Identification of lysophosphatidylcholine (LPC) and platelet activating factor (PAF) from PC12 cells and mouse cortex using liquid chromatography/multi-stage mass spectrometry (LC/MS3).

Lipids play essential roles in cellular structural support, energy storage and signal transduction. Recently, mass spectrometry (MS) has been used to produce three-dimensional maps that elucidate the lipid composition of complex cellular lysates. The identification of individual lipids within these maps is slow and requires the synthesis and spiking of each candidate lipid. We present a novel MS-based technique that rapidly elucidates the atomic connectivity of the fatty acid/alcohol substituent on the sn-1 position of several different families of glycerophosphocholine-containing lipids within the confines of a chromatographic separation. Sodiated lipid species were fragmented to produce radical cations which lost successive methylene groups upon further collisional activation to reveal the identity of the parent molecule. This approach was demonstrated to be effective on isobaric members of the lysophosphatidylcholine (LPC) and platelet activating factor (PAF) families of glycerophospholipids. We demonstrate the application of this technique to unambiguously identify these species within complex cellular lysates and tissue extracts.

Identification and quantitation of changes in the platelet activating factor family of glycerophospholipids over the course of neuronal differentiation by high-performance liquid chromatography electrospray ionization tandem mass spectrometry.

Glycerophospholipids are important structural lipids in membranes with changes associated with progressive neurodegenerative disorders such as Alzheimer disease. Synthesis of the platelet activating factor (PAF) glycerophospholipid subclass is implicated in the control of neuronal differentiation and death. In this article, we combine nanoflow HPLC and mass spectrometry to screen, identify, and quantitate changes in glycerophospholipid subspecies, specifically PAF family members, over the course of neuronal differentiation. Furthermore, precursor ion scans for fragments characteristic of PAF phosphocholine family members and the standard additions of PAF subspecies were combined to perform absolute quantitation of PAF lipids in undifferentiated and differentiated PC12 cells. Surprisingly, a marked asymmetry was detected in the two predominant PAF species (C16:0, C18:0) over the course of differentiation. These results describe a new technique for the sensitive analysis of lipids combining nanoflow HPLC, ESI-MS, and precursor ion scan. Limits of detection of as little as 2 pg of PAF and LPC were obtained, and analysis of the lipidome of as little as 70,000 cells was performed on this system. Furthermore, application to the PC12 model identified a quantifiable difference between PAF molecular species produced over the course of neuronal differentiation.

A differential phosphoproteomic analysis of retinoic acid-treated P19 cells.

External stimuli trigger internal signaling events within a cell that may represent either a temporary or permanent shift in the phosphorylation state of its proteome. Numerous reports have elucidated phosphorylation sites from a variety of biological samples and more recent studies have monitored the temporal dynamics of protein phosphorylation as a given system is perturbed. Understanding which proteins are phosphorylated as well as when they are phosphorylated may indicate novel functional roles within a system and allow new therapeutic avenues to be explored. To elucidate the dynamics of protein phosphorylation within differentiating murine P19 embryonal carcinoma cells, we induced P19 cells to differentiate using all-trans-retinoic acid and developed a strategy that combines isotopically labeled methyl esterification, immobilized metal affinity chromatography, mass spectrometric analysis, and a rigorous and unique data evaluation approach. We present the largest differential phosphoproteomic analysis using isotopically labeled methyl esterification to date, identifying a total of 472 phosphorylation sites on 151 proteins; 56 of these proteins had altered abundances following treatment with retinoic acid and approximately one-third of these have been previously associated with cellular differentiation. A series of bioinformatic tools were used to extract information from the data and explore the implications of our findings. This study represents the first global gel-free analysis that elucidates protein phosphorylation dynamics during cellular differentiation.

The proteomic reactor facilitates the analysis of affinity-purified proteins by mass spectrometry: application for identifying ubiquitinated proteins in human cells.

Mass spectrometry (MS) coupled to affinity purification is a powerful approach for identifying protein-protein interactions and for mapping post-translational modifications. Prior to MS analysis, affinity-purified proteins are typically separated by gel electrophoresis, visualized with a protein stain, excised, and subjected to in-gel digestion. An inherent limitation of this series of steps is the loss of protein sample that occurs during gel processing. Although methods employing in-solution digestion have been reported, they generally suffer from poor reaction kinetics. In the present study, we demonstrate an application of a microfluidic processing device, termed the Proteomic Reactor, for enzymatic digestion of affinity-purified proteins for liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. Use of the Proteomic Reactor enabled the identification of numerous ubiquitinated proteins in a human cell line expressing reduced amounts of the ubiquitin-dependent chaperone, valosin-containing protein (VCP). The Proteomic Reactor is a novel technology that facilitates the analysis of affinity-purified proteins and has the potential to aid future biological studies.

Multiplexed proteomic reactor for the processing of proteomic samples.

We report the development of a 96-well plate proteomic reactor for gel-free processing of minute amounts of complex proteomic samples. The device performs multiplexed trapping, enrichment, and biochemical processing of proteins, resulting in concentrated peptide solutions ready for mass spectrometric analysis. Individual wells on the reactor can process up to 2 microg of protein. We also report the coupling of the plate proteomic reactor with protein fractionation using size-exclusion chromatography for large-scale identification of proteins. To illustrate the potential of this approach, we separated 400 microg of MCF7 cell lysate using size-exclusion chromatography and processed 35 protein fractions on the reactor plate. Using stringent criteria when searching the data, a total of 875 unique proteins were identified. More relaxed searching conditions associated with a 1% false positive rate led to the identification of 2683 unique proteins, meaning that one protein was identified per 3-10 ng of total protein lysate loaded on the reactor plate.

The proteomic reactor: a microfluidic device for processing minute amounts of protein prior to mass spectrometry analysis.

Gel-free proteomics has emerged as a complement to conventional gel-based proteomics. Gel-free approaches focus on peptide or protein fractionation, but they do not address the efficiency of protein processing. We report the development of a microfluidic proteomic reactor that greatly simplifies the processing of complex proteomic samples by combining multiple proteomic steps. Rapid extraction and enrichment of proteins from complex proteomic samples or directly from cells are readily performed on the reactor. Furthermore, chemical and enzymatic treatments of proteins are performed in 50 nL effective volume, which results in an increased number of generated peptides. The products are compatible with mass spectrometry. We demonstrated that the proteomic reactor is at least 10 times more sensitive than current gel-free methodologies with one protein identified per 440 pg of protein lysate injected on the reactor. Furthermore, as little as 300 cells can be directly introduced on the proteomic reactor and analyzed by mass spectrometry.

Proteomic analysis of ubiquitinated proteins from human MCF-7 breast cancer cells by immunoaffinity purification and mass spectrometry.

Post-translational modification of proteins via the covalent attachment of Ubiquitin (Ub) plays an important role in the regulation of protein stability and function in eukaryotic cells. In the present study, we describe a novel method for identifying ubiquitinated proteins from a complex biological sample, such as a whole cell lysate, using a combination of immunoaffinity purification and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. We have demonstrated the applicability of this approach by identifying 70 ubiquitinated proteins from the human MCF-7 breast cancer cell line after treatment with the proteasome inhibitor MG132. This method will aid the study of protein ubiquitination and may be used as a tool for the discovery of novel biomarkers that are associated with disease progression.

Strategy to design improved proteomic experiments based on statistical analyses of the chemical properties of identified peptides.

Proteomics is an emerging field that uses many types of proteomic platforms however has few standardized procedures. Deciding which platform to use to perform large-scale proteomic studies is either based on personal preference or on so-called "figures of merit" such as dynamic range, resolution, and the limit of detection; these factors are often insufficient to predict the outcome of the experiment as the detection of peptides correlates to the chemical properties of each peptide. There is a need for a novel figure of merit that describes the overall performance of a platform based on measured output, which in proteomics is often a list of identified peptides. We report the development of such a figure of merit based on a predictive genetic algorithm. This algorithm takes into account the properties of the observed peptides such as length, hydrophobicity, and pI. Several large-scale studies that differed in sample type or platform were used to demonstrate the usefulness of the algorithm for improved experimental design. The figures that were obtained were clustered to find platforms that were biased in similar ways. Even though some platforms are different, they lead to the identification of similar peptide types and are thus redundant. The algorithm can thus be used as an exploratory tool to suggest a minimal number of complementary experiments in order to maximize experimental efficiency.