Profiling and Imaging Mass Spectrometry

Profiling and Imaging Mass SpectrometryProfiling and Imaging Mass Spectrometry

Compilation of Work from the Caprioli Lab

James Mobley, Ph.D.Director of Urologic ResearchAssociate Director of Mass SpectrometryUniversity of Alabama at [email protected]

Primary Focus of TodayPrimary Focus of Today’’s Lecture?s Lecture?

• Brief Overview of Biomarker Discovery (BMD) for Clinical Applications, Why do we do it, Why do we use MALDI-ToF?

• Understanding Advances in MALDI-ToF Driven Profiling of Tissue Sections for BMD, and the bottlenecks in this newly emerging field.

• How to produce a Mass Image from a Series of Profiles.• What to Do with All that Data (Following the Workflow

from Pre-prosessing to Statistical Analysis).• How to ID those Peaks, are they really Proteins?

First offFirst off………………....Why Do Biomarker Discovery?Why Do Biomarker Discovery?

• To find associations between biological components (i.e. SM, FA’s, Proteins) and any clinical endpoint quickly, non-invasively, affordably.

• To non-invasively determine…..– Pathologic Changes (i.e. early detection of cancer)– Aggressiveness/ Stage of Disease– Predicting Rx Response– Drug Target Discovery– Mechanistic Studies (Systems Biology)

• The Potential Clinical Impact is Tremendous!!

Using Proteins as an Example;How new is BMD?

1 2 3 4 5 6 7 8 9 100

1000

2000

3000ProteomicsBMD-Proteomics

Years Past

Num

ber

of P

ublic

atio

ns

Nearly half are reviews!

BMD Publications:51 real, 29 reviews

Bottlenecks in Biomarker Discovery

Biological Samples

Diagnostic Assay

Quick & Reproducible Protein Isolation

Increase Acquisition Speed & Sensitivity

Spectral Pre-processing & Data Analysis

Streamline Protein Characterization

Can 2D PAGE Get Us There?Can 2D PAGE Get Us There?

O. Golaz etal. (1993) Electrophoresis 14 1223-1231. Acidic region IPG, pH 3.5-10

Serum Proteome Anderson, PNAS, 1977

Direct Analysis ~1000 Protein Spots

Pieper, Proteome, 2003Affinity, SCX, Size Exclusion

Yielded 74 Fractions - 2D3700 Proteins Spots327 Distinct Proteins

25yrs later25yrs later

Primarily HMWPrimarily HMWProteins!Proteins!

MALDI-TofCrude Protein

2D PAGE, ID by MALDI-Tof

What About MALDIWhat About MALDI--ToFToF for Biomarker Discovery?for Biomarker Discovery?

Primarily LMW Proteins!Primarily LMW Proteins!

0 10 20 30 40 50

Molecular Weight (kDa)

100.0

25.0

6.3

1.6

0.4

0.1

Rel

ativ

e Pe

ak A

rea

(100

% =

1.6

fmol

e)

1.6 femptomole3.9 pg/ μL

cR

elat

ive

Peak

Are

a(1

00 %

set e

qual

to 1

.6 fm

ole) 1.6 fmole

0 10 20 30 40 50

Molecular Weight (kDa)

100.0

25.0

6.3

1.6

0.4

0.1

Rel

ativ

e Pe

ak A

rea

(100

% =

1.6

fmol

e)

1.6 femptomole3.9 pg/ μL

cR

elat

ive

Peak

Are

a(1

00 %

set e

qual

to 1

.6 fm

ole) 1.6 fmole

Sensitivity is Inversely Proportional to Mass(MALDI-ToF Example)

MALDIMALDI--TofTofLow Low AttomoleAttomole

2D PAGE2D PAGE

Primary Source: Cytokines Online Pathfinder Encyclopaediahttp://www.copewithcytokines.de/

Taking a Closer Look at our Proposed TargetTaking a Closer Look at our Proposed TargetPrimary Target: Many growth factors and cytokines are secreted into the plasma.Growth factor were previously referred to substances that promote cell growth. Promote/ Inhibit: mitogenesis, chemotaxis, apoptosis, angiogenesis, differentiation. Cytokines were simply known as proteins that exhibited immuno-modulating effects. A generic name for a diverse group humoral regulators.Chemokines are a family of cytokines previously referred to as the SIS, SIG, SCY, PF4, and Intercrine families. 8-10 kDa, chemotactic agents, with high homology (contain C, CC, CXC, or CX3C).Known In Late 90’s, the cytokine family was limited mainly consisted of 22 lymphokines; But Now, Cytokines, including those with no names: 491, Separate Chemokines: 345

Advantages of MALDI-ToF for Profiling Biological Soln’s and Tissue Sections

• Resistant to many impurities, robust!• Highly sensitive in low mass range, which is just

starting to be chartered.• Direct Analysis on Tissue; “very” little protein is

required for analysis.• Analysis of crude extracts of biological fluids.• Primarily 1+ charged proteins/ peptides (less

complex specta).• HTP!!

Acquire mass spectra

Slice frozen tissue on cryostat (~12 μm thick)

Thaw slice onto MALDI plate, allow to dry

Apply matrix

Droplets Spray coating

m/z 18 388

Non-DirectedDiscovery“Imaging”

Directed Discovery“Profiling”

Laser

+ + +

Laser

+ + +

Protein profiles Protein images

Human breast tumor needle biopsy

% In

tens

ity

17000 27600 38200 48800 59400 700000

2

4

6

8

10 28085

17253

17381

3101144085

43255

41854

6675322265

~50500

70000 116000 162000 208000 254000 300000Mass (m/z)

0

0.6

1.2

1.8

2.4

3 81934

132990

148196

x5

162805

78494

94266 97455

199988

215149

2000 5000 8000 11000 14000 170000

8

16

24

32

4015127α+

15869β+

7564α2+ 7934

β2+

6434

8810

140375360 10091

6633

1164913760

12344

9442

8686

1 mm

Protein Expression Profiling by MALDI-MS

Human Human GliomaGlioma BiopsyBiopsy

Schwartz et Al. Clin. Cancer Res., 10, 981-987 (2004).

15000

Tumor (A)

Tumor (B)

Non Tumor (C)

Non Tumor (D)

m/z4000 8000

Tumor (A)

Tumor (B)

Non Tumor (C)

Non Tumor (D)

X 5

A B

C

D

3 mm

Non-Tumor

Tumor

Principle of MALDI MS Imaging

Frozen section

Matrixapplication

MALDI Mass Spectrum

4000 6000 8000 10000m/z

MS Images

Raster of section

Spray Deposition of Matrix on Tissue SectionsSpray Deposition of Matrix on Tissue SectionsSpray nebulizer for TLC plates

NitrogenMatrix solution Tissue section

3000 8400 13800 19200 24600 30000Mass (m/z)

0

20

40

60

80

100%

Inte

nsity

5445

6228

95694965 6575

9906

11208 140051223926838

18310

16773

20746

3000 8400 13800 19200 24600 30000Mass (m/z)

0

20

40

60

80

100

% In

tens

ity

Spotted

Sprayed

5445

6228

9569

4965

6575

9906

11208

1400512239

268351677320746

200 μm

Comparing Matrix CoatingsComparing Matrix Coatings

50um Laser spot

4965

8565

4747

5444

5708

7810

, β2+

6573

5171

7537 9910

6719

6251

2000 3800 5600 7400 9200 11000

7492

, α2+

6545

1025

9

3347

9193

1213

4

1994

0

1164

1

1237

2

11000 13800 16600 19400 22200 25000Mass (m/z)

1679

0

2074

3

NT)

T)

2217

3

9975

1498

2, α

+

1561

8, β

+ x25

NT)

T)

Rel

ativ

e in

tens

ities

Tumor

Tumor Non tumor

2 mm

Glioma Mouse Model Glioma Mouse Model -- Intracranial Injection of GL261 Cancer CellsIntracranial Injection of GL261 Cancer Cells

m/z 12 134Cytochrome C

m/z 22 173

m/z 7539

m/z 14 039m/z 11 307 and 11 348Histone H4

m/z 13 804 and ~15 350Histones H2B1 and H3

m/z 18 420m/z 14 982 and 15 617α and β hemoglobins

m/z 6924

m/z 24 807

m/z 9910 Acyl CoA-binding protein2 mm

0 100%

Glioma Mouse Model. Imaging Resolution: 100 Glioma Mouse Model. Imaging Resolution: 100 μμm m

Chaurand et Al. Anal Chem, 76, 86A-93A (2004).

Can We Do Better?Tissue Profiling/Imaging – How Best to Apply Matrix?

Matrix Deposition Variables:

Time Repr. Inherent Spot Res. LaserDemand BG Dependent

Spot Size

Manual Spotting seconds variable N >1mm YRobotic Spotting minutes good N ~200µ YLaser Capture hours good Y ~7µ to 100 µ N

Current Laser Spot Size for Old STR: 25µ x 50µ

100 µ

Hand Spotting Vs. Pico Spotting Vs. LCM

PicoSpot ~200µ

10x

4xHandSpot ~2mm

The Robotic Spotter

Substrate image capturing

Drop imaging

MALDI target with sample

Acoustic drop ejector with reservoir

Computer controlled motorized stage

Strobe LED for drop illumination

Acoustic Drop Ejection Technology

yx “Ejection of microdroplets”Sample plate

Matrix reservoir

Coupling reservoir

Coupling membrane

Transducer

RF generator

Waveform generator

Current performances:- Spot size: ~180-200 µm

- Drop ejection rate: 10 Hz

- Drops per pixel: 60-80

Tissue Sectioning for Protein Profiling

Seamless High ResolutionImaging of Histological Slides

Whole Mouse Mammary Gland

Histology Directed Matrix Deposition

Whole Lung Sections

Lung Mets

A2 Not used A3

A4 A5 B1

B2B3 B4

C4 C3 C2 C1

Automating – “Whole Plate Profiling”

MALDI-Tof Plate

. . .

. . .. . .

. . . .

Rapid Spotting Over Entire Plate

Breast Cancer Early Vs. Late Disease

M.G. - NO Tumor

M.G. – Early BCa

M.G. – Late BCa

2 kDa 20 kDa

Results from Mass Profiling Tissue SectionsLung Mets Vs. Late Carcinoma

M.G. – Late BCa

2 kDa 20 kDa

Calgranulin A

Lung Mets

Lung Mets Vs. Late Carcinoma of the Breast

A)

B)

Manual Spotting Vs.Manual Spotting Vs.The Robotic SpottingThe Robotic Spotting

Mouse brain,Analysis of the corpus callosum

1000 μm

2014 15579 29144 42708 56273 69838Mass (m/z)

Inte

nsity

m/z 17885 m/z 11839 m/z 20688

m/z 11790 m/z 6759 m/z 7338

0 %0 % 100 100 %%

MS Imaging of a Mouse Brain SectionMS Imaging of a Mouse Brain SectionBy Robotic SpottingBy Robotic Spotting

Lung TumorHomogenate

Add Post-Translational Modification

MD HPLC

Fractions MwMatches

MwMatches

Database Search Protein ID

5000 12500 20000m/z

*8750 16250

MALDI MS

10000 12500 15000m/z

*Tryptic

DigestionMS/MS

100 300 500 700 900 1100 1300m/z

Schematic Representation of Protein Marker Identification

m/z 22535

m/z 8958

Combined Image

Combined Imagem/z 7930

Normal Human Colon Biopsy Normal Human Colon Biopsy

1000 µm

mucosa submucosa

muscle

Rat Kidney Sagittal Section

m/z 9943

m/z 9979

Overlay9950 10000 10050

100

200

300

400

500

600

Inte

nsity

Mass (m/z)

9943

9979

2.5 mm

m/z 17,514

m/z 10,935

Overlay

m/z 9943

m/z 9979

OverlayMass ( )

m/z 17,514

m/z 10,935

Overlay9900

0

cortexmedula

1 cm

Histological tumor margin

Tumor

Non-tumor

Skeletal muscle

Vessel

Fibrous stroma

Molecular Determination of Tumor Margins

Robert Caldwell,

15 x 100 spots250 µm resolution

A

B

C

D

E

A

B

C D

E

Histological vs. Molecular Assessment of the Tumor MarginHistological vs. Molecular Assessment of the Tumor Margin:

Proteins are OKProteins are OK…………But What About Drug Distribution/ But What About Drug Distribution/ Metabolism? BetterMetabolism? Better……..Correlating Drug Effects with ..Correlating Drug Effects with

Protein Expression.Protein Expression.

Dose animal♦ orally♦ i.v.

Laser

Remove tissue

♦Cut frozenslice (12 μm)

♦Apply matrix

♦Analyze by:MALDI MS andMALDI MS/MS

Reyzer ML et al, J Mass Spectrom, 38, 1081-1092 (2003)Reyzer ML et al, Cancer Res 64, 9093-9100 (2004)

Days post-treatment0 5 10 15 20 25 30 35

0

200

400

600

800

1000

Tum

or V

olum

e (m

m3 )

Captisol 200 µl (vector)OSI 774 100 mg/kg

MMTV/HER2 Transgenic Mouse Mammary TumorsMMTV/HER2 Transgenic Mouse Mammary Tumors

Contributed by M. Sliwkowski (Genentech, Inc.)

● MMTV/HER2 cells transplanted in FVB female mice.

● Tumor grown to a size of ~200 mm3.

● OSI 774 is an intracellular tyrosine kinaseEGF receptor inhibitor.

● Administered orally for 1 week

MS/MS Analysis of OSIMS/MS Analysis of OSI--774 in Tumor Tissue774 in Tumor TissueTumors removed after a single 100 mg/kg dose of OSITumors removed after a single 100 mg/kg dose of OSI--774774

1

65

432

4 hr

♦ Monitored CAD transition m/z 394 → 278

Spot #3

240 260 280 300 320 m/z

330

Inte

nsity

278

N

N

NH

CHC

O

O

O

O

m/z 278.1+H

m/z 336.2+H++H

N

N

NH

CHC

O

O

O

O

m/z 278.1+H

m/z 336.2+H++H

MW = 393.4

100 200 300 400 500

278.1

336.2

[OSI774 + H]+

m/z

Inte

nsity

100 200 300 400 500

278.1

336.2

[OSI774 + H]+

m/z

Inte

nsity

Dose Dependence of Protein Alteration (20 hr after dose)Dose Dependence of Protein Alteration (20 hr after dose)

9 control mice,Av of 54 spectra

3x3 dosed mice,Av of 9 spectra per dose

Rel

ativ

e in

tens

ity

thymosin β 4

4700 4780 4860 4940 5020 5100

8300 8400 8500 8600 8700 8800Mass (m/z)

Control10 mg/kg30 mg/kg100 mg/kg

Raw Spectra

Txt File Export

Baseline/ noise subtraction

Recalibration/ Realignment

Normalization

TIC

Wavelet

Processed Spectra

Peak Picking

Binning

Supervised Feature Selection

Supervised Classification (training set)

WMA

T-Test

Biomarker List

KNN

SVM/ LOOCV

Visual Inspection of Peaks

rule out noise

chemical adducts

Prep

roce

ssin

gPe

ak S

elec

tion

Analysis of MALDIAnalysis of MALDI--TofTof Data Data –– The WorkflowThe Workflow

Recent Example; No Data Processing – Raw Files

Check For Saturation!

2.5kDa 13.5kDa

Post Baseline and Normalization Post Baseline and Normalization –– Pre AlignmentPre Alignment

*

2.5kDa 13.5kDa

Check Alignment!

*

PostPost--BSL/Norm/AlignBSL/Norm/Align[No Smoothing][No Smoothing]

Good AlignmentDecreased ErrorSmaller Bin Sizes!

1. Baseline Correct (Efeckta)2. Smoothing (none) 3. Calibration (Efeckta)4. Normalize/ Transform (TIC, Wavelet, Log, Ln, CR)5. Standardization (none)6. Peak Picking (WMA – data dependent cutoff )

Testing the Approach:Testing the Approach:Liver Set Doped with Known Proteins Liver Set Doped with Known Proteins

10 Spectra/ Set10 Spectra/ Set

m/z mix 1 2 3 4Insulin (porcine) 5778.6 0.2375 0.11875 0.059375 0.02375Cytochrome C 12361.2 0.95 0.475 0.2375 0.095Apomyoglobin 16952.5 2.375 1.1875 0.59375 0.2375Trypsinogen 23982 2.5 3.75 4.375 4.75

Spectra contain liver extract proteins spiked with the standard proteins listed above. Concentrations are in pmol/uL (μM)

0 10k 20k 30k 40k

-0.8

0.0

0.8

Rel

ativ

e N

orm

aliz

ed In

tens

ities

m/z

NonNormalized TIC STD's CR Wavelet WaveletTIC

8.4k 8.5k

0.0

0.2

0.4

Rel

ativ

e N

orm

aliz

ed In

tens

ities

m/z

NonNormalized TIC STD's CR Wavelet WaveletTIC

InsulinInsulin

2+2+

cytCcytCApoMApoM

TrypTryp

Peaks Identified Using all Peaks Identified Using all Techniques with all Techniques with all stdsstds at 2 at 2 fold diff except fold diff except TrypTryp at 1.3. at 1.3.

TIC and TIC with Wavelet TIC and TIC with Wavelet work best!work best!

0 10k 20k 30k 40k

-0.8

0.0

0.8

Relat

ive

Nor

mliz

ed In

tens

ities

m/z

Std Set with 10 Fold Difference[Set 1 Vs. Set 4]

NonNormalized TIC STD's CR WaveletNorm WaveletSMNorm WaveletTIC

8.4k 8.5k 8.5k

0.0

Rel

ativ

e N

orm

lized

Inte

nsiti

es

m/z



-statistically relevant peaks that are not real!

17k 18k 19k

0.0

0.8

Rel

ativ

e N

orm

lized

Inte

nsiti

es

m/z



Peaks Identified Using all Techniques with all stds at 10 fold diff except Tryp at 2.0.

- logging techniques pick shoulders not peak asymptotes!

log (Protein Concentration)0.01 0.1 1 10

log

(Abs

olut

e In

tens

ity)

1e+3

1e+4

1e+5

Ins (1+)CC (1+)ApoM (1+)

UnprocessedUnprocessed

10000 20000 30000

0

500

1000

1500

2000

2500

3000

3500

4000

Inte

nsity

m /z


log

(Abs

olut

e In

tens

ity)

1e+3

1e+4

1e+5

Ins (1+)CC (1+)ApoM (1+)

UnprocessedUnprocessed

10000 20000 30000

0

500

1000

1500

2000

2500

3000

3500

4000

Inte

nsity

m /z


log

(Nor

mal

ized

Inte

nsity

)

1e+3

1e+4

1e+5

Ins (1+)CC (1+)ApoM (1+)

ProcessedProcessed

10000 20000 30000-500

0

500

1000

1500

2000

2500

3000

3500

4000

Nor

mal

ized

Inte

nsity

m /z

Spectral Processing Increases Detection Range and Sensitivity

These Techniques also Greatly Enhance MALDI Generated Images!

Raw Data Processed Data

Norris J.L., Cornett, D.S., Mobley J.A., Andersson M., Caprioli R.M. Processing MALDI Mass Spectra to Aid Biomarker Discovery and Improve Mass Spectral Image Quality, International Journal of Mass Spectrometry, 2006 (In Print).

What About Statistics?

• Now we can apply these data sets to really any type of statistics that one might want to employ.

• But be careful, we have to insure that the peaks are real and not noise and not adducts of sodium, potassium, or matrix.

• This is “very” common and the mass spec person needs to be involved again at this point!

2000 4000 6000 8000 10000 120000.0000

0.0015

0.00300.0000

0.0015

0.0030 Healthy Group

CaP Patients

2910 2925 2940 2955 29700.000

0.001

0.002

5760 5820 5880 594000000

00025

00050

00075

↓ CaPm/z 5913

↓ CaPm/z 2935, 2951

9080 9120 9160 9200

0.0003

0.0006

0.0009↓ CaP

m/z 9129

9360 9390 9420 9450 94800.0000

0.0008

0.0016↓ CaP

m/z 9420

Evaluation of Peaks in Evaluation of Peaks in PairwisePairwise AnalysisAnalysiswith a Std. Err Plotwith a Std. Err Plot

Hierarchical Clustering Analysis Carried out Hierarchical Clustering Analysis Carried out Statistically Differing Peaks from a Training SetStatistically Differing Peaks from a Training Set

56C

38C

64C

57C

37C

28C

62C

27C

71C

70C 9C 4C 50C

18C

17C

11C 8C 10C

79N

47N

48N

82N

61N

118N 9N 63N

44N 1N 11N

94N

93N

98N

64N

23N

119N 81N

97N

117N 62N

29N

28N

21N

78N

46N

66N

65N

80N

49N

115N

CaP NLTop 280 data Point s ( 36 Peaks)

688694209129456747074592492159133906295139194032236623492283293573277194713035652871238484482675567236668950297862202437223025132305253220662786

↑C

aP↓

CaP

M/Z

2935

--- --- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --- --- --- --- --- --- --- --- --- --- --- -- -- -- -- -- -- -- --- -- --- -- -- -- -- -- -- -- -- -- -- - -- - -- --- -- -- -- -- -- -- - --- - - - - - - - -- - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - -- --- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

0

20

40

60

80

100

120

CaP NLn=70 n=98

NormalLocal CaP

Metastatic CaP

g y p

-----

-

Clinical Serum Profiling Experiment;Clinical Serum Profiling Experiment;Example of Hierarchical Clustering AnalysisExample of Hierarchical Clustering Analysis

Carried out on 168 Patient Sample SetCarried out on 168 Patient Sample Set

General Stats:N Median Age PSA > 4.0 PSA < 4.0

Normals 98 55 12 (4.2-7.8) 86CaP 70 64 62 8 (1.9-3.6)

Clinical Test Based on Specific Protein PeaksClinical Test Based on Specific Protein Peaks

Diagnostic Efficiency as Determined through Class Prediction by Weighted Voting Scheme

Diagnostic Stats:

# Variables N Sensitivity Specificity PPV NPV Non-Predicted280 168 94.1 % 99.0 % 0.99 0.96 5

Sensitivity; TP/ (TP + FN)Specificity; TN/ (TN + FP)PPV; TP/ (TP + FP)NPV; TN/ (TN + FN)Note: Non-predicted values were not included in final calculations

TP – true positiveTN – true negativeFP – false positiveFN – false negative

Another Example:Biomarker Identification and Classification was Applied to a

Single Section; (May be able to differentiate DCIS from IMC??)

Cornett D.S., Mobley J.A., Dias, E.C., Andersson M., Arteaga C.L., Sanders M.E., Caprioli, R.M.; Histology Directed MALDI-MS Profiling Improves Throughput and Cellular Specificity in Human Breast Cancer, Journal of Molecular and Cellular Proteomics 2006 (in print).

What to Do with These Peaks?

• Top Down Proteomics• Bottom up Peptidomics• Medium Down Approaches• Top Down Directed

What to do With these Markers?

• First and foremost validation using immuno-directed or quantitative MS techniques must also be carried out.

• Mechanistic studies, i.e. knocking out a gene found to be involved in the disease process (LOF).

• This can be combined with global or directed stable isotope label studies in cell culture (i.e. SILAC, ITRAQ, ICAT).

Ex: HTP Validation of Novel Markers Ex: HTP Validation of Novel Markers with Multiplex Bead Assays (with Multiplex Bead Assays (LuminexLuminex))

Multiplex Bead Assay for cytokinesThe highlighted area represent populations of fluorescent beads, distinctively labeled, and carrying capture antibodies for sandwich assay of different cytokines. All detection antibodies carry the same fluorophore, which is read in a third channel to quantify sample cytokine concentration

Bosch I. et. al. work in progress!

Quantitative Proteomics(Mechanism Studies)

• Stable Isotope Tags:– ICAT (isotope coded affinity tag)– SILAC (stable isotope labeled AA in cell culture)– iTRAQ (Isotope tags for relative and absolute and

quantification)– 18O Digests (labels trypsin digested peptides at lysine

and arginine)– Many Other Chemical Tags

Conclusions??

• Draw your own…………..• Just Another Tool in the Toolbox???• Too Soon to Know, Let’s see where it takes us!

FundingNIH (NCI, GMS, NIDA)Vanderbilt University

Acknowledgments

OthersPer Andren, Uppsala UniversityRobert Weil, NIH Walter Korfmacher, Schering-PloughMarkus Stoeckli, Novartis

Vanderbilt CollaboratorsCarlos Arteaga (Breat SPORE Dir.)David Carbone (Lung SPORE Dir.)Robert Coffey Jr. (GI SPORE Dir.)Marie-Claire Orgebin-CristAriel DeutchDennis HallahanPat Levitt Robert MatusikJason Moore Hal MosesJennifer PietenpolNed Porter Yu ShyrRobert WhiteheadKen Schriver

MSRCRichard Caprioli (Director)Shannon CornettJames MobleyMichelle ReyzerJeremy NorrisRobert CaldwellErin SeeleyStacey OppenheimerSheerin Khatib-ShahidPierre ChaurrandKristin BurnumKristen HerringSaturday PuolitaivalHans Reudi AerniGreg Bowersock (IT)Ken Shriver

Questions?Questions?

Documents

Profiling and Imaging Mass Spectrometry