Transcript LC MALDI Method Development for Urine Peptidome Analysis

MASS-Conductor
Prediction analysis of microarray (PAM)
Predictor discovery
in training set
1
LCMS
raw spectra
Peak finding
peak alignment
feature extraction
Normalization
Remove
background signals
20937 unique
features
2
Training set
(10 AR, 10 STA, 6 BK)
Classifier
training
Six-fold
Cross-validation
Classify
AR, STA, BK
Predictor confirmation
in testing set
3
Testing set
(10 AR, 10 STA, 4 BK)
PAM
Class prediction algorithm
Predictors of
2 ~ 630 features
Calculate estimates of
predicted class probabilities
Analysis of goodness
of class separation
Cluster analysis
Platform Validation
Pattern analysis
in all set
4
5
All set
(20 AR, 20 STA,
10 BK, 10 NS, 10 HC)
2d hierarchical
clustering
heatmap plotting
Predictors of
53 features
2 peptide biomarkers
MRM assay
development
MRM assay
AR, STA, BK, NS, HC
Training + Testing Samples
Correlation Analysis
LC-MALDI  MRM
FIGURE 1A
Hypothesis of
Molecular Mechanism
Exploration Analysis
Confirmation Analysis
Exploration data set6
(TGCG)
Confirmation data set
(Stanford )
Observation of abundance patterns of
Urine peptide biomarkers in AR
Hypothesis 1
Gene expression
alteration in AR
Hypothesis 2
Protease expression
alteration in AR
Hypothesis 3
Protease inhibitor
expression
alteration in AR
Validation data set
(Stanford )
3
4
Affymetirics HG-U95Av2
Affymetirics HU-133
Quantitative RT-PCR
(AR: PBL, n=6; BX, n=7)
(STA: PBL, n=9; BX, n=10)
(NR: PBL, n=8; BX, n=5)
(HC: PBL, n=8; BX, n=9)
(AR: BX, n=37)
(HC: BX, n=23)
(AR: BX, n=14)
(STA: BX, n=10)
(HC: BX, n=10)
2
1
Validation Analysis
Expression analysis of peptide
biomarkers’ corresponding
precursor genes
Expression analysis of
metzincin superfamily genes
Confirmation
Discovery 
Validation
mechanism biomarkers
Expression analysis of
protease inhibitor genes
FIGURE 1B
Goodness of class separation – D probability
A
Feature#
AR
AR
STA
STA
BK
BK
Training
2
4
7
9
14
Testing
23
33
53
84
139
226
394
630
FIGURE 2
2
4
7
9
14
23
33
53
84
139
226
394 630
AR
STA
Probability
B
BK
Training
C
D
Training set
n = 26
Testing set
n = 24
AR STA BK
AR STA BK
AR STA BK NS HC
10
10
20
10 6
NON
Clinical
AR
-AR
diagnosis
n = 10 16
Probability
Predicted
as AR
9
Predicted
as Non-AR
1
0
8
16
Predicted
as NON-AR
2
Percent 90% 100%
+
Agreement
with clinical
96%
diagnosis
Overall
AR STA BK NS HC
Data set
n = 70
20 10 10 10
NON
Clinical
AR
-AR
diagnosis
n = 20 50
2d Cluster
PAM
Predicted
as AR
P = 3.2X10-6
Sample ID
10 4
NON
Clinical
AR
-AR
diagnosis
n = 10 14
PAM
Testing
E
2
Clustered
as AR
19
0
12
Clustered
as NON-AR
1
50
Percent 80% 85%
+
Agreement
with clinical
83%
diagnosis
Overall
P = 0.0027
FIGURE 2
95% 100%
Percent
+
Agreement
with clinical
98.5%
diagnosis
Overall
P =3.1X10-16
A
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COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A1
COL1A2
COL1A2
COL2A1
COL3A1
COL3A1
COL3A1
COL3A1
COL3A1
COL4A1
COL4A2
COL4A3
COL4A4
COL4A5
COL4A5
COL4A5
COL4A6
COL5A1
COL7A1
COL9A1
COL11A1
COL11A1
COL17A1
COL18A1
1235.56
1251.55
1322.57
1316.59
1409.66
2048.92
2064.91
2192.97
2362.12
2378.10
2645.24
1709.79
2031.95
2221.97
2205.99
2277.01
2293.01
2617.15
2086.93
2157.96
3014.41
1266.58
2129.99
2017.93
2081.94
2195.99
1861.85
1738.76
2008.93
2079.92
2565.18
2743.24
1424.66
1126.51
1161.52
1218.55
1144.52
1269.53
1733.76
1158.52
1748.82
1690.80
1732.84
1441.64
1828.84
1368.62
1142.51
APGDRGEPGPPGP
APGDRGEPGPPGP
APGDRGEPGPPGPA
DAGPVGPPGPPGPPG
GPPGPPGPPGPPGPPS
NGDDGEAGKPGRPGERGPPGP
NGDDGEAGKPGRPGERGPPGP
NGDDGEAGKPGRPGERGPPGPQ
GKNGDDGEAGKPGRPGERGPPGPQ
GKNGDDGEAGKPGRPGERGPPGPQ
GPPGKNGDDGEAGKPGRPGERGPPGPQ
PPGEAGKPGEQGVPGDLG
PPGEAGKPGEQGVPGDLGAPGP
ADGQPGAKGEPGDAGAKGDAGPPGP
ADGQPGAKGEPGDAGAKGDAGPPGP
ADGQPGAKGEPGDAGAKGDAGPPGPA
ADGQPGAKGEPGDAGAKGDAGPPGPA
GPPGADGQPGAKGEPGDAGAKGDAGPPGPA
EGSPGRDGSPGAKGDRGETGPA
AEGSPGRDGSPGAKGDRGETGPA
ESGREGAPGAEGSPGRDGSPGAKGDRGETGPA
SPGPDGKTGPPGPA
DGKTGPPGPAGQDGRPGPPGPPG
GRPGEVGPPGPPGPAGEKGSPG
DGPPGRDGQPGHKGERGYPG
NDGPPGRDGQPGHKGERGYPG
SNGNPGPPGPPGPSGKDGPK
NDGAPGKNGERGGPGGPGP
DGESGRPGRPGERGLPGPPG
DAGAPGAPGGKGDAGAPGERGPPG
GAPGQNGEPGGKGERGAPGEKGEGGPPG
KNGETGPQGPPGPTGPGGDKGDTGPPGPQG
PGQQGNPGAQGLPGP
GLPGLPGPKGFA
GEPGPPGPPGNLG
GLPGPPGPKGPRG
GPPGPPGPLGPLG
PGLDGMKGDPGLP
GIKGEKGNPGQPGLPGLP
GLPGPPGPPGPPS
KGPQGKPGLAGMPGANGPP
PGLPGQVGETGKPGAPGR
KRPDSGATGLPGRPGPPG
GPPGPPGLPGPQGPKG
DGPPGPPGERGPQGPQGPV
LPGPPGPPGSFLSN
GPPGPPGPPGPPS
B
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2.
3.
4.
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THP
THP
THP
THP
THP
THP
THP
THP
THP
THP
THP
THP
THP
THP
THP
THP
FIGURE 3
982.59
1047.48
1211.66
1225.69
1324.76
1423.83
1468.82
1510.87
1567.91
1581.91
1654.91
1680.98
1755.96
1768.01
1912.07
2040.16
VLNLGPITR
SGSVIDQSRV
DQSRVLNLGPI
SRVLNLGPITR
IDQSRVLNLGPI
VIDQSRVLNLGPI
DQSRVLNLGPITR
SVIDQSRVLNLGPI
GSVIDQSRVLNLGPI
IDQSRVLNLGPITR
SGSVIDQSRVLNLGPI
VIDQSRVLNLGPITR
SGSVIDQSRVLNLGPIT
SVIDQSRVLNLGPITR
SGSVIDQSRVLNLGPITR
SGSVIDQSRVLNLGPITRK
Signal Intensity/100
2.5
SAMPLE: URINE PEPTIDES
AR
STA
BK NS
HC
1.
2.
THP 1680.98
THP 1912.07
VIDQSRVLNLGPITR
SGSVIDQSRVLNLGPITR
2.0
1.5
1.0
Correlation coefficient analysis
between MRM and LC-MALDI data sets
method
Pearson
Kendall
Spearman
P-value
5.149 X 10-8
2.363 X 10-6
5.368 X 10-6
0.5
0
1
1
1
1
1
2
2
2
2
2
FIGURE 4A
Sensitivity
FIGURE 4B
1.0
1.0
0.8
0.8
AR versus STA
AR versus BK
AUC: 0.83
AUC: 0.92
0.6
0.6
AUC: 0.74
AUC: 0.83
0.4
0.4
SAMPLE: URINE PEPTIDES
0.2
THP 1680.98
0.2
VIDQSRVLNLGPITR
THP 1680.98
THP 1912.07 SGSVIDQSRVLNLGPITR
0.0
0.0
0.2
0.4
SAMPLE: URINE PEPTIDES
0.6
1- Specificity
0.8
THP 1912.07 SGSVIDQSRVLNLGPITR
0.0
1.0
VIDQSRVLNLGPITR
0.0
0.2
0.4
0.6
1- Specificity
0.8
1.0
log (Signal ratio (AR/HC))
3
FIGURE 5A
BIOPSIES RNA MICROARRAY
2
BIOPSIES RNA RT-PCR
1
0
-1
-2
URINE PEPTIDES
-3
THP
COL
1A1
COL
1A2
COL
2A1
COL
3A1
COL
4A1
COL
4A2
COL
4A3
COL
4A4
COL
4A5
COL
4A6
COL
7A1
COL
9A1
COL
11A1
COL
17A1
COL
18A1
º
ADAM
MEPRIN
6
10
MMP7
8
SERPING1
6
MMP
4
TIMP1
2
4
2
0
1
SERPING1
TIMP
Microarray Signal D (AR-HC)/100
º
Signal ratio (AR/HC)
RT-PCR
º microarray
FIGURE 5B
Renal peptides
FIGURE 6
Renal proteins
Endoproteases ( )
Protease inhibitor
ADAM, MMP, MEPRIN …
TIMP1, SERPING1
Precursor peptides
Allograft disease
Exoproteases ( )
Fragment peptides
unknown