β Scribed by Antonia Vlahou (editor)
No coin nor oath required. For personal study only.
In this book, a select group of researchers has contributed their state-of-the-art methodologies on protein profiling and identification of disease biomarkers in tissues, microdissected cells and body fluids. The book integrates biochemistry, pathology, analytical technology, bioinformatics, and proteome informatics. Experimental approaches are thoroughly detailed and explained through a step-by-step instructional format that ensures successful results.
Preface
Overview and Introduction to Clinical Proteomics
Part I Specimen Collection for ClinicalProteomics
Specimen Collection and Handling
Tissue Sample Collection for Proteomics Analysis
Part II Clinical Proteomics by 2DE and DirectMALDI/SELDI MS Profiling
Protein Profiling of Human Plasma Samplesby Two-Dimensional Electrophoresis
Analysis of Laser Capture Microdissected Cellsby 2-Dimensional Gel Electrophoresis
Optimizing the Difference Gel Electrophoresis(DIGE) Technology
MALDI/SELDI Protein Profiling of Serumfor the Identification of Cancer Biomarkers
Urine Sample Preparation and Protein Profilingby Two-Dimensional Electrophoresisand Matrix-Assisted Laser Desorption IonizationTime of Flight Mass Spectroscopy
Combining Laser Capture Microdissectionand Proteomics Techniques
Part III Clinical Proteomics by LC-MS Approaches
Comparison of Protein Expression by Isotope-CodedAffinity Tag Labeling
Analysis of Microdissected Cells by Two-DimensionalLC-MS Approaches
Label-Free LC-MS Method for the Identificationof Biomarkers
Analysis of the Extracellular Matrix and Secreted VesicleProteomes by Mass Spectrometry
Part IV Clinical Proteomics and Antibody Arrays
Miniaturized Parallelized Sandwich Immunoassays
Dissecting Cancer Serum Protein Profiles UsingAntibody Arrays
Part V Statistics and Bioinformatics in ClinicalProteomics Data Analysis
2D-PAGE Maps Analysis
Finding the Significant Markers
Web-Based Tools for Protein Classification
Open-Source Platform for the Analysis of LiquidChromatography-Mass Spectrometry (LC-MS) Data
Pattern Recognition Approaches for ClassifyingProteomic Mass Spectra of Biofluids
Index
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