Xiao-Jun Li

Bio: Xiao-Jun Li is an academic researcher from Institute for Systems Biology. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 12, co-authored 23 publications receiving 1453 citations.

Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry.

Abstract: We describe an algorithm for the automated statistical analysis of protein abundance ratios (ASAPRatio) of proteins contained in two samples. Proteins are labeled with distinct stable-isotope tags and fragmented, and the tagged peptide fragments are separated by liquid chromatography (LC) and analyzed by electrospray ionization (ESI) tandem mass spectrometry (MS/MS). The algorithm utilizes the signals recorded for the different isotopic forms of peptides of identical sequence and numerical and statistical methods, such as Savitzky-Golay smoothing filters, statistics for weighted samples, and Dixon's test for outliers, to evaluate protein abundance ratios and their associated errors. The algorithm also provides a statistical assessment to distinguish proteins of significant abundance changes from a population of proteins of unchanged abundance. To evaluate its performance, two sets of LC-ESI-MS/MS data were analyzed by the ASAPRatio algorithm without human intervention, and the data were related to the expected and manually validated values. The utility of the ASAPRatio program was clearly demonstrated by its speed and the accuracy of the generated protein abundance ratios and by its capability to identify specific core components of the RNA polymerase II transcription complex within a high background of copurifying proteins.

Quantitative phosphoproteome analysis using a dendrimer conjugation chemistry and tandem mass spectrometry.

Abstract: We present a robust and general method for the identification and relative quantification of phosphorylation sites in complex protein mixtures. It is based on a new chemical derivatization strategy using a dendrimer as a soluble polymer support and tandem mass spectrometry (MS/MS). In a single step, phosphorylated peptides are covalently conjugated to a dendrimer in a reaction catalyzed by carbodiimide and imidazole. Modified phosphopeptides are released from the dendrimer via acid hydrolysis and analyzed by MS/MS. When coupled with an initial antiphosphotyrosine protein immunoprecipitation step and stable-isotope labeling, in a single experiment, we identified all known tyrosine phosphorylation sites within the immunoreceptor tyrosine-based activation motifs (ITAM) of the T-cell receptor (TCR) CD3 chains, and previously unknown phosphorylation sites on total 97 tyrosine phosphoproteins and their interacting partners in human T cells. The dynamic changes in phosphorylation were quantified in these proteins.

Organ-specific proteins and methods of their use

Abstract: The present invention relates generally to methods for identifying and using organ-specific proteins and transcripts. The present invention further provides compositions comprising organ-specific proteins and transcripts encoding the same, detection reagents for detecting such proteins and transcripts, and diagnostic panels, kits and arrays for measuring organ-specific proteins/transcripts in blood, biological tissue or other biological fluid.

A Software Suite for the Generation and Comparison of Peptide Arrays from Sets of Data Collected by Liquid

Abstract: There is an increasing interest in the quantitative proteomic measurement of the protein contents of substantially similar biological samples, e.g. for the analysis of cellular response to perturbations over time or for the discovery of protein biomarkers from clinical samples. Technical limitations of current proteomic platforms such as limited reproducibility and low throughput make this a challenging task. A new LC-MS-based platform is able to generate complex peptide patterns from the analysis of proteolyzed protein samples at high throughput and represents a promising approach for quantitative proteomics. A crucial component of the LC-MS approach is the accurate evaluation of the abundance of detected peptides over many samples and the identification of peptide features that can stratify samples with respect to their genetic, physiological, or environmental origins. We present here a new software suite, SpecArray, that generates a peptide versus sample array from a set of LC-MS data. A peptide array stores the relative abundance of thousands of peptide features in many samples and is in a format identical to that of a gene expression microarray. A peptide array can be subjected to an unsupervised clustering analysis to stratify samples or to a discriminant analysis to identify discriminatory peptide features. We applied the SpecArray to analyze two sets of LC-MS data: one was from four repeat LC-MS analyses of the same glycopeptide sample, and another was from LC-MS analysis of serum samples of five male and five female mice. We demonstrate through these two study cases that the SpecArray software suite can serve as an effective software platform in the LC-MS approach for quantitative proteomics. Molecular & Cellular Proteomics 4: 1328–1340, 2005. The identification and quantification of the protein contents of biological samples plays a crucial role in biological and biomedical research (1–4). Due to the large dynamic range and the high complexity of most proteomes, it is very challenging to identify and accurately quantify the majority of proteins from such samples. LC-MS/MS-based methods are currently most efficient for the identification of a large number

A Blood-Based Proteomic Classifier for the Molecular Characterization of Pulmonary Nodules

Abstract: Each year, millions of pulmonary nodules are discovered by computed tomography and subsequently biopsied. Because most of these nodules are benign, many patients undergo unnecessary and costly invasive procedures. We present a 13-protein blood-based classifier that differentiates malignant and benign nodules with high confidence, thereby providing a diagnostic tool to avoid invasive biopsy on benign nodules. Using a systems biology strategy, we identified 371 protein candidates and developed a multiple reaction monitoring (MRM) assay for each. The MRM assays were applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and stage IA lung cancer matched for nodule size, age, gender, and clinical site, producing a 13-protein classifier. The classifier was validated on an independent set of plasma samples (n = 104), exhibiting a negative predictive value (NPV) of 90%. Validation performance on samples from a nondiscovery clinical site showed an NPV of 94%, indicating the general effectiveness of the classifier. A pathway analysis demonstrated that the classifier proteins are likely modulated by a few transcription regulators (NF2L2, AHR, MYC, and FOS) that are associated with lung cancer, lung inflammation, and oxidative stress networks. The classifier score was independent of patient nodule size, smoking history, and age, which are risk factors used for clinical management of pulmonary nodules. Thus, this molecular test provides a potential complementary tool to help physicians in lung cancer diagnosis.

Quantitative mass spectrometry in proteomics: a critical review

Abstract: The quantification of differences between two or more physiological states of a biological system is among the most important but also most challenging technical tasks in proteomics. In addition to the classical methods of differential protein gel or blot staining by dyes and fluorophores, mass-spectrometry-based quantification methods have gained increasing popularity over the past five years. Most of these methods employ differential stable isotope labeling to create a specific mass tag that can be recognized by a mass spectrometer and at the same time provide the basis for quantification. These mass tags can be introduced into proteins or peptides (i) metabolically, (ii) by chemical means, (iii) enzymatically, or (iv) provided by spiked synthetic peptide standards. In contrast, label-free quantification approaches aim to correlate the mass spectrometric signal of intact proteolytic peptides or the number of peptide sequencing events with the relative or absolute protein quantity directly. In this review, we critically examine the more commonly used quantitative mass spectrometry methods for their individual merits and discuss challenges in arriving at meaningful interpretations of quantitative proteomic data.

Proteomics by Mass Spectrometry: Approaches, Advances, and Applications

Abstract: Mass spectrometry (MS) is the most comprehensive and versatile tool in large-scale proteomics. In this review, we dissect the overall framework of the MS experiment into its key components. We discuss the fundamentals of proteomic analyses as well as recent developments in the areas of separation methods, instrumentation, and overall experimental design. We highlight both the inherent strengths and limitations of protein MS and offer a rough guide for selecting an experimental design based on the goals of the analysis. We emphasize the versatility of the Orbitrap, a novel mass analyzer that features high resolution (up to 150,000), high mass accuracy (2–5 ppm), a mass-to-charge range of 6000, and a dynamic range greater than 10 3 . High mass accuracy of the Orbitrap expands the arsenal of the data acquisition and analysis approaches compared with a low-resolution instrument. We discuss various chromatographic techniques, including multidimensional separation and ultra-performance liquid chromatography. Multidimensional protein identification technology (MudPIT) involves a continuum sample preparation, orthogonal separations, and MS and software solutions. We discuss several aspects of MudPIT applications to quantitative phosphoproteomics. MudPIT application to large-scale analysis of phosphoproteins includes (a) a fractionation procedure for motif-specific enrichment of phosphopeptides, (b) development of informatics tools for interrogation and validation of shotgun phosphopeptide data, and (c) in-depth data analysis for simultaneous determination of protein expression and phosphorylation levels, analog to western blot measurements. We illustrate MudPIT application to quantitative phosphoproteomics of the beta adrenergic pathway. We discuss several biological discoveries made via mass spectrometry pipelines with a focus on cell signaling proteomics.