# Presentation: 544523

## Proteomics Research Resource 

**Proteomics Research Resource **
**Center for Integrative Biology**
**Biological Sciences Division and Environmental Molecular Sciences Laboratory**
**Pacific Northwest National Laboratory**
**Richland, WA**

**Environmental Molecular Sciences Laboratory at PNNL**

## The PNNL Proteomics Resource Center

**P41 Biomedical Technology Research Resource Center funded in 2003; renewed in 2008**
**Developing and applying ultra-sensitive and high throughput mass spectrometry based proteomics technologies and supporting informatics capabilities (http://ncrr.pnl.gov/)**
**Leverages a large base of DOE instrumentation, infra-structure, and EMSL User Facility investments**
**Collaborative and service projects supporting a growing number of clinical/translational  proteomics applications (member UW CTSA; OHSU CTSA membership pending)**

## The Flow of Biological Information

** **

**Measurement technologies enable new biomedical applications**

**     ****DNA**
**   ****Genome**
**     ****Plan****	****                  Readout****	****         Execution**

******             ****RNA**
**      ****Transcriptome**

******       **** ****Proteins**
******       ****Proteome**

**The Flow of Biological Information**

## LC-tandem mass spectrometry (MS/MS) based shotgun proteomics

** ****LC-MS/MS analysis**

**Compare MS/MS spectrum to theoretical peptide spectra (e.g. from DNA sequences)**

**	****Identify peptide and infer parent protein**

**LC-tandem mass spectrometry (MS/MS) based shotgun proteomics**

**Peptide identification, its accurate mass, and its LC separation elution time provides an Accurate Mass and Time (AMT) tag for use in subsequent studies (i.e. without MS/MS)**

**Protein from tissue, blood, etc.**

**LC separation elution time**

**300**

**1000**

**1700**

**MS Spectrum**

**Peptide mass to charge ratio (m/z)**

- l

- i

**MS/MS Spectrum**

**200**

**600**

**1,000**

**1,400**

**1,800**

**LC Separation**

**Peptide mass to charge ratio (m/z)**

## Location of one peptide AMT tag derived from 

**Normalized LC separation elution times (NET)**

**Peptide mass**

**Location of one peptide AMT tag derived from **
**LC-MS/MS analysis enabling identification of a protein**

**.**

**Peptide= GITINTAHVEYQTETR**
**Mass=1830.1987**
**LC NET= 0.274**

## Use of peptide AMT tags greatly increases 

**Peptide AMT tags derived from **
**multiple “shotgun” LC-MS/MS runs**
** **

**Single LC-MS run with very **
**high mass accuracy analysis**

**Use of peptide AMT tags greatly increases **
**LC-MS proteome measurement throughput**

**Peptides identified**

**Mass**

**Mass**

**LC separation elution time (NET)**

**LC separation time (NET)**

## PRISM;  

**PRISM; **** **
**Proteomics Research **
**Information Storage **
**and Management **
**system**

## Informatics capabilities are central to successful  proteomics applications

***Informatics encompasses the capture, management, processing, analysis, and dissemination of data and related study information to facilitate understanding***

**Informatics capabilities are central to successful  proteomics applications**

**Terabytes of data**

**Multi**
**Disciplinary**
**Team**

**Dissemination**
**Data & Tools**

**Application**
**Development**

**Algorithm **
**Development**

**Data**
**Management**

**Work flow**
**Automation**

**Data**
**Facilitation**

**Meta data**
**Capture**

**Aid for**
**Experimental**
**Design**

**Biological**
**knowledge**

## http://ncrr.pnl.gov/software/

## Proteomics and the P41 Center

** ****Proteomics challenging, rapidly developing, and benefits from a larger Center infrastructure, team, and greater scale of efforts**
** ****Major focus of Center’s technology development efforts: increasing measurement quality, throughput and sensitivity**
** ****Presently thousands of proteins can be measured in single       LC-MS analysis; extended coverage of lower abundance proteins achievable with fractionation (trade-off with throughput)**
** ****Major types of applications: understanding biological systems and the identification of diagnostic or prognostic biomarkers**
**  ****Enabling new clinical applications: microscale proteomics (e.g., of microbiopsies and small blood cell populations)**

## Application projects scattered across the country

**Application projects scattered across the country**

## Application projects scattered across the country

**2**

**2**

**2**

**Locations of Present Center Collaborators (21)**

**Application projects scattered across the country**

## Service project: Providing Proteomics Core for WNPRC

**Service project: Providing Proteomics Core for WNPRC**

## Heat map showing protein abundance changes in a time course study of lung fluid from Monkeypox infected Macaques

**Heat map showing protein abundance changes in a time course study of lung fluid from Monkeypox infected Macaques**

**Time- **
**Course**

**Macaque 1**

**Macaque 2**

**Host & Viral Proteins**

**Lower**

**Higher**

**Post-infection**

**Abundance**

**Viral and “Inflammatory” proteins**

**Glyco-proteins, surfactant-associated proteins, actin associated proteins**

**“****Classical Plasma” Proteins and Immunoglobulins**

**Pre-infection**

**Post-infection**

**In conjunction with Prof. Scott Wong, and**** ****Oregon National Primate Research Center (OHSU)**

## Greater throughput and sensitivity 

**enable new applications**

**Sample size and**
**MS dynamic range**

**Lower limit of detection**

**Mass accuracy, resolution, and separation power**

*** *****As throughput increases:**
**  ****Cost/analysis decreases**
**  ****More replicates practical**
**  ****Complexity more addressable**

*** *****As sensitivity increases:**
**  ****More replicates feasible**
**  ****Complexity more addressable**
**  ****New applications enabled**

## 3-D mapping of proteins in mouse brain enabled by voxelation and quantitative HTP proteomics

**Analysis of one voxelated mouse brain at 1 mm resolution requires **
**proteome analysis of ~700 tissue samples  **

**diazepam binding inhibitor **

**Quantitation and spatial distributions obtained for >1000 distinct proteins**

**Collaboration with Prof. Desmond Smith; UCLA**

## Proteomics of ten mouse striata following neurotoxin treatments

**(Comparison with 10 controls)**

**A**

**I**

**B**

**H**

**E**

**C**

**D**

**F**

**G**

**J**

** ****215 of 1971 proteins show significant changes  for treatment vs. control**

**Methamphetamine (MA)-treated                   MPTP-treated**

**Log****2****Ratio (Stimulated/Control)**

**Up**

**Down**

**Proteins with differing responses**

**Proteins**

## Addressing the challenge of biological 

**  ****Requires sufficiently large sample cohorts for statistical confidence**
**  ****Biomarker development requires high quality proteomics data from large numbers of carefully selected samples**

**Addressing the challenge of biological **
**variation in human populations**

## Quantitative comparisons of blood plasma proteomes

**  ****Thousands of peptides covering 300 to >1000 proteins can be measured in each blood plasma proteome analysis**
**  ****Differences can arise from issues related to the measurements, normal biological variation, as well as possible disease states**
**  ****Pearson correlation coefficient for peptides between two different analyses; No difference =1.0**

**Quantitative comparisons of blood plasma proteomes**

**Background color code**

## Variation between Macaque serum and plasma measurements

**Plasma**
**Serum**

**        ****Plasma ****	****                      Serum**

**Variation between Macaque serum and plasma measurements**
**Comparison of plasma and serum levels for 325 proteins in three technical replicates (including sample preparation) for three animals (controls for longitudinal study of influenza infection) **

**With UWNPC, Prof. Michael Katze (UW), and Prof. Carol Baskin (ASU)**

## Biological variation in blood plasma proteome measurements

**Pearson Correlation Coefficient**

**Nine healthy humans**

**Nine mice (same strain, sex, and age)**

**0.85 ****± 0.06**

**0.92 ****± 0.05**

**Biological variation in blood plasma proteome measurements**

## Diversity of plasma proteomes for 38 burn patients 

**Service Project: Host Response to Injury Glue Grant     Prof. Ron Tompkins, Harvard Medical School, PI**

**In progress: proteomics measurements for plasma and three blood cell types     **
**(T cells, Neutrophils, and Macrophages) for 350 patients**

## Examples of plasma protein concentration variations

**10 healthy humans**

**10 severe burn patients**

**Relative concentration, deviation from mean (log2)**

***Gene***

**0**

**0.4**

**1.2**

## Targeted Proteomics Measurements

**Biomarker verification generally uses a different ‘targeted’ measurement platform providing higher throughput and for follow-up to broad ‘discovery’ proteomics**
**Multiple-reaction monitoring using LC-MS/MS with a triple quadrupole mass spectrometer is presently platform of choice **
**Provides higher selectivity and sensitivity, but only for limited numbers of pre-selected proteins**
**Circumventing this limitation is a major Center focus for technology development efforts**
**Goal: measurement platform combining attributes of both broad discovery and targeted platforms**

## MS

- IMS dimension

- MS dimension

**20****	****30 ****	****40 msec**

**MS**

**IMS**

**LC**

**Prototype LC-IMS-MS**

** ****Time scale for ion mobility separation < 100 msec**
** ****Combination with fast LC separation provides high 2-D separation power**

## 15 min LC-IMS-MS analysis of a mouse plasma sample

**Combined 2-D separations with MS allows detection of more proteins**

## Technology advances combine to 

**enable higher throughput**

**Multiplexing IMS separations allows analysis of >50-fold more ions than for conventional IMS-MS**
**All benefits and data qualities of conventional IMS-MS retained**
**Full benefits only gained in conjunction with improved nanoelectrospray ion source and ion funnel interface (providing improved sensitivity and quantitation) developed in Center **

**   ****Nanoelectrospray emitter array**

**Enhancement of sensitivity with new **
**ion source-mass spectrometer interface**

## Coefficients of variation (CV) for triplicate measurements of 

**Coefficients of variation (CV) for triplicate measurements of **
**20 peptides added to mouse plasma**

** ****ND****: Not detected**

|  |  | LC-IMS-MS | LC-LTQ-FT |
| --- | --- | --- | --- |
|  |  | CV % | CV % |
| 1 ng/mL | Fibrinopeptide A | 12 | ND |
| 1 ng/mL | ACTH Fragment 18-39 | 13 | ND |
| 10 ng/mL | Fibrinopeptide B | 13 | ND |
| 10 ng/mL | Bradykinin Fragment 1-7 | 13 | ND |
| 10 ng/mL | (D-Ala-6) LHRH | 14 | ND |
| 10 ng/mL | Renin Substrate Porcine | 10 | ND |
| 100 ng/mL | gamma-endorphin | 14 | 7.2 |
| 100 ng/mL | ProteoMass P14R MALDI-MS Standard | 11 | 3.4 |
| 100 ng/mL | Diazepam Binding Inhibitor | 6.0 | 19 |
| 100 ng/mL | Kemptide | 10 | 14 |
| 100 ng/mL | Leucine Enkephalin | 14 | 9.1 |
| 1 ug/mL | Methionine Enkephalin | 8.9 | 5.4 |
| 1 ug/mL | Des Pro Ala Bradykinin | 2.9 | 5.6 |
| 1 ug/mL | Syntide 2 | 5.6 | 13 |
| 1 ug/mL | 3X FLAG Peptide | 12 | 17 |
| 10 ug/mL | Tyr C Peptide | 12 | 84 |
| 10 ug/mL | Osteocalcin Fragment 7-19 Human | 3.2 | 9.1 |
| 10 ug/mL | [Ala92]-Peptide 6 | 2.7 | 5.8 |
| 10 ug/mL | [D-Ala2]-Deltorphin II | 9.4 | 9.5 |
| 10 ug/mL | Dynorphin A Porcine Fragment 1-13 | 1.8 | 13 |

_**Spiking Level**_**      **_**  Peptide added  **_

## Developing a broad spectrum of health biomarkers

**  ****Technology advances open the opportunity for proteomics  applied to large populations**
**  ****Blood proteomics may provide biomarkers for essentially every disease state**
**  ****As the studied population increases, a more detailed predictive capability should evolve**

## Our own populations within: Human Microbiomes

**  ****Largely uncharacterized, complex, and continually changing microbial communities exist in all of us, having mostly unknown health consequences **
**  ****NIH Roadmap Human Microbiome Projects are initially sequencing the DNA of selected microbiomes**
**  ****Proteomics studies of microbiome ****-**** host interactions are being enabled by this genomics data, as well as application of new informatics tools (e.g. developed by the new P41 Resource Center at UCSD; Prof. Pavel Pevzner, PI) **
**  ****Initial Center microbiome studies aided by approaches and tools developed to support DOE’s studies of environmental (e.g. ocean) microbial communities**
** **

## Summary

**  ****Proteomics capabilities will continue to evolve rapidly e.g. as measurement dynamic range and capabilities for identification of protein interaction partners and protein modifications are further extended**
**  ****The Center benefits from leveraging other investments, a diverse interdisciplinary team, the scale of operations, and an infrastructure facilitating remote interactions**
**  ****Advances in sensitivity and higher throughput are enabling new large scale proteomics applications**
**  ****Benefits particularly important for clinical applications and dealing with biological variation**

## The Proteomics Team

**Mike Belov**** ****	****	****Keqi Tang**
**Erin Baker****	****	****Aleksey Tolmachev**
**Ryan Kelly****	****	****Satendra Prasad**
**Jason Page****	****	****Yehia Ibrahim**
**Daniel Lopez-Ferrer****	****	****Ridha Mabrouki**
**Ioan Marginean****	****	****Errol Robinson**
**Yufeng Shen****	****         ****	****Kostas Petritis**
**Alex Shvartsburg****	****	**
**	****	**
**	****	**
**Informatics**
**Gordon Anderson**** ****	****	****Matt Monroe**
**Dave Clark****	****  ****	****Gary Kiebel **
**John Sandavol****	****   ****	****Deep Jaitly **
**Bill Danielson****	****	****Sam Purvine**
**Aleksey Tolmachev****	****	****Nikola Tolic**
**Ashoka Polpitya ****	****	****Xiuxia Du**
**Anoop Mayampurath****	****	****Ken Auberry**
**Brian LaMarche****	****	****Jaya Rao**
**Andrei Liyu****	****	**
** **

**	****	**
**Proteomics Applications**
**Joshua Adkins****	****Ljiljana Pasa-Tolic **
**Joseph Brown ****	****Juhi Jain**
**Joshua Turse****	****Weijun Qian**
**Jon Jacobs****	****Tao Liu**
**Vlad Petyuk  ****	****Charles Ansong**
**David Kaleta****	****Nathen Manes**
**Steve Callister****	****Angela Norbeck**
**David Kaleta ****	****Feng Yang**
**Sailful Chowdhury****	****Xu Zhang**
**Mary Lipton****	****Chris  Sorensen**
**Jianying Zhao****	**
**	**
**HTP Proteomics Measurements**
**Ron Moore****	****Brianna Ogata**
**David Anderson****	****Rui Zhao**
**Danial Orten****	****Anil Shukla ****	**
**Eric Livesay****	****Kim Hixson****	**
**Heather Mottaz ****	****Carrie Nicora**
**Marina Gritsenko****	****Therese Clauss**
**Rui Zhang****	****David Prior **
**Athena Schepmores****	****Karl Weitz****	****	****	**

**The Proteomics Team**

**Co-PI:  Steven Wiley**
**Associate Center Director:  David Camp **