Poster Reprint

ASMS 2020
MP 399Assessment of a Metabolomics
Automated Sample Prep Platform
for Low Volume Plasma Samples
Mark Sartain1, Manuel Gomez1, Genevieve
Van de Bittner1, Henry Shu1
1Agilent Technologies, Inc., Santa Clara, CA,USAIntroduction

In basic and translational research settings, sample
preparation prior to LC/MS based analysis of plasma
metabolites is challenging for several reasons
including the presence of compounds with different
physical properties, variability between operators andinter-day reproducibility. Additionally, in someresearch settings limited amounts of plasma can beobtained from infants/children or from animalmodels. Here we evaluate a modification to an
existing automated metabolomics sample prep
method to accommodate low volume plasma
samples (25 µL). This method precipitates plasma
proteins to quench enzymatic activity, depletes lipids,
and extracts metabolites, providing a clean metabolite
sample for LC/MS analysis. With this modified
protocol we evaluated metabolite recovery andreproducibility compared to a manual preparation
processed by multiple laboratory staff.
Experimental

Samples and Reagents
A single healthy pooled human plasma sample(BioIVT) was used for all experiments. Chemical
standards from the MSMLS library (IROATechnologies) were individually acquired with theLC/MS method to obtain retention times and MS/MSspectra. Unlabeled and 13C-labeled yeast metabolite
extracts (“ISO1-UNL” and “ISO1”, Cambridge Isotopes)
were used to aid in metabolite identification. ISO1
was additionally used as a spike-in for recovery
estimation and normalization purposes.
Software
Compounds confidently identified in plasma and yeast
samples were used to create a subset Personal
Compound Database and Library (PCDL) from theAgilent METLIN PCDL. The custom PCDL with curated
retention times was imported by Agilent MassHunter
Quantitative Analysis software (Ver 10.1) to easily
create a quantitative method.
Method and Workflow Overview
Beginning with the Agilent Bravo Metabolomics
Sample Prep Platform1, modifications were made to
the protocol that include reducing the starting plasma
volume from 100 µL to 25 µL:

Experimental
Low Volume Plasma Protocol
On-site version

Bravo automation steps
shown in green

Plasma, 25 µL per well is placedin Bravo 96-well plateTransfer 112.5 µL 1:1 ethanol/
methanol to plasma, pipet mix
and shake, wait 10 min
Transfer 87.5 µL water toquenched plasma, pipet mix
and shake, wait 10 min
Transfer sample to Captiva
EMR-lipid plate
Remove proteins and lipids.
Collect metabolites in filtrate.

Captiva EMR-Lipid 96-well plate
traps lipids efficiently

Wash Captiva EMR-lipid plate
twice with 250 µL 2:1:1
water/ethanol/methanol.
Collect metabolites in filtrate
Dry samples (optionally store).
Reconstitute samples in 100 µL
suitable LC/MS solvent
LC/MS Analysis

InfinityLab Poroshell 120 HILIC-Z column

6546 LC/Q-TOF System

A 1260 Infinity II Prime LC system was coupled to a6546 LC/Q-TOF with a Jet Stream ionization source.
Negative-ion mode LC conditions and MS parameters
were very similar to those previously described2.

2

Results and Discussion

Tiered Selection of Targets Provide Confident
Metabolite IDs
An approach was taken to select only the most confident
metabolite identifications for the following studies (Fig 1).
List from manufacturer of common metabolites foundin yeast (ISO1-UNL)
Observe single clear LC/MS peak from chemicalstandard library (MSMLS)?
Observe single clear LC/MS peak in ISO1-UNL
corresponding to MSMLS RT?
Same MS/MS spectra for MSMLS and ISO1-UNL?
Observe single clear 13C signal at same RT in ISO1(labeled yeast)?
Observe single clear endogenous 12C signal at same
RT with same MS/MS spectra in abundance?

RecoveryExperiment
Reproducibility
Experiment

Figure 3. Summary of recoveries

Figure 1. Metabolite selection strategy

Method Provides Overall Excellent Metabolite
Recoveries
The ISO113C-labeled yeast extract was spiked into plasma
before and after low volume Bravo metabolite extraction.
The 13C-compound peak area ratios from six pairs of preand post-spiked samples were used to calculate recovery.
Fig 2 shows example chromatograms for two
metabolites. Fig 3 shows a histogram summarizing therecoveries, and Table 1 lists individual results. Excellent
recoveries (>80%) were observed for 28 of the 32
compounds covering diverse chemical classes. One
compound showed poor recovery (D-fructose 1,6bisphosphate, 38.7%). However, this compound wasconsidered nonendogenous as it was not found at
detectable levels in plasma.
13C Fumaric acid (-EIC119.0171)

13C L-Arginine (-EIC 179.1245)
x10 5x10 5

Recovery
93.6%

2.5
221.5

1.5

110.5

0.5

00
9.5

1010.5
11
Counts vs. Acquisition Time (min)Recovery
64.7%

2.5

15

15.5
16
16.5
17
17.5
Counts vs. Acquisition Time (min)18Figure 2. Example EICs for two selected metabolites across 6 prespike samples (red) and 6 post-spike samples (black)

Spiked 13C Metabolite

Endogenous toPlasma

Amino Acids and Derivatives
Glycine
L-Alanine
L-Arginine
L-Asparagine
L-Aspartic Acid
L-Citrulline
L-Glutamic acidL-Glutamine
L-Histidine
L-Isoleucine
L-Leucine
L-Proline
L-Serine
L-Threonine
L-Tryptophan
L-Tyrosine
L-Valine
SAH / S-Adenosyl-L-homocysteine
Nucelobases, Nucleosides, and Nucleotides
Adenine5'-AMP / Adenosine 5'-monophosphate
IMP / Inosine 5'-monophosphate
UridineOrganic Acids
alpha-Ketoglutaric acid
Fumaric acid
D-Gluconic acid
Malic acid
Sugars, Sugar Alcohols, and Sugar Phosphates
D-Arabitol
D-Fructose 1,6-bisphosphate
D-Mannose 6-phosphate
Trehalose
Vitamins and Coenzymes
Nicotinamide adenine dinucleotide (NAD)

Table 1. Metabolite Recoveries% Recovery% RSD




91.6%
89.2%
64.7%
87.2%
91.6%
88.1%
92.5%
91.0%
90.0%
84.4%
84.8%
90.1%
96.1%
91.1%
94.6%
84.5%
78.6%
90.3%

4.6%
1.6%
2.4%
8.4%
5.6%
3.0%
5.1%
3.3%
6.3%
9.2%
6.2%
4.8%
7.8%
1.8%
8.1%
6.1%
8.9%
8.6%





77.1%
89.9%
84.3%
101.2%

13.2%
18.1%
16.4%
10.3%





95.5%
93.6%
92.7%
84.7%

10.2%
6.9%
6.8%
9.1%

92.3%
38.7%
72.8%
84.1%

2.5%
11.9%
12.7%
9.4%

81.3%

7.3%

86.2%

8.2%

Average3

Results and Discussion

Automation Improves Reproducibility

Metabolite Response Across Samples

versusBravo
Automation

ManualPreparation
3 Users

Normalized Peak Area

1E+6

L-Asparagine

1E+69E+5Bravo
RSD = 3.5%

User 2
RSD = 4.4%

8E+5User 3
RSD = 8.9%

User 1
RSD = 5.6%

7E+56E+5

Alpha-Ketoglutaric Acid

5E+5

User 2
RSD = 4.3%

4E+5Bravo
RSD = 5.9%

3E+52E+502040User 3
RSD = 8.9%

User 1RSD = 6.8%6080

100120Sample Number
Figure 4. Normalized peak area % RSDs for L-asparagine and alpha-ketoglutaric acid (the actual injection order was randomized)
Bravo

The performance of the automated method wascompared against manual preparation. Sixty 25-µL
plasma samples were processed with the low volume
plasma protocol using the Bravo instrument. A manual
version of the protocol, with the same key steps, wasprovided to three experienced technicians and eachprocessed twenty samples. Prior to drying andreconstitution, a 13C metabolite extract was added fornormalization purposes to remove effects from LC/MS
instrument variation. The sample injection order wasrandomized. Fig 4 shows results for two representative
metabolites, and Table 2 summarizes the results for all
metabolites. Bravo metabolite extraction reproducibility
was comparable to User 2, and outperformed User 1 and3. For all metabolites, the Bravo % RSDs weresignificantly lower than the combined % RSDs for the 60
manually-prepared samples across the three users.

α-Ketoglutaric acid
Fumaric acid
Glycine
L-Alanine
L-Arginine
L-Asparagine
L-Aspartic Acid
L-Citrulline
L-Glutamic acid
L-Glutamine
L-Histidine
L-Isoleucine
L-Methionine
L-Ornithine
L-Proline
L-Serine
L-Threonine
Malic acid
Average

n=60
5.9%
7.5%
4.1%
5.5%
6.0%
3.5%
3.9%
3.1%
3.3%
3.6%
3.2%
7.0%
5.1%
4.9%
6.6%
3.6%
4.7%
5.0%
4.8%

User 1
n=20
6.8%
5.2%
6.2%
8.0%
7.6%
5.6%
6.2%
5.5%
6.4%
5.1%
4.6%
8.5%
6.6%
6.8%
8.9%
5.3%
5.0%
4.9%
6.3%

User 2
n=20
4.3%
7.0%
5.2%
4.9%
5.7%
4.4%
4.7%
2.6%
2.8%
2.8%
2.7%
5.2%
3.4%
6.2%
5.3%
5.2%
4.2%
4.4%
4.5%

User 3
n=20
8.9%
9.8%
7.3%
9.8%
13.8%
8.9%
9.4%
8.5%
9.4%
9.4%
8.3%
10.6%
8.3%
13.2%
11.6%
8.5%
10.5%
6.8%
9.6%

Users
Combined
n=60
10.2%
10.3%
9.1%
11.4%
12.6%
9.9%
9.7%
9.4%
10.3%
10.1%
8.5%
11.1%
10.6%
12.3%
12.4%
9.4%
10.5%
7.5%
10.3%

Table 2. Normalized peak area % RSDs across metabolitesConclusions

We describe modifications to the Agilent Bravo Metabolomics Sample Prep Platform that reduce the required starting
plasma volume from 100 µL to 25 µL. Excellent metabolite recovery with the method was demonstrated acrossrepresentative chemical classes of compounds. We also showed that the automated method offers improved
reproducibility when compared to a laboratory environment where multiple users manually processed samples.
References
1Automated Metabolite Extraction for Plasma using the Agilent Bravo Platform.

Agilent Technologies Technical Overview,

publication number 5994-0685, 2019.
2Discovery Metabolomics LC/MS Methods Optimized for Polar Metabolites. Agilent Technologies Application Note,publication number 5994-1492, 2019

For Research Use Only. Not for use in diagnostic procedures.

This information is subject to change without notice.
© Agilent Technologies, Inc. 2020
Published in USA, June 1, 2020