1. Infusion MS/MSALL Workflow; TripleTOF™ 5600 System for Lipid Analysis
Brigitte Simons,
Sr. Applications and Sales Specialist at AB SCIEX

2. Infusion MS/MSALL Workflow On 5600
Basic Methodology
Lipid species confirmation is best carried out by high resolution MS/MS acquisition – MS is not sufficient as precursor ion information alone is compromised by high matrix interferences and isotopic overlap of many lipid species (across many classes) in a small mass range.
MS/MS triggered by IDA vs MS/MS collected without MS dependence
MS/MS collected without a dependence on MS survey information provides a an easy acquisition technique that can be applied generically to any experiment of desired polarity, ionization technique, or mass range.
Every precursor ion is captured by MS/MS and nothing is missed - CE is applied throughout a sweep to accumulate optimum ion current through many product ions.
MS/MS spectra can result in very rich signal intensities even if the precursor ion has a very low signal intensity
These files sizes are very small yet contain a very high # of MS/MS spectra per cycle
Quantification is carried out by complementary product ion information from the MS/MS spectrum and IS correction can be applied
This technique is supported by LipidView™ Software v1.1 for automated identification and quantitation of lipids using comprehensive library searching and MarkerView™ Software for PCA analysis and other statistical outputs

3. Infusion MS/MSALL Workflow What’s the Benefit From this Feature?
No method development and highly reproducible technique
Can be applied to acquisitions of any ionization mode (ESI, APCI, Positive, Negative, etc)
Everything is sampled by high resolution, you don’t miss anything!
Can be carried out in < 3 min
Is applicable to direct infusion, flow injection and directed lipid class LC techniques
An MS technique that is scalable and represents ease of high throughput 
EASY BUTTON

4. Tested and Published Quantitative Technique Where to find content for customers
AB SCIEX Technical Note
Publication in Metabolites 2012, 2(1), ; doi: /metabo
ASMS 2012 Posters:
Lipidomics Analysis of a Subset of Human Serum Samples from the Dallas Heart Study
Jeff Mcdonald1; Brigitte Simons2; Stefan Thibodeaux3; Jennifer Krone2; Phillip Sanders3
1UT Southwestern Medical Center, Dallas, TX; 2AB SCIEX, Concord, ON; 3Eli Lilly, Indianapolis, IN
Information Independent MS/MS Data Collection of All Precursors Using Time-of-Flight Mass Spectrometry.
Brigitte Simons; Eva Duchoslav ; Lyle Burton; Tanya Gamble; Ron Bonner
AB SCIEX, Concord, CANADA 4

5. Lipid Maps Consortium Uncovering The Human Plasma Lipidome
Quehenberger et al, J Lipid Res Nov;51(11):

6. Lipid Maps Consortium Uncovering The Human Plasma Lipidome
Complete quantitative analysis of over 580 lipids using MRM and PIS/NL on 4000 QTRAP® Systems
Synthetic lipid internal standards available from Avanti Polar Lipids, developed on QTRAP® Systems
Complete list of MRM methods available in Suppl. Methods for each of 8 lipid classes
Lead to the reviews on the future of MS techniques for lipidomics
Ion mobility MS
Time-of-flight MS for quant/qual
Tissue imaging
Applications of mass spectrometry to lipids and membranes.
Harkewicz R, Dennis EA.
Annu Rev Biochem Jun 7;80:

7. Lipid Analysis by Direct Infusion on the TripleTOF™ 5600 System

8. Fast Q1 precursor selection step-wise through mass range
MS/MSALL Information Independent Data Collection Product Ions from Every Precursor Q1 Q2
Fast Q1 precursor selection step-wise through mass range
CID Fragmentation
Direct infusion, flow injection, and lipid-class targeted LC techniques

9. MS/MSALL Product Ions from Every Precursor In Order
How to calculate cycle time?
Mass range
Step size to cover mass range
TOF MS/MS accumulation time (~50 – 100 ms)
Looping in a TOF MS scan (~ 200 ms)
Precursor mass m/z
Time (one cycle)

10. Acquisition Set-up MS/MSALL
Install MS/MSall mode Script from this location:
C:\\program files\Analyst\scripts
Restart Analyst Service
Turn on MS/Msall mode from Scripts Menu in Analyst TF
Important: using the .poap txt file
The ordered acquisition of MS/MS is carried out by inclusion list triggered IDA whereby the MS/MS is exclusively dictated by ordered masses in the inclusion list
User creates this inclusion list and names the file to exactly the .dam file name.txt
Inclusion list is created by excel and contains 3 columns: m/z, # of MS scans, # of cycles
It provides the appropriate mass defect as you increase m/z
It can be modified to provide a larger step or cover a different mass range
IMPORTANT: .poap file must have the same name as the acquisition method (.dam) file name and be saved to the Acquisition Methods folder in the Analyst project

11. Acquisition Parameters
MS/MSALL
A generic acquisition technique using Information Independent Logic and collecting 1000 MS/MS/cycle
Inclusion-based list depicting a mass at every 1 Da step
User indicates how frequent to acquire MS scan and how many cycles to complete.
CE is always a sweep; 50 ± 30 eV to capture all fragments
User creates this inclusion list and names the file to exactly the .dam file name.txt

12. MS/MS of Every Precursor Ordered in Time
Looking at the Raw Data in PeakView Software
Open Analyst TF raw data file displaying the TIC
SHOW>LC-MS Contour Plot
Total Ion Chromatogram
High Resolution MS/MS of every mass

13. MS/MS of Every Precursor Ordered in Time
Mining the Raw Data in PeakView Software
PROCESS < Fragment and Neutral Loss Filter

14. MS/MSALL Fragment and Neutral Loss Filter in PeakView Software
TAG Profiling
neutral loss filter =
Open MS/MS all data file in TIC mode
PROCESS> Fragment and Neutral Loss Filter
Precursor ion, fragment ion, and neutral loss filters
Set threshold and mass tolerance.
neutral loss filter =
TAG 52:2 + NH4
TAG 50:2 + NH4
TAG 50:1 + NH4
TAG 54:3 + NH4
neutral loss filter =
neutral loss filter =

15. MS/MSALL Fragment and Neutral Loss Filter
Precursors of m/z
Precursors of m/z
Precursors of neutral loss m/z

16. PeakView’s IDA Explore Mode Negative Mode TOF MS and MS/MS
Rat brain extracts (# 2) ~ 0.4 ng/mL
resolution
Ordered MS/MS view
High Resolution MS/MS of m/z
Resolution
Resolution
High Resolution TOF MS

17. Formula Finder Results from MS/MS spectrum
Accurate Mass Tools in PeakView™ Software 1
IDA explore
 MH- -CH3
- product ion of m/z 2
FA 22:6 3
- XIC of m/z
PC 44:12 + AcO- 4
Formula Finder Results from MS/MS spectrum
Flow of Lipid Identification from LC-MS/MS Workflow
Use of Lipid Catalogue and Calculators Ultilities for Fragment Interpretation

18. Lipid Analysis by Direct Infusion Using LipidView™ Software

19. Software Solutions for Automated Lipid Analysis Enabling the Identification and Quantitation of Lipid MS Data
Software Highlights:
More than 50 lipid classes containing 25,000+ lipid species represented in a lipid fragments database.
More than 600 characteristic lipid fragments lists are included.
Lipid catalogue and lipid calculator utilities are included.
Support of lipid bioinformatics in medium sample throughput with a seamless link to multivariate analysis tools in MarkerView™ software.
Building of target screening methods from identification results, with special focus on accurate mass data.
Semi-quantitative profiling with flexible use of internal standards.
AB Sciex now offers a complete software package dedicated to the analysis of Lipid data, called LipidView Software. It features a lipid fragments database composed of over 600 fragment ions across 44 lipid classes and over individual lipid species.
There are specialized utilities to browse the lipid database by lipid name or by mass and the software supports the batch processing of electrospray data from all AB Sciex Instruments.

20. Lipid Database for Accurate Mass
Collection of building blocks of lipid molecules and their arrangements within lipid classes and lipid categories.
Information on lipid collision-induced dissociation (CID) fragmentation.
Isotope correction factor.
Headgroup (HGS), neutral loss, and long change base (LCB) specific scan types.
Fatty acids (FA).
Detailed fragmentation information for each lipid species in both polarities.
Common adduct forms.
Lipid Catalogue Utility

21. Lipid Database for Accurate Mass
Prediction of lipid molecules based on the experimental m/z in either positive or negative polarity within lipid classes and lipid categories.
Information on formula composition and lipid class.
Mass accuracy and common species can be used to refine the search result.
Fatty acids (FA) and double bonds.
Common adduct forms.
Lipid Calculator Utility

22. MS/MSALL Automated Lipid Identification and Response Correction in LipidView™ Software
brain
liver
Automated lipid identification and relative quantification
Lipid class profiles (semi-quantitative) to measure ratios between samples

23. MS/MSALL Product Ions from Every Precursor In Order
Mean and % CV measurements across samples instantly calculated for each lipid
Excellent reproducibility is seen for lipid-class specific internal standards

24. MS/MSALL Automated Lipid Identification and Response Correction in LipidView™ Software
Table 1. A summary of the number of confirmed and common unique molecular lipid species identified by LipidView™ Software across the lipid extracts using MS/MSALL workflow.
Lipid Extract
Acquisition Mode
# of Glycerophospholipids identifications
# of Glycerol lipid identifications
# of Sphingolipid identifications
# of Sterol Lipid identifications
Liver
Negative mode MS/MSALL
800 3 0*
Positive mode MS/MSALL
152
125 40 12
Brain
1147
239
112
119
Totals
Liver (total = 1132)
Brain (total = 1620)
952
1386
128
n=5 replicates analyzed per extract; only confirmed and common lipid species reported
0* = not included in search

25. Linking Lipid Data to Statistical Analysis Reporting and Exporting Lipid Profiling Data
Through this function, you can export all data to MarkerView Software, as peak intensities or areas

26. Principal Component Analysis Multivariate Analysis Tools in MarkerView™ Software
Scores Plot
Loadings Plot
principal component analysis [PCA] for multivariate datasets to compare the lipid ids across multiple samples and sample groupings. The scores plot on the right allows you to visualize the measurements that discriminate between the groups and identify outliers through each PCA test. The loadings plot shown on the right provides you with valuable insight into variables that lead to sample clustering and illustrates which lipids represent the significant differences between groups.
So, this analysis benefits from the lipid id and m/z annotation from our lipids software for easier interpretation. Additionally, non-identified peaks can also be parsed from the dataset and subjected to PCA analysis for the discovery of novel lipid identifications that may be implicated in the lipidome studied.
Scores plot provides a visual distribution of the features and degree of discrimination between the sample groups
Loadings plot shows the variables (lipids) that lead to sample clustering

27. Principal Component Analysis Multivariate Analysis Tools in MarkerView™ Software
Differential Profiling
t-test Log Plot
Extraction of lipids from the data that show significant variation can be profiled across the samples
Log-fold change is visualized in t-test of liver lipids plotted against brain