increasing ms ifunnel technology for sensitivity for the qqq and … · 2016. 9. 4. ·...

Increasing MS

Sensitivity for

Proteomics

iFunnel Technology for

the QQQ and Q-TOF

Improving Sensitivity: iFunnel Technology in the

Agilent 6490 QQQ and 6550 Q-TOF

More efficient ionization

• Thermal confinement of

ESI ion plume

• Efficient desolvation to

create gas phase ions

2

Increased ion sampling

• 6 capillary inlets

• Samples 12x more ion rich

gas from the source

Greater ion transfer

• Removes the gas but

captures the ions

• Helps to remove source

generated noise

High

Pressure

Funnel

Low

Pressure

Funnel

MS Inlet

Nebulizer

Heated Sheath

Gas Thermal Gradient Focusing Region

Heat Sink with Forced Air

Cooling

Impact of Ion Funnel on QQQ Sensitivity

Observed a 5-10x increase in

sensitivity

Linear dynamic range (standard flow):

6460: 200 fmol to 25 pmol on-column

6490: 20 amol to 25 pmol on-column

6460 QQQ

6490 QQQ with iFunnel technology

1 fmol on-column

LVNEVTEFAK

575.5 937.5

3

Robust, Reproducible Quantitation in Digested

Plasma Using the UHPLC/QQQ Protein

Response

%RSD

Ret. Time

%RSD

Adiponectin:

IFYNQQNHYDGSTGK 9.8 0.13

Antithrombin-III :

DDLYVSDAFHK 4.7 0.16

Apolipoprotein A-II precursor:

SPELQAEAK 6.7 0.12

Apolipoprotein C-III:

GWVTDGFSSLK 2.3 0.08

Ceruloplasmin :

EYTDASFTNR 9.6 0.14

Heparin cofactor II:

TLEAQLTPR 6.1 0.15

Histidine-rich glycoprotein:

DGYLFQLLR 3.4 0.02

Kininogen-1:

TVGSDTFYSFK 3.3 0.13

L-selectin:

AEIEYLEK 9.5 0.15

Plasminogen:

LFLEPTR 2.2 0.13

Vitamin D-binding protein:

THLPEVFLSK 3.0 0.12

von Willebrand Factor:

ILAGPAGDSNVVK 9.5 0.15

The samples were provided by Derek Smith and Christoph H. Borchers from the UVic-Genome BC Proteomics Centre

2.2% RSD

n=110

4.7% RSD, n=4

7.9% RSD, n=4

12.3% RSD, n=4

Plasminogen LFLEPTR

4

HPLC-Chip/Q-TOF Increases Sensitivity for Protein

Identification

MS MS/MS

From 4-8 unique peptides (n=3)

10 amol BSA Digest On-column

5

Protein Discovery with

the 6550 Q-TOF

High Sensitivity Protein

Identification

Protein Identification

Identify Proteins

Shotgun:

find all proteins

present

Differentially

expressed (label and

label free)

Targeted

identification

Spectrum Mill

Scaffold MPP and Pathway Analysis

7

Spectrum Mill B.04.00: New Release

8

Spectrum Mill B.04 Just Released: New Features

• Automated workflows with saved parameter files

• Autovalidation with FDR and new “Auto Thresholding” strategies

• Variable Modification Localization (VML) probability scoring for PTMs

• MRM Selector for creating MRM methods from protein id results

• Peptide Selector for exporting targeted QQQ MRM, QTOF inclusion lists,

and accurate mass csv databases from a list of protein accession numbers

• Integration with MPP workflows via AMRT export and ID Browser

• Integrated Biology with MPP Pathways

• Support for Scaffold, PepXML export (Skyline, TPP)

9

Spectrum Mill B.04: Post-translational Modification

(PTM) Site Localization

VML score = Difference in Score of same identified sequences with different variable modification localizations

10

New Proteomics Software Suite Including Scaffold

• Scaffold Proteomics Software

– Scaffold Software from Proteomics Software of Portland, Oregon

– Allows users to combine results from multiple search engines (Spectrum Mill,

Mascot, SEQUEST, etc.) for increased confidence in protein identifications

– Scaffold contains tools to produce Venn diagrams of protein searches and

classify protein identifications by GO categories:

11

Spectrum Mill – MPP Data Exchange: A Unique

Agilent Advantage

12

Filtered data projected on curated pathways

Multi-Omics Analysis in MPP

Protein Data

Overlay

Metabolite

Data Overlay

13

Export a list of proteins based on pathway analysis

Protein Discovery to Targeted Protein Analysis

LBMSDG

14

• Protein data base search

• Protein-protein comparison of all results

• Export of protein “abundance” in MPP format Spectrum Mill

• Statistical analysis and visualization of differential proteins

• Pathway analysis of differential features

• Export of protein accession numbers

Mass Profiler Pro

• Use discovery data to develop MRM or inclusion list from protein accession numbers

• In silico prediction of peptides based on protein accession numbers

Spectrum Mill

• Inclusion list (with RT) or targeted mode on Q-TOF

• MRM or DMRM method on QQQ

• Export Quant results to MPP for analysis

Target Analysis (Q-TOF or

QQQ)

Genomics/Metabolomics Discovery to Target

Proteins Analysis

LBMSDG

15

• Process genomics/metabolomics data to find significant differences

• Map differential features to pathways

• Export protein accession numbers

Mass Profiler Pro or GeneSpring

• Peptide Selector predicts possible peptides and precursors for target proteins

• Export as inclusion list for data directed LC/MS/MS analysis

Spectrum Mill

• Acquire data on QTOF using inclusion list and data-directed mode

Q-TOF

• Search data in Spectrum Mill and find one-hit and missing

• Loop until satisfied with protein coverage and peptide surrogates

Spectrum Mill

Label-free Protein Discovery Results for HeLa Cell

Lysates

LBMSDG

16

• Treated HeLa cell lysates (Millipore) trypsinized then analyzed by LC/MS/MS (n=4)

• Protein database search in Spectrum Mill

• Protein-protein comparison in Spectrum Mill groups proteins across the entire set

• Color coding = abundance (based on EIC of peptides assigned to the protein)

• Export results to MPP

Protein

Group

Control HS-Ars IFN TNF

Injection

Treatments:

HS-Ars = heat-shock + arsenite

IFN = interferon

TNF = tumor necrosis factor

Statistical Analysis and Visualization of Protein

Identification Results

LBMSDG

17

One-way ANOVA followed by PCA of differential proteins

Statistical Analysis and Visualization of Protein

Identification Results

LBMSDG

18

Pathway Analysis of the Differential Proteins

Between HeLa Cell Lysates: Apoptotic Pathway

LBMSDG

19

Pathway Directed Experiment:

Target Protein List Is Exported To Spectrum Mill

Create list of target peptides for

proteomics study

• Measure changes in protein

expression level

• Detect post-translational

modifications

Copy protein accession numbers

from Pathway Architect

Generate peptide lists for:

• QQQ MRM

• Q-TOF target list

LBMSDG

20

Development of MRM-based Methods for Non-

Detected Target Proteins

LBMSDG

21

Improvements for Increasing Protein Identification

Increased

identifications

Q-TOF mass

spectrometer Chromatography

Data dependent

acquisition (DDA)

Recognize peptide

isotope pattern

Select “pure”

precursors

Optimize MS/MS

accumulation time

Better phase (Polaris)

Improved chip

manufacturing

Increase sensitivity

Increase speed to

acquire more MS/MS

22

DDA: On-the fly Determination of Precursor Purity

30% precursor

purity threshold

Intended precursor must be at least 30%

of precursor signal in the isolation window

600 601 602 603 604

Isolation window

Precursor

below purity

TH. MS/MS

will not be

done

Meets 30%

criteria –

mixed

MS/MS but

can ID

Dominant

precursor –

will give

good

MS/MS

23

MS precursor abundance

Acc.

Tim

e (

ms)

DDA: On-the fly Optimization of MS/MS Acquisition

Time Based on Precursor Intensity

Power ramp relationship

Increased protein

identified

Increased # of

MS/MS

Faster cycle times

24

Improved Chromatography With Polaris C18, 3 µm

Stationary Phase

Zorbax 300SB-C18

Jupiter

MagicAQ

SB-AQ/Reprosil

Extend C18/Reprosil

MetaSil

Polaris

609.7877 2+

982.9878 2+

706.3977 3+

Improved Protein ID

Best of tested phases Narrower peaks than

existing HPLC-Chips Good performance

with high sample load

25

Increase in Protein Identification With Increased

Sensitivity

10x less sample! 10x less sample!

Same number of identifications with 10x less sample!

0

200

400

600

800

1000

1200

0 100 200 300 400 500 600

Pro

tein

Id

en

tifi

ed

ng loaded

6530 vs. 6550: Proteins

6530

6550

0

1000

2000

3000

4000

5000

6000

7000

8000

0 100 200 300 400 500 600

Un

iqu

e P

ep

tid

es I

den

tifi

ed

ng loaded

6530 vs. 6550: Peptides

6530

6550

26

Effect of Gradient Time on Protein Identification:

E. coli Lysate

7000

7500

8000

8500

9000

9500

10000

0 50 100 150 200

Un

iqu

e P

ep

tid

es I

den

tifi

ed

Gradient Time

Unique Peptides

1000

1050

1100

1150

1200

1250

1300

1350

1400

1450

0 50 100 150 200

Un

iqu

e P

rote

ins I

den

tifi

ed

Gradient Time

Proteins

500 ng E. coli lysate

1% FDR

27

Targeted Proteomics

with the 6490 QQQ

High Sensitivity Peptide

Quantitation

Protein Discovery and Targeted Proteomic Workflows

Page 29

Target

Proteins

Proteomics Experiment

Literature and other sources

Metabolomics Experiment Genomics

Experiment

Mass

Profiler

Pro

Metlin

PCDL

Pathway

Architect

GeneSpring

29

Spectrum

Mill Mass

Profiler

Pro

Scaffold

Pathway

Architect

Skyline Targeted Proteomics Environment

30

• Open Source software

• Multi-vendor software

• Funded with CPTAC project

• Rapidly evolved using feedback

from top-labs

• Widely used and highly regarded

Human SRM Atlas

31

SISCAPA: Enrich Target Peptides and Decrease

Sample Complexity

32

Increasing Throughput Using SISCAPA to Enrich

Target Peptides and Reduce Sample Complexity

Standard flow ion funnel QQQ-MS

Mesothelin curves (log/log)

0.001

0.01

0.1

1

10

100

0.001 0.01 0.1 1 10 100 1000

L/H

(fw

d)

or

H/L

(re

v)

Are

a R

ati

o

fmol Spiked Varying Peptide

Agilent 6490 (400ul/min) + Bravo

Bravo1-Forward-Meso

Bravo1-Reverse-Meso

Endogenous level: 3fmol/10ul = 16ng/ml

0.001

0.01

0.1

1

10

100

0.001 0.01 0.1 1 10 100 1000

L/H

(fw

d)

or

H/L

(re

v)

Are

a R

ati

o

fmol Spiked Varying Peptide

AB 4000 Qtrap (300nl/min) + Kingfisher

Meso:Xlink:Reverse

Meso:Xlink:Forward

Endogenous level: 3fmol/10ul = 16ng/ml

Lorne Conference

33

Reducing analysis time to 3 min

Meso

CA

12

5

Her-

2

sT

fR

Tg

1 Tg

2

OP

N

FL

C

LPSBP

PC

I

AF

P

Magnetic Bead Implementation of SISCAPA Assay

Technology

Sample peptide

Labeled standard

MRM Chromatogram

Biomarker concentration

Agilent Bravo

34

42

Avg. Δ R.T ~ 2.4 s across 8 replicates over a 60-minute gradient