- Open Access
Random and independent sampling of endogenous tryptic peptides from normal human EDTA plasma by liquid chromatography micro electrospray ionization and tandem mass spectrometry
Clinical Proteomics volume 14, Article number: 41 (2017)
Normal human EDTA plasma samples were collected on ice, processed ice cold, and stored in a freezer at – 80 °C prior to experiments. Plasma test samples from the – 80 °C freezer were thawed on ice or intentionally warmed to room temperature.
Protein content was measured by CBBR binding and the release of alcohol soluble amines by the Cd ninhydrin assay. Plasma peptides released over time were collected over C18 for random and independent sampling by liquid chromatography micro electrospray ionization and tandem mass spectrometry (LC–ESI–MS/MS) and correlated with X!TANDEM.
Fully tryptic peptides by X!TANDEM returned a similar set of proteins, but was more computationally efficient, than “no enzyme” correlations. Plasma samples maintained on ice, or ice with a cocktail of protease inhibitors, showed lower background amounts of plasma peptides compared to samples incubated at room temperature. Regression analysis indicated that warming plasma to room temperature, versus ice cold, resulted in a ~ twofold increase in the frequency of peptide identification over hours–days of incubation at room temperature. The type I error rate of the protein identification from the X!TANDEM algorithm combined was estimated to be low compared to a null model of computer generated random MS/MS spectra.
The peptides of human plasma were identified and quantified with low error rates by random and independent sampling that revealed 1000s of peptides from hundreds of human plasma proteins from endogenous tryptic peptides.
The analysis of proteins and peptides from human blood by liquid chromatography, electrospray ionization and tandem mass spectrometry (LC–ESI–MS/MS) may permit the diagnosis of diseases and the evaluation of the efficacy of therapeutic treatments . Both MALDI and electrospray ionization methods previously demonstrated that tryptic protease activities cleave peptides from blood proteins that differed between control and heart attack samples [2, 3]. At least some peptides observed in serum samples are known to be ex vivo artefacts that represent the steady-state balance of endo- versus exopeptidase action after sample collection [2, 4]. The peptides of blood are in a dynamic steady state that can be easily perturbed by sample incubation conditions [2, 4]. Artefacts introduced into the blood fluids after sampling and prior to freezing may be a major source of pre-analytical variation [2, 5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]. There is considerable variation in the peptides observed, and even the trends reported, in the study of degradation of blood proteins that may differ between groups, likely from the large variation that occurs immediately after sample collection [2, 5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]. One key contribution of this study is the collection of EDTA plasma directly onto ice for cold processing to establish a reliable baseline compared to plasma peptides at room temperature.
There are many potential sources of error in the identification and quantification of peptides and therefore proteins . Collecting rich MS/MS spectra at a high signal-to-noise is the key to both reliable and sensitive identification and quantification of blood peptides and proteins with the X!TANDEM algorithm [23, 24]. It is necessary to pre-fractionate samples to prevent co-elution of peptides with similar m/z values. Selective extraction from the solid phase with organic aqueous solvent is a simple means to enrich endogenous peptides from blood fluids [2,3,4, 25]. Robust and sensitive ion traps may have advantages in analyzing low amounts of blood peptides and proteins where signal-to-noise filters and statistical controls are employed to control type I error rate [26,27,28]. The X!TANDEM algorithm is known to fit the MS/MS spectra from sensitive and robust ion traps with a high degree of statistical rigour using the default settings of ± 3 m/z of the precursor and ± 0.5 Da of the many fragments with up to three missed cleavages  with a low rate of type I error in the identification of peptides based on the goodness of fit of the MS/MS spectra [23, 24, 30, 31]. The proteins of human plasma contain many highly abundant proteins such as albumin, apolipoproteins, protease inhibitors and others that digest efficiently with the exogenous addition of trypsin and thus mask the detection of low abundance proteins by LC–ESI–MS/MS . Many of the well-known proteins of human blood [32, 33] are cleaved by endogenous tryptic endopeptidases to release fully tryptic peptides that may be identified by collection over C18 followed by LC–ESI–MS/MS with an ion trap . The peptides of blood fluids show good agreement on the detection of many unexpected cellular peptides by LC–ESI–MS/MS using both Qq-TOF and the sensitive ion trap [2,3,4, 25, 34]. The secretion or release of cellular proteins into extracellular space may result in the preferential cleavage of the cellular proteins upon exposure to circulating protease activities . Here, the random and independent sampling of endogenous tryptic peptides were compared from plasma collected and incubated on ice versus the same plasma incubated at room temperature and showed many plasma proteins are degraded over time by tryptic proteases with about a twofold higher frequency of many tryptic peptides at room temperature compared to ice cold samples. A second key contribution of this paper is to show that the peptides from proteins expressed in tissues and cells may be identified and quantified by micro electrospray with LC–ESI–MS/MS of endogenous peptides with a simple linear ion trap  that shows a low type I error rate by comparison to a null model of random MS/MS spectra, or noise spectra, and computing protein p-values using X!TANDEM that shows a low False Discovery Rate (low q-values) using the method of Benjamini and Hochberg .
The Agilent 1100 HPLC (Santa Clara, CA, USA) was coupled to an XL LTQ linear ion trap mass spectrometer (Thermo Electron Corporation, Madison, WI, USA) for LC–ESI–MS/MS. The protease inhibitors, salts, buffers, Coomassie Blue and ninhydrin were obtained from Sigma Aldrich (St. Louis, MO, USA). The #1 filter paper was obtained from Whatman (Maidstone, UK). The HPLC grade water and acetonitrile were obtained from Caledon Laboratories (Georgetown, ON, Canada).
Plasma sample collection
Human plasma was collected under a Comité National d’Ethique de Recherche (CNER) Protocol #201107 “Biospecimen Research” at the Centre Hospitalier de Luxembourg. The plasma was collected in EDTA tubes (Becton Dickenson) that were rapidly rotated 5 times before packing in ice. The ice cold plasma was then separated from blood cells at 12,000 RCF for 20 min at 4 °C prior to aliquoting to 225 µl samples on ice and randomly assigned to short term or long term experimental treatments. Plasma samples of 225 μl were previously shown to be sufficient for peptide extraction [4, 25, 34].
Plasma sample treatments
Random aliquots were maintained on ice, ice plus protease inhibitors, or incubated at room temperature for ≥ 96 h or more as indicated. Plasma samples were permitted to degrade at room temperature for 0 h (ICE), 1, 4, 8, 12, 24, 48 and 72 h alongside ice or ice plus inhibitor controls prior to random sampling and analysis. A total of 82 control plasma samples that were either frozen or never above ice cold (ALLICE), and 88 plasma samples that were incubated at room temperature for different lengths of time (ALLRT), were analyzed. The protease inhibitor treatment consisted of Sigma Eukaryotic protease inhibitor cocktail plus 2 mM AEBSF, 2 mM PMSF, 2 mM EDTA, 2 mM caproic acid, and 2 mM benzamidine. The Sigma Mammalian Protease inhibitor cocktail (contains at least: AEBSF, 104 mM, Aprotinin, 80 μM, Bestatin, 4 mM, E-64, 1.4 mM, Leupeptin, 2 mM, Pepstatin A, 1.5 mM) was used at 1/100 (v/v). Plasma samples (225 µl) from at least 10 different donors were tested at each time point and over the time course of degradation up to 72 h. At the end of each time period, the samples were frozen, freeze dried and stored dried at − 80 °C until analysis.
Protein assay, SDS-PAGE and free amine assays
Protein content of the plasma samples was determined in the presence of SDS detergent by the Dumbroff method  prior to separation by tricine SDS-PAGE followed by staining of the gel with Coomassie Brilliant Blue . Plasma samples were mixed 1:1 with 2 × tris sample buffer for SDS-PAGE and then diluted a further 10 fold in 2 × tris sample buffer into the linear BSA standard range for dot blot protein assays. The free amines were extracted in ethanol and measured using the Cd-ninhiydrin peptidase assay alongside glycine equivalents .
The mass spectrometer was cleaned, calibrated with the manufacturer’s standard mixture, tuned with GluFib and Angiotensin and tested for sensitivity prior to each replicate block by infusion of a dilution series of GluFib and Angiotensin. The mass accuracy and sensitivity of the LC–ESI–MS/MS system was tested with a tryptic digestion of a mixture of cytochrome C, glycogen phosphorylase B and alcohol dehydrogenase . The plasma peptides were collected over a C18 preparative column with elution in 2 µl of 5% formic acid and 65% acetonitrile and immediately diluted with 18 µl of 5% formic acid for injections via a 20 µl loop with a Rheodyne manual injector. A total of ~ 5 µg of extracted and purified peptides was injected for each analytical HPLC separation over a 300 micron ID column (15 cm) with inline filter frits. The peptides were ionized at 4.5 kV via a micro electrospray ion source with 10 L N2 per minute with a transfer capillary temperature of 200 °C into a Thermo Electron Corporation LTQ ion trap mass spectrometer . The peptides were randomly and independently sampled by MS and MS/MS without replacement as the peptides eluted from the HPLC column into the electrospray source from 350 to 2000 m/z.
Peptide MS/MS spectra correlation analysis
A federated library of 158,072 human proteins that differed by at least one amino acid was assembled from NCBI, Ensembl and Swiss Prot and made non-redundant using Structured Query Language (SQL). A physical filter of at least one thousand (E3) intensity counts for peptide parent ions was used to limit type I error [23, 24]. A sub-set of the data was analyzed by fully tryptic versus no enzyme specified to compare the sets of proteins identified. The MS and MS/MS spectra of all peptides recorded were correlated to the federated library set with fully tryptic enzyme specification, a charge state of 2+ or 3+ with up to three missed cleavages in SEQUEST [41, 42], MASCOT , OMSSA  and X!TANDEM  with ± 3 m/z for the precursor and with the fragments within 0.5 Da with up to three missed cleavage sites [23, 24, 26, 27, 30, 45]. The data from SEQUEST, provided via the manufacturer’s BIOWORKS algorithm, was further limited to the default setting of 0.05 maximum delta correlation of parent ions with maximum peptide mass set to 5000 Da and peptide length set to ≥ 6 amino acids. The authentic results were compared to random mis-correlations from unexpected modifications using computer generated random MS/MS spectra or from noise spectra by blank runs with HPLC grade solvents over naïve columns.
Computational analysis in SQL and statistical analysis with R
The combination of Chi Square and general linear models such as regression and ANOVA using a generic statistical analysis system are sufficient to provide a satisfying statistical analysis of LC–ESI–MS/MS data [26,27,28, 45]. The resulting peptide and protein identifications together with the parent and fragment m/z and intensity values were parsed into an SQL database . The charge state with the best score (Filter 1) and the peptide sequence with the best score (Filter 2) were used to control type I error of identification: thus, the SQL database utilized a unique hash tag for each MS–MS/MS event to ensure that only the best fit of each MS/MS spectra at only one charge state was accepted, and thus no MS/MS spectra was assigned to more than one peptide sequence. The peptide-to-protein counts of plasma samples were previously statistically analyzed in the generic statistical system S  or SAS [23, 24, 26, 27, 30, 31, 45]. However, in this study the data was analyzed using the generic open-source R statistical system . The R statistical system was also used to plot the peptide-to-protein distribution of authentic peptides compared to those of random spectra and to compute the cumulative p-value for protein Gene Symbols from the product of the observed peptides p-values from the best accession .
EDTA plasma collected directly onto ice were analyzed by C18 preparative chromatography followed by micro electrospray LC–ESI–MS/MS of samples incubated on ice or ice plus protease inhibitors. Samples that remained on ice showed about half of the endogenous tryptic peptides of samples incubated at room temperature, and thus many or most of the peptides observed in EDTA plasma over time are ex vivo artefacts.
Tricine SDS-PAGE showed no obvious protein degradation and no new bands were observed with time at room temperature (Fig. 1). The degradation of the plasma samples at room temperature for up to 96 h or more was not detectable by separation of ~ 100 µg of plasma proteins by tricine SDS-PAGE with staining by CBBR [2, 47]. The major protein bands of plasma from SDS-PAGE appear to be stable with incubation at room temperature even for many days and so do not appear to degrade much.
The protein content of the blood plasma samples in SDS-PAGE sample buffer were measured by the Dumbroff method  and was on average about 45 mg/ml but showed great variability within the randomly chosen samples. The total protein assays of the samples showed that there was a slight decline of about 10% in the protein content over the course of the 24 h at room temperature (Fig. 2). Protein content was too variable between individuals to be a useful measure of degradation but seems to indicate that some proteins were susceptible to degradation in the sample.
Free amine assay
The modified colorimetric (Cd-ninhydrin) method to detect peptidase activity by the release of free amines is sensitive to micromole amounts of released free amines . The free amine assay showed that there is a rapid and statistically significant release of alcohol-soluble amines over time from 350 to about 410 micro molar soon after incubation at room temperature (Fig. 3). The free amine results were consistent with the protein assay that shows large variation between individuals but also an apparent release of ethanol soluble amines early in the incubation of plasma at room temperature.
Random and independent sampling
A random and independent sampling of endogenous peptides from EDTA plasma maintained on ice versus at room temperature was made without replacement as the peptides eluted from the C18 HPLC column in the LC–ESI–MS/MS system. A total of 563,095 MS/MS spectra from precursors with intensity ≥ 1000 counts were obtained from LC–ESI–MS/MS analysis of 170 samples × 90 min C18 HPLC runs. The LC–ESI–MS/MS spectra and the results of the correlation algorithms were parsed into an SQL Server Database for statistical analysis with the generic statistical analysis system R [23, 24, 30, 31, 48].
Fully tryptic versus no enzyme correlation
Comparing the set of proteins identified by fully tryptic correlation versus no enzyme correlation (all possible human peptide sequences) showed essentially complete agreement on the set of proteins identified (Fig. 4). The no-enzyme search returned a greater number of peptides to the same small group of proteins identified by tryptic correlation and did not identify many new proteins from the much larger set of proteins not identified by tryptic correlation.
Filter by best charge state and peptide sequence
The MS/MS spectra were then correlated to 158,072 federated protein sequences by the MASCOT, OMSSA, X!TANDEM and SEQUEST algorithms. The results of the LC–ESI–MS/MS from the fully tryptic correlation algorithms were parsed together into an SQL Server Database for statistical analysis with the R generic statistical analysis system [23, 24, 30, 31, 48]. A major source of error in the results of LC–ESI–MS/MS analysis was the re-use of MS/MS spectra for more than one protein by MASCOT, OMSSA, SEQUEST and X!TANDEM (Table 1). Filtering out all hits that were not the best charge state, i.e. 2+ or 3+ (Filter 1) and then accepting only the best peptide sequence at the best charge state (Filter 2) eliminates more than 80% of the potential peptide correlations (Table 1). A total of 563,095 MS/MS spectra from parent ions ≥ E3 (1000) counts resulted in correlations to 729,533 peptides from a library of 158,072 proteins. A sum total of 3,788,530 peptides were correlated by the algorithms together over all treatments. After selecting only the best fit in terms of charge state and peptide sequence where the parent protein showed at least three independent correlations, the number of peptides and proteins collapsed to 729,533 peptides from 82,276 protein accessions.
From a total of 563,095 MS/MS spectra of greater than 1000 arbitrary counts, MASCOT fitted just over 60 proteins, OMSSA fitted 74 proteins, X!TANDEM fitted over 2068 protein sequences, and SEQUEST fitted 78,929 proteins that differ by at least one amino acid from the federated library of all known protein forms with at least three peptides (Fig. 5). Most of the peptides and proteins identified by X!TANDEM were further identified by SEQUEST. The peptides of normal human plasma extracted by C18 solid phase extraction and analyzed by micro electrospray are listed by accession number in the Additional file 1. Plotting the best fit data in terms of the averaged peptides to proteins from MASCOT, OMSSA, X!TANDEM and SEQUEST shows that some proteins show more than a hundred independent best fit correlations (Fig. 6). Since X!TANDEM previously showed a low type I error rate for proteins from fully tryptic peptides [23, 24, 26, 27] and since the goodness of fit of MS/MS spectra also sensitively identified far more proteins than either MASCOT or OMSSA, X!TANDEM was selected for subsequent statistical analysis. X!TANDEM fit some 583,927 random and independently sampled MS/MS spectra that showed 48,019 correlations to a set of 5855 peptides from 2068 proteins that reduced to 510 Gene Symbols with at least 5 peptides that had precursor intensity values greater than 1000 counts (E3). The MASCOT and X!TANDEM algorithms have been compared , in agreement with previous results we observed that under our conditions SEQUEST was the most sensitive, X!TANDEM was sensitive and MASCOT and OMSSA were less sensitive . Thus under these conditions MASCOT and OMSSA showed few proteins compared to X!TANDEM, that is known to be reliable compared to random simulations, and thus showed a large type II error.
Collecting EDTA plasma samples directly onto ice, followed by incubation on ice ± inhibitors, resulted in about 20,000 total peptide correlations per LC–ESI–MS/MS run but also prevented the generation of endogenous plasma peptides for days on ice especially if protease inhibitors were provided and so yields a stable baseline for degradation experiments. In contrast, samples that were collected on ice and then briefly incubated at room temperature showed an increase to about 40,000 thousand peptide MS/MS correlations per run by 1 h at room temperature (Table 2). The samples that were freeze dried and stored at room temperature or freeze dried and stored at − 20 °C both had about 20,000 thousand peptide correlations per LC–ESI–MS/MS run similar to samples collected and stored briefly on ice. Samples preserved by freezing at − 80 °C or liquid nitrogen showed slightly higher levels of peptides per run than ice or freeze drying (Table 2).
Analysis of ice versus room temperature samples
Samples maintained on ice showed proteins with up to a hundred correlated peptides after filtering (i.e. peptides to proteins) (Fig. 7a) but samples at room temperature showed greater numbers of correlations of peptides to proteins (Fig. 7b). Taking the ratio of room temperature (ALLRT) versus ice samples (ALLICE) showed more than one-hundred-fold variation of individual proteins with incubation at room temperature (Fig. 8). The proteins that showed the largest release of peptides at room temperature compared to ice was complement chain 4B (C4B) and C3 [2, 3]. Regression analysis revealed that most plasma proteins show on average a ~ twofold increase in sampling frequency at room temperature (Fig. 8).
X!TANDEM analysis of endogenous tryptic peptides
The results of the X!TANDEM algorithm were collected in SQL Server and analyzed compared to those of computer generated random MS/MS spectra or noise spectra (not shown) with the R statistical system that demonstrated a low probability of false positive identification (Fig. 9).
Most of the MS/MS spectra data was collected at values ranging from 350 to about 1700 m/z (Fig. 10a). The log10 distribution of precursor intensity increased with mass (momentum) as expected from impact detection (Fig. 10b); About 24,000 precursors with intensity values ranging from E3 to E6 arbitrary detector counts were observed (Fig. 10c). The quantile plot of the log10 distribution of precursor intensity values shows that after log10 transformation that intensity approaches Gaussian normality (Fig. 10d). The calculated peptide [M + H]+ values ranged from 1000 to 5000 Da (Fig. 10e). Moreover, the quantile plot strongly indicated that peptide [M + H]+ was sampled from a Gaussian (i.e. normal) population (Fig. 10f). The peptide delta mass values ranged from − 2 to + 4 Da consistent with the known error of the instrument and the presence of natural isotopes (Fig. 10g). The quantile plot of the delta mass values was apparently Gaussian with a mean of + 1, i.e. showed a normal statistical distribution with about 68% of the peptides within 1 Da of the mean predicted [M + H]+ while 95% were within 2 Da of the mean consistent with the presence of natural isotopes (Fig. 10h). The relationship between delta M + H versus peptide p-value was also apparently Gaussian (Fig. 10i).
There was no relationship between log p-value and the peptide intensity for precursors ≥ 1000 (E3) counts (Fig. 11a). The sorted p-values showed a linear relationship with an average p-value of about 0.05 (Fig. 11b) that was normally distributed (Fig. 11c). Computing the cumulative p-value for proteins with 3 or more peptides showed that the protein Gene Symbol p-values ranged from E-7 to E-200 (Fig. 11d).
The aim of this study was to perform the random and independent sampling of human plasma on ice versus room temperature to identify and quantify the peptides and proteins that were preferentially cleaved in normal human plasma during incubation at room temperature. In the case of HPLC we may consider that each peptide elutes in one Gaussian peak. The peptides may be sampled multiple times as they elute from the column and may not necessarily be sampled at the top of the Gaussian peak but randomly across the peak. Thus, the log10 precursor intensity values of LC–ESI–MS/MS approximate a Gaussian distribution [27, 28]. The instrument samples all precursors by MS and then samples the 5 most intense by MS/MS every second or two across the chromatogram. The instant that the data is sampled is random with respect to the starting time of each chromatogram in the automatically repeating sample cycle of 1 MS and then 5 MS/MS. The timing of the MS and MS/MS sampling cycles are essentially random across the replicate chromatography traces with respect to the start of the experiment and so the ions sampled in one replicate are independent from the next. Once the peptide peak passes it does not typically return and so the precursors in the HPLC effluent are sampled without replacement. Taken together then, the precursor intensity values from LC–ESI–MS/MS have been randomly and independently sampled without replacement from a Gaussian distribution and therefore may be analyzed by ANOVA. In contrast, the peptide counts approximate a Gamma distribution and may be analyzed by the Chi Square test.
The consistent SDS-PAGE banding patterns showing no change in the major plasma proteins indicates that many of the well known plasma proteins are relatively stable. In contrast, lower total protein levels, and a concomitant increase in alcohol soluble amines, early in the incubation at room temperature were both consistent with the degradation of a pool of proteins in plasma with time. The degradation of fresh human plasma itself was not reflected by changes in the banding patterns that were readily detectable by SDS-PAGE and this clearly rules out the presence of general proteases that visibly act on the many major plasma proteins with brief periods at room temperature [2, 47]. Moreover the stable banding pattern at room temperature observed here indicates the changes in SDS-PAGE banding patterns previously observed between different disease states were not merely due to differences in sample handling between normal and disease samples but rather must have resulted from the action of disease-specific protease activities [2, 47].
Comparison of correlation parameters and algorithms
The agreement on the set of proteins identified between no enzyme specified (that shows a large degree of freedom and might fit the MS/MS spectra to any human peptide sequence), versus the proteins identified by fully tryptic peptides, is powerful evidence in favour of the veracity and low type I error of MS/MS correlation with an ion trap. The close agreement between no enzyme and fully tryptic correlations that both show a similar small set of proteins with multiple peptides, while the vast majority of human protein sequences show no correlation, is entirely consistent with the high fidelity of MS/MS spectra matching to predicted fragmentation patterns as estimated from the agreement of blood proteins between groups [31, 48], the computation of p-values from the fit of MS/MS spectra  or from first principles using a null random model [23, 24, 26,27,28, 30] and recently confirmed by the elaborate synthesis of 300,000 peptides . With respect to fully tryptic peptides, the MASCOT, OMSSA, X!TANDEM and SEQUEST algorithms all agreed on the identity of the most frequently sampled peptides of EDTA plasma indicating that all the algorithms can identify the major plasma peptides. In agreement with previous results [23, 24], the endogenous tryptic peptides identified by X!TANDEM with at least three independent peptides also showed a low Type I (false positive) error rate after comparison to computer generated random (and noise) spectra consistent with the low cumulative protein p-values computed here. Conversely, MASCOT and OMSSA failed to identify many of the authentic peptides credibly detected by X!TANDEM and thus suffered from a high Type II (false negative) error rate under our conditions. The failure of MASCOT and OMSSA to identify many proteins may result from the heuristic nature of these algorithms that rely on arbitrary composite scoring schemes that include precursor mass accuracy and the fit of the MS/MS spectra relative measures instead of accepting the best match of the MS/MS data based on correlation coefficient or goodness of fit. Thus, MASCOT and OMSSA failed to meet the basic condition of providing the simplest model that explains all of the data. In contrast, SEQUEST that meets the obligate requirement to provide the simplest model that explains all data, frequently identified Titin and other giant proteins at a low frequency that likely results from the near random distribution of mis-correlations and so can be directly corrected by running noise or random MS/MS spectra simulations [23, 24, 26,27,28]. Thus for C18 solid phase extraction of endogenous peptides and micro scale LC–ESI–MS/MS of EDTA plasma with a simple linear ion trap, X!TANDEM was the best choice of algorithm to fit the MS/MS spectra without correction by random or noise spectra .
Baseline EDTA Plasma model
Collecting EDTA plasma directly onto ice and processing ice cold may be a practical way to avoid cellular or biochemical reactions that might vary over time and thus create pre-analytical variation. Here a solid baseline was established for the EDTA plasma by incubating on ice or ice plus inhibitors over a time compared to plasma incubated at room temperature. Many proteins showed about a two fold variation between ice and room temperature samples consistent with the degradation of plasma proteins over time and sample handling conditions [2, 5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]. Collecting the EDTA plasma samples directly onto ice demonstrates that, in contrast to many reports, the complement peptides are almost non-observable in the baseline EDTA plasma but are almost instantaneously released at room temperature. From this study it is clear that EDTA plasma may be collected and processed ice cold to avoid the rapid degradation of a pool of proteins that commences rapidly upon incubation at room temperature.
The tryptic peptides identified by X!TANDEM show a low type I error rate and are not heavily contaminated with mis-correlations to TTN or other giant proteins [23, 24, 27, 28]. The results of the X!TANDEM algorithm showed that the proteins identified by ≥ 3 tryptic peptides with intensity values ≥ E3 counts with a linear 2D quadrupole ion trap show a negligible type I error in agreement with previous studies [23, 26, 27, 30, 31]. The peptide fit by X!TANDEM reasonably explained the observed MS/MS fragmentation [2, 29, 52] and showed log intensity, log p-value and delta mass that were Gaussian and so were ideal for statistical analysis. From the computed p-values and the comparison to random spectra we can unambiguously conclude that plasma proteins identified with at least three peptides by X!TANDEM are statistically reliable enough for further study (~ E-3 to E-300). For simplicity, the peptides identified from SEQUEST that map to the proteins identified by X!TANDEM will reasonably meet the obligation to provide the simplest model to explain all data while avoiding type I error.
False discovery rate
The recent paper of Zolg et al. with 300,000 synthetic peptides confirmed the observed MS/MS spectra closely match those of the predicted MS/MS spectra . The residues of most amino acids (except Ile vs Leu 113 m/z and Gln vs. Lys 128 m/z (d 0.05)) can be resolved by the ion trap within 0.5 Da in the MS/MS but some high resolution mass spectrometers do not in practice distinguish these much better. While there may be some peptides that cannot be definitively discriminated by MS/MS with an ion trap there remain many that can and thus serve as the basis of a practical technology for identifying and quantifying peptides and proteins [53,54,55,56]. Thus multiple peptides from many proteins can be sensitively assigned using the robust ion trap with confidence using the MS/MS spectra. The X!TANDEM algorithm emphasizes the fit of the tandem MS/MS spectra within 0.5 Da from precursors ± 3 m/z [29, 52]. As previously demonstrated in a direct comparison of authentic MS/MS spectra to false positive noise or random MS/MS spectra: the p-value of the peptide fits cannot be effectively used to separate authentic data from false positive data. In contrast, the high frequency of peptide assignment to the small set of highly-sampled proteins efficiently separates authentic data from false positive noise and random data that is evenly distributed across the database at a low frequency [23, 24, 26,27,28]. It has been unambiguously and clearly demonstrated that the frequency of peptide correlation to proteins efficiently distinguishes authentic results from false-positive noise or random spectra results [23, 24, 27]. The computation of the False Discovery Rate by the method of Benjamini and Hochberg  agreed with null random models of noise and random spectra that the identification of proteins from multiple peptides showed a low type I error rate.
The cleavage products of EDTA plasma
The comparison of samples on ice versus room temperature shows that thousands of proteins may be directly detected and monitored from plasma by solid phase extraction with C18 peptide collection and robust analysis with micro electrospray. The experiment demonstrates it is feasible to compare plasma samples across experimental treatments using the endogenous peptide frequencies without extensive chromatographic pre-separation of proteins. The complement proteins such as C4 and C3 are in high abundance in blood and designed to be rapidly processed in response to biochemical signals that might be propagated via the action of kininogen [57, 58]. In contrast, fibrinogen peptides, that are abundant in serum , did not show highly elevated levels of peptides in plasma at room temperature where no clotting occurs in agreement with the theory.
Independent protein assays, free amine assays and LC–ESI–MS/MS methods all agree that a pool of proteins start to degrade soon after incubation of plasma samples at room temperature . LC–ESI–MS/MS of tryptic peptides was more sensitive than SDS-PAGE, protein assays or free amine assays for the detection of the ex vivo proteolytic degradation of plasma. The majority of blood peptides show a fairly robust two-fold increase in the random sampling frequency with incubation at room temperature indicating that most of the peptides observed were ex vivo artefacts. It was feasible to collect plasma samples from clinical subjects and identify the endogenous tryptic peptides, and thus proteins, with simple C18 solid phase extraction for identification and quantification by micro electrospray LC–ESI–MS/MS with a linear ion trap. Here we show for the first time that random and independent sampling of endogenous blood peptides results in a population of thousands of peptides from hundreds of proteins that show intensity, calculated mass and delta mass values that are normally distributed and have a low type I error rate based on comparison to a null model of random spectra or noise and the fitting of the MS/MS spectra by the rigorous X!TANDEM algorithm that directly generates a p-value from the goodness of fit of the MS/MS spectra within 0.5 Da [29, 52]. The sampling frequency of peptides by LC–ESI–MS/MS may be used to detect variation over experimental treatments. Since the observed peptide frequency may vary by about two with respect to ice cold controls, it follows that in general, differences between disease states or experimental treatments would have to exceed the threshold of twofold enrichment in order to rule out confounding effects from sample handling.
coomassie brilliant blue
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Zhu P, Bowden P, Zhang D, Marshall JG. Mass spectrometry of peptides and proteins from human blood. Mass Spectrom Rev. 2011;30:685–732.
Marshall J, Kupchak P, Zhu W, Yantha J, Vrees T, Furesz S, Jacks K, Smith C, Kireeva I, Zhang R, Takahashi M, Stanton E, Jackowski G. Processing of serum proteins underlies the mass spectral fingerprinting of myocardial infarction. J Proteome Res. 2003;2:361–72.
Marshall J, Jankowski A, Furesz S, Kireeva I, Barker L, Dombrovsky M, Zhu W, Jacks K, Ingratta L, Bruin J, Kristensen E, Zhang R, Stanton E, Takahashi M, Jackowski G. Human serum proteins preseparated by electrophoresis or chromatography followed by tandem mass spectrometry. J Proteome Res. 2004;3:364–82.
Williams D, Ackloo S, Zhu P, Bowden P, Evans KR, Addison CL, Lock C, Marshall JG. Precipitation and selective extraction of human serum endogenous peptides with analysis by quadrupole time-of-flight mass spectrometry reveals posttranslational modifications and low-abundance peptides. Anal Bioanal Chem. 2010;396:1223–47.
Banks RE. Preanalytical influences in clinical proteomic studies: raising awareness of fundamental issues in sample banking. Clin Chem. 2008;54:6–7.
Tuck MK, Chan DW, Chia D, Godwin AK, Grizzle WE, Krueger KE, Rom W, Sanda M, Sorbara L, Stass S, Wang W, Brenner DE. Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group. J Proteome Res. 2009;8:113–7.
Karsan A, Eigl BJ, Flibotte S, Gelmon K, Switzer P, Hassell P, Harrison D, Law J, Hayes M, Stillwell M, Xiao Z, Conrads TP, Veenstra T. Analytical and preanalytical biases in serum proteomic pattern analysis for breast cancer diagnosis. Clin Chem. 2005;51:1525–8.
Semmes OJ, Feng Z, Adam BL, Banez LL, Bigbee WL, Campos D, Cazares LH, Chan DW, Grizzle WE, Izbicka E, Kagan J, Malik G, McLerran D, Moul JW, Partin A, Prasanna P, Rosenzweig J, Sokoll LJ, Srivastava S, Thompson I, Welsh MJ, White N, Winget M, Yasui Y, Zhang Z, Zhu L. Evaluation of serum protein profiling by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry for the detection of prostate cancer: I. Assessment of Platform Reproducibility. Clin Chem. 2005;51:102–12.
de Noo ME, Tollenaar RA, Ozalp A, Kuppen PJ, Bladergroen MR, Eilers PH, Deelder AM. Reliability of human serum protein profiles generated with C8 magnetic beads assisted MALDI-TOF mass spectrometry. Anal Chem. 2005;77:7232–41.
Li J, Orlandi R, White CN, Rosenzweig J, Zhao J, Seregni E, Morelli D, Yu Y, Meng XY, Zhang Z, Davidson NE, Fung ET, Chan DW. Independent validation of candidate breast cancer serum biomarkers identified by mass spectrometry. Clin Chem. 2005;51:2229–35.
Pilny R, Bouchal P, Borilova S, Ceskova P, Zaloudik J, Vyzula R, Vojtesek B, Valik D. Surface-enhanced laser desorption ionization/time-of-flight mass spectrometry reveals significant artifacts in serum obtained from clot activator-containing collection devices. Clin Chem. 2006;52:2115–6.
Albrethsen J, Bogebo R, Olsen J, Raskov H, Gammeltoft S. Preanalytical and analytical variation of surface-enhanced laser desorption-ionization time-of-flight mass spectrometry of human serum. Clin Chem Lab Med. 2006;44:1243–52.
Koomen JM, Li D, Xiao LC, Liu TC, Coombes KR, Abbruzzese J, Kobayashi R. Direct tandem mass spectrometry reveals limitations in protein profiling experiments for plasma biomarker discovery. J Proteome Res. 2005;4:972–81.
Rai AJ, Gelfand CA, Haywood BC, Warunek DJ, Yi J, Schuchard MD, Mehigh RJ, Cockrill SL, Scott GB, Tammen H, Schulz-Knappe P, Speicher DW, Vitzthum F, Haab BB, Siest G, Chan DW. HUPO Plasma Proteome Project specimen collection and handling: towards the standardization of parameters for plasma proteome samples. Proteomics. 2005;5:3262–77.
Callesen AK, Vach W, Jorgensen PE, Cold S, Mogensen O, Kruse TA, Jensen ON, Madsen JS. Reproducibility of mass spectrometry based protein profiles for diagnosis of breast cancer across clinical studies: a systematic review. J Proteome Res. 2008;7:1395–402.
van Winden AW, Gast MC, Beijnen JH, Rutgers EJ, Grobbee DE, Peeters PH, van Gils CH. Validation of previously identified serum biomarkers for breast cancer with SELDI-TOF MS: a case control study. BMC Med Genomics. 2009;2:4.
Spencer K. The influence of different sample collection types on the levels of markers used for Down’s syndrome screening as measured by the Kryptor Immunosassay system. Ann Clin Biochem. 2003;40:166–8.
Govorukhina NI, de Vries M, Reijmers TH, Horvatovich P, van der Zee AG, Bischoff R. Influence of clotting time on the protein composition of serum samples based on LC-MS data. J Chromatogr, B: Anal Technol Biomed Life Sci. 2008;877:1281–91.
Gast MC, van Gils CH, Wessels LF, Harris N, Bonfrer JM, Rutgers EJ, Schellens JH, Beijnen JH. Influence of sample storage duration on serum protein profiles assessed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS). Clin Chem Lab Med. 2009;47:694–705.
West-Nielsen M, Hogdall EV, Marchiori E, Hogdall CK, Schou C, Heegaard NH. Sample handling for mass spectrometric proteomic investigations of human sera. Anal Chem. 2005;77:5114–23.
West-Norager M, Kelstrup CD, Schou C, Hogdall EV, Hogdall CK, Heegaard NH. Unravelling in vitro variables of major importance for the outcome of mass spectrometry-based serum proteomics. J Chromatogr, B: Anal Technol Biomed Life Sci. 2007;847:30–7.
van den Broek I, Romijn FP, Smit NP, van der Laarse A, Drijfhout JW, van der Burgt YE, Cobbaert CM. Quantifying protein measurands by peptide measurements: Where do errors arise? J Proteome Res. 2015;14:928–42.
Zhu P, Bowden P, Tucholska M, Marshall JG. Chi square comparison of tryptic peptide-to-protein distributions of tandem mass spectrometry from blood with those of random expectation. Anal Biochem. 2011;409:189–94.
Zhu P, Bowden P, Tucholska M, Zhang D, Marshall JG. Peptide-to-protein distribution versus a competition for significance to estimate error rate in blood protein identification. Anal Biochem. 2011;411:241–53.
Tucholska M, Scozzaro S, Williams D, Ackloo S, Lock C, Siu KWM, Evans KR, Marshall JG. Endogenous peptides from biophysical and biochemical fractionation of serum analyzed by matrix-assisted laser desorption/ionization and electrospray ionization hybrid quadrupole time-of-flight. Anal Biochem. 2007;370:228–45.
Bowden P, Thavarajah T, Zhu P, McDonell M, Thiele H, Marshall JG. Quantitative statistical analysis of standard and human blood proteins from liquid chromatography, electrospray ionization, and tandem mass spectrometry. J Proteome Res. 2012;11:2032–47.
Florentinus AK, Bowden P, Sardana G, Diamandis EP, Marshall JG. Identification and quantification of peptides and proteins secreted from prostate epithelial cells by unbiased liquid chromatography tandem mass spectrometry using goodness of fit and analysis of variance. J Proteomics. 2012;75:1303–17.
Florentinus AK, Jankowski A, Petrenko V, Bowden P, Marshall JG. The Fc receptor-cytoskeleton complex from human neutrophils. J Proteomics. 2011;75:450–68.
Craig R, Beavis RC. TANDEM: matching proteins with tandem mass spectra. Bioinformatics. 2004;20:1466–7.
Bowden P, Beavis R, Marshall J. Tandem mass spectrometry of human tryptic blood peptides calculated by a statistical algorithm and captured by a relational database with exploration by a general statistical analysis system. J Proteomics. 2009;73:103–11.
Bowden P, Pendrak V, Zhu P, Marshall JG. Meta sequence analysis of human blood peptides and their parent proteins. J Proteomics. 2010;73:1163–75.
Putnam F. The plasma proteins: structure function, and genetic control. 2nd ed. New York: Academic Press; 1975.
Burtis C, Bruns D (2007) Tietz fundamentals of clinical chemistry, 6th edn. Amsterdam, Netherlands: Elsevier.
Tucholska M, Florentinus A, Williams D, Marshall JG. The endogenous peptides of normal human serum extracted from the acetonitrile-insoluble precipitate using modified aqueous buffer with analysis by LC-ESI-Paul ion trap and Qq-TOF. J Proteomics. 2010;73:1254–69.
Marshall J, Bowden P, Schmit JC, Betsou F. Creation of a federated database of blood proteins: a powerful new tool for finding and characterizing biomarkers in serum. Clin Proteomics. 2014;11:3.
Schwartz JC, Senko MW, Syka JE. A two-dimensional quadrupole ion trap mass spectrometer. J Am Soc Mass Spectrom. 2002;13:659–69.
Benjamini Y, Hochberg Y. Controlling false discovery rate: a practical approach to multiple testing. J R Stat Soc. 1995;57:289–300.
Ghosh S, Gepstein S, Heikkila JJ, Dumbroff EB. Use of a scanning densitometer or an ELISA plate reader for measurement of nanogram amounts of protein in crude extracts from biological tissues. Anal Biochem. 1988;169:227–33.
Schagger H, von Jagow G. Tricine-sodium dodecyl sulfate-polyacrylamide gel electrophoresis for the separation of proteins in the range from 1 to 100 kDa. Anal Biochem. 1987;166:368–79.
Doi E, Shibata D, Matoba T. Modified colorimetric ninhydrin methods for peptidase assay. Anal Biochem. 1981;118:173–84.
Yates JR 3rd, Eng JK, McCormack AL, Schieltz D. Method to correlate tandem mass spectra of modified peptides to amino acid sequences in the protein database. Anal Chem. 1995;67:1426–36.
Yates JR III. Database searching using mass spectrometry data. Electrophoresis. 1998;19:893–900.
Perkins DN, Pappin DJ, Creasy DM, Cottrell JS. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis. 1999;20:3551–67.
Geer LY, Markey SP, Kowalak JA, Wagner L, Xu M, Maynard DM, Yang X, Shi W, Bryant SH. Open mass spectrometry search algorithm. J Proteome Res. 2004;3:958–64.
Florentinus AK, Bowden P, Barbisan V, Marshall J. Capture and qualitative analysis of the activated Fc receptor complex from live cells. Curr Protoc Protein Sci 2012;67:19.22:19.22.1–19.22.23. https://doi.org/10.1002/0471140864.ps1922s67.
Howard JC, Florentinus-Mefailoski A, Bowden P, Trimble W, Grinstein S, Marshall JG. OxLDL receptor chromatography from live human U937 cells identifies SYK(L) that regulates phagocytosis of oxLDL. Anal Biochem. 2016;513:7–20.
Zhang R, Barker L, Pinchev D, Marshall J, Rasamoelisolo M, Smith C, Kupchak P, Kireeva I, Ingratta L, Jackowski G. Mining biomarkers in human sera using proteomic tools. Proteomics. 2004;4:244–56.
Zhu P, Bowden P, Pendrak V, Thiele H, Zhang D, Siu M, Diamandis EP, Marshall J. Comparison of protein expression lists from mass spectrometry of human blood fluids using exact peptide sequences versus BLAST. Clin Proteomics. 2007;2:185–203.
Brosch M, Swamy S, Hubbard T, Choudhary J. Comparison of mascot and X!tandem performance for low and high accuracy mass spectrometry and the development of an adjusted mascot threshold. Mol Cell Proteomics. 2008;7:962–70.
Balgley BM, Laudeman T, Yang L, Song T, Lee CS. Comparative evaluation of tandem MS search algorithms using a target-decoy search strategy. Mol Cell Proteomics. 2007;6:1599–608.
Zolg DP, Wilhelm M, Schnatbaum K, Zerweck J, Knaute T, Delanghe B, Bailey DJ, Gessulat S, Ehrlich HC, Weininger M, Yu P, Schlegl J, Kramer K, Schmidt T, Kusebauch U, Deutsch EW, Aebersold R, Moritz RL, Wenschuh H, Moehring T, Aiche S, Huhmer A, Reimer U, Kuster B. Building ProteomeTools based on a complete synthetic human proteome. Nat Methods. 2017;14:259–62.
Craig R, Beavis RC. A method for reducing the time required to match protein sequences with tandem mass spectra. Rapid Commun Mass Spectrom. 2003;17:2310–6.
Link AJ, Eng J, Schieltz DM, Carmack E, Mize GJ, Morris DR, Garvik BM, Yates JR 3rd. Direct analysis of protein complexes using mass spectrometry. Nat Biotechnol. 1999;17:676–82.
McCormack AL, Schieltz DM, Goode B, Yang S, Barnes G, Drubin D, Yates JR 3rd. Direct analysis and identification of proteins in mixtures by LC/MS/MS and database searching at the low-femtomole level. Anal Chem. 1997;69:767–76.
Washburn MP, Wolters D, Yates JR 3rd. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol. 2001;19:242–7.
Wolters DA, Washburn MP, Yates JR 3rd. An automated multidimensional protein identification technology for shotgun proteomics. Anal Chem. 2001;73:5683–90.
Dobo J, Major B, Kekesi KA, Szabo I, Megyeri M, Hajela K, Juhasz G, Zavodszky P, Gal P. Cleavage of kininogen and subsequent bradykinin release by the complement component: mannose-binding lectin-associated serine protease (MASP)-1. PLoS ONE. 2011;6:e20036.
Michael IP, Sotiropoulou G, Pampalakis G, Magklara A, Ghosh M, Wasney G, Diamandis EP. Biochemical and enzymatic characterization of human kallikrein 5 (hK5), a novel serine protease potentially involved in cancer progression. J Biol Chem. 2005;280:14628–35.
JD calibrated, tuned and quality controlled the instruments and performed LC–ESI–MS/MS analysis and edited the manuscript. AF-M performed the sample aliquoting and performed LC–ESI–MS/MS analysis and edited the manuscript. JA performed LC–ESI–MS/MS analysis and edited the manuscript. AF performed SDS-PAGE & CBBR staining, proteins, Free amine assays and LC–ESI–MS/MS. PB collected the data into an SQL database for statistical analysis in R. JM planned the experiment, and performed the clinical plasma collection and sample treatments and storage experiments. All authors read and approved the final manuscript.
We gratefully acknowledge the review and critique of this manuscript by Dr. Fay Betsou and Dr. R.A. Phillips of IBBL.
The authors declare that they have no competing interests.
Availability of data and materials
The raw data is provided in the supplemental materials.
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No material from any other publication was used in this publication.
Ethics approval and consent to participate
Ethical approval and informed consent was obtained through the Comité National d’Ethique de Recherche (CNER) Protocol #201107 “Biospecimen Research” at the Centre Hospitalier de Luxembourg.
This research was paid for by the Fonds National de la Recherche, Mobility of Researcher award through Luxembourg Institute of Health (formerly CRP Sante) and the Integrated Biobank of Luxembourg to JGM.
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Dufresne, J., Florentinus-Mefailoski, A., Ajambo, J. et al. Random and independent sampling of endogenous tryptic peptides from normal human EDTA plasma by liquid chromatography micro electrospray ionization and tandem mass spectrometry. Clin Proteom 14, 41 (2017). https://doi.org/10.1186/s12014-017-9176-7
- EDTA Plasma
- Micro-liquid Chromatography
- Peptide Correlation
- False Positive Noise
- Human Peptide Sequences