Factors that contribute to the complexity of protein digests

Microfluidics for Peptidomics, Proteomics, and Cell Analysis

Microfluidics is the advanced microtechnology of fluid manipulation in channels with at least one dimension in the range of 1-100 microns. Microfluidic technology offers a growing number of tools for manipulating small volumes of fluid to control chemical, biological, and physical processes relevant to separation, analysis, and detection. Currently, microfluidic devices play an important role in many biological, chemical, physical, biotechnological and engineering applications. There are numerous ways to fabricate the necessary microchannels and integrate them into microfluidic platforms. In peptidomics and proteomics, microfluidics is often used in combination with mass spectrometric (MS) analysis. This review provides an overview of using microfluidic systems for peptidomics, proteomics and cell analysis. The application of microfluidics in combination with MS detection and other novel techniques to answer clinical questions is also discussed in the context of disease diagnosis and therapy. Recent developments and applications of capillary and microchip (electro)separation methods in proteomic and peptidomic analysis are summarized. The state of the art of microchip platforms for cell sorting and single-cell analysis is also discussed. Advances in detection methods are reported, and new applications in proteomics and peptidomics, quality control of peptide and protein pharmaceuticals, analysis of proteins and peptides in biomatrices and determination of their physicochemical parameters are highlighted.

The spectral networks paradigm in high throughput mass spectrometry

High-throughput proteomics is made possible by a combination of modern mass spectrometry instruments capable of generating many millions of tandem mass (MS(2)) spectra on a daily basis and the increasingly sophisticated associated software for their automated identification. Despite the growing accumulation of collections of identified spectra and the regular generation of MS(2) data from related peptides, the mainstream approach for peptide identification is still the nearly two decades old approach of matching one MS(2) spectrum at a time against a database of protein sequences. Moreover, database search tools overwhelmingly continue to require that users guess in advance a small set of 4-6 post-translational modifications that may be present in their data in order to avoid incurring substantial false positive and negative rates. The spectral networks paradigm for analysis of MS(2) spectra differs from the mainstream database search paradigm in three fundamental ways. First, spectral networks are based on matching spectra against other spectra instead of against protein sequences. Second, spectral networks find spectra from related peptides even before considering their possible identifications. Third, spectral networks determine consensus identifications from sets of spectra from related peptides instead of separately attempting to identify one spectrum at a time. Even though spectral networks algorithms are still in their infancy, they have already delivered the longest and most accurate de novo sequences to date, revealed a new route for the discovery of unexpected post-translational modifications and highly-modified peptides, enabled automated sequencing of cyclic non-ribosomal peptides with unknown amino acids and are now defining a novel approach for mapping the entire molecular output of biological systems that is suitable for analysis with tandem mass spectrometry. Here we review the current state of spectral networks algorithms and discuss possible future directions for automated interpretation of spectra from any class of molecules.

Digital microfluidic hydrogel microreactors for proteomics

Proteolytic digestion is an essential step in proteomic sample processing. While this step has traditionally been implemented in homogeneous (solution) format, there is a growing trend to use heterogeneous systems in which the enzyme is immobilized on hydrogels or other solid supports. Here, we introduce the use of immobilized enzymes in hydrogels for proteomic sample processing in digital microfluidic (DMF) systems. In this technique, preformed cylindrical agarose discs bearing immobilized trypsin or pepsin were integrated into DMF devices. A fluorogenic assay was used to optimize the covalent modification procedure for enzymatic digestion efficiency, with maximum efficiency observed at 31 μg trypsin in 2-mm diameter agarose gel discs. Gel discs prepared in this manner were used in an integrated method in which proteomic samples were sequentially reduced, alkylated, and digested, with all sample and reagent handling controlled by DMF droplet operation. Mass spectrometry analysis of the products revealed that digestion using the trypsin gel discs resulted in higher sequence coverage in model analytes relative to conventional homogenous processing. Proof-of-principle was demonstrated for a parallel digestion system in which a single sample was simultaneously digested on multiple gel discs bearing different enzymes. We propose that these methods represent a useful new tool for the growing trend toward miniaturization and automation in proteomic sample processing.

Optimized nLC-MS workflow for laser capture microdissected breast cancer tissue

Reliable sample preparation is of utmost importance for comparative proteome analysis, particularly when investigating minute amounts of clinical specimens, such as laser capture microdissected tumor tissue. In this study, we present an optimized nanoLC-MS workflow specifically for the analysis of laser capture microdissected breast cancer tissue. Analytical performance of different laser capture microdissection (LCM) functions available on the PALM system, time dependent trypsin digestion efficiency, effect of sample preparation and digestion time on peptide modification, semi-tryptic peptides and missed cleavages were evaluated. Our results show that microdissection from uncoated glass slides results in protein degradation; that protease and phosphatase inhibitors do not result in detectable improvement in number of peptides or semi-tryptic peptides; and that digestion time longer than four hours drastically reduces the number of missed cleavages, but also increases the number of unexpectedly modified peptides. Overalkylation was the most dominant side-reaction, which significantly increased overnight (P=0.05). The latter effect could almost completely be reverted by the use of a quenching agent (P=0.001). Taken together, our results show that it is of importance to carefully control sample handling steps so that reliable protein identification and quantitation can be performed within comparative proteomics studies using LCM. This article is part of a Special Issue entitled: Proteomics: The clinical link.

DeltAMT: a statistical algorithm for fast detection of protein modifications from LC-MS/MS data

Identification of proteins and their modifications via liquid chromatography-tandem mass spectrometry is an important task for the field of proteomics. However, because of the complexity of tandem mass spectra, the majority of the spectra cannot be identified. The presence of unanticipated protein modifications is among the major reasons for the low spectral identification rate. The conventional database search approach to protein identification has inherent difficulties in comprehensive detection of protein modifications. In recent years, increasing efforts have been devoted to developing unrestrictive approaches to modification identification, but they often suffer from their lack of speed. This paper presents a statistical algorithm named DeltAMT (Delta Accurate Mass and Time) for fast detection of abundant protein modifications from tandem mass spectra with high-accuracy precursor masses. The algorithm is based on the fact that the modified and unmodified versions of a peptide are usually present simultaneously in a sample and their spectra are correlated with each other in precursor masses and retention times. By representing each pair of spectra as a delta mass and time vector, bivariate Gaussian mixture models are used to detect modification-related spectral pairs. Unlike previous approaches to unrestrictive modification identification that mainly rely upon the fragment information and the mass dimension in liquid chromatography-tandem mass spectrometry, the proposed algorithm makes the most of precursor information. Thus, it is highly efficient while being accurate and sensitive. On two published data sets, the algorithm effectively detected various modifications and other interesting events, yielding deep insights into the data. Based on these discoveries, the spectral identification rates were significantly increased and many modified peptides were identified.

Trends in sample preparation for classical and second generation proteomics

Sample preparation is a fundamental step in the proteomics workflow. However, it is not easy to find compiled information updating this subject. In this paper, the strategies and protocols for protein extraction and identification, following either classical or second generation proteomics methodologies, are reviewed. Procedures for: tissue disruption, cell lysis, sample pre-fractionation, protein separation by 2-DE, protein digestion, mass spectrometry analysis, multidimensional peptide separations and quantification of protein expression level are described.

Peptide Sequence Analysis

In mass spectrometry (MS)-based protein studies, peptide fragmentation analysis (i.e., MS/MS experiments such as matrix-assisted laser desorption ionization [MALDI]-post-source decay [PSD] analysis, collision-induced dissociation [CID] of electrospray- and MALDI-generated ions, and electron-capture and electron-transfer dissociation analysis of multiply charged ions) provide sequence information and, thus, can be used for (i) de novo sequencing, (ii) protein identification, and (iii) posttranslational or other covalent modification site assignments. This chapter offers a qualitative overview on which kind of peptide fragments are formed under different MS/MS conditions. High-quality PSD and CID spectra provide illustrations of de novo sequencing and protein identification. The MS/MS behavior of some common posttranslational modifications such as acetylation, trimethylation, phosphorylation, sulfation, and O-glycosylation is also discussed.