Evaluation of Spin Columns for Human Plasma Depletion to Facilitate MS-Based Proteomics Analysis of Plasma.

High abundant protein depletion is a common strategy applied to increase analytical depth in global plasma proteomics experiment setups. The standard strategies for depletion of the highest abundant proteins currently rely on multiple-use HPLC columns or multiple-use spin columns. Here we evaluate the performance of single-use spin columns for plasma depletion and show that the single-use spin reduces handling time by allowing parallelization and is easily adapted to a nonspecialized lab environment without reducing the high plasma proteome coverage and reproducibility. In addition, we evaluate the effect of viral heat inactivation on the plasma proteome, an additional step in the plasma preparation workflow that allows the sample preparation of SARS-Cov2-infected samples to be performed in a BSL3 laboratory, and report the advantage of performing the heat inactivation postdepletion. We further show the possibility of expanding the use of the depletion column cross-species to macaque plasma samples. In conclusion, we report that single-use spin columns for high abundant protein depletion meet the requirements for reproducibly in in-depth plasma proteomics and can be applied on a common animal model while also reducing the sample handling time.

Use of Time-Resolved Fluorescence To Improve Sensitivity and Dynamic Range of Gel-Based Proteomics.

Limitations in the sensitivity and dynamic range of two-dimensional gel electrophoresis (2-DE) are currently hampering its utility in global proteomics and biomarker discovery applications. In the current study, we present proof-of-concept analyses showing that introducing time-resolved fluorescence in the image acquisition step of in-gel protein quantification provides a sensitive and accurate method for subtracting confounding background fluorescence at the photon level. In-gel protein detection using the minimal difference gel electrophoresis workflow showed improvements in lowest limit of quantification in terms of CyDye molecules per pixel of 330-fold in the blue-green region (Cy2) and 8000-fold in the red region (Cy5) over conventional state-of-the-art image acquisition instrumentation, here represented by the Typhoon 9400 instrument. These improvements make possible the detection of low-abundance proteins present at sub-attomolar levels, thereby representing a quantum leap for the use of gel-based proteomics in biomarker discovery. These improvements were achieved using significantly lower laser powers and overall excitation times, thereby drastically decreasing photobleaching during repeated scanning. The single-fluorochrome detection limits achieved by the cumulative time-resolved emission two-dimensional electrophoresis (CuTEDGE) technology facilitates in-depth proteomics characterization of very scarce samples, for example, primary human tissue materials collected in clinical studies. The unique information provided by high-sensitivity 2-DE, including positional shifts due to post-translational modifications, may increase the chance to detect biomarker signatures of relevance for identification of disease subphenotypes.

Tumor proteomics by multivariate analysis on individual pathway data for characterization of vulvar cancer phenotypes.

Vulvar squamous cell carcinoma (VSCC) is the fourth most common gynecological cancer. Based on etiology VSCC is divided into two subtypes; one related to high-risk human papilloma virus (HPV) and one HPV negative. The two subtypes are proposed to develop via separate intracellular signaling pathways. We investigated a suggested link between HPV infection and relapse risk in VSCC through in-depth protein profiling of 14 VSCC tumor specimens. The tumor proteomes were analyzed by liquid-chromatography tandem mass spectrometry. Relative protein quantification was performed by 8-plex isobaric tags for relative and absolute quantification. Labeled peptides were fractionated by high-resolution isoelectric focusing prior to liquid-chromatography tandem mass spectrometry to reduce sample complexity. In total, 1579 proteins were regarded as accurately quantified and analyzed further. For classification of clinical groups, data analysis was performed by comparing protein level differences between tumors defined by HPV and/or relapse status. Further, we performed a biological analysis on individual tumor proteomes by matching data to known biological pathways. We here present a novel analysis approach that combines pathway alteration data on individual tumor level with multivariate statistics for HPV and relapse status comparisons. Four proteins (signal transducer and activator of transcription-1, myxovirus resistance protein 1, proteasome subunit alpha type-5 and legumain) identified as main classifiers of relapse status were validated by immunohistochemistry (IHC). Two of the proteins are interferon-regulated and on mRNA level known to be repressed by HPV. By both liquid-chromatography tandem mass spectrometry and immunohistochemistry data we could single out a subgroup of HPV negative/relapse-associated tumors. The pathway level data analysis confirmed three of the proteins, and further identified the ubiquitin-proteasome pathway as altered in the high risk subgroup. We show that pathway fingerprinting with resolution on individual tumor level adds biological information that strengthens a generalized protein analysis.

Gender differences in the bronchoalveolar lavage cell proteome of patients with chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide and is increasing, primarily among women. Underdiagnosis is common, and because of the heterogeneous disease characteristics, molecular markers of specific disease phenotypes and more efficacious treatment regimens are urgently needed. OBJECTIVE: In this study the soluble proteome of bronchoalveolar lavage cells, primarily consisting of macrophages, was investigated with the aim of identifying phenotypic differences in early disease development. METHODS: Two-dimensional difference gel electrophoresis was used for relative quantification of protein levels, and multivariate modeling was applied to identify proteins of interest that were subsequently identified by means of liquid chromatography-mass spectrometry. RESULTS: Significant gender differences were unveiled, with numerous alterations in the bronchoalveolar lavage cell proteome occurring in female but not male patients with COPD. Specifically, a subset of 19 proteins provided classification of female healthy smokers from female patients with COPD with 78% predictive power. Subsequent pathway analyses linked the observed alterations to downregulation of the lysosomal pathway and upregulation of the oxidative phosphorylation pathway, possibly linking dysregulation of macroautophagy to a female-dominated COPD disease phenotype. CONCLUSION: This investigation makes an important contribution to the elucidation of putative molecular mechanisms underlying gender-based differences in the pathophysiology of COPD, linking alterations of specific molecular pathways to previously observed gender differences in clinical COPD phenotypes. Furthermore, these results stress the importance of the gender-specific search for biomarkers, diagnosis, and treatment in COPD.

Enhanced information output from shotgun proteomics data by protein quantification and peptide quality control (PQPQ).

We present a tool to improve quantitative accuracy and precision in mass spectrometry based on shotgun proteomics: protein quantification by peptide quality control, PQPQ. The method is based on the assumption that the quantitative pattern of peptides derived from one protein will correlate over several samples. Dissonant patterns arise either from outlier peptides or because of the presence of different protein species. By correlation analysis, protein quantification by peptide quality control identifies and excludes outliers and detects the existence of different protein species. Alternative protein species are then quantified separately. By validating the algorithm on seven data sets related to different cancer studies we show that data processing by protein quantification by peptide quality control improves the information output from shotgun proteomics. Data from two labeling procedures and three different instrumental platforms was included in the evaluation. With this unique method using both peptide sequence data and quantitative data we can improve the quantitative accuracy and precision on the protein level and detect different protein species.

Molecular evaluation of five different isolation methods for extracellular vesicles reveals different clinical applicability and subcellular origin.

Extracellular vesicles (EVs) are increasingly tested as therapeutic vehicles and biomarkers, but still EV subtypes are not fully characterised. To isolate EVs with few co-isolated entities, a combination of methods is needed. However, this is time-consuming and requires large sample volumes, often not feasible in most clinical studies or in studies where small sample volumes are available. Therefore, we compared EVs rendered by five commonly used methods based on different principles from conditioned cell medium and 250 µl or 3 ml plasma, that is, precipitation (ExoQuick ULTRA), membrane affinity (exoEasy Maxi Kit), size-exclusion chromatography (qEVoriginal), iodixanol gradient (OptiPrep), and phosphatidylserine affinity (MagCapture). EVs were characterised by electron microscopy, Nanoparticle Tracking Analysis, Bioanalyzer, flow cytometry, and LC-MS/MS. The different methods yielded samples of different morphology, particle size, and proteomic profile. For the conditioned medium, Izon 35 isolated the highest number of EV proteins followed by exoEasy, which also isolated fewer non-EV proteins. For the plasma samples, exoEasy isolated a high number of EV proteins and few non-EV proteins, while Izon 70 isolated the most EV proteins. We conclude that no method is perfect for all studies, rather, different methods are suited depending on sample type and interest in EV subtype, in addition to sample volume and budget.

In-depth human plasma proteome analysis captures tissue proteins and transfer of protein variants across the placenta.

Here, we present a method for in-depth human plasma proteome analysis based on high-resolution isoelectric focusing HiRIEF LC-MS/MS, demonstrating high proteome coverage, reproducibility and the potential for liquid biopsy protein profiling. By integrating genomic sequence information to the MS-based plasma proteome analysis, we enable detection of single amino acid variants and for the first time demonstrate transfer of multiple protein variants between mother and fetus across the placenta. We further show that our method has the ability to detect both low abundance tissue-annotated proteins and phosphorylated proteins in plasma, as well as quantitate differences in plasma proteomes between the mother and the newborn as well as changes related to pregnancy.

Two subgroups in systemic lupus erythematosus with features of antiphospholipid or Sjögren's syndrome differ in molecular signatures and treatment perspectives.

BACKGROUND: Previous studies and own clinical observations of patients with systemic lupus erythematosus (SLE) suggest that SLE harbors distinct immunophenotypes. This heterogeneity might result in differences in response to treatment in different subgroups and obstruct clinical trials. Our aim was to understand how SLE subgroups may differ regarding underlying pathophysiology and characteristic biomarkers. METHODS: In a cross-sectional study, including 378 well-characterized SLE patients and 316 individually matched population controls, we defined subgroups based on the patients' autoantibody profile at inclusion. We selected a core of an antiphospholipid syndrome-like SLE (aPL+ group; positive in the lupus anticoagulant (LA) test and negative for all three of SSA (Ro52 and Ro60) and SSB antibodies) and a Sjögren's syndrome-like SLE (SSA/SSB+ group; positive for all three of SSA (Ro52 and Ro60) and SSB antibodies but negative in the LA test). We applied affinity-based proteomics, targeting 281 proteins, together with well-established clinical biomarkers and complementary immunoassays to explore the difference between the two predefined SLE subgroups. RESULTS: The aPL+ group comprised 66 and the SSA/SSB+ group 63 patients. The protein with the highest prediction power (receiver operating characteristic (ROC) area under the curve = 0.89) for separating the aPL+ and SSA/SSB+ SLE subgroups was integrin beta-1 (ITGB1), with higher levels present in the SSA/SSB+ subgroup. Proteins with the lowest p values comparing the two SLE subgroups were ITGB1, SLC13A3, and CERS5. These three proteins, rheumatoid factor, and immunoglobulin G (IgG) were all increased in the SSA/SSB+ subgroup. This subgroup was also characterized by a possible activation of the interferon system as measured by high KRT7, TYK2, and ETV7 in plasma. In the aPL+ subgroup, complement activation was more pronounced together with several biomarkers associated with systemic inflammation (fibrinogen, α-1 antitrypsin, neutrophils, and triglycerides). CONCLUSIONS: Our observations indicate underlying pathogenic differences between the SSA/SSB+ and the aPL+ SLE subgroups, suggesting that the SSA/SSB+ subgroup may benefit from IFN-blocking therapies while the aPL+ subgroup is more likely to have an effect from drugs targeting the complement system. Stratifying SLE patients based on an autoantibody profile could be a way forward to understand underlying pathophysiology and to improve selection of patients for clinical trials of targeted treatments.

Normalization of mass spectrometry data (NOMAD).

iTRAQ and TMT reagent-based mass spectrometry (MS) are commonly used technologies for quantitative proteomics in biological samples. Such studies are often performed over multiple MS runs, potentially resulting in introduction of MS run bias that could affect downstream analysis. Such MS data have therefore commonly been normalized using a reference sample which is included in each MS run. We show, however, that reference normalization does not effectively remove systematic MS run bias. A linear model approach was previously proposed to improve on the reference normalization approach but does not computationally scale to larger data sets. Here we describe the NOMAD (normalization of mass spectrometry data) R package which implements a computationally efficient ANOVA normalization approach with protein assembly functionality. NOMAD provides the same advantages as the linear regression solution but is more computationally efficient which allows superior scaling to larger sample sizes. Moreover, NOMAD effectively removes bias which improves valid across MS run comparisons.

Single-Stranded Nucleic Acids Regulate TLR3/4/7 Activation through Interference with Clathrin-Mediated Endocytosis.

Recognition of nucleic acids by endosomal Toll-like receptors (TLR) is essential to combat pathogens, but requires strict control to limit inflammatory responses. The mechanisms governing this tight regulation are unclear. We found that single-stranded oligonucleotides (ssON) inhibit endocytic pathways used by cargo destined for TLR3/4/7 signaling endosomes. Both ssDNA and ssRNA conferred the endocytic inhibition, it was concentration dependent, and required a certain ssON length. The ssON-mediated inhibition modulated signaling downstream of TLRs that localized within the affected endosomal pathway. We further show that injection of ssON dampens dsRNA-mediated inflammatory responses in the skin of non-human primates. These studies reveal a regulatory role for extracellular ssON in the endocytic uptake of TLR ligands and provide a mechanistic explanation of their immunomodulation. The identified ssON-mediated interference of endocytosis (SOMIE) is a regulatory process that temporarily dampens TLR3/4/7 signaling, thereby averting excessive immune responses.

Quantitative accuracy in mass spectrometry based proteomics of complex samples: the impact of labeling and precursor interference.

Knowing the limit of quantification is important to accurately judge the results from proteomics studies. In order to investigate isobaric labels in combination with peptide pre-fractionation by high resolution isoelectric focusing in terms of limit of detection, quantitative accuracy and how to improve it, we used a human cell lysate spiked with 57 protein standards providing reference points across a wide concentration range. Specifically, the impact of precursor mixing (isolation interference and reporter ion interference) on quantitative accuracy was investigated by co-analyzing iTRAQ (8-plex) and TMT (6-plex) labeled peptides. A label-free analysis was also performed. Peptides, labeled or label-free, were analyzed by LC-MS/MS (Orbitrap Velos). We identified 3386 proteins by the label-free approach, 4466 with iTRAQ and 5961 with TMT. A linear range of quantification down to 1fmol was indicated for both isobaric and label-free analysis workflows, with an upper limit exceeding 60fmol. Our results indicate that 6-plex TMT is more sensitive than 8-plex iTRAQ. For isobaric labels, quantitative accuracy was affected by precursor mixing. Based on our evaluation on precursor mixing and accuracy of isobaric label quantification, we propose a cut off of <30% isolation interference for peptide spectrum matches (PSMs) used in the quantification. BIOLOGICAL SIGNIFICANCE: Quantitative proteome analysis by mass spectrometry offers opportunities for biological research. However, knowing the limit of quantification in biological samples is important to accurately judge the results. By using a high-complexity sample spiked with protein standards of known concentrations, we investigated the quantification limits of label-free and label-based peptide quantification, including an evaluation of precursor mixing and its impact on quantification accuracy by isobaric labels. We suggest limits of allowed precursor interference and believe that this study contributes with information useful in proteome quantification by mass spectrometry.

Stable isotope labeling methods in protein profiling.

Mass spectrometry (MS) analysis of peptides and proteins has evolved dramatically over the last 20 years. Improvement of MS instrumentation, computational data analysis, and the availability of complete sequence databases for many species have made large-scale proteomics analyses possible. The measurement of global protein abundance by quantitative mass spectrometry has the potential to increase both speed and impact of biological and clinical research. However, to be able to detect and identify potential biomarkers, reproducible and accurate quantification is essential. The following chapter describes how to perform quantitative protein profiling using stable isotope labeling methods. Throughout, there is a focus on guidance in selection of an appropriate labeling strategy. With that in mind, we have included a section on acquisition and understanding of the liquid chromatography-mass spectrometry (LC-MS) data format. Further, we describe the different stable isotope labeling methods and their pros and cons. We start by giving an overview of the overall quantitative proteomics workflow in which extracting relevant biological information from the acquired data is the ultimate goal.

Assessing recent smoking status by measuring exhaled carbon monoxide levels.

BACKGROUND: Cigarette smoke causes both acute and chronic changes of the immune system. Excluding recent smoking is therefore important in clinical studies with chronic inflammation as primary focus. In this context, it is common to ask the study subjects to refrain from smoking within a certain time frame prior to sampling. The duration of the smoking cessation is typically from midnight the evening before, i.e. 8 hours from sampling. As it has been shown that a proportion of current smokers underestimates or denies smoking, objective assessment of recent smoking status is of great importance. Our aim was to extend the use of exhaled carbon monoxide (CO(breath)), a well-established method for separating smokers from non-smokers, to assessment of recent smoking status. METHODS AND FINDINGS: The time course of CO(breath) decline was investigated by hourly measurements during one day on non-symptomatic smokers and non-smokers (6+7), as well as by measurements on three separate occasions on non-smokers (n = 29), smokers with normal lung function (n = 38) and smokers with chronic obstructive pulmonary disease (n = 19) participating in a clinical study. We used regression analysis to model the decay, and receiver operator characteristics analysis for evaluation of model performance. The decline was described as a mono-exponential decay (r(2) = 0.7) with a half-life of 4.5 hours. CO decline rate depends on initial CO levels, and by necessity a generic cut-off is therefore crude as initial CO(breath) varies a lot between individuals. However, a cut-off level of 12 ppm could classify recent smokers from smokers having refrained from smoking during the past 8 hours with a specificity of 94% and a sensitivity of 90%. CONCLUSIONS: We hereby describe a method for classifying recent smokers from smokers having refrained from smoking for >8 hours that is easy to implement in a clinical setting.

Fluorescence cross-correlation spectroscopy of a pH-sensitive ratiometric dye for molecular proton exchange studies.

Fluorescence fluctuation analysis of individual pH-sensitive fluorophores has recently proven to be a useful approach for biomolecular proton exchange studies. In this work, dual-color fluorescence cross-correlation spectroscopy (FCCS) is demonstrated on a ratiometric pH-sensitive dye, for which both the excitation and emission spectra shift as a function of pH. In the FCCS measurements, the fluorescence signal from the predominant emission wavelength range of the protonated form of the dye is cross-correlated with that of the deprotonated form. Two lasers are used alternatingly to excite predominantly the protonated and the deprotonated form of the dye. The alternating excitation modulation scheme is combined with detection gating, and is based on a recently developed concept that allows extraction of correlation data for all correlation times regardless of the chosen modulation period. The scheme can thus be applied without concern for the time-scales of the molecular dynamic processes to be studied. By this combined discrimination based on both excitation and emission, spectral cross-talk is dramatically reduced and a very distinct and unambiguous anticorrelation can be recorded in the correlation curves as a consequence of the proton exchange. The strong discrimination power makes the approach applicable also to ratiometric dyes with less pronounced spectral shifts. It should also be useful in combination with ratiometric dyes sensitive to other ambient conditions and ions, such as the biologically very important calcium ion.