Plasma, the soluble component of the human blood, is believed to harbor thousands of distinct proteins, which originate from a variety of cells and tissues through either active secretion or leakage from blood cells or tissues. The dynamic range of plasma protein concentrations comprises at least nine orders of magnitude. Proteins involved in coagulation, immune defense, small molecule transport, and protease inhibition, many of them present in high abundance in this body fluid, have been functionally characterized and associated with disease processes. For example, protein sequence mutations in coagulation factors cause various serious disease states. Diagnosing and monitoring such diseases in blood plasma of affected individuals has typically been conducted by use of enzyme-linked immunosorbent assays, which using a specific antibody quantitatively measure only the affected protein in the tested plasma samples. The discovery of protein biomarkers in plasma for diseases with no known correlations to genetic mutations is challenging. It requires a highly parallel display and quantitation strategy for proteins. We fractionated blood serum proteins prior to display on two-dimensional electrophoresis (2-DE) gels using immunoaffinity chromatography to remove the most abundant serum proteins, followed by sequential anion-exchange and size-exclusion chromatography. Serum proteins from 74 fractions were displayed on 2-DE gels. This approach succeeded in resolving approximately 3700 distinct protein spots, many of them post-translationally modified variants of plasma proteins. About 1800 distinct serum protein spots were identified by mass spectrometry. They collapsed into 325 distinct proteins, after sequence homology and similarity searches were carried out to eliminate redundant protein annotations. Although a relatively insensitive dye, Coomassie Brilliant Blue G-250, was used to visualize protein spots, several proteins known to be present in serum in < 10 ng/mL concentrations were identified such as interleukin-6, cathepsins, and peptide hormones. Considering that our strategy allows highly parallel protein quantitation on 2-DE gels, it holds promise to accelerate the discovery of novel serum protein biomarkers.

Many low-abundance biomarkers for early detection of cancer and other diseases are invisible to mass spectrometry because they exist in body fluids in very low concentrations, are masked by high-abundance proteins such as albumin and immunoglobulins, and are very labile. To overcome these barriers, we created porous, buoyant, core–shell hydrogel nanoparticles containing novel high affinity reactive chemical baits for protein and peptide harvesting, concentration, and preservation in body fluids. Poly(N-isopropylacrylamide-co-acrylic acid) nanoparticles were functionalized with amino-containing dyes via zero-length cross-linking amidation reactions. Nanoparticles functionalized in the core with 17 different (12 chemically novel) molecular baits showed preferential high affinities (KD < 10–11 M) for specific low-abundance protein analytes. A poly(N-isopropylacrylamide-co-vinylsulfonic acid) shell was added to the core particles. This shell chemistry selectively prevented unwanted entry of all size peptides derived from albumin without hindering the penetration of non-albumin small proteins and peptides. Proteins and peptides entered the core to be captured with high affinity by baits immobilized in the core. Nanoparticles effectively protected interleukin-6 from enzymatic degradation in sweat and increased the effective detection sensitivity of human growth hormone in human urine using multiple reaction monitoring analysis. Used in whole blood as a one-step, in-solution preprocessing step, the nanoparticles greatly enriched the concentration of low-molecular weight proteins and peptides while excluding albumin and other proteins above 30 kDa; this achieved a 10,000-fold effective amplification of the analyte concentration, enabling mass spectrometry (MS) discovery of candidate biomarkers that were previously undetectable.

Liquid chromatography tandem mass spectrometry (LC-MS/MS) and multiple reaction monitoring mass spectrometry (MRM-MS) proteomics analyses were performed on eccrine sweat of healthy controls, and the results were compared with those from individuals diagnosed with schizophrenia (SZ). This is the first large scale study of the sweat proteome. First, we performed LC-MS/MS on pooled SZ samples and pooled control samples for global proteomics analysis. Results revealed a high abundance of diverse proteins and peptides in eccrine sweat. Most of the proteins identified from sweat samples were found to be different than the most abundant proteins from serum, which indicates that eccrine sweat is not simply a plasma transudate, and may thereby be a source of unique disease-associated biomolecules. A second independent set of patient and control sweat samples were analyzed by LC-MS/MS and spectral counting to determine qualitative protein differential abundances between the control and disease groups. Differential abundances of selected proteins, initially determined by spectral counting, were verified by MRM-MS analyses. Seventeen proteins showed a differential abundance of approximately two-fold or greater between the SZ pooled sample and the control pooled sample. This study demonstrates the utility of LC-MS/MS and MRM-MS as a viable strategy for the discovery and verification of potential sweat protein disease biomarkers.

Cationic antimicrobial peptides and their therapeutic potential have garnered growing interest because of the proliferation of bacterial resistance. However, the discovery of new antimicrobial peptides from animals has proven challenging due to the limitations associated with conventional biochemical purification and difficulties in predicting active peptides from genomic sequences, if known. As an example, no antimicrobial peptides have been identified from the American alligator, Alligator mississippiensis, although their serum is antimicrobial. We have developed a novel approach for the discovery of new antimicrobial peptides from these animals, one that capitalizes on their fundamental and conserved physico-chemical properties. This sample-agnostic process employs custom-made functionalized hydrogel microparticles to harvest cationic peptides from biological samples, followed by de novo sequencing of captured peptides, eliminating the need to isolate individual peptides. After evaluation of the peptide sequences using a combination of rational and web-based bioinformatic analyses, forty-five potential antimicrobial peptides were identified, and eight of these peptides were selected to be chemically synthesized and evaluated. The successful identification of multiple novel peptides, exhibiting antibacterial properties, from Alligator mississippiensis plasma demonstrates the potential of this innovative discovery process in identifying potential new host defense peptides.

Mass spectrometry (MS) is an attractive alternative to quantification of proteins by immunoassays, particularly for protein biomarkers of clinical relevance. Reliable quantification requires that the MS-based assays are robust, selective, and reproducible. Thus, the development of standardized protocols is essential to introduce MS into clinical research laboratories. The aim of this study was to establish a complete workflow for assessing the transferability and reproducibility of selected reaction monitoring (SRM) assays between clinical research laboratories. Four independent laboratories in North America, using identical triple-quadrupole mass spectrometers (Quantum Ultra, Thermo), were provided with standard protocols and instrumentation settings to analyze unknown samples and internal standards in a digested plasma matrix to quantify 51 peptides from 39 human proteins using a multiplexed SRM assay. The interlaboratory coefficient of variation (CV) was less than 10% for 25 of 39 peptides quantified (12 peptides were not quantified based upon hydrophobicity) and exhibited CVs less than 20% for the remaining peptides. In this report, we demonstrate that previously developed research platforms for SRM assays can be improved and optimized for deployment in clinical research environments.