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Mass Spectrometry

Mass Spectrometry . David Graham, Ph.D. dgraham@jhmi.edu Jennifer Van Eyk , Ph.D. jvaneyk1@ jhmi.edu. Lab Goals. Familiarization with how to manipulate finished data sets and extract biological meaning Familiarization with some of the bioinformatics tools

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Mass Spectrometry

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  1. Mass Spectrometry David Graham, Ph.D. dgraham@jhmi.edu Jennifer Van Eyk, Ph.D. jvaneyk1@jhmi.edu

  2. Lab Goals • Familiarization with how to manipulate finished data sets and extract biological meaning • Familiarization with some of the bioinformatics tools • Generate a figure based upon the data

  3. Comparative Heart Region Proteome • Comparative study of Rabbit heart regional proteome emphasizing on the functionally critical proteins (Calcium channels, Calcium handling protiens, Kinases, Signaling, Receptors etc.) • Tissues isolated from 5 different tissue regions (Left ventricle, Right Ventricle, Left Atrium, Right Atrium, Septum), 3 technical replicates

  4. Sample Preparation and Acquisition • Sample Preparation: • Heart regions were carefully disected rinsed in ice cold PBS and snap frozen in liquid nitrogen • Pulvarized with a morter and pestle under liquid nitrogen • Solubilized in 8M Urea 4% Chaps • TCA (in acetone) precipitation • Multiple Acetone washes • Digestion: • Pellet resuspended in 8M urea for 1 hours • Diluted to 2M urea • Digested with 1:100 trypsin:protein following Rapigest (Waters) protocol without rapigest • Instrumentation: • AB Sciex 5600 in IDA mode (data dependent discovery) choosing 40 precursors per second • 150 uM ID external column 180 minute gradient

  5. Search Details Database: Swissprotmammals Data Search: Mascot 2.3 Parameters: Mass tolerance: 50 ppm, 0.1 Da ms/ms Modifications: Acetylation, Carbamidomethylation, Deamidation, Carbamylation and Oxidation Post data import into Scaffold 4.0

  6. Question 1: Survey your data • Using Scaffolds built in functions: • Determine the reproducibility of your samples • Construct a venn diagram comparing replicates

  7. Question:2 • Data Normalization; find a common protein represented in all samples and normalize the data with it • Do statistics on data using Excel comparing heart regions (T test) • Find the proteins that are differently expressed in the samples • Identify the common and unique proteins among all three samples. Represent it with a venndiagram

  8. Question:3 • Data Annotation; find the biological process, and molecular functions of the data and classify the data accordingly. Create a venn diagram for the annotation • Classify the proteins into membrane and soluble proteins • Find the potential membrane proteins and classify them based on function. For eg. enzymatic proteins (Kinases, Dehydrogenase), structural proteins, channel proteins, receptor proteins. Create a bar graph with this data

  9. Question:4 • Pathway and Functional relationship; use a pathway explorer tool to generate the functional association of genes and build relationship between genes. • String (for this lab) Others: • or IPA tool for gene association • Cytoscape, Pathview

  10. Tools for Analysis

  11. Tools. Question 1: • Scaffold Question 2: • Excel, xlstat • Databases: Uniprot Question3 • TMHMM Server for trans membrane prediction • http://www.cbs.dtu.dk/services/TMHMM/ • Databases: Uniprot

  12. Question3: Tools for data analysis: DAVIDhttp://david.abcc.ncifcrf.gov/

  13. Question3: Tools for data analysis: STRINGhttp://string-db.org/

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