Supplemental Methods and Results.
MALDI-TOF-MS Profiling Comparison, Optimization, and Reproducibility.
Methods:
Our goal for this study was to determine the optimal serum volume for use in our
2D-MALDI-MS approach, and to determine the level of reproducibility using the
optimized volume, while also carrying out a direct comparison to previously published
MALDI-MS profiling reports. To do this, we first took a single human volunteer serum
sample and carried out our 2D workflow using 25µl, 50µl, 100µl, and 200µl of serum in
triplicate (n=3). We then applied the optimized volume of serum at 100µl and ran the
same sample through eight experimental replicates (n=8) to compare the reproducibility
and efficiency of each fraction. Finally, we compared three different previously
published analyses to our 2D approach (n=3): 1) dilute and shoot (DS – 1µl serum), 2)
C18 cleanup (10µl serum), and 3) 1D SCX/ C18 cleanup (50µl serum) [1-3]. Briefly, the
DS approach only required that the ~1µl of serum be diluted in the matrix and analyzed
directly, the C18 cleanup approach required ~10µl of serum, utilizing a solid core C18
resin that absorbs the proteins/ peptides allowing desalting, while enriching LMW
proteins over HMW proteins. For the 1D/ cleanup fractionation approach we used 50µl
of serum fractionated as indicated for the 2D approach, followed by desalting using the
same reverse phase resin with a single 80% organic bump prior to analysis by MALDIMS.
All spectra were converted to .txt files, analyzed, and the features were extracted
as indicated in the main methods section. For analysis of the reproducibility
experiments, normalized feature intensities were converted to natural log (ln) values (yaxis) and plotted against the m/z values (x-axis) using Origin v.7.5 (OriginLab,
Northampton, MA), and Pearson’s r coefficients (R2) were also calculated between ion
intensities for each individual analysis versus the average.
Results:
We first compared increasing volumes of a single human serum sample (25µl,
50µl, 100µl, 200µl) using our 2D MALDI-MS approach (average values; n=3), followed
by carrying out an experimental replicate study (n=8) on the optimized volume of 100µl,
and then compared that optimized volume to other more common and previously
published MALDI-MS profiling methods (average values; n=3) (Supplimental Figure
1a). When comparing increasing volumes of sera with the 2D MALDI-MS approach,
25µl of serum generated 351 features, 50µl generated 566 features, 100µl generated
637 features, and 200µl generated 671 features. Based on this data we felt that 100µl of
serum was optimal when considering the lack of serum often provided by a single
animal combined with the few additional features generated by 200µl compared to
100µl of serum. The optimal volume was then further used to measure experimental
robustness. To accomplish this task, we generated ln-ln plots to measure any deviation
in linearity, while measuring the number of features along with %CV for each fraction
grouped by % organic in the second dimension. The detailed summation of this data is
indicated (Supplemental Figure 1b, c, d), with an average of ~50 features generated
per fraction, an average CV of ~0.2, and average R2 of ~0.99.
When comparing across a few of the more common MALDI-MS profiling
methods, the DS approach was by far the most straightforward, while requiring the least
amount of serum at 1µl, presenting with an average of 37 features. The C18 cleanup
approach was also very straightforward, required only 10µl of serum, and presented
with an average of 63 features. The 1D/ cleanup approach was considerably more
involved, and required 25-50µl of serum, generating an average of 257 features. The
optimized 2D MALDI-MS approach required the most serum at 100µl, and was also the
most involved, generating a significant increase to 637 features on average. While the
absolute number of features may differ from previously published data, we applied very
strict methods for data analysis and feature extraction, applied equally across all of
these experiments so that an accurate comparison could be applied.
Protein Identification by a Top-Down Directed Workflow.
Sera from the same set of animals studied above were sub-pooled (6-7 animals
per sub-pool, one sub-pool per genotype), and 100μL of each sub-pooled serum
specimen were processed via a top-down-directed (TDD) approach used to carry out
feature identification (Supplemental Figure 2) [4-6]. Each sample was depleted of the
seven serologically most abundant proteins using ProteomeLabTM IgY-R7 LC10
Proteome Partitioning kit (Cat.# A25403-AA, Beckman Coulter) following manufacturers’
instructions and in-line with a LC-10AT HPLC (Shimadzu, Columbia, MD). The FT and
bound fractions were collected separately by hand into 50mL conical tubes. The FT
fractions were then quantified using a BCA Protein Assay Reagent (Cat.#, 23225,
Pierce, Thermo Scientific, Rockford, IL) as per manufacturers’ instructions, and 200ug
of protein were concentrated and desalted using a Protein C4 Macrotrap (Cat.#
TR1/25108/53, Michrom Bioresources Inc., Auburn, California) as per manufacturers’
instructions, followed by dilution to 20% ACN in 50mM NaOAc, pH3.8 and fractionated
using a SCX Macrotrap (Cat.# TR1/25108/55, Michrom Bioresources Inc., Auburn,
California) with the same salt bumps as described for the 2D MALDI-MS approach.
Each fraction for each genotype were then loaded onto a BioBasic4 column 150mm x
2.5mm (Cat#.72305-153030, Thermo Fisher, Waltham, MA) and further separated into
96 fractions using a LC-10AT HPLC (Shimadzu, Columia, MD) at a flow rate of
0.4mL/min and collected at 0.5min/fraction in-line with an automated fraction collector
(FC203B, Gilson, Middletown, WI), using Whatman™ 96-Well Polypropylene UniPlate™
Microplate, v-shaped, natural, 250µl (Cat.# 7701-5250, Thermo Fisher, Waltham, MA).
Mobile phases used included solvent A (0.1%TFA in ddH2O) and solvent B (0.1%TFA in
ACN). The instrument settings were as follows: 0-2min 8% solvent B, 2-6min 18%
solvent B, 6-40min 42% solvent B, 40-45min 65% solvent B, 45-48min 90% solvent B,
50-55min 0% solvent B. All 96 fractions were taken over the first 48 minutes, and
concentrated to near dryness using a Savant SpeedVac (Thermo Fisher, Waltham, MA)
and diluted to 20μL/ fraction with 0.1% TFA in 50:50 ACN:ddH2O out of which 1.5μL per
fraction was spotted on a MALDI target plate as described for the 2D MALDI-MS
approach, with external standards spotted in the middle of every eight spots, and
analyzed with the UltraflexIII (Bruker Daltonics, Billerica, MA, USA) using WARP-LC
(Bruker Daltonics, Billerica, MA, USA). Each fraction containing features of interest were
identified using SurveyViewer (Bruker Daltonics, Billerica, MA, USA), and were loaded
onto Novex® 10-20% 1.0mm Tricine gels (Cat.# EC6625BOX, Life Technologies,
Carlesbad, CA). The gels were stained with SYPRO Ruby (Cat#S-12000, Life
Technologies, Carlesbad, CA) as per manufacturers’ instructions. Bands corresponding
to the proteins of interest were excised and digested with Trypsin Gold Mass
Spectrometry Grade (Cat.# V5280, Pierce, Thermo Scientific, Rockford, IL) as per
manufacturers’ instructions. Digests were then reconstituted in 10μL of 0.1%FA in 5:95
ACN:ddH2O, and analyzed using an LTQ XL linear ion trap mass spectrometer fitted
with a nano ESI source (Thermo Fisher, San Jose, CA). The LCMS settings were
described previously [5]. The data was searched using Mascot (v2.1, .mgf files, Matrix
Science, Boston, MA), which was set for two missed cleavages, a precursor mass
window of 0.45 Da, Tryptic, static modification C at 57.0293, and a variable modification
M at 15.9949. For the fragment-ion mass tolerance, the Mascot default of 0.8Da was
used. The searches were performed with a mouse subset of the UniRef100 database,
which included common contaminants such as digestion enzymes and human keratins.
All proteins identified using this approach are discussed in the results section and
listed with their corresponding m/z values in Supplemental Table 2.
1.
2.
3.
4.
5.
6.
Taguchi, F., et al., Mass spectrometry to classify non-small-cell lung cancer
patients for clinical outcome after treatment with epidermal growth factor receptor
tyrosine kinase inhibitors: a multicohort cross-institutional study. J Natl Cancer
Inst, 2007. 99(11): p. 838-46.
Villanueva, J., et al., Serum peptide profiling by magnetic particle-assisted,
automated sample processing and MALDI-TOF mass spectrometry. Anal Chem,
2004. 76(6): p. 1560-70.
Mobley, J.A., et al., Monitoring the serological proteome: the latest modality in
prostate cancer detection. J Urol, 2004. 172(1): p. 331-7.
Chaurand, P., et al., New developments in profiling and imaging of proteins from
tissue sections by MALDI mass spectrometry. J Proteome Res, 2006. 5(11): p.
2889-900.
Kojima, K., et al., Applying proteomic-based biomarker tools for the accurate
diagnosis of pancreatic cancer. J Gastrointest Surg, 2008. 12(10): p. 1683-90.
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Supplemental Figure 1. 2D-MALDI-MS Optimization & Reproducibility (a; n=3, bd; n=8). a) the number of observed features with S/N>3 are indicated vs. method
applied (top) including 1) “DS” dilute and shoot, where serum is combined directly with
MALDI matrix, 2) “Clean up”, where serum is diluted 1:10 with 0.1%TFA prior to solid
phase extraction (SPE) with a C18 resin, 3) “1D”, where serum is partitioned using four
salt bumps with a strong cation exchange (SCX) resin, followed by a SPE desalting step
with a C18 resin, and 4) “2D” as outlined in Figure 2, where the volume used was also
optimized at 100µl (bottom). b-d))The number of features observed and %CV’s for each
of 12 SPE fractions is indicated for the 2D MALDI-TOF-MS approach using 100µL of
serum, and the Pierson’s coefficient measurement for the combined SCX fractions
following reverse fractionation is also indicated for the three organic extractions at 35%,
45%, and 80% respectively.
Supplemental Figure 2. Top-Down-Directed (TDD) Workflow for Protein ID. a) LCMALDI-MS directed “intact” protein identification workflow, b) visual inspection of each
statistically significant feature is important prior to purification and characterization. An
example at 6,568, m/z is shown, c) following top-down-directed LC-MALDI-MS, and 1D
PAGE, the molecular weight (MW) region of interest was digested with trypsin for nLCMS analysis and protein identification.
Supplemental Figure 3. Network Analysis. One of the top networks identified using
systems biology analysis was related to the acute-phase and innate immune response.
This analysis is based on previously published associations with the biomarkers directly
identified in this study. There is a strong indication that the JAK/STAT pathway is
activated, and while in this figure it is indicated that IL-6 is the highlighted driving force,
this pathway could be activated similarly through alternative mechanisms.
Supp. Table 1. Summary of Pancreatic Histopathology. Histologic evaluation was
carried out on FFPE H&E sections taken from the pancreas of each mouse used in this
study.
Genotype ID
Pdx-Cre-1
Pdx-Cre-2
Pdx-Cre-3
Pdx-Cre-4
Pdx-Cre-5
Pdx-Cre-6
Pdx-Cre-7
Pdx-Cre-8
Pdx-Cre/ Ras-1
Pdx-Cre/ Ras-2
Pdx-Cre/ Ras-3
Pdx-Cre/ Ras-4
Pdx-Cre/ Ras-5
Pdx-Cre/ Ras-6
Pdx-Cre/ Ras-7
Pdx-Cre/ Ras-8
Ducts Observed
7
9
7
Abnormal Ducts
0
0
0
6
7
8
9
9
7
17
21
12
10
11
12
6
0
0
0
0
0
0
5
7
1
0
1
5
1
Pathology
Non-observed
Non-observed
Hypertrophy of Islet
Cells
Non-observed
Non-observed
Non-observed
Non-observed
Non-observed
Non-observed
PanIN(2)
A-typical/ Unclear
PanIN(1)
Non-observed
A-typical/ Unclear
PanIN(1)
PanIN(2)