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{{PrinceLab}}
{{PrinceLab}}


==List of Current Projects==
==Introduction==
[[Prince:Notebook | Projects]]


# [[Prince:Notebook/Mouse_Brain_Phosphoproteomics|Mouse Brain Phosphoproteomics]]
We use sophisticated mass spectrometers to measure hundreds to thousands of proteins, lipids, and other metabolites from a variety of biological sources (cell culture, tissue, blood, etc.).  We develop novel sample preparation and mass spectrometry methods and couple these with innovative computational approaches to identify and quantify bio-analytes of most interest.
# [[Prince:Notebook/Proteomics_Analysis_Pipeline|Proteomics Analysis Pipeline]]
# [[Prince:Notebook/PPIX|Protein-Protein Interaction Crosslinker]]
# [[Prince:Notebook/Depression_Biomarkers|Depression Biomarkers]]
# [[Prince:Notebook/Asthma_Omics|Asthma Omics]]


==[[Prince:Lab Equipment & Instruments Description|Lab Equipment & Instruments Description]]==
* [[Prince:Mission Statement|Prince lab Mission Statement]]
* [[Prince:Lab Equipment & Instruments Description|Lab Equipment & Instruments]]
* github: [https://github.com/princelab princelab]


==The Dynamic Proteome==
==Lipidomics==


Goal: To create predictive models of cellular behavior, with a focus on modeling signal transduction dynamics.
Lipids are fundamental components of biological systems and play crucial roles in cellular systems. Besides their structural role as compartment boundaries, they play an intimate role in cell signaling, energy storage, and in modulating key mitochondrial activities such as electron transport and apoptosis initiation. Lipidomics, or the analysis of lipid composition, localization, and activity, is rapidly increasing in importance. The preeminent platform for lipid analysis from complex samples is Mass spectrometry (MS).


Our lab runs a world-class mass spectrometer (LTQ-Orbitrap XL +ETD) that allows us to identify and quantify hundreds to thousands of proteins, lipids, and other metabolites in a single runWith this instrument, we can pose highly informative queries of cell state.  We are currently focusing our efforts on these specific objectives:
We currently use shotgun lipidomic techniques to analyze thousands of lipids in a complex sampleCurrent projects include


===Protein Protein Interactions===
* Examining cellular changes in lipid constitution during epithelial-mesenchymal transition (EMT).
* Identifying lipids involved in pancreatic cancer and the lipids responsible for patient outcomes in chronic lymphocytic leukemia.
* Computationally modeling how lipids fragment to increase our ability to identify lipids.
* Using machine learning to classify lipids based on their structure.


Proteins interact with one another in order to transmit signals from the outside environment (signal transduction), form complex molecular machines, and localize to specific areas of the cell.  Current methods for capturing protein-protein interactions (PPIs) lack context and/or specificity.  We are developing methods that will enable us to capture ''in vivo'' PPIs involving new biochemical, mass spectrometric, and computational techniques.  See [[Prince:Notebook/PPIX|PPIX project page]].
==Proteomics==


===Protein post-translational modifications===
Proteins make up the sophisticated machinery responsible for maintaining cellular life and they form a significant portion of the structure of living organism (e.g., cytoskeleton).  We analyze proteomes to identify proteins and track changes in different conditions.  Current projects include:


Proteins undergo state changes through post-translational modifications. These chemical modifications determine the activity of enzymes, the localization of proteins, and a protein's interactions.  We are working to perfect high-throughput methodologies to measure global protein phosphorylation.
* Analyzing proteins responsible for salt tolerance in halophytes.
* Identifying the complete proteome of bacteriaphages.


In collaboration with Emily Bates' lab, we are currently analyzing differential phosphorylation in brain tissue between migraine susceptible and wild-type mice.  See [[Prince:Notebook/Mouse_Brain_Phosphoproteomics|Mouse Brain Phosphoproteomics project page]].
==Protein-Protein Interactions==


===Lipidomics & Proteomics of Asthma===
Proteins interact with one another in order to transmit signals from the outside environment (signal transduction), form complex molecular machines, and localize to specific areas of the cell.


Building on the work and expertise of the BYU Lipidomics Team, we are collaborating with Srirama Rao to apply lipidomic and proteomic techniques to mouse asthma models and human lung tissue.  Specifically, we are asking:
===Protein complex immunoprecipitation (Co-IP)===


* Can particular phosphatidyl ethanolamine/choline species be used as biomarkers?
Using antibodies to pull down one member of a complex is a fantastic way to identify binding partners.  An alternative to Western blotting is to use mass spectrometry to comprehensively identify binding partners.  Our lab currently uses a sophisticated hierarchical Bayes estimation of generalized linear mixed effects model ([http://www.ncbi.nlm.nih.gov/pubmed/18644780 Qspec]) to report binding partners with the high sensitivity and robust measures of confidence.
* Can proteomics be coupled with lipidomics to provide mechanistic explanations for different lung phenotypes?
* Can these analyses be used on human biopsies to differentiate and ultimately explain asthma from different origins?


===Systems Biology===
===Crosslinking===


The integration of measures of protein state to create meaningful models of cellular behavior is an ongoing challenge in Systems Biology.
We are working with the [http://www.chem.byu.edu/faculty/barry-m-willardson/ the Willardson lab] to determine protein complex architecture  using chemical crosslinking methods.


We are beginning work with Sean Warnick's lab to apply mathematical models to proteomic measurements in order to deduce protein network structure.  To accomplish this, prescribed sets of time series data and over/under expression of particular nodes are used with Dynamical Structure Analysis (DSA), yielding a useful model of protein interactions and dynamics.  We are addressing how to:
==Open computational tools for mass spectrometry omics==


* Scale DSA to larger numbers of measurements.
Mass spectrometry based proteomic data sets are challenging to analyze due to their enormous size, complexity, and changing specifications. As signatories of [https://github.com/pjotrp/bioinformatics The Small Tools Manifesto for Bioinformatics] we support the development of modular, well-tested tools that can be combined in new analytical approaches.
* Deal with noise and signal loss in measures of concentration.
* Supplement network reconstruction with direct measures of protein interactions.  


Relevant publications from the Warnick lab: [http://idealabs.byu.edu/publications/conference/ReconBioNetCDC08.pdf CDC08.pdf],[http://idealabs.byu.edu/publications/conference/FOSBE-ModelRed.pdf FOSBE09], [http://idealabs.byu.edu/publications/conference/FOSBE-Comparison.pdf FOSBE09-2],[http://idealabs.byu.edu/publications/conference/CDC2009-ModelRed.pdf CDC09]
Besides a collection of small tools ([https://github.com/princelab github projects]), we also are the creators/maintainers of:


===Open, Dynamic Tools for Proteomics===
* [http://mspire.rubyforge.org/ mspire] library for common mass spectrometry calculations and that enables programmatic access to a variety of mass spectrometry data.
 
* [https://github.com/princelab/rubabel rubabel] Ruby interface to the OpenBabel ruby bindings providing a high-level interface to fast cheminformatics software.
Mass spectrometry based proteomic data sets are challenging to analyze due to their enormous size, complexity, and changing specifications.  The development of '''fast''', '''easily modifiable''' software enables researchers to glean much additional information from existing data sets and rapidly test new analytical approaches.
 
We are the main support behind the [http://mspire.rubyforge.org/ mspire] libraries, enabling programmatic access to a variety of mass spectrometry data. In particular, we offer:
 
* a unified interface offering random scan access to all versions of mzXML and the new mzML standard format.
* the only existing free & open source converter for Bioworks .srf files
* a universal converter from pepXML to the new mzIdentML standard
 
We are also currently working on:
 
* A dynamic, massively parallel data analysis pipeline, KatamariDotei ("clod identification").
** Integrates 4 different search engines (3 of which are free and/or open source).
** Utilizes [http://per-colator.com/ Percolator]'s Support Vector Machines to distinguish genuine from spurious hits.
* Fast visualization of mass spectrometry data sets.

Latest revision as of 13:44, 24 April 2014

Home        Lab Members         Research         Publications         Internal         Mass Spec         Contact        


Introduction

We use sophisticated mass spectrometers to measure hundreds to thousands of proteins, lipids, and other metabolites from a variety of biological sources (cell culture, tissue, blood, etc.). We develop novel sample preparation and mass spectrometry methods and couple these with innovative computational approaches to identify and quantify bio-analytes of most interest.

Lipidomics

Lipids are fundamental components of biological systems and play crucial roles in cellular systems. Besides their structural role as compartment boundaries, they play an intimate role in cell signaling, energy storage, and in modulating key mitochondrial activities such as electron transport and apoptosis initiation. Lipidomics, or the analysis of lipid composition, localization, and activity, is rapidly increasing in importance. The preeminent platform for lipid analysis from complex samples is Mass spectrometry (MS).

We currently use shotgun lipidomic techniques to analyze thousands of lipids in a complex sample. Current projects include

  • Examining cellular changes in lipid constitution during epithelial-mesenchymal transition (EMT).
  • Identifying lipids involved in pancreatic cancer and the lipids responsible for patient outcomes in chronic lymphocytic leukemia.
  • Computationally modeling how lipids fragment to increase our ability to identify lipids.
  • Using machine learning to classify lipids based on their structure.

Proteomics

Proteins make up the sophisticated machinery responsible for maintaining cellular life and they form a significant portion of the structure of living organism (e.g., cytoskeleton). We analyze proteomes to identify proteins and track changes in different conditions. Current projects include:

  • Analyzing proteins responsible for salt tolerance in halophytes.
  • Identifying the complete proteome of bacteriaphages.

Protein-Protein Interactions

Proteins interact with one another in order to transmit signals from the outside environment (signal transduction), form complex molecular machines, and localize to specific areas of the cell.

Protein complex immunoprecipitation (Co-IP)

Using antibodies to pull down one member of a complex is a fantastic way to identify binding partners. An alternative to Western blotting is to use mass spectrometry to comprehensively identify binding partners. Our lab currently uses a sophisticated hierarchical Bayes estimation of generalized linear mixed effects model (Qspec) to report binding partners with the high sensitivity and robust measures of confidence.

Crosslinking

We are working with the the Willardson lab to determine protein complex architecture using chemical crosslinking methods.

Open computational tools for mass spectrometry omics

Mass spectrometry based proteomic data sets are challenging to analyze due to their enormous size, complexity, and changing specifications. As signatories of The Small Tools Manifesto for Bioinformatics we support the development of modular, well-tested tools that can be combined in new analytical approaches.

Besides a collection of small tools (github projects), we also are the creators/maintainers of:

  • mspire library for common mass spectrometry calculations and that enables programmatic access to a variety of mass spectrometry data.
  • rubabel Ruby interface to the OpenBabel ruby bindings providing a high-level interface to fast cheminformatics software.