ASMS 2013 Unknown to Known Screening and

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Addressing the Bottlenecks in Metabolomics: Making an Expedient Transition from Global Profiling to Targeted Quantitation
Mark Sanders1, Kevin McHale1, Adam Takvam2 , Michael Athanas3, Mark Szewc1, Jamie Humphries2, Michael Pacold4, David Sabatini4
1Thermo Fisher Scientific, Somerset, NJ, 2Thermo Fisher Scientific, Austin, TX, 3Thermo Fisher Scientific, San Jose, CA, 4Whitehead Institute, Cambridge, MA
Overview
Results
Purpose: Tools were developed to augment commercially available software packages
(SIEVE and TraceFinder) to provide automation and drive efficiency in the transition
from global profiling to targeted quantitation.
The challenge of moving from profiling to quantitation has been that traditionally two
different instrument platforms were used, HRAM instrument and triple quads
respectively. With the latest generation of HRAM instruments you can collect
untargeted data with little to no compromise in quantitative performance1. This
provides new data processing possibilities, i.e. combining targeted and untargeted
approaches on the same data set. In addition some studies , such as the one
discussed here, can be further consolidated by using pos/neg switching. Whether the
follow up quantitative analysis is performed on the same instrument or methods need
to be developed on other instruments, the set up can be labor intensive. The software
tools described here facilitate efficient transition from global profiling to targeted
quantitation on a large number of endogenous metabolites. The full workflow is
described in Figs. 4-14.
Methods: Breast cancer cell lines grown under different conditions were analyzed by
high resolution/accurate mass (HRAM) LCMS on a Q Exactive. Using the SIEVE plugin for TraceFinder components were accurately detected from full scan data, identified
where possible and statistically evaluated. Components of interest were selected and
quantitative methods were automatically generated for TraceFinder.
Results: The HRAM data from the Q Exactive facilitated the transition from profiling to
quantitation with little to no compromise in quantitative performance, with the
advantage of being able to retrospectively interrogate the data based on new
information. Combining the capabilities of SIEVE with TraceFinder enabled an
automated transition from untargeted profiling/relative quantitation to targeted, full
featured quantitation
Introduction
The goal of global metabolite profiling or untargeted metabolomics is to make a
quantitative assessment of as many endogenous metabolites as possible. This
approach is very effective for hypothesis generation and revealing the involvement of
metabolic pathways that may not have been predicted. The downside of this approach
is that massive amounts of data are collected and the unbiased data processing
algorithms are relatively slow. This form of data processing is impractical for larger
scale validation studies and in most cases, as you learn more about the biology under
study, what starts as global profiling becomes a more targeted assay on the
components of interest. Here we demonstrate some new tools to increase efficiency in
this process
Methods
In this approach a subset of samples are subject to unbiased analysis. The samples
could be from an initial discovery study or from pooled controls. The important factor is
that these samples are representative of those in the full study. These results yield a
list of both known and unknown components that can be evaluated statistically to
determine which may be of interest . All information needed for a targeted assay is
determined from the preliminary data analysis. For larger scale or follow up studies the
levels of the components of interest can be measured in a targeted approach. The
main benefits being processing speed and peak detection can be customized per
component (Figs. 9, 10).
FIGURE 4. Sample assignment and data acquisition is performed within
TraceFinder. Once acquisition is complete component detection is initiated
using from the SIEVE icon on the TraceFinder tool bar.
FIGURE 9. Peak detection and integration parameters can be customized for
each compound to accommodate the wide range of chromatographic
performance that is expected in an untargeted method.
FIGURE 5. Component detection and identification is performed using the
SIEVE plug-in for TraceFinder.
FIGURE 10. Pos/neg switching data can be efficiently processed within the same
method.
SIEVE is the engine for unbiased component detection and the workflow is shown in
Fig. 1. SIEVE removes non-sample ions and data is further reduced by extensive
grouping of related ions (Fig. 2). This reduces statistical noise and improves the ability
to detect differences between sample groups2 (Fig.3).
While it is not absolutely necessary, the quantitative assessment is better performed
when compound identifications are made. For this a novel database of MSn spectral
trees, m/zCloud, was used to either identify or determine compound classes3. Even if a
compound is not fully identified knowing some structural information can aid in
choosing a related surrogate for a suitable calibration curve (Fig. 13).
Sample Preparation.
Breast cancer cell lines, MDA-MB-231 - low 3-phosphoglycerate dehydrogenase
(PHGDH) and MDA-MB-468 cells - high PHGDH were grown in DMEM (low amino
acids) and RPMI (high amino acids) media. Aqueous extractions were performed on
~5x10^6 cells with ice-cold methanol. 500 µL of each extract was lyophilized and
stored at -80 C. Prior to LCMS analysis samples were reconstituted in water/methanol
90:10, 500 µL. Extractions were performed in triplicate.
FIGURE 1. SIEVE component detection
workflow.
.
1
Liquid Chromatography
LC:
Thermo Scientific Ultimate 3000 RSLC system
Column:
Luna NH2 100A, 3µm, 150 x 2 mm
Column temp.: 15°C
AS tray temp. 10°C
Inj. volume:
1 µL
Wash solvent: 90% MeOH
Mobile phase: A = 10 mM NH4OAc, pH 9.9
B = Acetonitrile
Gradient:
90-10% B in 20 min, hold at 10% B for 5 min.
Flow rate:
250 µL/min
Mass Spectrometry
MS:
Source:
Thermo Scientific Q Exactive mass spectrometer
Heated Electrospray Ionization probe v.2 (HESI 2)
Cap. Temp:
275
Vap. Temp:
350
Sheath gas:
40
Aux gas:
15
S-Lens:
40
Polarity mode: Alternate positive, negative switching
Mass Range: m/z 70 – 1000
Resolution:
70,000 [at m/z 200]
Calibration:
External
FIGURE 2. Component detection in
SIEVE recognizes 17 related ions for
Uric acid
Neg
• Distinguish analyte signals from noise (background
subtraction)
3
• Automatically interpret spectra, reduce signal peaks
into components
4
• Comparison of components among the different
conditions
5
• Identification using accurate mass/elemental
composition database searching w/wo RT
Pos
FIGURE 6. Using various filters and statistical tools, components of interest are
identified.
• Chromatographic alignment
2
FIGURE 12. Data review by sample.
Showing all compounds within a
selected sample
FIGURE 7. Components of interest are selected.
FIGURE 3. SIEVE component detection removes noise allowing differences to
be more easily identified
Intelligent sequencing is another feature of TraceFinder designed to maximize data
quality. In a targeted quantitation mode. This allows the results from processed data to
determine which sample/experiment the instrument will run next. It is particularly useful
for large unattended studies with limited amounts of sample. A simple example of
intelligent sequencing would be that if a blank sample showed significant analyte levels
then the instrument could either reinject the blank sample until a blank was obtained, or
the whole analysis could be paused, avoiding injecting precious sample into a
compromised system.
The custom report builder plug-in for TraceFinder (Fig. 14) provides an easy, flexible
way to generate reports customized for various workflows and/or studies. Working in
an Excel-like environment, quantitative results, chromatograms/spectra, and
charts/graphs can be automatically reported on a batch or single file basis.
The software tools developed here were designed to make the transition from global
profiling to targeted quantitation more efficient and automated. They include modules
for data reduction, component detection and identification, automated information
transfer, intelligent sample queuing and customized reporting.

The Q Exactive, HRAM platform can be used for both global profiling and
targeted quantitation.

Global profiling has the benefit of an untargeted analysis so unexpected
changes are not missed and data can be retrospectively interrogated as new
information is gained.

Targeted quantitation has the benefit of processing speed for large studies and
peak detection and integration parameters can be customized for each targeted
metabolite, improving the quality of data.

The analytical strategies described here take advantage of the benefits of
unbiased global profiling as well as the data quality aspects of targeted
quantitation. The software tools provide an easy transition between the two.
Components
Fed
Large intra group
variability
Female
No group
separation
Results (cont.)
Conclusion
FIGURE 11. Data review by
compound. A selected compound
viewed across all samples.
m/z Features
FIGURE 14. Rapidly build custom reports with the new report builder plug-in for
TraceFinder
Male
Fasted
FIGURE 8. Selected components are automatically imported into TraceFinder as
a compound database. Identified compounds are named, unknowns are labeled
with a mass and retention time.
FIGURE 13. The amount of any compound can be estimated based on a
calibration curve from any other compound. Usually a compound in the same
chemical class, or for endogenous compounds a calibration curve is often
generated from a stable label standard.
References
1.
Accurate and sensitive all-ions quantitation using ultra-high resolution LCMS, Mark
Sanders, Josef Ruzicka, Kevin McHale and Petia Shipkova, Thermo Fisher Scientific
Apps Note 476.
2.
Metabolomic profiling in drug discovery: understanding the factors that influence a
metabolomics study and strategies to reduce biochemical and chemical noise, Mark
Sanders, Serhiy Hnatyshyn, et.al., Thermo Fisher Scientific Apps Note 562.
3.
Untargeted metabolomics: From statistical objects to the efficient identification of
"known unknowns“, Robert Mistrik and Juraj Lutisan, ASMS 2013, ThOB am, 10:10am
Data Analysis
Excel is a trademark of Microsoft Corporation. All other trademarks are the property of Thermo Fisher
Data analysis was performed with Thermo Scientific TraceFinder 3.1 with the plug-in
for Thermo Scientific SIEVE 2.1.
Scientific and its subsidiaries
This information is not intended to encourage use of these products in any manners that might infringe the
intellectual property rights of others.
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