Jian Liu

University of Toronto, Toronto, Ontario, Canada

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Publications (8)33.62 Total impact

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    ABSTRACT: Mass spectrometry-based targeted proteomic assays are experiencing a surge in awareness due to the diverse possibilities arising from the re-application of traditional LC-SRM technology. The FDA-approved quantitative LC-SRM-pipeline in drug discovery motivates the use to quantitatively validate putative proteomic biomarkers. However, complexity of biological specimens bears a huge challenge to identify, in parallel, specific peptides and proteins of interest from large biomarker candidate lists. Methods have been devised to increase scan speeds, improve detection specificity and verify quantitative SRM-features. In contrast, high-resolution mass spectrometers could be used to improve reliability and precision of targeted proteomics assays. Here, we present a new method for identifying, quantifying and reporting peptides in high-resolution targeted proteomics experiments performed on an Orbitrap hybrid instrument using stable isotope-labeled internal reference peptides. This high precision TPM method has unique advantages over existing techniques, including the need to only detect the most abundant product ion of a given target for confident peptide identification using a scoring function that evaluates assay performance based on 1) m/z-mass accuracy, 2) retention time accuracy of observed species relative to prediction, and 3) retention time accuracy relative to internal reference peptides. Further, we show management of multiplexed precision TPM-assays using sentinel peptide standards. This article is part of a Special Issue entitled: Proteomics from protein structures to clinical applications (CNPN 2012).
    Journal of proteomics 11/2012; · 5.07 Impact Factor
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    ABSTRACT: The experimental isolation and characterization of stable multi-protein complexes are essential to understanding the molecular systems biology of a cell. To this end, we have developed a high-throughput proteomic platform for the systematic identification of native protein complexes based on extensive fractionation of soluble protein extracts by multi-bed ion exchange high performance liquid chromatography (IEX-HPLC) combined with exhaustive label-free LC/MS/MS shotgun profiling. To support these studies, we have built a companion data analysis software pipeline, termed ComplexQuant. Proteins present in the hundreds of fractions typically collected per experiment are first identified by exhaustively interrogating MS/MS spectra using multiple database search engines within an integrative probabilistic framework, while accounting for possible post-translation modifications. Protein abundance is then measured across the fractions based on normalized total spectral counts and precursor ion intensities using a dedicated tool, PepQuant. This analysis allows co-complex membership to be inferred based on the similarity of extracted protein co-elution profiles. Each computational step has been optimized for processing large-scale biochemical fractionation datasets, and the reliability of the integrated pipeline has been benchmarked extensively. This article is part of a Special Issue entitled: Proteomics from protein structures to clinical applications (CNPN 2012).
    Journal of proteomics 10/2012; · 5.07 Impact Factor
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    ABSTRACT: A post-translational modification (PTM) is a chemical modification of a protein that occurs naturally. Many of these modifications, such as phosphorylation, are known to play pivotal roles in the regulation of protein function. Henceforth, PTM perturbations have been linked to diverse diseases like Parkinson's, Alzheimer's, diabetes and cancer. To discover PTMs on a genome-wide scale, there is a recent surge of interest in analyzing tandem mass spectrometry data, and several unrestrictive (so-called 'blind') PTM search methods have been reported. However, these approaches are subject to noise in mass measurements and in the predicted modification site (amino acid position) within peptides, which can result in false PTM assignments. To address these issues, we devised a machine learning algorithm, PTMClust, that can be applied to the output of blind PTM search methods to improve prediction quality, by suppressing noise in the data and clustering peptides with the same underlying modification to form PTM groups. We show that our technique outperforms two standard clustering algorithms on a simulated dataset. Additionally, we show that our algorithm significantly improves sensitivity and specificity when applied to the output of three different blind PTM search engines, SIMS, InsPecT and MODmap. Additionally, PTMClust markedly outperforms another PTM refinement algorithm, PTMFinder. We demonstrate that our technique is able to reduce false PTM assignments, improve overall detection coverage and facilitate novel PTM discovery, including terminus modifications. We applied our technique to a large-scale yeast MS/MS proteome profiling dataset and found numerous known and novel PTMs. Accurately identifying modifications in protein sequences is a critical first step for PTM profiling, and thus our approach may benefit routine proteomic analysis. Our algorithm is implemented in Matlab and is freely available for academic use. The software is available online from http://genes.toronto.edu.
    Bioinformatics 01/2011; 27(6):797-806. · 5.47 Impact Factor
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    ABSTRACT: Effective methods to detect and quantify functionally linked regulatory proteins in complex biological samples are essential for investigating mammalian signaling pathways. Traditional immunoassays depend on proprietary reagents that are difficult to generate and multiplex, whereas global proteomic profiling can be tedious and can miss low abundance proteins. Here, we report a target-driven liquid chromatography-tandem mass spectrometry (LC-MS/MS) strategy for selectively examining the levels of multiple low abundance components of signaling pathways which are refractory to standard shotgun screening procedures and hence appear limited in current MS/MS repositories. Our stepwise approach consists of: (i) synthesizing microscale peptide arrays, including heavy isotope-labeled internal standards, for use as high quality references to (ii) build empirically validated high density LC-MS/MS detection assays with a retention time scheduling system that can be used to (iii) identify and quantify endogenous low abundance protein targets in complex biological mixtures with high accuracy by correlation to a spectral database using new software tools. The method offers a flexible, rapid, and cost-effective means for routine proteomic exploration of biological systems including "label-free" quantification, while minimizing spurious interferences. As proof-of-concept, we have examined the abundance of transcription factors and protein kinases mediating pluripotency and self-renewal in embryonic stem cell populations.
    Molecular &amp Cellular Proteomics 05/2010; 9(11):2460-73. · 7.25 Impact Factor
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    ABSTRACT: IntroductionTandem mass spectrometry (MS/MS) has emerged as a cornerstone of proteomic screens aimed at discovering putative protein biomarkers of disease with potential clinical applications. Systematic validation of lead candidates in large numbers of samples from patient cohorts remains an important challenge. One particularly promising high throughout technique is multiple reaction monitoring (MRM), a targeted form of MS/MS by which precise peptide precursor–product ion combinations, or transitions, are selectively tracked as informative probes. Despite recent progress, however, many important computational and statistical issues remain unresolved. These include the selection of an optimal set of transitions so as to achieve sufficiently high specificity and sensitivity when profiling complex biological specimens, and the corresponding generation of a suitable scoring function to reliably confirm tentative molecular identities based on noisy spectra. MethodsIn this study, we investigate various empirical criteria that are helpful to consider when developing and interpreting MRM-style assays based on the similarity between experimental and annotated reference spectra. We also rigorously evaluate and compare the performance of conventional spectral similarity measures, based on only a few pre-selected representative transitions, with a generic scoring metric, termed T corr, wherein a selected product ion profile is used to score spectral comparisons. ConclusionsOur analyses demonstrate that T corr is potentially more suitable and effective for detecting biomarkers in complex biological mixtures than more traditional spectral library searches.
    Clinical Proteomics 01/2009; 5(1):3-14.
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    ABSTRACT: Tandem mass spectrometry is the prevailing approach for large-scale peptide sequencing in high-throughput proteomic profiling studies. Effective database search engines have been developed to identify peptide sequences from MS/MS fragmentation spectra. Since proteins are polymorphic and subject to post-translational modifications (PTM), however, computational methods for detecting unanticipated variants are also needed to achieve true proteome-wide coverage. Different from existing "unrestrictive" search tools, we present a novel algorithm, termed SIMS (for Sequential Motif Interval Search), that interprets pairs of product ion peaks, representing potential amino acid residues or "intervals", as a means of mapping PTMs or substitutions in a blind database search mode. An effective heuristic software program was likewise developed to evaluate, rank, and filter optimal combinations of relevant intervals to identify candidate sequences, and any associated PTM or polymorphism, from large collections of MS/MS spectra. The prediction performance of SIMS was benchmarked extensively against annotated reference spectral data sets and compared favorably with, and was complementary to, current state-of-the-art methods. An exhaustive discovery screen using SIMS also revealed thousands of previously overlooked putative PTMs in a compendium of yeast protein complexes and in a proteome-wide map of adult mouse cardiomyocytes. We demonstrate that SIMS, freely accessible for academic research use, addresses gaps in current proteomic data interpretation pipelines, improving overall detection coverage, and facilitating comprehensive investigations of the fundamental multiplicity of the expressed proteome.
    Analytical Chemistry 10/2008; 80(20):7846-54. · 5.70 Impact Factor
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    ABSTRACT: In breast cancer, there is a significant degree of molecular diversity among tumors. Multiple perturbations in signal transduction pathways impinge on transcriptional networks that in turn dictate malignant transformation and metastatic progression. Detailed knowledge of the sequence-specific transcription factors that become activated or repressed within a tumor and comparison of their relative levels of expression in cancer versus normal tissue should therefore provide insight into disease mechanisms, improving patient stratification and facilitating personalized treatment. While high-throughput tandem mass spectrometry methods for global proteome profiling have been developed, existing approaches have limited sensitivity and are often unable to detect low-abundance transcription factors in a complex biological specimen like a biopsy or tumor cell extract. To this end, we have undertaken a systematic comparative evaluation of three MS/MS methods for the ability to detect reference transcription factors spiked in known amounts into a cell-free breast cancer nuclear extract: Data-Dependent Acquisition (DDA), wherein precursor ion intensity dictates selection for fragmentation; Targeted Peptide Monitoring (TPM), a directed approach using successive isolation and fragmentation of predefined m/ z ratios; and Multiple Reaction Monitoring (MRM), in which specific precursor ion to product ion transitions are selectively monitored. Through a series of controlled, parallel benchmarking experiments, we have determined the relative figures-of-merit of each approach, and have established that prior knowledge of signature proteotypic peptides markedly improves overall detection sensitivity, reliability, and quantification.
    Journal of Proteome Research 05/2008; 7(4):1529-41. · 5.06 Impact Factor
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