Applications of Ultraperformance Nanoscale Liquid ... · Applications of Ultraperformance Nanoscale...

Applications of Ultraperformance Nanoscale Liquid Chromatography

and High Resolution Accurate Mass Tandem Mass Spectrometry in ‘Omic Biomedical Studies

Laura Dubois, Matt Foster, M. Arthur Moseley

J. Will Thompson, Meredith Turner and Erik Soderblom

Duke Proteomics Core Facility, Institute for Genome Sciences & Policy,

Duke University School of Medicine, Durham , NC

Duke Proteomics Core Facility

Collaborative creation of the Duke School of Medicine, the Institute for Genome Sciences & Policy - established 2007, fully on-line 2008 Provides support for basic and clinical research scientists - Support for >800 projects for >150

Principle Investigators - > 10,000 samples

Name Changing in 2013 to

“Duke Proteomics and Biological Mass Spectrometry Facility”

- Addition of support for small

molecule analysis, including DMPK, metabolomics, and lipidomics

www.genome.duke.edu/cores/proteomics

9/10/2013 2

http://www.genome.duke.edu/cores/proteomics

Outline of Presentation

• Toolkits of an ‘Omic Lab • Reproducibility requirements for ‘omic analyses

– Within a project – Across projects – Across laboratories

• Enrichments for Post-Translational Modifications • Secretomics • Integration of ‘Omic Strategies

– Why? • this a pressing medical need?

– How? • Microfluidic LC/MS/MS

9/10/2013 Duke Proteomics Core Facility 3

Common and Emerging Workflows in our Lab

Qualitative Proteomics Experiments

• Protein ID, confirmation

• Immunoprecipitation, Protein/Protein Interaction

PTM Characterization

• Phosphorylation (TiO2 Enrichments), S-Nitrosylation (RAC enrichments),

• Acetylation (Antibody Based Enrichment)

• Qualitative or Quantitative Analysis (Label-Free or various SILAC/covalent

• labeling strategies)

Differential Expression and/or Targeted Quantitation

• Global or targeted quantitation of individual proteins expression as a function of disease, treatment, time, etc.

Metabolite Quantitation, Pharmacokinetic Analysis

• Non-targeted analysis of polar or nonpolar metabolites

• Targeted quantitation of metabolites

• Drug metabolism and Pharmacokinetic Analysis (DMPK)

A C

B

A

Vs.

• Protein and Peptide Separations

– Four Waters Nanoscale UPLC

– Two Waters Nanoscale UPLC/UPLC

• ‘Omic Qualitative and Quantitative Biomarker Discovery

– Five high resolution, accurate mass, tandem mass spectrometers

• One hybrid quadrupole / time-of-flight systems

– Waters Q-Tof Ultima

• Three hybrid quadrupole/ion-mobility/time-of-flight tandem mass spectrometer

– Waters Synapt G1 HDMS

– Waters Synapt G2 HDMS with ETD

• One hybrid LTQ / Orbitrap system

– Thermo LTQ-Orbitrap

• Targeted Peptide and Protein Quantitation

– One triple quadrupole tandem mass spectrometer

• Waters Xevo

Waters UPLCs

Four 1D systems

Two 2D systems

Waters Synapt G1 HDMS

Waters Synapt G2 HDMS (x2)

Waters Q-Tof Ultima

Thermo LTQ-Orbitrap

(HHMI owned)

Waters Xevo Triple Quad

Duke ‘Omic Hardware Toolkit

Advion

NanoMate

OFFGEL

pI Fractionation

GELFREE

MW Fractionation


Duke ‘Omic Software Toolkit

Acquiring data is (relatively) easy, acquiring knowledge is hard

• Qualitative Analyses

– Data Dependant Acquisition Database Searching

– Matrix Science Mascot Software – runs across 40 processor equivalents

– Automated Processing Pipeline with Mascot Demon, Mascot Distiller, and Mascot Server

– Dell Blade Cluster - 40 processor equivalents

– Data Independent Database Searching

• Waters PLGS/IdentityE Software

• Two ‘Home Brew’ Super-Computers - 1,000 X Cray-1 speed

– Data Visualization Software (data return to customers)

• Proteome Software Scaffold

• Quantitative and Qualitative Analyses

– Rosetta Elucidator Software

• Data processing, data statistics, data visualizations

– Dell Server R900 (largest single server at Duke)

• 4 Quad Core Processors - 64 GB of RAM

– Rosetta Oracle DB running on a Blade

10 quad core processors – 32 GB of RAM

– Waters – Nonlinear Dynamics TransOmics

• Pathway Analyses

– Ingenuity Pathway Analyses

• Data Storage

– 72 Terabytes of NetApps Enterprise Quality Storage

– data mirrors for data security

– ~50 TB ‘Cold’ Data Storage on DROBOs

– Transitioning to Amazon Glacier

Dell R-900 Server

NetApps

Data Storage

Dell Blade Server

Scaffold

Mascot

Elucidator

Oracle DB

Ingenuity

Pathway Analysis


Depletion and/or

Selective Enrichment

Depleted / Enriched Proteome

Peptide and Protein Quantitation

nanoscale

UPLC

MS

Quantitative Pipeline Qualitative Pipeline

Automated data transfer to NetApp enterprise data storage

Integration of quantitative and qualitative data

(Waters’ TransOmics or Rosetta Elucidator)

Automated translation to DB searchable format

Waters’ PLGS, Rosetta Elucidator, Matrix Science Distiller

Image Conversion, Image alignment and Quantitative Analysis

(Rosetta Elucidator or Waters’ TransOmics)

Database search of product ion spectra

(Waters’ IdentityE and Matrix Science Mascot)

Peptide ID quality scoring & translating peptides to proteins

(Rosetta Elucidator or Proteome Software’s Scaffold)

MSE or MS/MS

Rigorously use of Quantitatively Reproducible Analytical Methods Discovery Quantitative Proteomic LC/MS/MS

DIGEST

High resolution , Accurate Mass Mass Spectrometry

High Resolution Accurate Mass Measurements Precursor Ions and Product Ions

Sample (lysate, sub-cellular fraction)

(or Multidimensional UPLC)

9/10/2013 7

Label-Free Quantitation Strategy - flexibility to fit clinical study design

Image Translation

Retention Time Alignment

Intensity Normalization

m/z

Cohort 1

Cohort 3

Cohort 2

Cohort 4

Retention Time

… (n)

Retention Time

Retention Time

m/z

Retention Time

m/z

Master Image

Retention Time

Inte

nsi

ty

Individual Feature

8

Increasing Information Content

DIA using Ion-Mobility - HDDIA or HDMSe

Ion mobility

Improved Database Search results due to increased specificity

in High Energy Spectra

DIA or

MSe

HDDIA or

HDMSe


Rigorously use Quantitatively Reproducible Analytical Methods Forget not the basics of analytical chemistry

• Highly reproducible chromatography is required

• A high sampling rate across the chromatographic peak is required for accurate quantitation

•Ideally want 15-20 sampling points across chromatographic profile •Highly reproducible chromatography is required for

• High resolution, accurate mass (precursor & products) tandem mass spectrometry technology is required

• For quantitative selectivity (near isobaric cross-talk)

• For accurate qualitative identifications 1% FPR at peptide level (Decoy DB; Peptide Prophet)

• No QCs = No Quantifiably Reliable Data

• No Replication = No Quantifiably Reliable Data

• No Common Standard = No Meaningful Comparison across Projects


Column Condition

QC1 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 7 Sample 8 Sample 9 Sample 10 QC 2 Sample 11 Sample 12 Sample 13

Rigorously use Quantitatively Reproducible Analytical Methods Daily QC Checks of Data Acquisition Precision and Reproducibility

Instrument Performance Checks Day 1(+) QCs Column Conditioning Preliminary database searches

Day 2: Data Collection Day 3: Data Collection

QC X-1 Sample

X-5 Sample

X-4 Sample

X-3 Sample

X-2 Sample

X-1 Sample

X QC X ………

Day X: Data Collection

• Want to maximize biological powering - analyzing as many samples as possible

• Must use robust LC-MS platform and singlicate analysis of each sample

• Data QC is performed by daily injections of a “standard” of the same sample (pool of cohort) • Common surrogate used in all samples in all projects – QC tracking across projects


Breast Tumor Needle Biopsy Samples in OCT -phenotype changes pre- and post-drug treatment

• QC 1: yeast ADH spiked into each sample at a constant fmol quantity per ug of lysate

• QC 2: create a pooled sample from equal portions of all samples and run (same analytical method) periodically throughout the study

n= 17 n= 16

Pre-Treatment Needle Biopsy Tumor Samples

Post-Treatment Needle Biopsy

Tumor Samples

Solubilize

Reduce/alkylate/trypsin digest

5-Fraction 2D UPLC/UPLC Synapt G2 Resolution Mode IM-DIA

Rosetta Elucidator Label-Free Quantitation Peptide Teller Annotation (1% FDR)


Breast Tumor Needle Biopsy Samples in OCT Coefficient of Variation Distributions

7 QC pool injections over 9 days of 5-fraction LC/LC/MS/MS data acquisition

0%

20%

40%

60%

80%

100%

0

50

100

150

200

250

300

350

400

450

5

15 25 35 45 55 65 75 85 95

Mo

re

Cu

mu

lati

ve %

Fre

qu

en

cy

Coefficient of Variation % (bins of 5%)

Average CV: 11.9% Median CV: 8.8% 80% of the signals have CVs < 16.4% 50% of the signals have CVs < 8.8%

Average CV: 16.2% Median CV: 12.0% 80% of the signals have CVs < 22.0% 50% of the signals have CVs < 12.0%

0%

20%

40%

60%

80%

100%

0

200

400

600

800

1000

1200

1400

1600

1800

2000

5

15 25 35 45 55 65 75 85 95

Mo

re

Cu

mu

lati

ve %

Fre

qu

en

cy

Coefficient of Variation % (bins of 5%)

Protein (n=1,278) Peptides (n=6024)


Breast Tumor Needle Biopsy Samples in OCT -phenotype changes pre- and post-drug treatment


LC-MS based Phosphoproteomic Workflows

P

Native Peptide Mixture

Phosphopeptide Enriched Mixture

Phosphopeptide Enrichment (TiO2, IMAC)

P P

P P UPLC/UPLC MS/MS Analysis

Sample Soluble Protein

Solubilize Digest

SCX or HILIC – 6-24 fractions

Pre-Fractionation Alternative Enrichment

Antibody Based Enrichments

Single Antibody “Targeted” Discovery

Multiple Antibodies “Multiplexed Targeted Discovery”

Dr. Erik Soderblom, DPCF (ASMS 2013)

Multiple Motif-Specific Antibody Enrichment

Equal molar quantities of seventeen motif specific antibodies (112 pmol per antibody per enrichment) incubated overnight at 4C with Protein A agarose resin in PBS, pH 7.4

Multi-Motif Antibody Protein A Bead Conjugation

pY phospho-tyrosine

MAPK, CDK,

tP, tPE

AMPK

PKD

AKT, PKA, PKC

CK1, PLK1

PDK1 Bas

op

hili

c

Motif Antibody Motif

Akt Substrate RXX(s/t)

Akt Substrate RXRXX(s/t)

PKA Substrate (K/R)(K/R)X(s/t)

PKC Substrate (K/R)XsX(K/R)

PKD Substrate LXRXP(s/t)

CDK Substrate (K/R)sPX(K/R)

AMPK LxRXX(s/t)

MAPK Substrate PXsP

tPE Motif tPE, tP

PLK Binding motif StP

tXR Motif tXR, tPR

14-3-3 (R/K)XXsXP

Phosphotyrosine y

ATM/ATR Substrate (s/t)QG

ATM/ATR Substrate sQ

CK Substrate t(D/E)X(D/E)

PDK1 Docking Motif (F/Y)(s/t)(F/Y)

Pro

line

Dir

ect

ed

A

typ

ical

The Human Kinome

Phosphoproteome Profiles – Qualitative Comparison Data Imported in Scaffold (v4.0.3)

Annotated at 0.91% peptide FDR using Peptide Prophet Algorithm

847 1016

59 (3.1% overlap)

323 332

205 (23.8% overlap)

Embryo Tissue – 1888 Phosphopeptides

Embryo Tissue – 960 Phosphoproteins

467 319

174 (18.1% overlap)

67 (3.5% overlap)

1038 783

TiO2 Antibody TiO2 Antibody

Brain Tissue – 1922 Phosphopeptides

Brain Tissue – 860 Phosphoproteins

TiO2 Antibody TiO2 Antibody

Stringent Localization Increases Uniqueness

Apply Mascot Delta Ion Score Filter ~1% False Localization Rate

Kuster, B. et. al. Mol Cell Proteomics. 2011 February; 10(2)

444 569

24 (2.3% overlap)

TiO2 Antibody

Brain Tissue – Phosphopeptides Embryo Tissue – Phosphopeptides

569 445

15 (1.4% overlap)

TiO2 Antibody

Antibody Enrichment Comparison with Literature Compare unique phosphorylated residues with three publicly

available mouse brain datasets

Dataset 1 Dataset 2 Dataset 3

Adult Mouse Brain Adult Mouse Brain Neonatal Mouse Brain

Huttlin, Gygi, et. al. Jedrychowski, Gygi, et. al. Goswami, Ballif, et. al.

Cell 2010 Dec; 143 MCP 2011 Dec;10(12) Proteomics 2012 Jul; 12(13)

SCX-IMAC (12 fractions) LTQ-Oribtrap



73.6% Unique in Ab 73.0% Unique in Ab 86.3% Unique in Ab

633

13984 227 232 118

11669 4401

628 742

Dataset 1

Dataset 2

Dataset 3

Ab Ab Ab

SCX-IMAC SCX-IMAC SCX-IMAC

Carnitine Acetyltransferase Defends Against Hyperacetylation of Mitochondrial Proteins Michael Davies* & Lilja Kjalarsdottir*, Will Thompson, Laura Dubois, Dorothy Slentz, Olga Ilkayeva and Deborah Muoio

Cold Springs Harbor Conference “Metabolic Signaling & Disease” August 2013

~1,400 K-Acetylated peptides qualitatively and quantitatively characterized across cohort

New Technologies – Acetylomics

The DPCF uses CST antibodies with high affinity to acetylated-lysine (Ac-K) peptides for enrichment followed by LC-MS/MS analysis for quantitative differential expression acetylomics

New Technologies Bioorthogonal Labeling

- for selective pulldowns and for selective imaging

“Functional tools are needed to understand complex biological systems.

Here we review how chemical reporters in conjunction with bioorthogonal labeling methods can be used to image and retrieve nucleic acids, proteins, glycans, lipids

and other metabolites in vitro, in cells as well as in whole organisms.

By tagging these biomolecules, researchers can now monitor their dynamics in living systems and discover specific substrates of cellular pathways.

These advances in chemical biology are thus providing important tools to

characterize biological pathways and are poised to facilitate our understanding of human diseases.”

Secretomics - no, not this kind…….

Secretomics - Tjalsma et al. first coined the term secretomics in 2000

• Subset of proteomics addressing the secreted proteins (cell or tissue)

• Secreted proteins are involved in – cell signaling – matrix remodeling – invasion and metastasis of malignant cells

• Secretory proteins include – hormones – enzymes – toxins

• Secreted proteins are of interest for – biomarkers of disease – disease targets for therapeutic intervention

Types of Intracellular Signaling via Secreted Proteins

• Endocrine signaling

– cell-cell communications over long distances

• Paracrine signaling

– cell-cell communications over short distances

• Autocrine signaling

– intracellular communication

http://www.hartnell.edu/tutorials/biology/signaltransduction.html

http://www.hartnell.edu/tutorials/biology/signaltransduction.html

First Implementation of Bioorthogonal Labeling in DPCF

- Click Chemistry Labeling and Pulldown L-Azidohomoalanine (AHA) – Methionine Mimetic - Fast, sensitive, non-toxic and non-radioactive alternative to 35S-methionine pulse-chase analyses - AHA is incorporated into proteins as a methionine. Pull-down of AHA-proteins accomplished by Click-reaction between AHA’s azide and bead-bound alkyne moieties allows for selective enrichment and subsequent analysis by LC/MS/MS. - Alternative Click-Reagents for fluorescence or PET imaging

DPCF AHA Pilot Secretome of Macrophages following LPS Stimulation

(secretome proteins usually obscured by FBS culture media)

Time 0 hours 5.5 6 8

Switch to Met-deficient media AHA supplementation (+/- LPS) Sample collection (cells, secretome)

LPS - + - + AHA - - + +

Proteins Unique to Secretome +LPS +AHA

tumor necrosis factor

interleukin-1 receptor antagonist protein isoform 1

interleukin-27 subunit alpha precursor

plasminogen activator inhibitor 1 precursor

legumain precursor

C-C motif chemokine 2 precursor

C-C motif chemokine 9 precursor

histocompatibility 2, Q region locus 1 precursor

histocompatibility 2, D region locus L

granulocyte colony-stimulating factor precursor

cathepsin S isoform 2 preproprotein

hemoglobin subunit beta-1

serum amyloid A-3 protein precursor

Top Pathway Match – Inflammatory Response (p-value 5.1E-11 to 7.1E-4 for 12 of 13 proteins)

AHA approach equally applicable to cytosolic proteins

Dr. Matt Foster, DPCF

• …..The ability to simultaneously measure thousands of molecular variables and assess their relationship with clinical data collected during the course of care could enable reclassification of disease not only by gross phenotypic observation but according to underlying molecular mechanism and influence of social determinants.


Combining clinical and molecular data will redefine how we manage diseases.

• Quantify risk

• Establish diagnoses earlier

• Prevent disability by treating earlier

• Predict death and disability

• Use healthcare resources strategically

Barriers to Data Acquisition Multiple ‘Omics = Multiple Samples

Multiple Samples No New MS/MS

9/10/2013 30

Option for Increasing Throughput - TRIZAIC 150 (beta)

• Evaluation of potential improvements in TRIZAIC compared to our standard nanoscale capillary LC configuration – Improved sample throughput through the use of high

flow rates • Decreased method development time • Increase in information content per unit time

– Same information content with higher sample throughput – Higher information content with same sample throughput

– Improved ruggedness and ease of use – Expand application space for existing nanoAcquity

systems


Dr. Will Thompson, DPCF (ASMS 2013)

Tile Design and Flow Diagram

Incoming flow

Analytical Column

Trap Column

Electrical Connections (EEPROM, Heater)

ESI Emitter Assembly

Benefits and Compromises of Changing Column Diameters for Various Applications

0.075 mm

0.150 mm

2.1 mm

Benefits 2-4x Speed & Efficiency/time 1/20 Solvent/Sample Consumption 20-40x Sensitivity Benefits

Compromises 4-5x sample required 10-20% increase in time Source/ionization flexibility Compromises

Must be weighed for each individual application


Chromatographic Performance and Raw Signal Intensity, Nano (75 um) versus 150 um

Peptide K.TFAEALR.I from Enolase

Peptide R.EALDFFAR.G from ADH

Nano LC conditions

F = 0.4 ul/min W1/2 = 12 sec

F = 3 ul/min W1/2 = 2.4 sec

T150 Conditions


150 um Tile Loading Test q1D configuration, 150 um x 100 mm

Loading test performed with E. Coli lysate Method: 5 to 40% MeCN in 37.1 min, 3 ul/min, 35C Only very minor effects on chromatographic efficiency at 4 ug load


Common and Emerging Workflows in our Lab - where would decreasing analysis time add value?

Qualitative Proteomics Experiments

• Protein ID, confirmation

• Immunoprecipitation, Protein/Protein Interaction

PTM Characterization

• Phosphorylation (TiO2 Enrichments), S-Nitrosylation (RAC enrichments),

• Acetylation (Antibody Based Enrichment)

• Qualitative or Quantitative Analysis (Label-Free or various SILAC/covalent

• labeling strategies)

Differential Expression and/or Targeted Quantitation

• Global or targeted quantitation of individual proteins expression as a function of disease, treatment, time, etc.

Metabolite Quantitation, Pharmacokinetic Analysis

• Non-targeted analysis of polar or nonpolar metabolites

• Targeted quantitation of metabolites

• Drug metabolism and Pharmacokinetic Analysis (DMPK)

A C

B

A

Vs.

Evaluation Areas for Prototype 150 um Tile

ID09969_01_UCA195_3302_030513.raw:1

ID09969_01_UCA195_3302_030513.raw : 1

Label-Free Quantitation, Proteomics Targeted Peptide Quant,

Method Development and Deployment 23:05:17 27-Sep-2012UCA1953 ug EColi, 5 fxn, 37 min grad, HDMSE, Fxn 5

Time5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00

%

0

100

5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00

%

0

100

5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00

%

0

100

5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00

%

0

100

5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00

%

0

100

ID09049_01_UCA195_3252_092712_05 1: TOF MS ES+ BPI

1.31e522.85995.21

20.31748.48

18.32748.928.94

478.81

7.62585.84

13.28622.88

22.91995.21

39.16795.79

30.30706.7528.48

1196.2732.42964.08

38.46987.5534.83

750.96

39.261022.21

43.99995.54


9.02e418.19858.47

12.57549.8410.76;720.95

9.26613.40

7.11617.37

4.43;409.25

15.63724.43

18.34858.47 20.63

983.53 24.96995.54 28.75

774.41 31.62748.42

32.74935.25

39.16949.15


1.27e516.25901.51

8.79613.35

7.11510.81

6.17;486.33

13.53586.36

10.00602.00

16.35901.51

26.14812.92

18.69777.36

21.54792.97

23.03798.98

34.981257.62

29.63832.42

38.441310.63


7.68e49.94

859.966.64878.984.20

562.64

3.47548.32

12.23645.02

14.45575.32 17.62

888.78 20.67926.77

26.82739.70

24.40533.02

26.93739.70

32.211133.05

39.191003.55

43.91765.08


6.61e43.43

811.01

3.22590.32

3.47;811.01

39.191003.55

8.90517.286.52

642.8716.07526.2814.26

590.83

10.59573.83

23.57995.22

17.27655.30

18.87870.42

22.99652.42

29.321196.2923.91

1014.23 30.28;836.7234.96750.98

39.261022.22

Metabolomics (RPLC and HILIC) Lipid Profiling (Flow Injection)


Bradford Assay, 1.8mg/sample (normalize by total lysate)

Cell Disruption (Sonication in AmBic pH8)

Polar Metabolites ~48%

80/20 MeOH/water 1 hr extraction, N2 dry

Lipids ~48%

80/20 MTBE/MeOH 1 hr extraction, N2 dry

Proteins ~4% 0.25% w/v Rapigest DTT/IAA/trypsin overnight

Resuspend 2/1/0.2 MeCN/

Formic Acid/HFBA Inject 1% for LC-MS/MS

(30 min/sample)

Resuspend 4/2/1

IPA/MeOH/CHCl3

Inject 4% for FIA (10 min/sample)

Acidify 1/2/97 TFA/MeCN/water Inject 20% for 2DLC-MS/MS (3 hr/sample)

Summary of Multi-Omics Sample Preparation Strategy






Lipids ~48%





(30 min/sample)

Resuspend 4/2/1

IPA/MeOH/CHCl3




Arginine

Phenylalanine

BP

I

MP

B C

om

po

s.

S-adenosyl methionine (SAM)

5-methylthioadenosine (MTA)

m/z

Time (min)

RPLC Metabolomics Method Analysis used 1% of isolate: 150 um x 10 cm 1.7 um BEH C18 tile, F = 2.0 ul/min at 45°C Mobile phase A: 0.1% Formic acid, 0.02% HFBA, in water Mobile Phase B: 0.1% Formic acid in 10/90 IPA/MeCN Mass Spectrometry: Synapt G2 HDMS, Resolution mode (25,000 Rs) @ 5Hz






Lipids ~48%





(30 min/sample)

Resuspend 4/2/1

IPA/MeOH/CHCl3




Lipid Profiling using Flow Injection Analysis and an Infusion Tile

ID09969_01_UCA195_3302_030513.raw:1

ID09969_01_UCA195_3302_030513.raw : 1

Approximately 600 unique lipid species quantified in a 4 minute run (5 min cycle)

Ion Mobility

m/z

Analysis of the Lipid Isolate from MCF7 cells (prepared using MTBE/MeOH extraction). - Ion-Mobility Data-Independent Analysis - Synapt G2, 0.6 sec scans (6V or 15-45V) - 3 ul/min flow rate - Mobile phase was 10/90 IPA/MeCN

with 0.1% formic acid

9/10/2013 42





Lipids ~48%





(30 min/sample)

Resuspend 4/2/1

IPA/MeOH/CHCl3




Goals for High-Throughput Proteomics Analysis Using 2DLC and TRIZAIC

Time per sample (hr)

0 1 2 3 4 5

Type Column F F

/min

1D Nano* 112 0.8

2D Nano* TriZAIC

295*405

1.0 1.35

2D TriZAIC 350 2.0

2D** TriZAIC 350 2.6**

90 min gradient @ 0.4 ul/min

37 min gradient @ 0.4 ul/min (nano) or 3 uL/min (Tile)

18.5 min gradient @ 3 uL/min (Tile)

18.5 min gradient @ 3 uL/min (Tile)

**

* Current “standard” configurations **Potential elimination of between-fraction trapping time with dual-trap 2DLC prototype (K. Fadgen and M. Staples)

Initial Trapping Step

Fraction Elution to 2nd Dimension

Analytical Separation

*

*


Timeline for Multi-’Omic Analysis on a Single System 3 x 3 = two conditions, three biological replicates each

LIPID (ESI+)

LIPID (ESI-)

transition

Metabolite (RPLC, ESI+)

Metabolite (HILIC, ESI-)

transition to 2D

Proteome (5-fraction RP/RPLC, ESI+)

Hours 1 5 10 15 16.5

Goal was “3x3” Quantitative Profiling in 24 hours: - At least 600 lipid species - At least 5,000 soluble metabolite species - At least 2,000 proteins

Goals achieved “3x3” Quantitative Profiling in 24 hours: - 882 lipid species - 3,766 soluble metabolite species - 2,207 proteins

Multi-Omics Profiling of Methionine-Restricted MCF7 Cells in 24 Hours Using a Prototype UPLC-Compatible Microfluidic Device (ASMS 2013)

J. Will Thompson 1; Jay Johnson2; Giuseppe Astarita2; Xiaohu Tang1; Giuseppe Paglia3; Jim Murphy2; Steven Cohen2; Mark Bennett4;

Jen-Tsan Chi1; James Langdridge2; Geoff Gerhardt2; M. Arthur Moseley1 1Duke University School of Medicine, Durham , NC; 2Waters Corporation, Milford,

MA; 3Center for Systems Biology, Univ of Iceland, Reykjavik, Iceland;4Nonlinear Dynamics, Durham, NC

32


Acknowledgments Duke University Proteomics Core Facility

http://www.genome.duke.edu/cores/proteomics/

Funding NIH S10 grant

Duke School of Medicine CTSA grant UL1RR024128

Jay Johnson1, Giuseppe Astarita1, Giuseppe Paglia2, Jim Murphy2, Steven Cohen2,

Jim Langridge2, Geoff Gerhardt2

1Waters Corporation, Milford, MA;

1Center for Systems Biology, University of Iceland,

3Waters Corporation, Manchester, UK


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