bladder cancer proteome: a multiplexing approach using online 2d

BLADDER CANCER PROTEOME: A MULTIPLEXING APPROACH USING ONLINE 2D RP-RP CHROMATOGRAPHY
COUPLED WITH DATA INDEPENDENT ION MOBILITY
Lee A Gethings1, Zhuowei Wang2, Bo Wen3, Ju Zhang3, Quanhui Wang3, Liang Lin3, Christopher Hughes1, Johannes PC Vissers1, Richard Lock1, James I Langridge1, Siqi Liu3
1
Waters Corporation, Manchester, United Kingdom, 2Waters Corporation, Beijing, China, 3Beijing Genomics Institute, Beijing, China
RESULTS
Peptide Comparison
A comparative study between the different acquisition strategies (1D MSE, 1D HDMSE and 2D 5 fraction HDMSE) was utilized for the three individual cell lines. All acquisitions were performed in triplicate. Figure 4 are typical chromatograms for individual fractions resulting from a 2D 5 fraction experiment.
70000
25000
20000
50000
# mutant peptides
60000
15000
10000
Mass Spectrometry
A Synapt G2-S was used for data acquisition. Data were acquired using data independent analysis (DIA) in conjunction
with ion mobility (IM), as shown in Figure 3.
Fraction #
1
2
3
4
5
% ACN
10.8
14.0
16.7
20.4
45.0
Fraction 5
# peptides
5000
40000
SV-HUC
5637
0
1D MSE
1D HDMS
2D 5 fraction HDMS
T24
30000
20000
SV-HUC 1-1 Fraction 5
11025_005
1: TOF MS ES+
BPI
2.31e5
100
Bioinformatics
10000
0
1D MSE
0
20.00
Fraction
11025_004
SV-HUC 1-1 Fraction 4
40.00
60.00
80.00
2D 5 Fraction HDMS
7000
4
1: TOF MS ES+
BPI
3.33e5
100
0
Fraction
11025_003
SV-HUC 1-1 Fraction 3
40.00
60.00
3
80.00
Time
1: TOF MS ES+
BPI
1.94e5
100
900
800
700
6000
5000
20.00
Figure 7. Normalized protein abundance curve for the SV-HUC
cell line, including identifications from 1D MSE (blue), 1D
HDMSE (red) and 2D 5 fraction HDMSE (grey) experiments. Coefficient of variance to determine protein quantification reproducibility of all cell lines is shown inset.
Time
# proteins
Single
Pump
Trapping
1D HDMS
Protein Comparison
4000
# mutant proteins
The LC-MS peptide data were processed and searched with
ProteinLynx GlobalSERVER. A curated human database consisting of protein sequences derived from in-house genomic and
transcriptomic studies (BGI) was used for searching. The resulting data was also subjected to pathway analysis using Ingenuity (IPA) software.
%
Bladder cancer arises from malignancy of cells
located within the epithelial lining. It is
estimated that there are over 380,000
diagnosed cases worldwide. The presence of
blood in the urine and frequent urination are the
typical symptoms of the condition. However
detailed molecular mechanisms regarding the
processes involved for tumor development in
cases of bladder cancer are not clearly
understood. Therefore using three different
human cell lines we aim to use qualitative and
quantitative proteomics data to characterize
and determine changes in the proteomes related
with the type of cancer cell lines, which is useful
for discovering potential bladder cancer
biomarkers.
2D RP-RP chromatography. Peptides were loaded onto a first
dimension column (XBridge BEH 130 C18, 300 µm x 50 mm)
using 20 mM ammonium formate (pH 10). A discontinuous
step gradient of acetonitrile at a flow rate of 2 µL/min was
used to provide a 5 fraction strategy. Second dimension separation was performed as described for 1D chromatography.
500 ng material was loaded on-column (first dimension) for all
2D analyses.
%
INTRODUCTION
600
500
400
300
200
SV-HUC
100
5637
0
3000
1D MSE
1D HDMS
T24
2D 5 Fraction HDMS
2000
%
1000
0
1D MSE
0
20.00
Fraction
11025_002
SV-HUC 1-1 Fraction 2
60.00
2
80.00
Time
1: TOF MS ES+
BPI
1.83e5
100
2D 5 Fraction HDMS
Figure 5. Number of identified peptides (top) and proteins
(bottom) for the three cell lines over the various acquisition
schemes with corresponding mutant information shown inset.
Of the mutant proteins identified, approximately 20% show
overlap between the three cell lines. Functional analysis using
IPA appears to show a strong association of these mutants
with cancer derived pathways and networks involving DNA replication, recombination, repair, energy production and nucleic
acid metabolism. A total of 465 mutant genes have been identified with 184 selected as potential biomarkers (Figure 9).
Figure 9. Functional analysis of the identified mutant proteins
from the three cell lines. Of the 796 genes mapped, 465 are
positively identified as being associated with bladder cancer.
The majority of which originate from the cytoplasm and nucleus.
CONCLUSIONS
%
Figure 1. Transitional cell carcinoma. The most common type
of cells responsible for the onset of bladder cancer.
2D RP-RP with dilution
40.00
1D HDMS
Label-free LC-IM-DIA-MS quantitation can highlight protein expression fold changes for specific proteins of interest. A example is provided in Figure 8 using hierarchical clustering to
group proteins together which share similar expression profiles.
METHODS
Figure 2. LC configurations: 1D (single pump trapping) and 2D
RP-RP with dilution
0
20.00
40.00
60.00
Fraction 1
11025_001
Sample preparation
Peptide Identification
Intersection
low energy
%
liquid phase
separation
elevated energy
retention time aligned precursor and product ions
LC conditions
1D chromatography. 1D LC experiments consisted of a 90 min
gradient from 1 to 40% acetonitrile (0.1% formic acid) at 300
nL/min using a nanoACQUITY system, configured in single
trapping mode with a HSS T3 1.8 µm C18 reversed phase (75
µm x 15 cm) nanoscale LC column and Symmetry C18
trapping column (180 µm x 20 mm). 100 ng material was
loaded on-column for all 1D analyses.
Implementing online 2D RP-RP provides an average
of 30% more protein identifications compared with
1D LC.
Based on 5-fraction 2D RP-RP data, an average of
5800 proteins are identified (over the three cell
lines), with 13% being assigned as mutant proteins.
Identified mutants can be mapped to 58% of known
genes associated with cancer. A large proportion of
which are over expressed.
A label-free proteomics (IM-DIA-MS) approach has
been applied for the analysis of bladder cancer,
providing a potential biomarker list of 184 gene
candidates.
Time
1: TOF MS ES+
BPI
1.53e5
100
Two different bladder cancer cell lines (5637 and T24) were
prepared, in addition to the control cell line (SV-HUC-1). In all
cases two biological replicates were prepared for each cell line.
Cell lines were cultured in media containing fetal bovine
serum, which was removed and cell lines rinsed with
phosphate buffer solution. Cells were centrifuged, supernatant
removed and resuspended with lysis buffer before sonication.
Insoluble proteins were separated by centrifugation before gelassisted (trypsin) digestion.
80.00
SV-HUC 1-1 Fraction 1
ion mobility/gas phase
separation
drift time aligned precursor and product ions
Figure 3. Retention and drift time principle ion mobility enabled
data-independent analysis (IM-DIA-MS).
0
20.00
40.00
60.00
80.00
Time
Figure 4. Representative BPI chromatograms for a 5 fraction
experiment of the SV-HUC cell line. Percentages of acetonitrile
used for the discontinuous step gradient are shown top left.
Peptide and protein identifications following database searching are provided in Figure 5. Comparing 1D MSE with 1D
HDMSE, an average increase of 45% in protein identifications is
observed. Incorporating the fractionation strategy provides an
additional 30%. A proportion of mutant peptides and proteins
were also derived from the results (inset of Figure 5).
A gauge of reproducibility over technical and biological replicates is provided at the peptide and protein level (Figure 6).
Label-free quantitative data over a wide dynamic range is also
demonstrated using IM-DIA-MS data (Figure 7).
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Protein Identification
Intersection
Figure 6. Identification reproducibility at the peptide and protein level, representing four biological replicates of the T24 cell
line. 23788 (39.95%) of the identified peptides replicated in
more than two replicates, whilst at the protein level this corresponded to 2091 (42.64%).
Figure 8. Subset hierarchical clustering results (log2 converted
within sample amounts) for the 5 fraction experiments, showing significantly regulated and unique proteins, contrasting
control (SV-HUC) with cancer cell lines (5637 & T24).
References
1.An Ion Mobility Assisted Data Independent LC-MS Strategy for the Analysis of Complex
Biological Samples. Rodriguez-Suárez E, Hughes C, Gethings L, Giles K, Wildgoose J,
Stapels M, Fadgen KE, Geromanos SJ, Vissers JPC, Elortza F, Langridge JI. CAC , 2012(8).
2.Database searching and accounting of multiplexed precursor and product ion spectra from
the data independent analysis of simple and complex peptide mixtures. Li GZ, Vissers JP,
Silva JC, Golick D, Gorenstein MV, Geromanos SJ. Proteomics. 2009 Mar;9(6):1696-719.
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