生物质谱与蛋白质组学

蛋白质组学是后基因组学时代最受关注的研究领域之一,其核心的鉴定技术——生物质谱近年来在仪器设计以及鉴定通量、分辨率和灵敏度等各方面均有质的飞跃,促进了蛋白质表达谱作图、定量蛋白质组分析、亚细胞器蛋白质组作图、蛋白质翻译后修饰以及蛋白质相互作用等蛋白质组研究各个领域的飞速发展。本综述了生物质谱技术的最新进展,及其在蛋白质组学研究中的应用。     

Recent advances in biomedical applications of accelerator mass spectrometry

The use of radioisotopes has a long history in biomedical science, and the technique of accelerator mass spectrometry (AMS), an extremely sensitive nuclear physics technique for detection of very low-abundant, stable and long-lived isotopes, has now revolutionized high-sensitivity isotope detection in biomedical research, because it allows the direct determination of the amount of isotope in a sample rather than measuring its decay, and thus the quantitative analysis of the fate of the radiolabeled probes under the given conditions. Since AMS was first used in the early 90's for the analysis of biological samples containing enriched ¹⁴C for toxicology and cancer research, the biomedical applications of AMS to date range from in vitro to in vivo studies, including the studies of 1) toxicant and drug metabolism, 2) neuroscience, 3) pharmacokinetics, and 4) nutrition and metabolism of endogenous molecules such as vitamins. In addition, a new drug development concept that relies on the ultrasensitivity of AMS, known as human microdosing, is being used to obtain early human metabolism information of candidate drugs. These various aspects of AMS are reviewed and a perspective on future applications of AMS to biomedical research is provided.     

Correction: Recent advances in biomedical applications of accelerator mass spectrometry

After publication of our article, it was noted that we inadvertently failed to include the complete list of authors. The full list, including co-authors, has now been added and the Authors' contributions and Competing interests sections modified accordingly.     

Molecular mass spectrometry imaging in biomedical and life science research

This review describes the current state of mass spectrometry imaging (MSI) in life sciences. A brief overview of mass spectrometry principles is presented followed by a thorough introduction to the MSI workflows, principles and areas of application. Three major desorption-ionization techniques used in MSI, namely, secondary ion mass spectrometry (SIMS), matrix-assisted laser desorption ionization (MALDI), and desorption electrospray ionization (DESI) are described, and biomedical and life science imaging applications of each ionization technique are reviewed. A separate section is devoted to data handling and current challenges and future perspectives are briefly discussed at the end.     

Recent developments and applications of electron transfer dissociation mass spectrometry in proteomics

Electron transfer dissociation (ETD) has been developed recently as an efficient ion fragmentation technique in mass spectrometry (MS), being presently considered a step forward in proteomics with real perspectives for improvement, upgrade and application. Available also on affordable ion trap mass spectrometers, ETD induces specific N–Cα bond cleavages of the peptide backbone with the preservation of the post-translational modifications and generation of product ions that are diagnostic for the modification site(s). In addition, in the last few years ETD contributed significantly to the development of top-down approaches which enable tandem MS of intact protein ions. The present review, covering the last 5 years highlights concisely the major achievements and the current applications of ETD fragmentation technique in proteomics. An ample part of the review is dedicated to ETD contribution in the elucidation of the most common posttranslational modifications, such as phosphorylation and glycosylation. Further, a brief section is devoted to top-down by ETD method applied to intact proteins. As the last few years have witnessed a major expansion of the microfluidics systems, a few considerations on ETD in combination with chip-based nanoelectrospray (nanoESI) as a platform for high throughput top-down proteomics are also presented.     

Bioinformatics in mass spectrometry data analysis for proteomics studies

Mass spectrometry is a technique widely employed for the identification and characterization of proteins. The role of bioinformatics is fundamental for the elaboration of mass spectrometry data due to the amount of data that this technique can produce. To process data efficiently, new software packages and algorithms are continuously being developed to improve protein identification and characterization in terms of high-throughput and statistical accuracy. However, many limitations exist concerning bioinformatics spectral data elaboration. This review aims to critically cover the recent and future developments of new bioinformatics approaches in mass spectrometry data analysis for proteomics studies.     

Bioinformatics in mass spectrometry data analysis for proteomics studies

Mass spectrometry is a technique widely employed for the identification and characterization of proteins. The role of bioinformatics is fundamental for the elaboration of mass spectrometry data due to the amount of data that this technique can produce. To process data efficiently, new software packages and algorithms are continuously being developed to improve protein identification and characterization in terms of high-throughput and statistical accuracy. However, many limitations exist concerning bioinformatics spectral data elaboration. This review aims to critically cover the recent and future developments of new bioinformatics approaches in mass spectrometry data analysis for proteomics studies.     

A noise model for mass spectrometry based proteomics

Motivation: Mass spectrometry data are subjected to considerablenoise. Good noise models are required for proper detection andquantification of peptides. We have characterized noise in bothquadrupole time-of-flight (Q-TOF) and ion trap data, and haveconstructed models for the noise. Results: We find that the noise in Q-TOF data from Applied BiosystemsQSTAR fits well to a combination of multinomial and Poissonmodel with detector dead-time correction. In comparison, iontrap noise from Agilent MSD-Trap-SL is larger than the Q-TOFnoise and is proportional to Poisson noise. We then demonstratethat the noise model can be used to improve deisotoping forpeptide detection, by estimating appropriate cutoffs of thegoodness of fit parameter at prescribed error rates. The noisemodels also have implications in noise reduction, retentiontime alignment and significance testing for biomarker discovery. Contact: pdu{at}us.ibm.com Supplementary information: Supplementary data are availableat Bioinfomatics Online. Associate Editor: Olga Troyanskaya     

Charge state estimation for tandem mass spectrometry proteomics

High-throughput protein analysis by tandem mass spectrometry produces anywhere from thousands to millions of spectra that are being used for peptide and protein identifications. Though each spectrum corresponds only to one charged peptide (ion) state, repetitive database searches of multiple charge states are typically conducted since the resolution of many common mass spectrometers is not sufficient to determine the charge state. The resulting database searches are both error-prone and time-consuming. We describe a straightforward, accurate approach on charge state estimation (CHASTE). CHASTE relies on fragment ion peak distributions, and by using reliable logistic regression models, combines different measurements to improve its accuracy. CHASTE's performance has been validated on data sets, comprised of known peptide dissociation spectra, obtained by replicate analyses of our earlier developed protein standard mixture using ion trap mass spectrometers at different laboratories. CHASTE was able to reduce number of needed database searches by at least 60% and the number of redundant searches by at least 90% virtually without any informational loss. This greatly alleviates one of the major bottlenecks in high throughput peptide and protein identifications. Thresholds and parameter estimates can be tailored to specific analysis situations, pipelines, and instrumentations. CHASTE was implemented in Java GUI-based and command-line-based interfaces.     

Decision tree-driven tandem mass spectrometry for shotgun proteomics

Mass spectrometry has become a key technology for modern large-scale protein sequencing. Tandem mass spectrometry, the process of peptide ion dissociation followed by mass-to-charge ratio (m/z) analysis, is the critical component for peptide identification. Recent advances in mass spectrometry now permit two discrete, and complementary, types of peptide ion fragmentation: collision-activated dissociation (CAD) and electron transfer dissociation (ETD) on a single instrument. To exploit this complementarity and increase sequencing success rates, we designed and embedded a data-dependent decision tree algorithm (DT) to make unsupervised, real-time decisions of which fragmentation method to use based on precursor charge and m/z. Applying the DT to large-scale proteome analyses of Saccharomyces cerevisiae and human embryonic stem cells, we identified 53,055 peptides in total, which was greater than by using CAD (38,293) or ETD (39,507) alone. In addition, the DT method also identified 7,422 phosphopeptides, compared to either 2,801 (CAD) or 5,874 (ETD) phosphopeptides.     

Electron transfer dissociation mass spectrometry in proteomics

Mass spectrometry has rapidly evolved to become the platform of choice for proteomic analysis. While CID remains the major fragmentation method for peptide sequencing, electron transfer dissociation (ETD) is emerging as a complementary method for the characterization of peptides and post-translational modifications (PTMs). Here, we review the evolution of ETD and some of its newer applications including characterization of PTMs, non-tryptic peptides and intact proteins. We will also discuss some of the unique features of ETD such as its complementarity with CID and the use of alternating CID/ETD along with issues pertaining to analysis of ETD data. The potential of ETD for applications such as multiple reaction monitoring and proteogenomics in the future will also be discussed.     

Surface plasmon resonance mass spectrometry in proteomics

Due to the enormous complexity of the proteome, focus in proteomics shifts more and more from the study of the complete proteome to the targeted analysis of part of the proteome. The isolation of this specific part of the proteome generally includes an affinity-based enrichment. Surface plasmon resonance (SPR), a label-free technique able to follow enrichment in real-time and in a semiquantitative manner, is an emerging tool for targeted affinity enrichment. Furthermore, in combination with mass spectrometry (MS), SPR can be used to both selectively enrich for and identify proteins from a complex sample. Here we illustrate the use of SPR-MS to solve proteomics-based research questions, describing applications that use very different types of immobilized components: such as small (drug or messenger) molecules, peptides, DNA and proteins. We evaluate the current possibilities and limitations and discuss the future developments of the SPR-MS technique.     

Clinical proteomics and mass spectrometry profiling for cancer detection

A key challenge in the clinical proteomics of cancer is the identification of biomarkers that would enable early detection, diagnosis and monitoring of disease progression to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer a unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and mass spectrometry is one of the most interesting new approaches for cancer diagnostics using proteomic profiling. This review presents two technologies in this field, surface-enhanced laser desorption/ionization time-of-flight and Clinprot, and aims to summarize the results of studies obtained with the first of them for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems, and still requires some efforts to be standardized and validated.     

Performance of a genetic algorithm for mass spectrometry proteomics

Background  

Recently, mass spectrometry data have been mined using a genetic algorithm to produce discriminatory models that distinguish healthy individuals from those with cancer. This algorithm is the basis for claims of 100% sensitivity and specificity in two related publicly available datasets. To date, no detailed attempts have been made to explore the properties of this genetic algorithm within proteomic applications. Here the algorithm's performance on these datasets is evaluated relative to other methods.     

Spectral quality assessment for high-throughput tandem mass spectrometry proteomics

Current techniques in tandem mass spectrometric analyses of cellular protein contents often produce thousands to tens of thousands of spectra per experiment. This study introduces a new algorithm, named SPEQUAL, which is aimed at automated tandem mass spectral quality assessment. The quality of a given spectrum can be evaluated from three basic components: (i) charge state differentiation, (ii) total signal intensity, and (iii) signal-to-noise estimates. The differentiation between single and multiple precursor charge states (i) provides a binary score for a given spectrum. Components (ii) and (iii) provide partial scores which are subsequently summarized and multiplied by the first score. SPEQUAL was applied to over 10,000 data files derived from almost 3,000 tandem mass spectra, and the results (final cumulative scores) were manually verified. SPEQUAL's performance was determined to have high sensitivity and specificity and low error rates for both spectral quality estimates in general and precursor charge state differentiation in particular. Each of the partial scores is controlled by adjustable thresholds to fine-tune SPEQUAL's performance for different analysis pipelines and instrumentation. This spectral quality assessment tool is intended to act in an advisory role to the researcher, assisting in filtration of thousands of spectra typically produced by high throughput tandem mass spectrometric proteome analyses. Lastly, SPEQUAL was implemented as Java GUI-based and command-line-based interfaces freely available for both academic and industrial researchers.     

Clinical proteomics and mass spectrometry profiling for cancer detection

A key challenge in the clinical proteomics of cancer is the identification of biomarkers that would enable early detection, diagnosis and monitoring of disease progression to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer a unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and mass spectrometry is one of the most interesting new approaches for cancer diagnostics using proteomic profiling. This review presents two technologies in this field, surface-enhanced laser desorption/ionization time-of-flight and Clinprot?, and aims to summarize the results of studies obtained with the first of them for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems, and still requires some efforts to be standardized and validated.     

Applications of MALDI-TOF mass spectrometry in clinical proteomics

Introduction: The development of precision medicine requires advanced technologies to address the multifactorial disease stratification and to support personalized treatments. Among omics techniques, proteomics based on Mass Spectrometry (MS) is becoming increasingly relevant in clinical practice allowing a phenotypic characterization of the dynamic functional status of the organism. From this perspective, Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) MS is a suitable platform for providing a high-throughput support to clinics.

Areas covered: This review aims to provide an updated overview of MALDI-TOF MS applications in clinical proteomics. The most relevant features of this analysis have been discussed, highlighting both pre-analytical and analytical factors that are crucial in proteomics studies. Particular emphasis is placed on biofluids proteomics for biomarkers discovery and on recent progresses in clinical microbiology, drug monitoring, and minimal residual disease (MRD).

Expert commentary: Despite some analytical limitations, the latest technological advances together with the easiness of use, the low time and low cost consuming and the high throughput are making MALDI-TOF MS instruments very attractive for the clinical practice. These features offer a significant potential for the routine of the clinical laboratory and ultimately for personalized medicine.