Plasma Proteome Profiling to detect and avoid sample‐related biases in biomarker studies
2019; Springer Nature; Volume: 11; Issue: 11 Linguagem: Inglês
10.15252/emmm.201910427
ISSN1757-4684
AutoresPhilipp E. Geyer, Eugenia Voytik, Peter V. Treit, Sophia Doll, Alisa Kleinhempel, Lili Niu, Johannes Müller, Marie‐Luise Buchholtz, Jakob M. Bader, Daniel Teupser, Lesca M. Holdt, Matthias Mann,
Tópico(s)Blood properties and coagulation
ResumoReport30 September 2019Open Access Transparent process Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies Philipp E Geyer Philipp E Geyer orcid.org/0000-0001-7980-4826 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Eugenia Voytik Eugenia Voytik Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Peter V Treit Peter V Treit Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Sophia Doll Sophia Doll Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Alisa Kleinhempel Alisa Kleinhempel Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany Search for more papers by this author Lili Niu Lili Niu NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Johannes B Müller Johannes B Müller Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Marie-Luise Buchholtz Marie-Luise Buchholtz Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany Search for more papers by this author Jakob M Bader Jakob M Bader Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Daniel Teupser Daniel Teupser Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany Search for more papers by this author Lesca M Holdt Lesca M Holdt Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany Search for more papers by this author Matthias Mann Corresponding Author Matthias Mann [email protected] orcid.org/0000-0003-1292-4799 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Philipp E Geyer Philipp E Geyer orcid.org/0000-0001-7980-4826 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Eugenia Voytik Eugenia Voytik Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Peter V Treit Peter V Treit Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Sophia Doll Sophia Doll Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Alisa Kleinhempel Alisa Kleinhempel Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany Search for more papers by this author Lili Niu Lili Niu NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Johannes B Müller Johannes B Müller Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Marie-Luise Buchholtz Marie-Luise Buchholtz Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany Search for more papers by this author Jakob M Bader Jakob M Bader Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Daniel Teupser Daniel Teupser Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany Search for more papers by this author Lesca M Holdt Lesca M Holdt Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany Search for more papers by this author Matthias Mann Corresponding Author Matthias Mann [email protected] orcid.org/0000-0003-1292-4799 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Author Information Philipp E Geyer1,2, Eugenia Voytik1, Peter V Treit1, Sophia Doll1,2, Alisa Kleinhempel3, Lili Niu2, Johannes B Müller1, Marie-Luise Buchholtz3, Jakob M Bader1, Daniel Teupser3, Lesca M Holdt3 and Matthias Mann *,1,2 1Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany 2NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark 3Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany *Corresponding author. Tel: +49 89 8578 2557; E-mail: [email protected] EMBO Mol Med (2019)11:e10427https://doi.org/10.15252/emmm.201910427 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Plasma and serum are rich sources of information regarding an individual's health state, and protein tests inform medical decision making. Despite major investments, few new biomarkers have reached the clinic. Mass spectrometry (MS)-based proteomics now allows highly specific and quantitative readout of the plasma proteome. Here, we employ Plasma Proteome Profiling to define quality marker panels to assess plasma samples and the likelihood that suggested biomarkers are instead artifacts related to sample handling and processing. We acquire deep reference proteomes of erythrocytes, platelets, plasma, and whole blood of 20 individuals (> 6,000 proteins), and compare serum and plasma proteomes. Based on spike-in experiments, we determine sample quality-associated proteins, many of which have been reported as biomarker candidates as revealed by a comprehensive literature survey. We provide sample preparation guidelines and an online resource ( www.plasmaproteomeprofiling.org) to assess overall sample-related bias in clinical studies and to prevent costly miss-assignment of biomarker candidates. Synopsis This study describes marker panels for the systematic assessment of plasma samples that report on erythrocyte lysis, platelet contamination and coagulation. Marker panels can assess entire clinical studies or individual samples for quality issues and allow evaluation of biomarker candidates. Deep reference proteome data (> 6,000 proteins) from erythrocytes, platelets, platelet-rich plasma, platelet-free plasma and whole blood from 20 individuals, distilled in three quality marker panels of 30 proteins each. The influence of blood sampling equipment and sample processing protocols on the plasma proteome is compared. The www.plasmaproteomeprofiling.org website allows the automated quality assessment of single samples and clinical studies. A general guideline for minimizing pre-analytical variations in future clinical studies is proposed. Evaluation of 210 biomarker studies reveal that about 50% of them report proteins of the quality marker panels, indicating potential sample handling issues. Introduction Protein levels determined in blood-based laboratory tests can be useful proxies of diseases. These biomarkers assess normal physiological status, pathogenic processes, or a response to an exposure or intervention (FDA-NIH:Biomarker-Working-Group, 2016). Proteins and enzymes constitute the largest proportion of laboratory tests, reflecting the importance of the plasma proteome in clinical diagnostics (Geyer et al, 2017). Typical protein biomarkers such as the enzymes aspartate aminotransferase (ASAT) and alanine aminotransferase (ALAT) for the diagnosis of liver diseases or cardiac troponins indicating myocardial necrosis are used routinely in clinical decision making. Enzymatic activity or antibody-based laboratory tests are performed in high-throughput and at relatively low costs, as the standard of health care. However, specific biomarkers are only available for a very limited number of conditions and most have been introduced decades ago (Anderson et al, 2013). There is thus a critical need to make the biomarker discovery process more efficient. Protein-binder assays quantifying many plasma proteins in parallel have become available (Gold et al, 2010; Assarsson et al, 2014), resulting in large-scale biomarker mining efforts (Ganz et al, 2016; Herder et al, 2018; Sun et al, 2018). Orthogonal to those technologies, mass spectrometry (MS)-based proteomics has become increasingly powerful in all domains of protein research (Aebersold & Mann, 2003, 2016; Munoz & Heck, 2014). MS measures the mass and fragmentation spectra of tryptic peptides derived from the sample with very high accuracy. Because these peptide and fragment masses are unique, MS-based proteomics is inherently specific, which can be an advantage over enzyme tests and immunoassays (Wild, 2013). Within its limit of detection, MS-based proteomics can analyze all proteins in a system and is unbiased and hypothesis-free in this sense. The proteomic community has developed guidelines for the development, specificity, and potential clinical application of biomarkers. These discuss quality standards and emphasize the importance of selecting cohorts that are appropriate in size, thus ensuring the statistical significance of potential findings (Mischak et al, 2010; Surinova et al, 2011; Skates et al, 2013; Hoofnagle et al, 2016; Geyer et al, 2017). That being said, there are no systematic procedures in place to assess the proteome-wide effects of pre-analytical handling of blood-based samples. Considering that plasma samples are often collected during daily clinical routine and variably processed, sample collection and processing clearly have the potential to negatively influence clinical studies, making it difficult to uncover true biomarkers, while potentially contributing incorrect ones. Especially in case–control studies, any difference in the collection and processing of samples may result in systematic bias. So far, relatively little attention has been paid to this crucial aspect on a proteome-wide scale and these studies mainly investigate pre-analytical effects (Rai et al, 2005; Timms et al, 2007; Schrohl et al, 2008; Qundos et al, 2013; Hassis et al, 2015). Recently, we developed “Plasma Proteome Profiling”, an automated MS-based pipeline for high-throughput screening of plasma samples (Geyer et al, 2016a). In this article, we apply this technology to systematically assess the quality of individual samples and clinical studies with the aim to identify generally applicable quality marker panels. Blood collection and subsequent errors in preparation are likely sources of plasma contamination. To address this issue, we construct proteomic catalogs of contaminating cell types as well as proteomic changes that may be induced during processing. This results in three panels of contaminating proteins, recommendations for assessing the quality of plasma samples and for consistent sample processing. We develop an online tool for biomarker studies and test the applicability of the panels on a recent investigation on the effects of weight loss on the plasma proteome (Geyer et al, 2016b). A comprehensive literature review of plasma proteome studies highlights that about half of them potentially suffer from limitations related to sample processing. Results Erythrocyte and platelet proteins in the plasma proteome During the development of our Plasma Proteome Profiling pipeline and its optimization for high-throughput screening of human cohorts (Geyer et al, 2016a), we repeatedly observed proteins that tended to emerge as groups of statistically significant outliers but appeared to be independent of the particular study. We hypothesized that they reflected sample quality issues. Manual and bioinformatic inspection revealed three classes of origin: erythrocytes, platelets, and the blood coagulation system. Consequently, we designed experiments to systematically characterize these main quality issues of the plasma proteome. First, we acquired reference proteomes of erythrocytes and platelets, which are by far the most abundant cellular components (5 × 106 and 3 × 105 cells per μl). We harvested these cellular components from 10 healthy females and 10 males to obtain representative erythrocytes, platelets, and pure (platelet-free) plasma and further collected platelet-rich plasma and whole blood (Fig 1A; see Materials and Methods). Cell counting confirmed the purity of the samples (Table EV1). All five blood fractions were separately prepared for each individual by our automated proteomic sample preparation pipeline, followed by liquid chromatography coupled to high-resolution mass spectrometry (LC-MS/MS). To create reference proteomes, we generated a very deep library from pooled samples by analyzing extensively pre-fractionated peptides (Kulak et al, 2017; see Materials and Methods). A total of 6,130 different proteins were identified from 61,654 sequence-unique peptides (Fig 1B and C). The platelet proteome was the most extensive (5,793 proteins), whereas we detected 2,069 proteins in erythrocytes, 1,682 in platelet-rich plasma, and 912 in platelet-free plasma. The comparison of platelet-rich plasma to platelet-free plasma (84% additional proteins) demonstrates the extent of proteins that can be introduced by platelets. Figure 1. Identification of blood cell markers A. Study outline and proteomic workflow. Erythrocytes, thrombocytes, platelet-rich, and platelet-free plasma were generated from 10 healthy female and male individuals by differential centrifugation and successive purification steps. To generate reference proteomes for each of the blood compartments, the respective protein samples of the 20 study participates were digested to peptides. B, C. Proteins (B) and peptides (C) identified for platelets, erythrocytes, platelet-rich, and platelet-free plasma. D, E. Selection of the most suitable quality marker proteins for (D) platelet contamination (blue dots) and (E) erythrocyte contamination (red dots) based on their abundance, the platelet/erythrocyte-to-plasma ratio, and the coefficient of variation. Proteins that were only detected in platelets or erythrocytes, but not in plasma are aligned on the right side of the graph. Download figure Download PowerPoint Next, we investigated purified samples for all 20 study participants individually. The average numbers of identified proteins and peptides were very consistent in all individuals (Appendix Fig S1). To construct panels of easily detectable and robust quality markers, we calculated the average protein intensities and the coefficient of variation (CV) across the study participants. As a prerequisite, we required that the proteins should be substantially more abundant in erythrocytes as well as platelets rather than in plasma. According to these criteria, we selected the 30 most abundant proteins with CVs below 30% and at least a 10-fold higher expression level in the contaminating cell type than in plasma (Fig 1D and E). NIF3-like protein 1 (NIF3L1), a low-abundance erythrocyte-specific protein, was excluded, because it was inconsistently identified as was the platelet-bound coagulation factor F13A1, whose function makes it an unsuitable platelet marker. The remaining proteins represent our cellular quality marker panels (Table EV2). They overlap by just two proteins (actin/ACTB and glyceraldehyde-3-phosphate dehydrogenase/GAPDH), and their quantities were not correlated with each other (Appendix Fig S2). Thus, they are specific and independent indicators for the origin of plasma quality. Comparing median expression values of proteins shared between the blood components revealed that plasma proteins do correlate with whole blood (Pearson's correlation coefficient R = 0.43), as expected. In contrast, there was no correlation between the platelet, erythrocyte, and plasma proteomes (Appendix Fig S2). This indicates that the levels of cellular proteins in plasma are not a constant fraction of those in the cellular proteomes. The platelet panel was enriched in platelet-rich plasma compared to normal (platelet-free) plasma. Both panels are de-enriched in pure plasma compared to whole blood, however, this effected the erythrocyte panel even more strongly, because centrifugation removes erythrocytes more efficiently than platelets. A histogram of both panels over the abundance range visualizes their distribution in the different blood compartments (Appendix Fig S2). Erythrocytes are 10-fold more abundant and fourfold larger than platelets, and indeed, the corresponding panel proteins have a 42-fold difference in whole blood. In plasma, however, their ratio was nearly one to one, again pinpointing a more efficient removal of erythrocytes than of platelets in standard sample preparation. The fact that several proteins of both panels were still detectable in pure plasma indicates a baseline level of contaminants due to imperfect de-enrichment or the life cycle of these cells. The four most abundant erythrocyte proteins, HBA1, HBB, CA1, and HBD, were present in pure plasma of almost all individuals, whereas lower abundant proteins were only sporadically identified. In contrast, platelet proteins were quantified over a larger abundance range and some of them were found in every individual. In addition to the sum of panel protein abundances, we calculated their correlation to the standard reference panel defined by the 20 participants to several hundred plasma samples of a previous study (Geyer et al, 2016b). A distinct contamination of erythrocyte proteins seems to be a part of the plasma proteome as the erythrocyte panel has in general a relatively high correlation between the reference cohort erythrocyte levels and the plasma samples in the above-mentioned study. In contrast, in many plasma samples there was no correlation detectable between the reference cohort platelet levels and the plasma samples in the study. In practice, a correlation > 0.5 indicated that the proteins are present as a result of contamination (Appendix Fig S3A–C). Note that an apparent contaminant protein could still be applied as a biomarker—however, in this case its abundance value should be different from the pattern in the reference quality panel. Serial dilution experiments validate the erythrocyte and platelet quality marker panels To determine whether the two protein panels correctly quantify contamination in plasma, we generated four pools of erythrocytes and platelets from five study participants at a time. These pools were diluted in nine steps into platelet-free plasma for a total range of 107, followed by cell counting and proteomic analysis (Fig 2A). This resulted in an expected decrease in the cellular proteome ratio to plasma (Fig 2B and C). All but two of the panel proteins were consistently quantified over the dilution range. As the protein within each panel has the same origin, we defined a single variable for each cell type by summing their intensities and dividing by the summed intensities of all quantified plasma proteins. This yielded two remarkably robust “contamination indices” that turned out to be linear with respect to the cell numbers determined by cell cytometry (Table EV3; R = 0.98 and 0.99, Fig 2D and E). Spiked-in contaminations of 1:100 could readily be detected, which corresponds to a concentration of 70,000 erythrocytes or 30,000 platelets per μl plasma. Figure 2. Spike-in of erythrocyte and platelet fractions into pure plasma A. Dilution and analysis scheme. B, C. Protein intensities were Z-scored across the dilution series (B) for the 29 quality markers of the erythrocyte panel and (C) for the 29 markers of the platelet panel as a function of their spike-in proportion to plasma. Whiskers indicate 10–90 percentiles, and horizontal lines denote the mean. D. Correlation of erythrocyte count to the “contamination index” for the erythrocyte marker panel. E. Correlation of platelet count to contamination index for the platelet marker panel. Download figure Download PowerPoint Quality marker panel for blood coagulation In addition to contamination due to cellular constituents, partial and variable coagulation could contribute to systematic bias in biomarker studies. Indeed, we had found coagulation-related proteins to be connected to sample handling from finger pricks while developing our plasma proteomics pipeline (Geyer et al, 2016a). In clinical practice, an anticoagulant is pre-added to commercially available containers so that it is combined with blood upon withdrawal. Prompt inversion mixes the anticoagulant with the blood, yielding pure plasma after centrifugation (Fig 3A). Any delay in adding or mixing could cause partial coagulation—in the extreme case of missing anticoagulant and waiting for 30 min, one would obtain serum instead of plasma. Figure 3. Quality marker panel for blood coagulation A. Preparation of plasma and serum samples. EDTA was used as anticoagulation agent, and incubation and centrifugation values are indicated. B. Volcano plot comparing 72 plasma vs. 72 serum proteomes. Proteins highlighted in yellow were chosen according to their P-value as markers for coagulation. Only the plasma-enriched proteins (compared to serum) were used in the calculation of the coagulation contamination index. C. Ratio of the summed intensities of all plasma or serum proteins to the sum of the plasma-enriched panel proteins is plotted for all samples. Whiskers indicate the 10–90 percentile, and horizontal lines denote the mean. D. Overlap of the three quality marker panels. Download figure Download PowerPoint To generate a panel for assessing blood coagulation, we systematically compared 72 plasma vs. 72 serum samples (four individuals, 18 aliquots). From a total of 2,099 quantified proteins, 299 were significantly altered (Fig 3B). The most significantly de-enriched proteins after clotting were typical constituents of the coagulation cascade such as fibrinogen chains alpha (FGA), beta (FGB), and gamma (FGG) (P < 10−130, > 40-fold), whereas the platelet-associated coagulation factor F13A1 and antithrombin-III (SERPINC1) decreased by more than half. Interestingly, the strongest elevated proteins in serum were highly abundant platelet proteins: platelet basic protein (PPBP), platelet glycoprotein Ib alpha chain (GP1BA), thrombospondin 1 (THBS1), and platelet glycoprotein V (GP5) (P < 10−10; twofold to fivefold increase). In total, 208 proteins increased and 91 decreased due to coagulation. The former set of proteins, which have higher levels in serum than in plasma, were also quantitatively enriched with high-abundant platelet proteins (P < 10−5; median rank 699 of 3,150 proteins), indicating coagulation-induced activation of platelets. To define a robust panel of quality markers for the extent of coagulation, we first selected the 30 most significantly altered proteins between serum and plasma. Although not among the top 30, we added the platelet factor 4 variant 1 (PF4v1; P < 10−11, 2.2-fold up in serum), because it was an excellent indicator of coagulation in our studies and has already been reported in the context of pre-analytical variation (Timms et al, 2007). In contrast to the erythrocyte and platelet panels, proteins of the coagulation panel increase or decrease due to blood clotting and the fold changes vary strongly between them. Because fold changes are greatest for the decreasing proteins, we calculated the coagulation marker ratio only from them (sum of all plasma proteins divided by sum of plasma-elevated coagulation proteins). This ratio was very robust when comparing serum and plasma, clearly separating them with median ratios of 9 and 120 for these distinct sample types (Fig 3C). Of the coagulation marker panel, only F13A1, PPBP, and THBS1 were in common with the platelet panel and none with the erythrocyte panels (Fig 3D). The low overlap observed for the three quality marker panels should make them highly specific tools to elucidate the presence and origin of sample-related bias. Application of the quality marker panels to a biomarker study The above-defined marker panels can assess sample-related issues at three levels: the quality of each sample in a clinical cohort, potential systematic bias in the entire study, and the likelihood that individual biomarker candidates belong to the contaminant proteomes. We recently investigated changes in the plasma proteome upon weight loss (Geyer et al, 2016a,b). Briefly, caloric restriction in 52 individuals for 2 months was followed by weight maintenance for 1 year. Plasma Proteome Profiling of seven longitudinal samples revealed significant changes in the profile of apolipoproteins, a decrease in inflammatory proteins and markers correlating with insulin sensitivity. Given that protein abundance changes of < 20% were often highly significant, we expected that overall sample quality was high, making this study suitable for testing the practical applicability of the quality marker panels. First, we assessed the quality of each sample separately by calculating the three contamination indices and plotting their distribution in the total of 318 measurements. For each index, we initially defined potentially contaminated samples as those with a value more than two standard deviations above the mean (red lines in Fig 4A). This flagged 12 samples, six with platelet contamination, one with increased erythrocyte levels, and five with signs of partial coagulation. Resolving the three quality marker panels to the levels of individual proteins resulted in almost perfectly parallel trajectories (Appendix Fig S4A–C). Accordingly, the correlations to the reference quality marker panels were substantial (R > 0.77). Overall, the variation of the contamination indices was highest for the platelets also visible by a contamination index difference (max/min ratio) of a factor 182 between the least and the most contaminated sample, followed by erythrocytes (max/min 23), and lowest for coagulation (max/min 5). The platelet proteins talin-1 (TLN1), myosin-9 (MYH9), and alpha-actinin-1 (ACTN1) had the largest variations, all with maximal changes > 5,000-fold. Catalase (CAT), carbonic anhydrase 1 and 2 (CA1, CA2) from the erythrocyte index varied maximally by more than 500-fold. The three fibrinogens in the coagulation panel changed by up to 20-fold, indicating that only partial coagulation events took place (Fig 4A). Figure 4. Quality marker panels in a weight loss study and literature study A. Assessment of individual sample quality with respect to the three contamination indices using the online tool at www.plasmaproteomeprofiling.org. Samples with indices that are more than two standard deviations from the mean (horizontal red lines) are flagged as potentially contaminated (red bars and sample numbers). B. Volcano plot of the proteome comparison of time point 1 vs. 4. Proteins of the platelet panel are highlighted in blue and two additional significantly regulated proteins in red. C. Global correlation map on the left with an inset of the platelet cluster on the right. The two significant outliers of the volcano plot in (B) are marked in red. Platelet panel proteins are highlighted in blue in the inset. Red patches in the global correlation map indicate positive and blue patches negative correlations. D. Literature analysis of 210 publications using MS-based plasma proteomics to identify new biomarkers. The number of quality markers reported as biomarker candidates in these studies is indicated. E. Distribution of the reported quality markers according to the three types of likely contaminations. The distribution is shown across studies that report one, two, or three proteins of the same quality marker panel. Download figure Download PowerPoint Note that evaluating individual sample quality based on the standard deviation of all samples, as done here, has the benefit of being independent of the specific proteomic method used to measure protein amounts. However, this requires that most samples have low levels of contamination, so that outliers of the statistical distribution are clearly apparent. If this is not the case, we propose using general, study-independent cutoff values to differentiate between samples of high and poor quality in such studies. To assess potential systematic bias for groups of samples such as cases and controls or different time points, we applied a t-test based volcano plot. Most of the significantly upregulated proteins at time point 4 were members of the platelet panel (Fig 4B). With this information in hand, we contacted our collaboration partners, who tracked down the platele
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