Page 81 - CRFTApplication Compendium_090613

3 Thermo Scienti c Poster Note • PN ASMS13_M503_ENiederko er_E 07/13S
ents and quantitation on
gical levels for research.
ientific™ MSIA™ (Mass
ction efficiency of all insulin
g HR/AM MS and MS/MS data
meter.
sma for all variants used in the
addition, robust qual/quan
ed at different levels.
utic analogs has become
n is typically present at sub
equiring extraction/enrichment
ntitation. In addition to
ed to stimulate the same
slight sequence variations to
sub ng/mL levels. To reduce
required to facilitate
ntitation. In addition, the LC-MS
quantification of known and
sma. To each well a 500 µL
rcine insulin and used as an
e prepared in the wells. The first
nge of 0.015 to 0.96 nM
had one insulin variant spiked
le set 1 except Humulin® S
The last set of samples spiked
ntration range with Humulin S
sample was extracted using a
omated Research Tips)
thod using the Thermo
llowing insulin extraction,
dried down and then
/MeCN/formic acid with 15
rations)
C system was used for all
d on a 100 x 1 mm Thermo
column using a linear gradient
cid in water and B) 0.1% formic
re of 50 ºC.
s spectrometer operated in
g of 70,000 (@m/z 200) was
ull scan MS data was acquired
lusion list was used to trigger all
oint™ 1.3 software. HR/AM
ide additional levels of
r charge states per insulin
topes per charge state. A mass
ualitative scoring was based on
topic overlap as well as
ach sample. Product ion data
C values for porcine insulin was
Results
The protocol for targeted detection and quantification of insulin and different insulin
sequence variants must have specific attributes to be effective. The sensitivity and
selectivity of extraction and detection methods must reach biological levels as well as
provide qualitative measurements per target. A useful internal standard was included
to normalize the entire method – from the sample preparation, LC-MS analysis, and
data processing. Lastly, the protocol must be effective for most insulin variants to
reduce cost and complexity for the workflow.
Our workflow has been shown to reach the required biological levels, facilitate a low-
cost internal standard in porcine insulin, and automate the workflow to expedite
sample analysis and data processing. The key aspect is based on effective targeted
extraction using the Ab coated MSIA tips. Figure 1 shows the automated steps to first
bind the insulin variants, wash off background compounds, and elution into a new
plate. Once the extraction was performed, the plate was then prepped for LC-MS
analysis. This process eliminates the two steps previously reported while increasing
the detection/quantitative capabilities using the Q Exactive mass spectrometer.
FIGURE 1. Targeted extraction process using covalently bounded pan-insulin Ab
to MSIA D.A.R.T tips. All samples were processed using the same protocol.
Following automated extraction, washing, and elution, the samples were dried
down prior to being reconstituted in an LC-MS solvent composition.
FIGURE 2. Targeted data extraction approach in the Pinpoint 1.3 software based
on HR/AM MS data. Data from each targeted insulin variant was extracted
based on isotopic m/z values from three precursor charge states. Integrated
AUC values from each isotope was co-added to generate the reported values.
In addition, qualitative analysis was performed to score each insulin variant
based on 2A) comparative peak profiles (peak stop and stop, apex, and tailing
factors) as well as 2b) isotopic distribution overlap.
2a 2b
FIGURE 3. Comparative analysis
workflow, including the same tip
processing. Each sample was pr
different wells. The measured re
results for porcine (Figure 3e). T
precursor m/z values for each va
Hum Lan Bov Nov
Humulin S
Novorapid
Hum Lan Bov
69:100:28
3.87e7
0.998
0.88 ppm
63:100:31
4.70e6
0.991
0.73 ppm
3b 3a
3d
R2 = 0.9
y = 74399239x – 1
FIGURE 4. Targeted quantitation
were summed from 18 isotopic
The subsequent LC-MS detection using HR/AM MS data enabled sufficient selectivity to
distinguish insulin variants from the background signal using multiple precursor charge
states and isotopes. The data extraction approach as shown in Figure 2 demonstrates
multliple verification attributes from the LC and MS profiles. Data dependent MS/MS
acquisition can also be used for specific variant determination as well. (data not shown)
Decoupling the quantitative method (MS data) from sequence confirmation (MS/MS)
enables the method to probe not only for known variants, but to perform significant post-
acquisition processing as new variants become known, provided the b-chain epitope
region remains consistent. Figure 3 shows the comparative extraction and detection
efficiency of the workflow across five different insulin variants. FIGURE 5. Qualitative output fro
charge state and 5b) +5 isotopic
plasma.
0.0150.030.060.12 0.240.48 0.96 Amount Spiked into Plasm
Humulin S