further investigations to better characterise their role in early CRC carcinogenesis and their potential as early CRC markers.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-017-9143-3) contains supplementary material, which is available to authorized users.

Gender differences in responses in Gammarus pulex exposed to BDE-47: A gel-free proteomic approach

Very few ecotoxicological studies have considered differences in toxic effects on male and female organisms. Here, we investigated protein expression differences in caeca of Gammarus pulex males and females under control conditions (unexposed) and after 96h exposure to BDE-47. Using gel-free proteomic analysis, we have identified 45 proteins, of which 25 were significantly differently expressed according to sex and/or BDE-47 exposure. These proteins were involved in several biological processes such as energy metabolism, chaperone proteins, or transcription/translation. In unexposed amphipods, 11 proteins were significantly over-expressed in females, and 6 proteins were over-expressed in males. Under BDE-47 stress, 7 proteins were differently impacted according to sex. For example, catalase was over-expressed in exposed females and under-expressed in exposed males, as compared to respective controls. Conversely, proteins involved in energy metabolism were up-regulated in males and down-regulated in females. Our proteomic study showed differences in responses of males and females to BDE-47 exposure, emphasizing that sex is a confounding factor in ecotoxicological assessment. However, due to the limited information existing in databases on Gammarids, it was difficult to define a BDE-47 mechanism of action. The gel-free proteomic seems to be a promising method to develop in future ecotoxicological studies and thus, to improve our understanding of the mechanism of action of xenobiotics.

Comparison of two FFPE preparation methods using label-free shotgun proteomics: Application to tissues of diverticulitis patients

Formalin-fixed paraffin-embedded (FFPE) specimens of patients are useful sources of materials for clinical research and have recently gained interest for use in the discovery of clinical proteomic biomarkers. However, the critical step in this field is the ability to obtain an efficient and repeatable extraction using the limited quantities of material available for research in hospital biobanks. This work describes the evaluation of the peptide/protein extraction using FFPE sections treated by the following two methods before shotgun proteomic analysis: a commercial solution (FFPE-FASP) (filter aided sample preparation) and an antigen retrieval-derived protocol (On Slice AR). Their efficiencies and repeatabilities are compared using data-independent differential quantitative label-free analysis. FFPE-FASP was shown to be globally better both qualitatively and quantitatively than On Slice AR. FFPE-FASP was tested on several samples, and differential analysis was used to compare the tissues of diverticulitis patients (healthy and inflammatory tissues). In this differential proteomic analysis using retrospective clinical FFPE material, FFPE-FASP was reproducible and provided a high number of confident protein identifications, highlighting potential protein biomarkers.

BIOLOGICAL SIGNIFICANCE

In clinical proteomics, FFPE is an important resource for retrospective analysis and for the discovery of biomarkers. The challenge for FFPE shotgun proteomic analysis is preparation by an efficient and reproducible protocol, which includes protein extraction and digestion. In this study, we analyzed two different methods and evaluated their repeatabilities and efficiencies. We illustrated the reproducibility of the most efficient method, FFPE-FASP, by a pilot study on diverticulitis tissue and on FFPE samples amount accessible in hospital biobanks. These data showed that FFPE is suitable for use in clinical proteomics, especially when the FFPE-FASP method is combined with label-free shotgun proteomics as described in the workflow presented in this work.