FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2121863513> ?p ?o ?g. }

• W2121863513 endingPage "1744" @default.
• W2121863513 startingPage "1727" @default.
• W2121863513 abstract "Recent advances in proteomics technologies provide tremendous opportunities for biomarker-related clinical applications; however, the distinctive characteristics of human biofluids such as the high dynamic range in protein abundances and extreme complexity of the proteomes present tremendous challenges. In this review we summarize recent advances in LC-MS-based proteomics profiling and its applications in clinical proteomics as well as discuss the major challenges associated with implementing these technologies for more effective candidate biomarker discovery. Developments in immunoaffinity depletion and various fractionation approaches in combination with substantial improvements in LC-MS platforms have enabled the plasma proteome to be profiled with considerably greater dynamic range of coverage, allowing many proteins at low ng/ml levels to be confidently identified. Despite these significant advances and efforts, major challenges associated with the dynamic range of measurements and extent of proteome coverage, confidence of peptide/protein identifications, quantitation accuracy, analysis throughput, and the robustness of present instrumentation must be addressed before a proteomics profiling platform suitable for efficient clinical applications can be routinely implemented. Recent advances in proteomics technologies provide tremendous opportunities for biomarker-related clinical applications; however, the distinctive characteristics of human biofluids such as the high dynamic range in protein abundances and extreme complexity of the proteomes present tremendous challenges. In this review we summarize recent advances in LC-MS-based proteomics profiling and its applications in clinical proteomics as well as discuss the major challenges associated with implementing these technologies for more effective candidate biomarker discovery. Developments in immunoaffinity depletion and various fractionation approaches in combination with substantial improvements in LC-MS platforms have enabled the plasma proteome to be profiled with considerably greater dynamic range of coverage, allowing many proteins at low ng/ml levels to be confidently identified. Despite these significant advances and efforts, major challenges associated with the dynamic range of measurements and extent of proteome coverage, confidence of peptide/protein identifications, quantitation accuracy, analysis throughput, and the robustness of present instrumentation must be addressed before a proteomics profiling platform suitable for efficient clinical applications can be routinely implemented. Advances in MS technologies, high resolution liquid phase separations, and informatics/bioinformatics for large scale data analysis have made MS-based proteomics an indispensable research tool with the potential to broadly impact biology and laboratory medicine (1Aebersold R. Mann M. Mass spectrometry-based proteomics.Nature. 2003; 422: 198-207Crossref PubMed Scopus (5191) Google Scholar). In particular, proteomics technologies have been increasingly applied to the study of disease-related clinical samples (e.g. human blood serum/plasma, proximal fluids, and disease tissues) for the purposes of identifying novel disease-specific protein biomarkers, gaining better understandings of disease processes, and discovering novel protein targets for therapeutic interventions and drug developments (2Hanash S. Disease proteomics.Nature. 2003; 422: 226-232Crossref PubMed Scopus (813) Google Scholar).Proteomics-based candidate biomarker discovery efforts have recently gained significant attention due to the power of these technologies for analyzing complex protein mixtures and their potential for identifying novel markers indicative of disease. It is widely believed that many complex human diseases, including cancers, might be more effectively cured if specific disease biomarkers were available to enable detection and treatment at very early stages of disease (3Etzioni R. Urban N. Ramsey S. McIntosh M. Schwartz S. Reid B. Radich J. Anderson G. Hartwell L. The case for early detection.Nat. Rev. Cancer. 2003; 3: 243-252Crossref PubMed Google Scholar). Despite noteworthy efforts, only a handful of cancer biomarkers have been approved by the United States Food and Drug Administration (FDA) 1The abbreviations used are: FDA, Food and Drug Administration; SCX, strong cation exchange chromatography; NET, normalized elution time; AMT, accurate mass and time; IMS, ion mobility spectrometry; 2D, two-dimensional; RPLC, reversed phase LC; MARS, multiple affinity removal system; HUPO, Human Proteome Organization; LPS, lipopolysaccharide; MRM, multiple reaction monitoring. 1The abbreviations used are: FDA, Food and Drug Administration; SCX, strong cation exchange chromatography; NET, normalized elution time; AMT, accurate mass and time; IMS, ion mobility spectrometry; 2D, two-dimensional; RPLC, reversed phase LC; MARS, multiple affinity removal system; HUPO, Human Proteome Organization; LPS, lipopolysaccharide; MRM, multiple reaction monitoring. for clinical use, with the majority of these being protein biomarkers (4Ludwig J.A. Weinstein J.N. Biomarkers in cancer staging, prognosis and treatment selection.Nat. Rev. Cancer. 2005; 5: 845-856Crossref PubMed Scopus (1180) Google Scholar). Although existing markers play a significant role in screening, monitoring, and staging, effective biomarkers are not currently available for most cancers and are generally nonexistent for early detection (3Etzioni R. Urban N. Ramsey S. McIntosh M. Schwartz S. Reid B. Radich J. Anderson G. Hartwell L. The case for early detection.Nat. Rev. Cancer. 2003; 3: 243-252Crossref PubMed Google Scholar). Therefore, there is a clear need for applying advanced technologies such as these based on proteomics in the quest for novel candidate clinical biomarkers.Although widely speculated that advances in genomics and proteomics would alter the landscape of clinical biomarker discovery and validation, the declining trend of new FDA-approved biomarkers reported over the last decade (5Anderson N.L. Anderson N.G. The human plasma proteome: history, character, and diagnostic prospects.Mol. Cell. Proteomics. 2002; 1: 845-867Abstract Full Text Full Text PDF PubMed Google Scholar) highlights the magnitude of the challenges associated with human clinical samples and validation of candidate biomarkers. Contributing to these challenges are the substantial complexity of the human proteome and the heterogeneity of the human population, both of which make the search for biomarkers from either biofluids or disease tissues a daunting task. As a result of the heterogeneous nature of humans and the complexity of diseases, e.g. cancers, a panel of biomarkers rather than a single marker may be required to achieve the high sensitivity and specificity required for clinical applications (3Etzioni R. Urban N. Ramsey S. McIntosh M. Schwartz S. Reid B. Radich J. Anderson G. Hartwell L. The case for early detection.Nat. Rev. Cancer. 2003; 3: 243-252Crossref PubMed Google Scholar). Proteomics technologies offer significant potential for discovering such marker panels.Many different technologies have been applied for biomarker discovery and other clinical applications, including two-dimensional (2D) gel-electrophoresis (6Zhou G. Li H. DeCamp D. Chen S. Shu H. Gong Y. Flaig M. Gillespie J.W. Hu N. Taylor P.R. Emmert-Buck M.R. Liotta L.A. Petricoin III, E.F. Zhao Y. 2D differential in-gel electrophoresis for the identification of esophageal scans cell cancer-specific protein markers.Mol. Cell. Proteomics. 2002; 1: 117-124Abstract Full Text Full Text PDF PubMed Google Scholar), LC-MS, and protein- and antibody-based microarrays (7Zangar R.C. Varnum S.M. Bollinger N. Studying cellular processes and detecting disease with protein microarrays.Drug Metab. Rev. 2005; 37: 473-487Crossref PubMed Scopus (0) Google Scholar, 8Janzi M. Odling J. Pan-Hammarstrom Q. Sundberg M. Lundeberg J. Uhlen M. Hammarstrom L. Nilsson P. Serum microarrays for large scale screening of protein levels.Mol. Cell. Proteomics. 2005; 4: 1942-1947Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, 9Uhlen M. Bjorling E. Agaton C. Szigyarto C.A. Amini B. Andersen E. Andersson A.C. Angelidou P. Asplund A. Asplund C. Berglund L. Bergstrom K. Brumer H. Cerjan D. Ekstrom M. Elobeid A. Eriksson C. Fagerberg L. Falk R. Fall J. Forsberg M. Bjorklund M.G. Gumbel K. Halimi A. Hallin I. Hamsten C. Hansson M. Hedhammar M. Hercules G. Kampf C. Larsson K. Lindskog M. Lodewyckx W. Lund J. Lundeberg J. Magnusson K. Malm E. Nilsson P. Odling J. Oksvold P. Olsson I. Oster E. Ottosson J. Paavilainen L. Persson A. Rimini R. Rockberg J. Runeson M. Sivertsson A. Skollermo A. Steen J. Stenvall M. Sterky F. Stromberg S. Sundberg M. Tegel H. Tourle S. Wahlund E. Walden A. Wan J. Wernerus H. Westberg J. Wester K. Wrethagen U. Xu L.L. Hober S. Ponten F. A human protein atlas for normal and cancer tissues based on antibody proteomics.Mol. Cell. Proteomics. 2005; 4: 1920-1932Abstract Full Text Full Text PDF PubMed Scopus (856) Google Scholar). LC-MS- or tandem MS (MS/MS)-based proteomics technologies offer highly sensitive analytical capabilities and a relatively large dynamic range of detection and have increasingly become the method of choice for in depth profiling of complex protein mixtures (1Aebersold R. Mann M. Mass spectrometry-based proteomics.Nature. 2003; 422: 198-207Crossref PubMed Scopus (5191) Google Scholar). In addition, the relatively high throughput of LC-MS technologies is amenable to clinical applications that involve human biofluids and disease tissues. The application of LC-MS/MS for human biofluid protein profiling was initiated by the first global shotgun proteomics study of human plasma/serum published in 2002 by Adkins et al. (10Adkins J.N. Varnum S.M. Auberry K.J. Moore R.J. Angell N.H. Smith R.D. Springer D.L. Pounds J.G. Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry.Mol. Cell. Proteomics. 2002; 1: 947-955Abstract Full Text Full Text PDF PubMed Scopus (688) Google Scholar). An explosion of LC-MS-based applications in human plasma/serum and various biofluids soon followed due to the tremendous interest in identifying disease-related proteins (11Jacobs J.M. Adkins J.N. Qian W.J. Liu T. Shen Y. Camp II, D.G. Smith R.D. Utilizing human blood plasma for proteomic biomarker discovery.J. Proteome Res. 2005; 4: 1073-1085Crossref PubMed Scopus (241) Google Scholar, 12Veenstra T.D. Conrads T.P. Hood B.L. Avellino A.M. Ellenbogen R.G. Morrison R.S. Biomarkers: mining the biofluid proteome.Mol. Cell. Proteomics. 2005; 4: 409-418Abstract Full Text Full Text PDF PubMed Scopus (200) Google Scholar). Various depletion/fractionation/enrichment techniques have been developed along the way and coupled to LC-MS to increase coverage of the biofluid proteomes (13Lee H.J. Lee E.Y. Kwon M.S. Paik Y.K. Biomarker discovery from the plasma proteome using multidimensional fractionation proteomics.Curr. Opin. Chem. Biol. 2006; 10: 42-49Crossref PubMed Scopus (90) Google Scholar).Human blood serum/plasma remains the most commonly used clinical sample to date for proteomics applications because it may include specific biomarkers for virtually all human diseases due to its either direct or indirect interaction with the entire cell complement of the body, i.e. tissue-specific proteins may be released into the blood stream upon cell damage or cell death. Additionally serum/plasma can be readily obtained by clinical sampling. However, the magnitude of the previously mentioned challenges associated with human clinical samples coupled with the anticipation that potential biomarkers of interest could be present at extremely low concentrations in plasma has raised doubts as to whether disease biomarkers can be accurately detected or identified from plasma using a proteomics approach. As a result, analysis of various other biofluids/tissues has gained increasing attention. Due to their proximity to the source of disease or perturbation in the body, tissues (14Wright M.E. Han D.K. Aebersold R. Mass spectrometry-based expression profiling of clinical prostate cancer.Mol. Cell. Proteomics. 2005; 4: 545-554Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar) and various biofluids such as cerebrospinal fluid (15Hu Y. Malone J.P. Fagan A.M. Townsend R.R. Holtzman D.M. Comparative proteomic analysis of intra- and interindividual variation in human cerebrospinal fluid.Mol. Cell. Proteomics. 2005; 4: 2000-2009Abstract Full Text Full Text PDF PubMed Scopus (102) Google Scholar), bronchoalveolar lavage fluid (16Wattiez R. Falmagne P. Proteomics of bronchoalveolar lavage fluid.J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2005; 815: 169-178Crossref PubMed Scopus (110) Google Scholar), synovial fluid (17Liao H. Wu J. Kuhn E. Chin W. Chang B. Jones M.D. O’Neil S. Clauser K.R. Karl J. Hasler F. Roubenoff R. Zolg W. Guild B.C. Use of mass spectrometry to identify protein biomarkers of disease severity in the synovial fluid and serum of patients with rheumatoid arthritis.Arthritis Rheum. 2004; 0: 3792-3803Crossref Scopus (228) Google Scholar), nipple aspirate fluid (18Varnum S.M. Covington C.C. Woodbury R.L. Petritis K. Kangas L.J. Abdullah M.S. Pounds J.G. Smith R.D. Zangar R.C. Proteomic characterization of nipple aspirate fluid: identification of potential biomarkers of breast cancer.Breast Cancer Res. Treat. 2003; 80: 87-97Crossref PubMed Scopus (93) Google Scholar), saliva (19Xie H. Rhodus N.L. Griffin R.J. Carlis J.V. Griffin T.J. A catalogue of human saliva proteins identified by free flow electrophoresis-based peptide separation and tandem mass spectrometry.Mol. Cell. Proteomics. 2005; 4: 1826-1830Abstract Full Text Full Text PDF PubMed Scopus (144) Google Scholar), and urine (20Theodorescu D. Wittke S. Ross M.M. Walden M. Conaway M. Just I. Mischak H. Frierson H.F. Discovery and validation of new protein biomarkers for urothelial cancer: a prospective analysis.Lancet Oncol. 2006; 7: 230-240Abstract Full Text Full Text PDF PubMed Scopus (351) Google Scholar) are believed to provide a more focused pool of potential biomarkers of interest. In addition, tumor interstitial fluids have also been reported as a novel source for proteomics biomarker and therapeutic target discovery (21Celis J.E. Gromov P. Cabezon T. Moreira J.M. Ambartsumian N. Sandelin K. Rank F. Gromova I. Proteomic characterization of the interstitial fluid perfusing the breast tumor microenvironment: a novel resource for biomarker and therapeutic target discovery.Mol. Cell. Proteomics. 2004; 3: 327-344Abstract Full Text Full Text PDF PubMed Scopus (261) Google Scholar), offering a promising alternative to direct tissue analysis. In the following review, we highlight LC-MS-based proteomics profiling for clinical applications by summarizing recent advances as well as the major challenges facing this technology for more effective candidate biomarker discovery.CHALLENGES AND REQUIREMENTS FOR DESIGNING A ROBUST LC-MS DISCOVERY PLATFORMThe distinctive nature of human biofluid proteomes, in particular the serum/plasma proteome, presents significant challenges for current analytical technologies aimed at quantitative protein profiling and biomarker discovery. First, the serum/plasma protein content is dominated by several very abundant proteins (i.e. the 22 most abundant proteins represent ∼99% of the total protein mass in plasma) yet at the same time presents an extraordinary dynamic range (>10 orders of magnitude) in protein concentrations that begins with serum albumin at ∼45 mg/ml and extends to cytokines (and potentially many disease-related proteins) at around 1–10 pg/ml or lower (5Anderson N.L. Anderson N.G. The human plasma proteome: history, character, and diagnostic prospects.Mol. Cell. Proteomics. 2002; 1: 845-867Abstract Full Text Full Text PDF PubMed Google Scholar). Second, the serum/plasma proteome presents tremendous biological complexity as a result of tissue “leakage” proteins from the entire body, complex post-translational protein modifications such as glycosylation, and the existence of various forms (i.e. splice variants, proteolytic products, and the tremendous variability in the immunoglobulin class) for each expressed gene. Finally the substantial genetic and non-genetic biological variability of human clinical samples contributes significantly to the overall analytical challenge.Despite significant recent advances, major challenges remain to prevent routine implementation of an LC-MS protein profiling platform suitable for efficient biomarker discovery (Table I). To effectively address these challenges, a protein profiling platform suitable for biomarker discovery and clinical applications must provide at the very minimum 1) overall high dynamic range of measurements and extensive coverage of the proteome for effective detection of low abundance proteins, 2) highly confident and specific protein identifications, 3) accurate quantitation of relative protein abundances across many clinical samples, and 4) high throughput capable of analyzing large numbers of clinical samples to provide sufficient statistical power needed to address biological variability. In addition, the platform, including both sample processing and LC-MS instrumentation, must be robust and include efficient informatics software capabilities for data mining and statistical analyses. Currently there is a broad consensus that no existing platform meets all of these requirements for effective biomarker discovery.Table IChallenges and limitations of current LC-MS-based proteomics technologies applied to biomarker discoveryChallengeCurrent techniques for addressing the challengeLimitationsDynamic range of measurementsImmunoaffinity depletion and multidimensional fractionation coupled with high resolution LC-MS or MS/MS instrumentationLow throughput, requires relatively large sample sizesSensitivitySmall inner diameter LC column (50 μm or less) coupled with nanoflow electrospray ionization and advanced MS instrumentation (i.e. FTICR, LTQ-FT)Issues in robustness and expenseReproducibility and quantitationPlatform automation (including sample processing), label-free direct quantitation, and isotope labeling-based quantitationVariations from multistep sample processing, ionization suppression and instrument variations, labeling efficienciesThroughputAutomated fast LC and gas phase ion mobility separationsLimited dynamic range or coverageFalse positive identificationsImproved database searching algorithms and statistical modelsLack of consensus Open table in a new tab Fig. 1 shows a component-based diagram of an LC-MS protein profiling platform. Note that such a platform is not based on a single instrument but rather on a compilation of current technologies to achieve high dynamic range quantitative proteome profiling for clinical samples. A key performance factor of any such platform is the overall dynamic range of detection and extent of proteome coverage, which in turn dictates its ability to detect low abundance proteins. Many disease-specific proteins in plasma/serum are anticipated to be present at very low levels (ng/ml or even lower), e.g. within the same range as current FDA-approved markers such as prostate-specific antigen (0.01–100 ng/ml) and Troponin-T (0.02–100 ng/ml). This is particularly obvious for cancer markers of early detection where tumor size is very small (millimeter size), and cancer-specific proteins in plasma may present at pg/ml or lower levels. This overall dynamic range presents a tremendous challenge for any MS-based technology. The achievable dynamic range or proteome coverage for a platform depends on the peak capacity (the number of chromatographic peaks that can be fit into the length of separation) of the on-line LC separations prior to MS measurements, the dynamic range of the MS instrumentation, and the efficiency of sample enrichment or fractionation steps at both protein and peptide levels prior to LC-MS analyses. Analysis throughput inevitably determines the size of any clinical study sample set and largely depends on factors such as automation of each platform component, LC-MS analysis duty cycle, and the extent of prefractionation prior to LC-MS analysis. Although the application of more extensive fractionation can lead to a higher dynamic range of detection, the overall throughput can be severely reduced. Other key performance factors are the confidence of protein identifications and the quantitative accuracy, which determine the ability of the platform to confidently identify a potential biomarker based on the abundance differences between healthy and diseased conditions. Both the reproducibility of sample processing/fractionation prior to LC-MS and the LC-MS instrumentation will contribute to the accuracy of quantitation.ADVANCES IN LC-MS TECHNOLOGIESA high resolution LC (or LC/LC) separation coupled on line with MS is the central component of many proteomics platforms. Over the past decade, there have been significant advances in LC separations as well as in MS instrumentation and ESI. To date, the “bottom-up” proteomics strategy that combines high efficiency separations with MS to characterize highly complex peptide mixtures still accounts for the majority of proteomics measurements. This strategy relies on the identification of peptides sufficiently unique for protein identification. Protein mixtures from cellular lysates or biofluids are typically digested by trypsin (or other proteases) into polypeptides, which are then separated by capillary LC and analyzed by MS on line via an ESI interface. Peptide sequences are identified by using automated database searching algorithms such as SEQUEST (22Yates III, J.R. Eng J.K. McCormack A.L. Mining genomes: correlating tandem mass spectra of modified and unmodified peptides to sequences in nucleotide databases.Anal. Chem. 1995; 67: 3202-3210Crossref PubMed Google Scholar), MASCOT (23Perkins D. Pappin D. Creasy D. London U. Probability-based protein identification by searching sequence databases using mass spectrometry data.Electrophoresis. 1999; 20: 3551-3567Crossref PubMed Google Scholar), or X!Tandem (24Craig R. Beavis R.C. TANDEM: matching proteins with tandem mass spectra.Bioinformatics. 2004; 20: 1466-1467Crossref PubMed Scopus (1841) Google Scholar) to correlate experimental MS/MS spectra to theoretical mass spectra based on sequences in a given protein database for a specific organism. With the recent development of high speed 2D linear ion trap instruments, i.e. LTQ, the protein profiling coverage has been greatly enhanced compared with traditional three-dimensional ion trap systems (25Mayya V. Rezaul K. Cong Y.S. Han D. Systematic comparison of a two-dimensional ion trap and a three-dimensional ion trap mass spectrometer in proteomics.Mol. Cell. Proteomics. 2005; 4: 214-223Abstract Full Text Full Text PDF PubMed Scopus (44) Google Scholar). When coupled with SCX fractionation either on line or off line (26Wolters D.A. Washburn M.P. Yates J.R. An automated multidimensional protein identification technology for shotgun proteomics.Anal. Chem. 2001; 73: 5683-5690Crossref PubMed Scopus (1474) Google Scholar, 27Wang H. Qian W.J. Chin M.H. Petyuk V.A. Barry R.C. Liu T. Gritsenko M.A. Mottaz H.M. Moore R.J. Camp II, D.G. Khan A.H. Smith D.J. Smith R.D. Characterization of the mouse brain proteome using global proteomic analysis complemented with cysteinyl-peptide enrichment.J. Proteome Res. 2006; 5: 361-369Crossref PubMed Scopus (104) Google Scholar), LC-MS/MS technologies now routinely allow for identification of thousands of proteins from complex mammalian tissues and cells. Although routinely used for peptide/protein identifications, data-dependent LC-MS/MS still has an inherent “undersampling” limitation whereby only a portion of the species observed in the survey MS scan is selected for fragmentation (28Tabb D.L. MacCoss M.J. Wu C.C. Anderson S.D. Yates J.R. Similarity among tandem mass spectra from proteomic experiments: detection, significance, and utility.Anal. Chem. 2003; 75: 2470-2477Crossref PubMed Scopus (131) Google Scholar).To overcome the undersampling issue, our laboratory developed an accurate mass and time (AMT) tag approach that utilizes highly accurate mass measurements from a high resolution mass spectrometer (e.g. FTICR or TOF mass spectrometer) in conjunction with accurate elution time measurements from high resolution capillary LC separations to achieve high throughput proteome profiling without routine MS/MS measurements (29Smith R.D. Anderson G.A. Lipton M.S. Pasa-Tolic L. Shen Y. Conrads T.P. Veenstra T.D. Udseth H.R. An accurate mass tag strategy for quantitative and high throughput proteome measurements.Proteomics. 2002; 2: 513-523Crossref PubMed Scopus (398) Google Scholar, 30Qian W.J. Camp D.G. Smith R.D. High throughput proteomics using Fourier transform ion cyclotron resonance (FTICR) mass spectrometry.Expert Rev. Proteomics. 2004; 1: 89-97Crossref Scopus (38) Google Scholar). The concept of this AMT tag approach is based on the principle that the accurate mass and time measurements will allow reliable peptide identifications by correlating the mass and time of detected peaks to a pre-established peptide AMT tag reference library for a particular biological system (e.g. plasma). With this approach, LC-MS/MS proteome analyses coupled with extensive fractionation only need to be performed once to create an effective reference database of peptide markers defined by accurate masses and elution times, i.e. AMT tags. The AMT tag database then serves as a comprehensive “look-up table” for subsequent higher throughput LC-MS analyses, allowing many peptides in each spectrum to be identified without MS/MS. Fig. 2 exemplifies an LC chromatogram and 2D display of ∼2,800 peptides identified using the AMT tag strategy resulting from a single LC-FTICR analysis of a ProteomeLab™ IgY-12 depleted human plasma sample.Fig. 2.A typical LC-FTICR analysis of an IgY-12 depleted human plasma sample.A, the base peak chromatogram. B, a 2D display of ∼2,800 identified species at the mass and NET space. The analysis was performed using a Bruker 9.4-tesla FTICR instrument coupled with an LC system equipped with a 150-μm-inner diameter and 65-cm-long capillary column operated at 5,000 p.s.i.View Large Image Figure ViewerDownload Hi-res image Download (PPT)The fact that application of the AMT tag approach obviates the need for routine MS/MS is particularly attractive in high throughput repeated analyses of similar samples (e.g. serum/plasma) in clinical proteomics studies. We have recently demonstrated the application of the AMT tag approach coupled with 18O labeling for quantitative profiling of the human plasma proteome in response to lipopolysaccharide administration (31Qian W.J. Monroe M.E. Liu T. Jacobs J.M. Anderson G.A. Shen Y. Moore R.J. Anderson D.J. Zhang R. Calvano S.E. Lowry S.F. Xiao W. Moldawer L.L. Davis R.W. Tompkins R.G. Camp D.G. Smith R.D. Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 16O/18O labeling and the accurate mass and time tag approach.Mol. Cell. Proteomics. 2005; 4: 700-709Abstract Full Text Full Text PDF PubMed Scopus (145) Google Scholar). The availability of commercial high performance mass spectrometers (e.g. ThermoElectron Finnigan LTQ-FT and LTQ-Orbitrap) will likely lead to an even broader range of applications based on this LC-MS-only approach for higher throughput peptide identifications.As mentioned previously, the achievable dynamic range for the LC-MS platform depends significantly on the peak capacity of the on-line gradient reversed phase separations, the dynamic range of the MS system, and the efficiency and stability of the ESI interface. A single MS spectrum can provide a dynamic range of up to 103 for a high resolution instrument (e.g. FTICR), and one would expect to achieve a dynamic range of at least 105 by coupling this instrument to an on-line high resolution LC separation that provides a peak capacity of ∼1,000. However, the observed dynamic range of measurements can be significantly reduced for complex biological samples such as human plasma due to the charge competition of co-eluting high abundance species, leading to ion suppression of the relatively low abundance species. Ion suppression is a particular issue when analyzing human biofluid samples as these samples are dominated by a handful of highly abundant proteins. Significant ion suppression will occur when peptides originating from low abundance proteins of interest co-elute with peptides originating from high abundance proteins, leading to the inability to detect the co-eluting low abundance peptides.Table II provides a summary of the relative proteome coverage and estimated dynamic ranges achieved by coupling high resolution reversed phase capillary LC separations with either MS/MS using an LTQ instrument or MS using a 9.4-tesla FTICR instrument. The enhanced coverage and dynamic ranges obtained by the removal of high abundance proteins and SCX fractionation are illustrated. All results shown in Table II are based on triplicate experiments that involved a pooled plasma sample from healthy subjects. The number of peptide identifications are reported with >95% confidence based on either a reversed database evaluation for MS/MS data (32Qian W.J. Liu T. Monroe M.E. Strittmatter E.F. Jacobs J.M. Kangas L.J. Petritis K. Camp D.G. Smith R.D. Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and" @default.
• W2121863513 created "2016-06-24" @default.
• W2121863513 creator A5023982706 @default.
• W2121863513 creator A5032251290 @default.
• W2121863513 creator A5043928018 @default.
• W2121863513 creator A5067276579 @default.
• W2121863513 creator A5075235007 @default.
• W2121863513 date "2006-10-01" @default.
• W2121863513 modified "2023-10-10" @default.
• W2121863513 title "Advances and Challenges in Liquid Chromatography-Mass Spectrometry-based Proteomics Profiling for Clinical Applications" @default.
• W2121863513 cites W1550214637 @default.
• W2121863513 cites W1597678601 @default.
• W2121863513 cites W1729812787 @default.
• W2121863513 cites W1953681441 @default.
• W2121863513 cites W1970696723 @default.
• W2121863513 cites W1970824545 @default.
• W2121863513 cites W1971094103 @default.
• W2121863513 cites W1971280282 @default.
• W2121863513 cites W1973753936 @default.
• W2121863513 cites W1975938671 @default.
• W2121863513 cites W1977184198 @default.
• W2121863513 cites W1977839167 @default.
• W2121863513 cites W1978576205 @default.
• W2121863513 cites W1979221552 @default.
• W2121863513 cites W1981593008 @default.
• W2121863513 cites W1982517443 @default.
• W2121863513 cites W1984853246 @default.
• W2121863513 cites W1984871500 @default.
• W2121863513 cites W1985493919 @default.
• W2121863513 cites W1985723706 @default.
• W2121863513 cites W1985923228 @default.
• W2121863513 cites W1986249771 @default.
• W2121863513 cites W1988201426 @default.
• W2121863513 cites W1989711406 @default.
• W2121863513 cites W1992367328 @default.
• W2121863513 cites W1995070541 @default.
• W2121863513 cites W1995636176 @default.
• W2121863513 cites W1997504014 @default.
• W2121863513 cites W1998179715 @default.
• W2121863513 cites W2010640075 @default.
• W2121863513 cites W2012942725 @default.
• W2121863513 cites W2013255353 @default.
• W2121863513 cites W2014809334 @default.
• W2121863513 cites W2015556811 @default.
• W2121863513 cites W2021513832 @default.
• W2121863513 cites W2022452100 @default.
• W2121863513 cites W2023096047 @default.
• W2121863513 cites W2024720397 @default.
• W2121863513 cites W2029220511 @default.
• W2121863513 cites W2029503203 @default.
• W2121863513 cites W2033883485 @default.
• W2121863513 cites W2035037428 @default.
• W2121863513 cites W2035901782 @default.
• W2121863513 cites W2043113652 @default.
• W2121863513 cites W2044970750 @default.
• W2121863513 cites W2045340003 @default.
• W2121863513 cites W2052014891 @default.
• W2121863513 cites W2055063265 @default.
• W2121863513 cites W2056048546 @default.
• W2121863513 cites W2056467018 @default.
• W2121863513 cites W2059315009 @default.
• W2121863513 cites W2059469939 @default.
• W2121863513 cites W2062049401 @default.
• W2121863513 cites W2065188739 @default.
• W2121863513 cites W2065436349 @default.
• W2121863513 cites W2067074165 @default.
• W2121863513 cites W2068565992 @default.
• W2121863513 cites W2072000480 @default.
• W2121863513 cites W2072244397 @default.
• W2121863513 cites W2076376894 @default.
• W2121863513 cites W2079182665 @default.
• W2121863513 cites W2080872214 @default.
• W2121863513 cites W2082006641 @default.
• W2121863513 cites W2082601326 @default.
• W2121863513 cites W2083309296 @default.
• W2121863513 cites W2083932946 @default.
• W2121863513 cites W2084151531 @default.
• W2121863513 cites W2084270048 @default.
• W2121863513 cites W2087746647 @default.
• W2121863513 cites W2089492568 @default.
• W2121863513 cites W2090089390 @default.
• W2121863513 cites W2091380860 @default.
• W2121863513 cites W2092612030 @default.
• W2121863513 cites W2093828656 @default.
• W2121863513 cites W2095175548 @default.
• W2121863513 cites W2095489676 @default.
• W2121863513 cites W2097229040 @default.
• W2121863513 cites W2099277710 @default.
• W2121863513 cites W2099794827 @default.
• W2121863513 cites W2101938218 @default.
• W2121863513 cites W2106876831 @default.
• W2121863513 cites W2111208057 @default.
• W2121863513 cites W2112078820 @default.
• W2121863513 cites W2114329163 @default.
• W2121863513 cites W2114781040 @default.
• W2121863513 cites W2116200464 @default.
• W2121863513 cites W2123124042 @default.
• W2121863513 cites W2123778163 @default.

• next