Improved detection of DNA replication fork-associated proteins.

Innovative methods to retrieve proteins associated with actively replicating DNA have provided a glimpse into the molecular dynamics of replication fork stalling. We report that a combination of density-based replisome enrichment by isolating proteins on nascent DNA (iPOND2) and label-free quantitative mass spectrometry (iPOND2-DRIPPER) substantially increases both replication factor yields and the dynamic range of protein quantification. Replication protein abundance in retrieved nascent DNA is elevated up to 300-fold over post-replicative controls, and recruitment of replication stress factors upon fork stalling is observed at similar levels. The increased sensitivity of iPOND2-DRIPPER permits direct measurement of ubiquitination events without intervening retrieval of diglycine tryptic fragments of ubiquitin. Using this approach, we find that stalled replisomes stimulate the recruitment of a diverse cohort of DNA repair factors, including those associated with poly-K63-ubiquitination. Finally, we uncover the temporally controlled association of stalled replisomes with nuclear pore complex components and nuclear cytoskeleton networks.

RIPPER: a framework for MS1 only metabolomics and proteomics label-free relative quantification.

UNLABELLED: RIPPER is a framework for mass-spectrometry-based label-free relative quantification for proteomics and metabolomics studies. RIPPER combines a series of previously described algorithms for pre-processing, analyte quantification, retention time alignment, and analyte grouping across runs. It is also the first software framework to implement proximity-based intensity normalization. RIPPER produces lists of analyte signals with their unnormalized and normalized intensities that can serve as input to statistical and directed mass spectrometry (MS) methods for detecting quantitative differences between biological samples using MS. AVAILABILITY AND IMPLEMENTATION: http://www.z.umn.edu/ripper CONTACT: vanr0014@umn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improved intensity-based label-free quantification via proximity-based intensity normalization (PIN).

Researchers are increasingly turning to label-free MS1 intensity-based quantification strategies within HPLC-ESI-MS/MS workflows to reveal biological variation at the molecule level. Unfortunately, HPLC-ESI-MS/MS workflows using these strategies produce results with poor repeatability and reproducibility, primarily due to systematic bias and complex variability. While current global normalization strategies can mitigate systematic bias, they fail when faced with complex variability stemming from transient stochastic events during HPLC-ESI-MS/MS analysis. To address these problems, we developed a novel local normalization method, proximity-based intensity normalization (PIN), based on the analysis of compositional data. We evaluated PIN against common normalization strategies. PIN outperforms them in dramatically reducing variance and in identifying 20% more proteins with statistically significant abundance differences that other strategies missed. Our results show the PIN enables the discovery of statistically significant biological variation that otherwise is falsely reported or missed.

Mass spectrometry-based proteomics: basic principles and emerging technologies and directions.

As the main catalytic and structural molecules within living systems, proteins are the most likely biomolecules to be affected by radiation exposure. Proteomics, the comprehensive characterization of proteins within complex biological samples, is therefore a research approach ideally suited to assess the effects of radiation exposure on cells and tissues. For comprehensive characterization of proteomes, an analytical platform capable of quantifying protein abundance, identifying post-translation modifications and revealing members of protein complexes on a system-wide level is necessary. Mass spectrometry (MS), coupled with technologies for sample fractionation and automated data analysis, provides such a versatile and powerful platform. In this chapter we offer a view on the current state of MS-proteomics, and focus on emerging technologies within three areas: (1) New instrumental methods; (2) New computational methods for peptide identification; and (3) Label-free quantification. These emerging technologies should be valuable for researchers seeking to better understand biological effects of radiation on living systems.

Sample collection and handling considerations for peptidomic studies in whole saliva; implications for biomarker discovery.

BACKGROUND: Proteomic studies in saliva have demonstrated its potential as a diagnostic biofluid, however the salivary peptidome is less studied. Here we study the effects of several sample collection and handling factors on salivary peptide abundance levels. METHODS: Salivary peptides were isolated using an ultrafiltration device and analyzed by tandem mass spectrometry. A panel of 41 peptides common after various treatments were quantified and normalized. We evaluated the effects of freezing rate of the samples, nutritional status of the donors (fed vs. fasted), and room-temperature sample degradation on peptide abundance levels. Repeatability of our sample processing method and our instrumental analysis method were investigated. RESULTS: Increased sample freezing rate produced higher levels of peptides. Donor nutritional status had no influence on the levels of measured peptides. No significant difference was detected in donors' saliva following 5, 10 and 15 min of room-temperature degradation. Sample processing and instrumental variability were relatively small, with median CVs of 9.6 and 6.6. CONCLUSIONS: Peptide abundance levels in saliva are rather forgiving towards variations in sample handling and donor nutritional status. Differences in freezing methods may affect peptide abundance, so consistency in freezing samples is preferred. Our results are valuable for standardizing sample collection and handling methods for peptidomic-based biomarker studies in saliva.

Hexapeptide libraries for enhanced protein PTM identification and relative abundance profiling in whole human saliva.

Dynamic range compression (DRC) by hexapeptide libraries increases MS/MS-based identification of lower-abundance proteins in complex mixtures. However, two unanswered questions impede fully realizing DRC's potential in shotgun proteomics. First, does DRC enhance identification of post-translationally modified proteins? Second, can DRC be incorporated into a workflow enabling relative protein abundance profiling? We sought to answer both questions analyzing human whole saliva. Addressing question one, we coupled DRC with covalent glycopeptide enrichment and MS/MS. With DRC we identified ∼2 times more N-linked glycoproteins and their glycosylation sites than without DRC, dramatically increasing the known salivary glycoprotein catalog. Addressing question two, we compared differentially stable isotope-labeled saliva samples pooled from healthy and metastatic breast cancer women using a multidimensional peptide fractionation-based workflow, analyzing in parallel one sample portion with DRC and one portion without. Our workflow categorizes proteins with higher absolute abundance, whose relative abundance ratios are altered by DRC, from proteins of lower absolute abundance detected only after DRC. Within each of these salivary protein categories, we identified novel abundance changes putatively associated with breast cancer, demonstrating feasibility and benefits of DRC for relative abundance profiling. Collectively, our results bring us closer to realizing the full potential of DRC for proteomic studies.

LTQ-iQuant: A freely available software pipeline for automated and accurate protein quantification of isobaric tagged peptide data from LTQ instruments.

Pulsed Q dissociation enables combining LTQ ion trap instruments with isobaric peptide tagging. Unfortunately, this combination lacks a technique which accurately reports protein abundance ratios and is implemented in a freely available, flexible software pipeline. We developed and implemented a technique assigning collective reporter ion intensity-based weights to each peptide abundance ratio and calculating a protein's weighted average abundance ratio and p-value. Using an iTRAQ-labeled standard mixture, we compared our technique's performance to the commercial software MASCOT, finding that it performed better than MASCOT's nonweighted averaging and median peptide ratio techniques, and equal to its weighted averaging technique. We also compared performance of the LTQ-Orbitrap plus our technique to 4800 MALDI TOF/TOF plus Protein Pilot, by analyzing an iTRAQ-labeled stem cell lysate. We found highly correlated protein abundance ratios, indicating that the LTQ-Orbitrap plus our technique yields results comparable to the current standard. We implemented our technique in a freely available, automated software pipeline, called LTQ-iQuant, which is mzXML-compatible; supports iTRAQ 4-plex and 8-plex LTQ data; and can be modified for and have weights trained to a user's LTQ and other isobaric peptide tagging methods. LTQ-iQuant should make LTQ instruments and isobaric peptide tagging accessible to more proteomic researchers.

A dynamic range compression and three-dimensional peptide fractionation analysis platform expands proteome coverage and the diagnostic potential of whole saliva.

Comprehensive identification of proteins in whole human saliva is critical for appreciating its full diagnostic potential. However, this is challenged by the large dynamic range of protein abundance within the fluid. To address this problem, we used an analysis platform that coupled hexapeptide libraries for dynamic range compression (DRC) with three-dimensional (3D) peptide fractionation. Our approach identified 2340 proteins in whole saliva and represents the largest saliva proteomic dataset generated using a single analysis platform. Three-dimensional peptide fractionation involving sequential steps of preparative isoelectric focusing (IEF), strong cation exchange, and capillary reversed-phase liquid chromatography was essential for maximizing gains from DRC. Compared to saliva not treated with hexapeptide libraries, DRC substantially increased identified proteins across physicochemical and functional categories. Approximately 20% of total salivary proteins are also seen in plasma, and proteins in both fluids show comparable functional diversity and disease-linkage. However, for a subset of diseases, saliva has higher apparent diagnostic potential. These results expand the potential for whole saliva in health monitoring/diagnostics and provide a general platform for improving proteomic coverage of complex biological samples.