The objective of this overview is to introduce bottom-up mass spectrometry (MS)–based proteomics approaches and strategies, widely used in other biomedical research fields, to the wound-healing research community.
Approaches:
Two major proteomics workflows are discussed: gel-based and gel-free chromatographic separation to reduce the complexity of the sample at protein and peptide level, respectively, prior to nano–liquid chromatography–tandem mass spectrometry analysis. Other strategies to discover less abundant proteins present in the sample, are also briefly discussed along with label-free and label-incorporated methods for protein quantification. Overall, the experimental workflows are designed and continually improved to increase the number of proteins identifiable and quantifiable.
Discussion:
Recent advances and improvements in all areas of proteomics workflow from sample preparation, to acquisition of massive amounts of data, to bioinformatics analysis have made this technology an indispensable tool for in-depth large-scale characterization of complex proteomes. This technology has been successfully applied in studies focusing on biomarker discovery, differential protein expression, protein–protein interactions, and post-translational modifications in complex biological samples such as cerebrospinal fluid, serum and plasma, and urine from patients. The publications from these studies have reported greater number of identified proteins, novel biomarker candidates, and post-translational modifications previously unknown.
Conclusions:
The qualitative and quantitative protein analysis of the protein population of wound tissues or fluids at different stages is important in wound healing research. Given the complexities and analytical challenges of these samples, MS-based proteomic workflows further improved with recent advances offer a powerful and attractive technology for this purpose.
Get full access to this article
View all access options for this article.
References
1.
ZhouG, LiH, DeCampD, ChenS, ShuH, GongY, FlaigM, GillespieJW, HuN, TaylorPR, Emmert-BuckMR, LiottaLA, PetricoinEF3rd, ZhaoY. 2D differential in-gel electrophoresis for the identification of esophageal scans cell cancer-specific protein markers. Mol Cell Proteomics, 2002; 1:117.
2.
CurretteO, BuckhardPR, SanchezJC, HochstrasserDF. State-of-the-art two dimensional gel electrophoresis: a key tool of proteomics research. Nat Protoc, 2006; 1:812.
3.
ZangerRC, VarnumSM, BollingerNM. Studying cellular processes and detecting disease with protein microarrays. Drug Metab Rev, 2005; 37:473.
4.
JanziM, OdlingJ, Pan-HammarstromQ, SundbergM, LundebergJ, UhlenM, HammarstromL, NilssonP. Serum microarrays for large scale screening of protein levels. Mol Cell Proteomics, 2005; 4:1942.
5.
UhlenM, BjorlingE, AgatonC, SzigyartoCA, AminiB, AndersenE, AnderssonAC, AngelidouP, AsplundA, AsplundC, BerglundL, BergstromK, BrumerH, CerjanD, EkstromM, ElobeidA, ErikssonC, FagerbergL, FalkR, FallJ, ForsbergM, BjorklundMG, GumbelK, HalimiA, HallinI, HamstenC, HanssonM, HedhammarM, HerculesG, KampfC, LarssonK, LindskogM, LodewyckxW, LundJ, LundebergJ, MagnussonK, MalmE, NilssonP, OdlingJ, OksvoldP, OlssonI, OsterE, OttossonJ, PaavilainenL, PerssonA, RiminiR, RockbergJ, RunesonM, SivertssonA, SkollermoA, SteenJ, StenvallM, SterkyF, StrombergS, SundbergM, TegelH, TourleS, WahlundE, WaldenA, WanJ, WernerusH, WestbergJ, WesterK, WrethagenU, XuLL, HoberS, PontenF. A human protein atlas for normal and cancer tissues based on antibody proteomics. Mol Cell Proteomics, 2005; 4:1920.
6.
QinXJ, LingBX. Proteomics studies in breast cancer. Oncol Lett, 2012; 3:735.
7.
GalvaoER, MartinsLMS, IbiapinaJO, AndradeHM, MonteSJH. Breast cancer proteomics: a review for clinicians. J Cancer Res Clin Oncol, 2011; 137:915.
8.
DekkersDHW, BezstarostiK, KusterD, VerhoevenAJM, DasDK. Application of proteomics in cardiovascular research. Curr Proteomics, 2010; 7:108.
9.
GersztenRE, AsnaniA, CarrSA. Status and prospects for discovery and verification of new biomarkers of cardiovascular disease by proteomics. Circ Res, 2011; 109:463.
10.
MicallefJ, DharseeM, ChenJ, AcklooS, EvansK, QiuLQ, ChangH. Applying mass spectrometry based proteomic technology to advance the understanding of multiple myeloma. J Hematol Oncol, 2010; 3:13.
11.
ParkerCE, PearsonTW, AndersonNL, BorchersCH. Mass-spectrometry-based clinical proteomics: a review and prospective. Analyst, 2010; 135:1830.
12.
PalmbladM, TissA, CramerR. Mass spectrometry in clinical proteomics-from the present to the future. Proteomics Clin Appl, 2009; 3:6.
13.
Di PalmaS, HennrichML, HeckAJR, MohammedS. Recent advances in peptide separation by multidimensional liquid chromatography for proteome analysis. J Proteomics, 2012; 75:3791.
14.
WashburnMP, WoltersD, YatesJR3rd. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol, 2001; 19:242.
15.
ZhangG, FenyoD, NeubertTA. Use of DNA ladders for reproducible protein fractionation by SDS-PAGE for quantitative proteomics. J Proteome Res, 2008; 8:678.
16.
ChoJC, ShanJS, WinsorJE, DiamandisEP. Proteomic analysis of human amniotic fluid. Mol Cell Proteomics, 2007; 6:1406.
17.
WickramasekaraS, NeilsonJ, PatelN, BreciL, HilderbrandA, MaierRM, WysockiV. Proteomics analyses of the opportunistic pathogen Burkholderia vietnamiensis using protein fractionation and mass spectrometry. J Biomed Biotechnol, 2011; 2011:701928.
18.
ZougmanA, PilchB, PodtelejnikovA, KiehntopfM, SchnabelC, KumarC, MannM. Integrated analysis of the cerebrospinal fluid peptidome and proteome. J Proteome Res, 2008; 7:386.
19.
De SouzaGA, de GodoyLMF, MannM. Identification of 491 proteins in the tear fluid proteome reveals a large number of proteases and protease inhibitors. Genome Biol, 2006; 7:R72.
20.
AdachiJ, KumarC, ZhangY, OlsenJV, MannM. The human urinary proteome contains more than 1500 proteins including a large proportion of membrane proteins. Genome Biol, 2006; 7:R80.
21.
SadygovRG, CociorvaD, YatesJR3rd. Large-scale database searching using tandem mass spectra: looking up the answer in the back of the book. Nat Methods, 2004; 1:195.
22.
NesvizhskiiAI, VitekO, AebersoldR. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat Methods, 2007; 4:787.
23.
RappsilberJ, MannM. What does it mean to identify a protein in proteomics?Trends Biochem Sci, 2002; 27:74.
24.
SteenH, MannM. The ABC's (and XYZ's) of peptide sequencing. Nat Rev Mol Cell Biol, 2004; 5:699.
25.
BaldwinMA. Protein identification by mass spectrometry; issues to be considered. Mol Cell Proteomics (Perspective), 2004; 3:1.
26.
MalikR, DullaK, NiggEA, KörnerR. From proteome lists to biological impact-tools and strategies for the analysis of large MS data sets. Proteomics, 2010; 10:1270.
27.
GlatterT, LudwigC, AhrneE, AebersoldR, HeckAJ, SchmidtA. Large-scale quantitative assessment of different in-solution protein digestion protocols reveals superior cleavage efficiency of tandem Lys-C/trypsin over trypsin digestion. J Proteome Res, 2012; 11:5145.
DunnJD, ReidGE, BrueningML. Techniques for phosphopeptide enrichment prior to analysis by mass spectrometry. Mass Spectrom Rev, 2010; 29:29.
30.
BervenFS, AhmadR, ClauserKR, CarrSA. Optimizing performance of glycopeptide capture for plasma proteomics. J Proteome Res, 2010; 9:1706.
31.
DomonB, AebersoldR. Mass spectrometry and protein analysis. Science, 2006; 312:212.
32.
HanX, AslanianA, YatesJR3rd. Mass spectrometry for proteomics. Curr Opin Chem Biol, 2008; 12:483.
33.
LuiH, SadygovRG, YatesJR. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem, 2004; 76:4193.
34.
GokceE, ShufordCM, FranckWL, DeanRA, MuddimanDC. Evaluation of normalization methods on GeLC-MS/MS label-free spectral counting data to correct for variation during proteomic workflows. J Am Soc Mass Spectrom, 2011; 22:2199.
BroadbentJ, WalshT, UptonZ. Proteomics in chronic wound research: potentials in healing and health. Proteomics Clin Appl, 2010; 4:204.
38.
TarranSL, CraftGE, ValovaV, RobinsonPJ, ThomasG, MarkhamR, LangloisNE, VanezisP. The use of proteomics to study wound healing: a preliminary study for forensic estimation of wound age. Med Sci Law, 2007; 47:134.
39.
OhJE, KrapfenbauerK, LubecG. Proteomic identification of collagens and related proteins in human fibroblasts. Amino Acids, 2004; 27:305.
40.
SteinsträßerL, JacobsenF, HirschT, KestingM, ChojnackiC, KrispC, WoltersD. Immunodepletion of high-abundant proteins from acute and chronic wound fluids to elucidate low-abundant regulators in wound healing. BMC Res Notes, 2010; 3:335.
41.
EmingSA, KochM, KriegerA, BrachvogelB, KreftS, Bruckner-TudermanL, KriegT, ShannonJD, FoxJW. Differential proteomic analysis distinguishes tissue repair biomarker signatures in wound exudates obtained from normal healing and chronic wounds. J Proteome Res, 2010; 9:4758.
42.
KrispC, KubutatC, KyasA, SteinsträßerL, JacobsenF, WoltersD. Boric acid gel enrichment of glycosylated proteins in human wound fluids. J Proteomics, 2011; 74:502.
43.
AebersoldR, MannM. Mass spectrometry-based proteomics. Nature, 2003; 422:198.
44.
HagerJW. Recent trends in mass spectrometer development. Anal Bioanal Chem, 2004; 378:845.
45.
MakarovA. Electrostatic axially harmonic orbital trapping: a high-performance technique of mass analysis. Anal Chem, 2000; 72:1156.
46.
GilletLC, NavarroP, TatesS, RöstH, SelevsekN, ReiterL, BonnerR, AebersoldR. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics, 2012; 11:O111.016717.
47.
GeigerT, CoxJ, MannM. Proteomics on an orbitrap benchtop mass spectrometer using all-ion fragmentation. Mol Cell Proteomics, 2010; 9:2252.
48.
LiGZ, VissersSJ, SilvaJC, GolickD, GorensteinMV, GeromanosSJ. Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures. Proteomics, 2009; 9:1683.
49.
NagarajaN, KulakNA, CoxJ, NeuhouserN, MayrK, HoerningO, VormO, MannM. System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC on a bench top orbitrap. Mol Cell Proteomics, 2012; 11:M111.013722.
50.
RichardsS. Proteome portraits: innovations in mass spectrometry are making quick, comprehensive, and easy proteome mapping more attainable than ever. The Scientist, 2012; 8:57.
51.
De GodoyLMF, OlsenJV, CoxJ, NielsenML, HubnerNC, FröhlichF, WaltherTC, MannM. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature, 2008; 455:1251.
MichalskiA, CoxJ, MannM. More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. J Proteome Res, 2011; 10:1785.
