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Center for Intelligent Chemical Instrumentation 1
Chemometric Considerations in Proteomic Analyses by Mass
Spectrometry
Peter de B. Harrington* Mariela L. Ochoa*, Sanford P. Markey+, Claudine Laurent+, Kuniaki Saito+, & Alfred L. Yergey^
*Ohio University, Center for Intelligent Chemical, Instrumentation, Department of Chemistry and Biochemistry, Athens, OH 45701-2979, [email protected]
+Laboratory of Neurotoxicology, National Institute of Mental Health, Building 10 Room 3D42, MSC 1262, 10 Center Drive, Bethesda, MD 20892-1262
^Section on Mass Spectrometry and Metabolism, Building 10, Room 9D52, National Institute of Child Health and Human Development, 10 Center Drive, Bethesda, MD 20892-1580
Center for Intelligent Chemical Instrumentation 2
Center for Intelligent Chemical Instrumentation 3
Chemometrics
Chemometrics is a discipline that is devoted to maximizing the amount and quality of information obtained from chemical or molecular measurements.
Chemometrics uses mathematical, statistical, logical, and computational tools.
For scientists, an important chemometric topic is the statistical design of experiments.
Center for Intelligent Chemical Instrumentation 4
‘Omics Era
• Besides the proteome over 50 other ‘omes*.
• Complex biological systems• Reductionism is difficult because of the
large degree of interaction.• The interesting proteins are the ones
that are difficult to detect.
*http://www.genomicglossaries.com/content/omes.asp, accessed on 16-Mar-2004.
Center for Intelligent Chemical Instrumentation 5
Chemometrics and Proteomics
KnowledgeBasesBiology
Sample
Preparation
Instrumental Measurement
Data Information
Center for Intelligent Chemical Instrumentation 6
Some Statistics Concerning Foodborne Bacteria Pathogens
• In the U.S., 76,000,000 foodborne illnesses occur each year (325,000 hospitalizations and up to 5,000 deaths).
• Escherichia coli O157:H7 foodborne poisoning:– Largest outbreak (1993): more than 700 people ill and 4 deaths– Up to 75,000 infections estimated annually
• Listeria monocytogenes foodborne poisoning:– Largest outbreak reported in 1985– About 2,500 cases of Listeriosis every year – 500 deaths attributed to Listeriosis
Buzby, J. C., Frenzen, P. D. and Rasco, B., Product Liability and Microbial Foodborne Illness. Agricultural Economic Report Nº 799; Food and Rural Economics Division, Economic Research Service, U.S. Department of Agriculture: Washington, DC. April 2001 p 1., Website http://www.about-listeria.com/aer799.pdf, (accessed Feb 2004).
Center for Intelligent Chemical Instrumentation 7
IMS and MALDI TOF-MS as Attractive Methods for Foodborne Bacteria Characterization
Ion Mobility Spectrometry (IMS)
1. Presumptive technique
2. Ion mobility spectra may furnish useful information for bacteria species/ strains characterization and differentiation
3. Fast analysis time for rapid screening of foodborne pathogens
4. Portable instruments, attractive for on-site monitoring
Matrix-Assisted Laser Desorption/Ionization
(MALDI) TOF-MS1. Confirmatory technique2. Provides a fingerprint of proteins
for bacteria of interest3. Comparison against database
containing the bacteria genome alleviates the issue with spectral reproducibility
4. Rapid analysis time
Center for Intelligent Chemical Instrumentation 8
Identification of Foodborne Pathogens Using Molecular Weight Database Search
The ExPASy (Expert Protein Analysis System) proteomics server of the Swiss Institute of Bioinformatic (SIB) Home Page http://us.expasy.org/srs/ (accessed Oct 2003).
Database Search for Organism’s Protein Molecular Weight
Center for Intelligent Chemical Instrumentation 9
Problems Associated with Microbiological Food Analysis
• Detection of small number of pathogens hampered by large numbers of harmless background microflora
• Culture enrichment steps necessary to amplify target analytes before traditional methods of detection can be applied
• Affinity capture techniques (i.e., immunomagnetic separations –IMS) to isolate target bacteria from complex food matrices
Madonna, A. J.; Basile, F.; Furlong, E.; Voorhees, K. J. Rapid Commun. Mass Spectrom. 2001, 15, 1068-1074.
Center for Intelligent Chemical Instrumentation 10
MALDI as an Ionization Method
• Introduced by Karas and Hillenkamp (1987) as ionization method for non-volatile polar biological and organic macromolecules and polymers
• Low concentration of analyte uniformly dispersed in solid or liquid matrix
• Matrix should have strong absorbance at laser excitation wavelength and low sublimation temperature
• Three main processes occur: formation of solid solution, matrix excitation, and analyte ionization
Karas, M.; Bachmann, D.; Bahr, U.; Hillenkamp, F. Int. J. Mass Spectrom. Ion Process. 1987, 78, 53-68.
Center for Intelligent Chemical Instrumentation 11
M@ldi-LRTM Mass Spectrometer Time-of-Flight by Micromass (UK)
Instrumental parameters
• Laser: Nitrogen UV (337 nm)
– Firing rate: 5 Hz
– 10 shots/spectrum
• Ion optics: Linear TOF path
length 0.7 m
• Ion source: Grounded “time lag
focusing” source (delayed
extraction) ~ 500 ns
• Accelerating voltage: 15 kV
• Detector: Fast dual micro-
channel plate (MCP)
Center for Intelligent Chemical Instrumentation 12
MALDI-Time-of-Flight Mass Spectrometry (TOF-MS)
Center for Intelligent Chemical Instrumentation 13
Variation in the MALDI Mass Spectrum
• Compare the signal averaged spectrum to a collection of single laser shots.
• Single scan spectra are from individual laser shots.
• Historically spectra were signal averaged because of computational limits on storing large amounts of data.
• Modeling the single scan spectra can be beneficial.
Center for Intelligent Chemical Instrumentation 14
1 1.5 2 2.5
x 104
0
5
10
15
20
25
30
35
m/z
Inte
nsi
ty (
cou
nts
)
Average MALDI-MS Spectrum for a Protein Standard Mixture
Cytochrome cMyoglobinTrypsinogen
Cytochrome cMyoglobinTrypsinogen
Center for Intelligent Chemical Instrumentation 15
Baseline Correction
• Polynomial or exponential fitThe model usually depends on the instrument or matrix conditions
• Reduce the least squares error between the spectrum and the model
20 1ˆ b xy b b e
Center for Intelligent Chemical Instrumentation 16
1 1.5 2 2.5
x 104
-5
0
5
10
15
20
25
30
m/z
Inte
nsi
ty (
cou
nts
)
Baseline Corrected MALDI-MS Spectrum for a Protein Standard Mixture
Cytochrome cMyoglobinTrypsinogen
Cytochrome cMyoglobinTrypsinogen
Center for Intelligent Chemical Instrumentation 17
Wavelet Compression
• For large data sets, modest linear wavelet compression can improve efficiency.
• The biorthogonal wavelets, such as the Villasenor preserve the peak locations and avoid the extra step of reconstruction.
• Compressed using 4 levels and a biorthogonal filter with 3 vanishing moments.
• Improves signal-to-noise ratio by removing high frequency components.
Center for Intelligent Chemical Instrumentation 18
0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6
x 104
-1
0
1
2
3
4
5
6
7
8x 10
4
m/z
Inte
nsi
ty (
cou
nts
)
Wavelet Compressed Average Spectrum
Average Spectrum 100KWavelet Coefficients 6K
1.4 1.42 1.44 1.46 1.48 1.5 1.52
x 104
-2000
0
2000
4000
6000
8000
10000
m/z
Inte
nsi
ty (
cou
nts
)
Wavelet Compressed Average Spectrum
Average Spectrum 100KWavelet Coefficients 6K
Center for Intelligent Chemical Instrumentation 19
Modern Approach
• Compress single shot scans • Baseline correct• Align m/z drift for each individual
scan (i.e., single laser shot spectrum).
• Model using multivariate curve resolution
Center for Intelligent Chemical Instrumentation 20
Multivariate Curve Resolution
• Simple linear models based on transient behavior of the data
• Separate correlated spectral information based on temporal response
• Simple-to-use interactive mixture analysis (SIMPLISMA).
• Alternating least squares (ALS)
Center for Intelligent Chemical Instrumentation 21
Con
cen
trat
ion
Spectra
TD = CS E
Model = Product of analyte concentration and analyte
sensitivity
= +
Error
Center for Intelligent Chemical Instrumentation 22
Principal Component Analysis• Decomposition into
orthogonal matrices C and S
• The matrices maximize variance
• The matrices are abstract in that they do not represent physical or chemical trends
Center for Intelligent Chemical Instrumentation 23
SIMPLISMA
Willem Windig and Jean Guilment, Anal. Chem. 1991, 63, 1425-1432.
11 1,2
1
iii
i iii
r rp
r r
Instead of detecting peaks, SIMPLISMA selects points or columns in the data matrix D that have a maximum purity.
The two criteria for a pure variable are:
1. the point characterizes a variance
2. the point varies independently with other points in the model
Center for Intelligent Chemical Instrumentation 24
SIMPLISMA Decomposition
• The columns of the data matrix D are used as initial estimates for the concentration profiles C.
• Spectra are obtained by least squares regression of C onto D.
• The spectra are normalized to unit vector length.
• Concentration profiles are obtained from regression of the normalized spectra S onto D.
2
T T -1
T -1
S = D C(C C)
S = S/ S
C = DS(S S)
Center for Intelligent Chemical Instrumentation 25
Alternating Least Squares (ALS)
• Alternating procedure of regression with constraints
• Concentrations and spectra should not be negative. Use non-negative constrained least squares for the regression.
-1
-1
f ( )
f ( )
Tn+1 n
n+1 n+1
S = D C
C = D SJ. C. Hamilton and P. J. Gemperline, "Mixture Analysis Using Factor Analysis II: Self Modeling Curve Resolution," J. Chemometrics, 1990, 4, 1-13.
Center for Intelligent Chemical Instrumentation 26
Center for Intelligent Chemical Instrumentation 27
1 1.5 2 2.5
x 104
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
Mass- to-charge Ratio (m/z)
No
rmal
ized
In
ten
sity
Simplisma Spectra of Unaligned Scans
Component #1Component #2
Center for Intelligent Chemical Instrumentation 28
2.38 2.4 2.42 2.44 2.46 2.48 2.5
x 104
0
0.05
0.1
0.15
0.2
Mass- to-charge Ratio (m/z)
No
rmal
ized
In
ten
sity
Simplisma Spectra of Unaligned Scans
Component #1Component #2
Center for Intelligent Chemical Instrumentation 29
2.39 2.4 2.41 2.42 2.43 2.44 2.45 2.46
x 104
0
20
40
60
80
100
120
140
160
Mass- to-charge Ratio (m/z)
Inte
nsi
ty (
Co
un
ts)
First 10 Unaligned Scans
Center for Intelligent Chemical Instrumentation 30
Mass Alignment
• Before alignment each scan is wavelet compressed and baseline corrected.
• Align each spectrum so that the correlation with the average spectrum is maximized.
• The alignment is obtained by a quadratic fit of the m/z of each spectral scan.
• Linear interpolation is used to match the scan m/z to the average m/z of the data set.
Center for Intelligent Chemical Instrumentation 31
1 1.5 2 2.5
x 104
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Mass- to-charge Ratio (m/z)
No
rmal
ized
In
ten
sity
Simplisma Spectra of Aligned Scans
Component #1Component #2
Center for Intelligent Chemical Instrumentation 32
2.35 2.4 2.45 2.5
x 104
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Mass- to-charge Ratio (m/z)
No
rmal
ized
In
ten
sity
Simplisma Spectra of Aligned Scans
Component #1Component #2
Center for Intelligent Chemical Instrumentation 33
2.38 2.39 2.4 2.41 2.42 2.43 2.44 2.45
x 104
0
20
40
60
80
100
120
140
160
Mass- to-charge Ratio (m/z)
Inte
nsi
ty (
Co
un
ts)
First 10 Aligned Scans
Center for Intelligent Chemical Instrumentation 34
0 100 200 300 400 500 600-500
0
500
1000
1500
2000
2500
3000
Scan Number
Inte
nsi
ty (
Co
un
ts)
Concentration Profiles of Aligned Scans
Component #1Component #2
Center for Intelligent Chemical Instrumentation 35
2.38 2.4 2.42 2.44 2.46 2.48
x 104
0
0.02
0.04
0.06
0.08
0.1
0.12
Mass- to-charge Ratio (m/z)
No
rmal
ized
In
ten
sity
Comparison of Signal Averaged and SIMPLISMA Spectrum
SimplismaProc-Mean
Center for Intelligent Chemical Instrumentation 36
-1000 0 1000 2000 3000
-500
-400
-300
-200
-100
0
100
200
300
400
500
1
2345678 9101112
131415
16
171819
2021222324
2526
27
28
29
30
31323334
353637
383940414243444546474849
50
5152535455565758
596061626364656667686970717273747576 77
787980
818283848586878889
90 9192
9394959697
9899100101102103104105106107108109110
111112113114115116117118
119120
121
122123124125126127128129
130131132133134135136 137138139
140141142143
144145146147148149150151152153154155156157158159160161162163
164165166167168169170171172173174175176177178179180181182183184185186187188189190191
192193194195196
197
198199
200201202203204205206207
208209
210211212213214215216217218219220221222223224225226227228229230231232233234
235
236237238239240241242243
244245246247248249250251252253254255256
257258259
260261262263264265266267
268269270271272273
274275
276277
278279 280281
282
283284285286
287288
289
290291292293294295296297
298299
300301
302
303
304305306307308
309310
311
312313314315316317
318319320
321322323324
325326327328329
330331
332333334335336337338339340341342343344345346347348349350351352353
354355
356357358359360361362363364
365366367368369370371
372373
374
375376377378
379
380381382383384385
386387388
389390391392
393394395396397
398399400
401
402403
404
405
406
407
408409410411
412413414415416
417418419
420
421422423424
425426
427428
429
430431432
433
434
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440441
442
443444
445446
447448449450451452453454
455456457458459460461
462463464
465466
467468469470
471
472473
474475
476477478
479480 481
482
483
484485
486487
488489
490491
492
493494
495 496
497498499
500501
502503504505506507508509510511512513514515
516517
518519520521522
523524525526
527528529
530531532533534535536537538539540541
542543
544
545
546
547548
549
550551
552
553
554
555
556
557558559
560561562563564565566
567
568569
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572573
574575576577
578579
580581582
583584
585586
587
588589590
591
592593
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595596
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598
599600601
602
603604605606607608609610611
612
613614615
616617
618
619
620621622623624625626627628
A
PC #1 (81%)
PC #
2 (
2%
)PCA Score Plot for Processed MS
Plotted with respect to scan number.
Center for Intelligent Chemical Instrumentation 37
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
2 3
456
78
91011
121314
1516
1718192021222324
252627282930313233343536
37383940
41
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48 49 50
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6263
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7879
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8283848586
878889
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102103104105
106
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109110111
112113
114115
116117118
119
120121
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124 125126127
128
129130131132133134135136
137138139140141142143
144
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160161162
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262 263264
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270271272273274
275276
277278279280281282283
284285
286287288 289290
291292293294295296297298
299300301302
303
304305
306
307308 309310
311312313
314
315316317
318319 320
321322323
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329 330
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333334
335
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341342
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352353 354355
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366367368
369370
371
372373 374375376
377378379380381382383384385386387
388389390391392393394395
396397398399
400
401
402403
404405406407
408 409410
411
412
413
414
415
416
417418
419
420421422423424425426427428429430431432433
434435436437438439440441442443444445446447448449450451452453
454
455
456
457 458459
460
461
462463
464465
466467468
469470471472473474475476477478479480481482483484485486487488489490491492
493494495496497498
499500501502
503504505506507508
509510511512513514515
516517
518519520
521
522
523524
525526
527
528529
530
531
532 533
534
535536
537
538
539 540
541542543544545546547
548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578
579580581582583584585586587
588589590591592593594
595596
597598599600601602603604
605
606
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609
610
611612
613
614
615 616
617
618
619
620621
622623
624
625626
627
628
A
PC #1 (17%)
PC #
2 (
7%
)
PCA Score Plot of Normalized Processed Scan
Center for Intelligent Chemical Instrumentation 38
1 1.5 2 2.5
x 104
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
Mass- to-charge Ratio (m/z)
No
rmal
ized
In
ten
sity
Variable Loadings of the First 2 Principal Components
Component #1Component #2
Center for Intelligent Chemical Instrumentation 39
1 1.5 2 2.5
x 104
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Mass- to-charge Ratio (m/z)
No
rmal
ized
In
ten
sity
Comparison Between ALS and Mean Spectra
ALS SpectrumMean Spectrum
Center for Intelligent Chemical Instrumentation 40
2.35 2.4 2.45 2.5
x 104
0
0.05
0.1
0.15
0.2
Mass- to-charge Ratio (m/z)
No
rmal
ized
In
ten
sity
Comparison Between ALS and Mean Spectra
ALS SpectrumMean Spectrum
Center for Intelligent Chemical Instrumentation 41
1.4 1.42 1.44 1.46 1.48 1.5 1.52
x 104
0
0.005
0.01
0.015
0.02
Mass- to-charge Ratio (m/z)
No
rmal
ized
In
ten
sity
Comparison Between ALS and Mean Spectra
ALS SpectrumMean Spectrum
Center for Intelligent Chemical Instrumentation 42
Prediction of Risk for Premature Delivery from MALDI-MS of Amniotic Fluid
• Control was a pooled amniotic fluid from women who produced excessive volumes of amniotic fluid (AF)
• Women who are at risk for premature delivery from two individuals
• Three replicates of each sample were studied at different times
• Each replicate was subject to one of four sample preparation procedures
• After sample preparation 3 more replicates were obtained to characterize measurement variations
Center for Intelligent Chemical Instrumentation 43
Sample Preparation• The matrix was formed from saturated sinnapinic
acid in a 1:1 mix of acetonitrile (ACN) and 0.1% trifluoroacetic acid (TFA)
• Four sample preparation procedures were evaluated
• The samples were diluted 10-fold to volume with 0.1% TFA
• Method 1 adds a 1.0 L of this solution to the MALDI plate
• Method 2 extracts 15 L with a ZipTip and elute with 5 L of a 1:1 mix of TFA 0.1% and ACN
• Method 3 extracts a diluted 10-fold solution with 5 L of methylene chloride
• Method 4 is method 3 followed by method 2
Center for Intelligent Chemical Instrumentation 44
MALDI-MS Conditions for Amniotic Fluid Study
• ABI Voyager DE-STR – Linear mode– Delayed extraction 375 ns– Accelerating voltage 25 kV– Grid 95%– Guide wire 0.1%– Mass range 3-20 kDa– Low mass gate 2 kDa– Laser shots per spectrum 250
Center for Intelligent Chemical Instrumentation 45
Analysis of Variance (ANOVA)
• The data set comprised 108 spectra with 38,970 mass measurements
• Additive variance model coupled with PCA
Pr Pr
( ) ( ) ( )
( ) ( ) ( ) ( )Treatment Patient Treatment Sample Patient
ep Sample MS ep Int MS I nt
x x x x x x x x
x x x x x x x x
Center for Intelligent Chemical Instrumentation 46
-0.6 -0.4 -0.2 0 0.2 0.4
-0.4
-0.2
0
0.2
0.4
0.6
1
2
34
56
78
9101112
131415
16
1718
19
2021
22
2324
252627
28
2930
313233
34 3536
3738
39
404142
434445 46
47
48
495051
525354
55 5657
5859
60
6162
63
6465
66
6768 69
7071 72
73 7475
76 7778
798081
82
8384
8586
87
8889
90
9192 93
94
9596
979899
100101
102
103104105
106107108
ControlPre
PC #1 (28%)
PC
#2
(24%
)Total Variance
Center for Intelligent Chemical Instrumentation 47
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
1
2
34
56
78
9
10
1112
131415
16
17
18
19
202122
23242526
27
28
2930313233
34
35
36
37
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40
4142
4344
45
46
47
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49505152535455 5657
58
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676869
70
7172
73
7475
76
7778
798081
82
8384
85 8687
88
89
90
91
92 93
94
95
96
9798 99
100
101
102
103104105
106
107
108
ControlPre
PC #1 (47%)
PC
#2
( 2%
)
Treatment vs Residual Variance
Center for Intelligent Chemical Instrumentation 48
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2
x 104
-0.1
-0.05
0
0.05
0.1
0.15
m/z
Rel
ativ
e In
tens
ityVariable Loadings of Treatment
Center for Intelligent Chemical Instrumentation 49
-0.2 -0.1 0 0.1 0.2 0.3
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
12
3 45
6 789
10
1112
131415 1617 18 19202122 2324252627 282930 31323334
35 3637 3839 404142 4344
45
46
4748 4950
51 525354 555657 58
59
6061
6263 6465
66
676869 70
7172 737475 767778 798081
82
8384
85868788
89
90919293
9495
96 979899100
101102 103104105
106107108
ControlStudy 1Study 2
PC #1 (15%)
PC
#2
( 4%
)
Study vs Residual Variance
Center for Intelligent Chemical Instrumentation 50
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2
x 104
-0.04
-0.02
0
0.02
0.04
0.06
0.08
m/z
Rel
ativ
e In
tens
ity
Variable Loadings of Study
Center for Intelligent Chemical Instrumentation 51
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
1
23
45
6
78
9101112
13
1415
16
1718
19
2021
22
2324
252627
28
2930
313233
343536
37
38
39
404142
434445
46
47 48
495051
52
5354
55
5657
58
5960
61
62
63
6465 66
676869
70
71
72
7374
75
76
77
78
79
8081
82
8384
8586
878889
90
91
92
9394
9596
97
98
99
100
101
102
103104105
106
107
108
Ex-ZTExZTNoZT
PC #1 (35%)
PC
#2
( 3%
)
Pretreatment vs Residual Variance
Center for Intelligent Chemical Instrumentation 52
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2
x 104
-0.04
-0.02
0
0.02
0.04
0.06
0.08
m/z
Rel
ativ
e In
tens
ity
Variable Loadings of Pretreatment
Center for Intelligent Chemical Instrumentation 53
-0.6 -0.4 -0.2 0 0.2 0.4 0.6
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
12 3
456
789
10
1112
131415
161718
192021
222324
252627
282930
313233
34
3536
37
38
39
404142
4344
45
46
47 48
495051
525354
55
565758
59
60
61
62
63
64
65
666768
6970
71
72
73
7475
76
7778
79
8081
828384
8586
87
8889
90
91
92
93
94
9596
97
98
99
100
101
102
103104
105106
107108
Ex-ZTExZTNoZT
PC #1 (18%)
PC
#2
( 7%
)
Interaction vs Residual Variance
Center for Intelligent Chemical Instrumentation 54
Follow-up ExperimentDay 1 Day 2 Day 3 SB1b-n PB2c-n SB3b-nPA1a-ZT SB2c-ZT PA3b-nPB1a-ZT SB2a-ZT PC3b-ZTPA1b-n SA2a-n SC3a-nPC1c-ZT PC2c-ZT SB3b-ZTSC1c-n PB2b-n SA3c-nPA1c-ZT PA2b-n SB3a-nSC1c-ZT PB2c-ZT SA3a-nSC1a-n SA2b-ZT SC3b-nSC1a-ZT PC2b-n PC3a-ZTPA1c-n SB2c-n SA3a-ZTSA1b-n PB2b-ZT SC3a-ZTSC1b-n PC2c-n SA3b-nSB1b-ZT SC2b-ZT PB3a-nPC1a-n SA2a-ZT SA3c-ZTPB1b-n SA2c-ZT SC3b-ZTPC1c-n SB2b-ZT PB3c-nSB1a-ZT PA2c-n PA3a-n
Day 1 Day 2 Day 3 PB1b-ZT SC2c-n PB3b-ZTSB1a-n SC2c-ZT PC3a-nSB1c-n PB2a-n PA3c-ZTSA1a-ZT SC2a-n PB3b-nSA1a-n PC2a-n PA3b-ZTPB1a-n PB2a-ZT PB3a-ZTSA1c-ZT PA2a-n SA3b-ZTPC1a-ZT SA2b-n SB3c-ZTPC1b-n PC2a-ZT SB3c-nSB1c-ZT SB2b-n PC3c-nPA1b-ZT SC2a-ZT PB3c-ZTPB1c-ZT PA2c-ZT SB3a-ZTPB1c-n SB2a-n PA3c-nPC1b-ZT SC2b-n PC3b-nSA1b-ZT PC2b-ZT PC3c-ZTSA1c-n PA2bZT SC3c-ZTSC1b-ZT PA2a-ZT SC3c-nPA1a-n SA2c-n PA3a-ZT
Center for Intelligent Chemical Instrumentation 55
-0.1 -0.05 0 0.05 0.1 0.15 0.2
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15 1
2
3
4
5
6
7
8
9
10
11
12
13
1415
161718
1920
2122
2324
25
26
27
28
29
30
3132 33
34 3536
37
38
39
40
41
42
43
44
45
46
47
48
49
5051
5253
5455
56
5758
5960
61
62
63
64
65
666768 6970 7172
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
8889
90
91
92
93
94
95
9697
98
99100 101
102103
104105106
107108
Single PatientPooled
PC #1 (49%)
PC #
2 (
35
%)
Total Variance
Center for Intelligent Chemical Instrumentation 56
-0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
1
23
4
5
6
7
8
910
11
12 13
1415
1617
18
19
202122
23
24
25
2627
28
29
30
313233
34
35
36
37
383940
41
42
43
44
45
46
47
48 4950
51
525354
555657585960
6162636465
66676869
707172
73 7475767778
79808182
83
84 8586878889 90
919293
94
95
9697
9899100 101102
103104105106
107108
Single PatientPooled
PC #1 (73%)
PC #
2 (
6%
)Treatment vs. Residual
Center for Intelligent Chemical Instrumentation 57
-0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
1
2
3
4
5
6
7
8
910
11
12 13
1415
1617
18
19
202122
23
24
25
2627
28
29
30 3132
33
34
35
36
37
383940
41
42
43
44
45
46
47
48 4950
51
525354
555657585960 616263
6465
6667
6869707172
73 747576
7778
79808182
8384 8586878889 90
919293
94
9596
979899
100 101102103
104105106
107108
Day 1Day 2Day 3
PC #1 (76%)
PC #
2 (
3%
)
Day vs. Residual
Center for Intelligent Chemical Instrumentation 58
-0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
1
23
4
5
6
7
8
910
11
12
13
1415
16
17
18
19
202122
23
24
25
26
2728
29
30
3132333435 36
37
383940
41
42
43
44
45
46
47
48
4950
51
52
5354
55 5657585960 61
6263
6465 66
67686970
7172
7374
7576
7778
79808182
8384
858687888990
919293
9495
9697
98
99
100101
102
103104105106107108
ABC
PC #1 (68%)
PC #
2 (
12
%)
Sample vs. Residual
Center for Intelligent Chemical Instrumentation 59
-0.15 -0.1 -0.05 0 0.05 0.1
-0.2
-0.15
-0.1
-0.05
0
0.05
1
2
3
4
5
6
7
8
9
10
11
1213
14
15
16
17
18
19
20
21
22
23
24
25
26
27
2829
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60 6162
63
64
65
66
67
68
69
70
71
72
73
74
75
76
7778
79
80
81
82
83
84 8586
87
88
89
90
91
92
93
94
95
96 97
98
99100
101
102
103
104
105
106
107
108
ZipTipNothing
PC #1 (68%)
PC #
2 (
23
%)
Center for Intelligent Chemical Instrumentation
• Proteins are captured, retained and purified directly on the chip (affinity capture)
Laser
“Homogeneous” Capture Surface
The SELDI Process and ProteinChipThe SELDI Process and ProteinChip®® Arrays Arrays
• Sample goes directly onto the ProteinChip™ Array
• Array is “read” by Surface-Enhanced Laser Desorption/Ionization (SELDI)
• Retained proteins can be processed directly on the chip
ProteinChipTM Array
Sample
Trace proteins (targets/markers)
Center for Intelligent Chemical Instrumentation 61
ProteinChip® Array Surfaces
Preactivated Surfaces for Specific Protein Interaction Studies
Chromatographic Surfaces for General Profiling
(Reverse Phase) (Cation Exchange) (Metal Ion) (Normal Phase)
(PS-1 or PS-2)(Antibody - Antigen) (Receptor - Ligand) (DNA - Protein)
(Anion Exchange)
Center for Intelligent Chemical Instrumentation 62
ProteinChip® Detection Technology: Laser Desorption Time-of Flight MS
• Retained proteins are detected Laser Desorption Ionization • Simple Linear, TLF TOF MS• Orthogonal Quadrupole TOF MS and MS/MS
Dete
ctor
Dete
ctor
Laser
TOF-MS
0
2.5
5
7.5 Spectra View
8000
2000 4000 6000
2
4
6
2185.8+H
2369.9+H2528.2+H
2781.9+H
3172.3+H
3915.2+H
4000.2+H4345.6+H
4618.2+H
4730.4+H
5045.2+H
7977.1+H
Map View
2000
2000 4000 6000 8000
4000 6000 8000 Gel View
Center for Intelligent Chemical Instrumentation 63
Protein Profiling: Three Dimensions of Resolution
Org
anic
Org
anic
Dete
rgent
Dete
rgent
Salt
Salt
Wate
rW
ate
r
pH
pH
Ure
aU
rea
CH
APS
CH
APS
Imid
azo
lIm
idazo
l
Wash Wash ConditionsConditions
0
2.5
5
7.5
2000 4000 6000 8000
Su
rface
Type
Su
rface
Type
Measured m/zMeasured m/z
12 x 8-spot ProteinChip® Arrays match the footprint of a 96 well microplate
Center for Intelligent Chemical Instrumentation 64
Sample Preparation
• Pre-wash chips with 5% ACN/Methanol
• Deposit 1 μL of sample• Wash chips with 5% ACN• Spot 0.5/1μL of matrix solution ( 3,5-
dimethoxy-4-hydroxycinnamic acid in ACN/H2O/TFA 50/50/0.1)
Center for Intelligent Chemical Instrumentation 65
-1000 -500 0 500
-400
-200
0
200
400
600
1
2
34
5
6
7
8
9
1011
12
13
1415
16
17
18
19
20
21
2223
2425
2627
2829
30
3132
33
34
35
36
373839
4041
42
43
44
45
46
47
48
49
50
51
5253
5455
56
5758
59
60
61
62
63
64
65
66
67
68
69
70
717273
74
75
76
77
7879
80
81
82
83
84
85
86
878889
90
91
9293
94
95
96
97
98
99100101
102
103
104
105106
107
108
109
110
111
112
113114
115
116117
118119
120121
122
123
124
125
126127
128
129130
131132
133
134
135
136137
138
139
140
141142143144145
146147
148
149
150
151
152
153
154
155
156
157
158
159160
161
162163
164165
166167168169
170
171
172
173
174
175176177
178
179
180181
182
183
184
185
186187 188189
190
191
192
193
194
195 196
197
198
199
200
201
202203204205
206
207
208
209
210
211212
213
214
215
216217218
219
220
221
222
223
224
225
03kr7003kr7603kr7803kr7903kr275
PC #1 (59%)
PC #
2 (
12
%)
45 single shot spectra from 5 control serum samples
Center for Intelligent Chemical Instrumentation 66
-400 -200 0 200 400 600 800
-300
-200
-100
0
100
200
1
2
3
4
5
03kr7003kr7603kr7803kr7903kr275
PC #1 (85%)
PC #
2 (
11
%)
Average Spectra
Center for Intelligent Chemical Instrumentation 67
0 2 4 6 8 10
x 104
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
m/z
Rel
ativ
e In
ten
sity
Variable Loadings for the Distribution of the Average Spectra
PC #1PC #2
Center for Intelligent Chemical Instrumentation 68
-1000 -500 0 500 1000
-600
-400
-200
0
200
400
600
1
2
34
5
6
7
8
9
101112
13
1415
16
17
18
19
20
21
2223
2425
2627
282930
3132
33
34
35
36
373839
40
41
42
43
44
45
4647
48
49
50
51
5253
5455
56
575859
6061 62
63
64
65
6667
68
6970
7172
73
74
75
76
77
7879
80
81
82
83
8485
86
878889
9091
9293
9495
96
97
98
99100101
102103
104
105106107
108109
110
111
112
113114
115
116117
118119
120121
122
123
124
125
126127
128
129130
131
132 133134
135
136137
138
139
140
141142143144145
146147
148
149
150
151
152
153
154
155
156
157
158
159160
161
162163164165
166167168169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188189
190
191
192
193
194
195
196
197
198
199
200
201
202
203204205
206
207
208
209
210
211212
213
214
215
216
217218
219
220
221
222
223
224
225
03kr7003kr7603kr7803kr7903kr275
PC #1 (40%)
PC #
2 (
16
%)
Residual Spectra
Center for Intelligent Chemical Instrumentation 69
0 2 4 6 8 10
x 104
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
m/z
Rel
ativ
e In
ten
sity
Variable Loadings for the Residual Spectra
PC #1PC #2
Center for Intelligent Chemical Instrumentation 70
SELDI Protein Profiles After Depletion of the Highest-abundant Serum Proteins
(albumin, IgG, antitrypsin, IgA, transferrin, haptoglobin) a. Pooled sera from 5 Japanese subjects (01/12/04)b. Individual sera from 5 Caucasian control subjects
(02/19/04)c. Individual sera from 10 Japanese prostate cancer
subjects and 10 matched controls (03/08/04)d. Individual sera from 5 Japanese control subjects,
diluted to 20% and 50% of original concentration and spotted with 1 μL of matrix solution (03/12/04)
e. Individual sera from 3 Japanese control subjects, diluted to 20% and 50% of original concentration and spotted with 0.5 μL of matrix solution
Center for Intelligent Chemical Instrumentation 71
2000 4000 6000 8000 10000
0.005
0.01
0.015
0.02
0.025
0.03
m/z
No
rmal
ized
In
ten
sity
Center for Intelligent Chemical Instrumentation 72
-0.3 -0.2 -0.1 0 0.1 0.2-0.15
-0.1
-0.05
0
0.05
0.1
0.15
1
2
34
5 678
9
10
11
1213
14
15
16
17
18
19
20
21
22
2324
25
26
27
28
29
3031
32
33
34
35
36
37
38
39
40
4142
43
4445
46
47
48
4950
51
52
53
54
55
56
57
58
59
60
6162
63
64
65
66
67
68
69
70
71
72
73
74
7576
7778
79
80
81
82
83
84
85
86
87
8889
90
91
ControlProstate
PC #1 (61%)
PC #
2 (
18
%)
Center for Intelligent Chemical Instrumentation 73
-0.3 -0.2 -0.1 0 0.1 0.2-0.15
-0.1
-0.05
0
0.05
0.1
0.15
1
2
34
5 678
9
10
11
1213
14
15
16
17
18
19
20
21
22
2324
25
26
27
28
29
3031
32
33
34
35
36
37
38
39
40
4142
43
4445
46
47
48
4950
51
52
53
54
55
56
57
58
59
60
6162
63
64
65
66
67
68
69
70
71
72
73
74
7576
7778
79
80
81
82
83
84
85
86
87
8889
90
91
ControlProstate
PC #1 (61%)
PC #
2 (
18
%)
Treatment vs. Residual
Center for Intelligent Chemical Instrumentation 74
Concluding Thoughts• Variability of spectra from MALDI and SELDI
sources are attributable to shot-to-shot variations that are not independent or random.
• Modeling single scans can display chemical and instrumental variations and provide higher quality spectra.
• Mass alignment should be accomplished prior to averaging as opposed to afterwards.
• All the above statements are likely to be attributable to ESI spectra as well.
• PCA coupled to separation of experimental sources of variation provides a useful graphical tool for evaluating experimental procedures.
Center for Intelligent Chemical Instrumentation 75
Acknowledgements
• Students– Libo Cao Matt Rainsberg– George Bota Ping Chen– Preshious Rearden Leyna Denapoli– Leanna Kishler
• Federal Aviation Administration - Donation of a Barringer Ionscan 350
• Ion Track Instruments for Support and Donation of the Itemizer 2 and VaporTracer 1
• Sionex for the donation of DMS– Erkin Nazarov for DMS Slides
• National Biscuit Company - Donation of a GC-MS• U.S. Army EBCB - GeoCenters Donation of 4 Chemical
Agent Monitors and Funding• Research Opportunity Award-Research Corporation • Wright-Patterson Air Force Base-INNSSI Fuel Analysis