Peter Bowden

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankinds scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

Human serum proteins fractionated by preparative partition chromatography prior to LC-ESI-MS/MS.

Many proteomics studies are limited to the identification of only the most abundant proteins in a sample due to the high sample complexity in most proteomes. We have here addressed this problem by prefractionation of human blood samples using microchromatography. We show that our approach resulted in high-stringency tryptic peptides identified by LC-ESI-MS/MS. Serum proteins were fractionated by batch and stepwise preparative chromatography using various types of chromatography resins (propyl sulfate, quaternary amine, diethylaminoethanol, cibachron blue, phenol Sepharose, carboxy methyl sepharose, hydroxyl apatite, heparin, concanavalin A and protein G) that were compared. The efficacy of sample fractionation was determined by protein assays, electrophoresis, and mass spectrometry. Tryptic peptides were separated by C18 liquid chromatography with electrospray ionization via metal needle at 2 microL/min with ion trap tandem mass spectrometry. The MS/MS spectra were correlated to some 4396 distinct sequences of the human forward RefSeq by X!TANDEM. Of these, 61% have been detected by other algorithms, but 3219 (73%) were never previously reported from blood by X!TANDEM. The use of a simple apparatus for making gravity microchromatography columns that permits the rapid side-by-side fractionation of many serum samples is described. Disposable microcolumns rapidly prepared blood samples for LC ESI-MS/MS that detected both tissue and cell leakage proteins known to exist in the approximately 1 ng/mL range and some circulating receptor sequences. Our results demonstrate that the depletion of albumin or IgG was not necessary prior to LC-MS/MS and that multiple forms of protein chromatography will be useful for complete identification of blood proteins.

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.

A goodness of fit test may be used to assign tandem mass spectra of peptides to amino acid sequences and to directly calculate the expected probability of mis-identification. The product of the peptide expectation values directly yields the probability that the parent protein has been mis-identified. A relational database could capture the mass spectral data, the best fit results, and permit subsequent calculations by a general statistical analysis system. The many files of the Hupo blood protein data correlated by X!TANDEM against the proteins of ENSEMBL were collected into a relational database. A redundant set of 247,077 proteins and peptides were correlated by X!TANDEM, and that was collapsed to a set of 34,956 peptides from 13,379 distinct proteins. About 6875 distinct proteins were only represented by a single distinct peptide, 2866 proteins showed 2 distinct peptides, and 3454 proteins showed at least three distinct peptides by X!TANDEM. More than 99% of the peptides were associated with proteins that had cumulative expectation values, i.e. probability of false positive identification, of one in one hundred or less. The distribution of peptides per protein from X!TANDEM was significantly different than those expected from random assignment of peptides.

AMP-Activated Protein Kinase Regulates the Cell Surface Proteome and Integrin Membrane Traffic

The cell surface proteome controls numerous cellular functions including cell migration and adhesion, intercellular communication and nutrient uptake. Cell surface proteins are controlled by acute changes in protein abundance at the plasma membrane through regulation of endocytosis and recycling (endomembrane traffic). Many cellular signals regulate endomembrane traffic, including metabolic signaling; however, the extent to which the cell surface proteome is controlled by acute regulation of endomembrane traffic under various conditions remains incompletely understood. AMP-activated protein kinase (AMPK) is a key metabolic sensor that is activated upon reduced cellular energy availability. AMPK activation alters the endomembrane traffic of a few specific proteins, as part of an adaptive response to increase energy intake and reduce energy expenditure. How increased AMPK activity during energy stress may globally regulate the cell surface proteome is not well understood. To study how AMPK may regulate the cell surface proteome, we used cell-impermeable biotinylation to selectively purify cell surface proteins under various conditions. Using ESI-MS/MS, we found that acute (90 min) treatment with the AMPK activator A-769662 elicits broad control of the cell surface abundance of diverse proteins. In particular, A-769662 treatment depleted from the cell surface proteins with functions in cell migration and adhesion. To complement our mass spectrometry results, we used other methods to show that A-769662 treatment results in impaired cell migration. Further, A-769662 treatment reduced the cell surface abundance of β1-integrin, a key cell migration protein, and AMPK gene silencing prevented this effect. While the control of the cell surface abundance of various proteins by A-769662 treatment was broad, it was also selective, as this treatment did not change the cell surface abundance of the transferrin receptor. Hence, the cell surface proteome is subject to acute regulation by treatment with A-769662, at least some of which is mediated by the metabolic sensor AMPK.

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.

The proteins secreted by prostate cancer cells (PC3(AR)6) were separated by strong anion exchange chromatography, digested with trypsin and analyzed by unbiased liquid chromatography tandem mass spectrometry with an ion trap. The spectra were matched to peptides within proteins using a goodness of fit algorithm that showed a low false positive rate. The parent ions for MS/MS were randomly and independently sampled from a log-normal population and therefore could be analyzed by ANOVA. Normal distribution analysis confirmed that the parent and fragment ion intensity distributions were sampled over 99.9% of their range that was above the background noise. Arranging the ion intensity data with the identified peptide and protein sequences in structured query language (SQL) permitted the quantification of ion intensity across treatments, proteins and peptides. The intensity of 101,905 fragment ions from 1421 peptide precursors of 583 peptides from 233 proteins separated over 11 sample treatments were computed together in one ANOVA model using the statistical analysis system (SAS) prior to Tukey-Kramer honestly significant difference (HSD) testing. Thus complex mixtures of proteins were identified and quantified with a high degree of confidence using an ion trap without isotopic labels, multivariate analysis or comparing chromatographic retention times.

Comparison of protein expression lists from mass spectrometry of human blood fluids using exact peptide sequences versus BLAST

The proteins in blood were all first expressed as mRNAs from genes within cells. There are databases of human proteins that are known to be expressed as mRNA in human cells and tissues. Proteins identified from human blood by the correlation of mass spectra that fail to match human mRNA expression products may not be correct. We compared the proteins identified in human blood by mass spectrometry by 10 different groups by correlation to human and nonhuman nucleic acid sequences. We determined whether the peptides or proteins identified by the different groups mapped to the human known proteins of the Reference Sequence (RefSeq) database. We used Structured Query Language data base searches of the peptide sequences correlated to tandem mass spectrometry spectra and basic local alignment search tool analysis of the identified full length proteins to control for correlation to the wrong peptide sequence or the existence of the same or very similar peptide sequence shared by more than one protein. Mass spectra were correlated against large protein data bases that contain many sequences that may not be expressed in human beings yet the search returned a very high percentage of peptides or proteins that are known to be found in humans. Only about 5% of proteins mapped to hypothetical sequences, which is in agreement with the reported false-positive rate of searching algorithms conditions. The results were highly enriched in secreted and soluble proteins and diminished in insoluble or membrane proteins. Most of the proteins identified were relatively short and showed a similar size distribution compared to the RefSeq database. At least three groups agree on a nonredundant set of 1671 types of proteins and a nonredundant set of 3151 proteins were identified by at least three peptides.

Quantitative statistical analysis of standard and human blood proteins from liquid chromatography, electrospray ionization, and tandem mass spectrometry.

It will be important to determine if the parent and fragment ion intensity results of liquid chromatography, electrospray ionization and tandem mass spectrometry (LC-ESI-MS/MS) experiments have been randomly and independently sampled from a normal population for the purpose of statistical analysis by general linear models and ANOVA. The tryptic parent peptide and fragment ion m/z and intensity data in the mascot generic files from LC-ESI-MS/MS of purified standard proteins, and human blood protein fractionated by partition chromatography, were parsed into a Structured Query Language (SQL) database and were matched with protein and peptide sequences provided by the X!TANDEM algorithm. The many parent and/or fragment ion intensity values were log transformed, tested for normality, and analyzed using the generic Statistical Analysis System (SAS). Transformation of both parent and fragment intensity values by logarithmic functions yielded intensity distributions that closely approximate the log-normal distribution. ANOVA models of the transformed parent and fragment intensity values showed significant effects of treatments, proteins, and peptides, as well as parent versus fragment ion types, with a low probability of false positive results. Transformed parent and fragment intensity values were compared over all sample treatments, proteins or peptides by the Tukey-Kramer Honestly Significant Difference (HSD) test. The approach provided a complete and quantitative statistical analysis of LC-ESI-MS/MS data from human blood.

The Fc receptor-cytoskeleton complex from human neutrophils.

The Fc receptor complex and its associated phagocytic cytoskeleton machinery were captured from the surface of live cells by IgG coated microbeads and identified by mass spectrometry. The random and independently sampled intensity values of peptides were similar in the control and IgG samples. After log transformation, the parent and fragment intensity values showed a normal distribution where ≥99.9% of the data was well above the background noise. Some proteins showed significant differences in intensity between the IgG and control samples by ANOVA followed by the Tukey-Kramer honestly significant difference test. However many proteins were specific to the IgG beads or the control beads. The set of detected cytoskeleton proteins, binding proteins and enzymes detected on the IgG beads were used to predict the network of actin-associated regulatory factors. Signaling factors/proteins such as PIK3, PLC, GTPases (such CDC42, Rho GAPs/GEFs), annexins and inositol triphosphate receptors were all identified as being specific to the activated receptor complex by mass spectrometry. In addition, the tyrosine kinase Fak was detected with the IgG coated beads. Hence, an activated receptor cytoskeleton complex and its associated regulatory proteins were captured from the surface of live human primary leukocytes.

Chi-square comparison of tryptic peptide-to-protein distributions of tandem mass spectrometry from blood with those of random expectation.

Proteomics uses tandem mass spectrometers and correlation algorithms to match peptides and their fragment spectra to amino acid sequences. The replication of multiple liquid chromatography experiments with electrospray ionization of peptides and tandem mass spectrometry (LC-ESI-MS/MS) produces large sets of MS/MS spectra. There is a need to assess the quality of large sets of experimental results by statistical comparison with that of random expectation. Classical frequency-based statistics such as goodness-of-fit tests for peptide-to-protein distributions could be used to calculate the probability that an entire set of experimental results has arisen by random chance. The frequency distributions of authentic MS/MS spectra from human blood were compared with those of false positive MS/MS spectra generated by a computer, or instrument noise, using the chi-square test. Here the mechanics of the chi-square test to compare the results in toto from a set of LC-ESI-MS/MS experiments with those of random expectation is detailed. The chi-square analysis of authentic spectra demonstrates unambiguously that the analysis of blood proteins separated by partition chromatography prior to tryptic digestions has a low probability that the cumulative peptide-to-protein distribution is the same as that of random or noise false positive spectra.

Meta sequence analysis of human blood peptides and their parent proteins

Sequence analysis of the blood peptides and their qualities will be key to understanding the mechanisms that contribute to error in LC-ESI-MS/MS. Analysis of peptides and their proteins at the level of sequences is much more direct and informative than the comparison of disparate accession numbers. A portable database of all blood peptide and protein sequences with descriptor fields and gene ontology terms might be useful for designing immunological or MRM assays from human blood. The results of twelve studies of human blood peptides and/or proteins identified by LC-MS/MS and correlated against a disparate array of genetic libraries were parsed and matched to proteins from the human ENSEMBL, SwissProt and RefSeq databases by SQL. The reported peptide and protein sequences were organized into an SQL database with full protein sequences and up to five unique peptides in order of prevalence along with the peptide count for each protein. Structured query language or BLAST was used to acquire descriptive information in current databases. Sampling error at the level of peptides is the largest source of disparity between groups. Chi Square analysis of peptide to protein distributions confirmed the significant agreement between groups on identified proteins.