Robert E. Synovec

Department of Chemistry University of Washington

Seattle, Washington

Implementation of Advanced Gas Chromatography:

From the Discovery Stage to Real-Time On-Line Analysis

13th CPAC Summer Institute, Seattle WA

• Instrumentation Development

• Methodology Design and Optimization

• Data Analysis – Chemometrics – Software

Research in Separation Technology

From high - speed analysis of simple mixtures to the analysis of complex samples:

Analytical Chemistry…advances in measurement science

OVERVIEW

- GC x GC separations and metabolomics,and the role of GC x GC-TOFMS

- Three dimensional separations

- Rapid (potentially on-line) GC-MS retention time alignment to enhance data analysis

DetectorPrimaryInjector

SecondaryInjector

Column 1

Column 2

Instrument Schematic

SampleIntroduction

Modulator

Comprehensive Two-Dimensional (2D) Separations

Apply two separation columns with complementary separation mechanisms or stationary phases to optimize use of 2D separation space “peak capacity”

Comprehensive Two-Dimensional Gas Chromatography (GC x GC), pioneered by John B. Phillips

FID

Sig

nal

15 Component Mixture:REAL-TIME separation into different chemical classes!

Column 1 (Non-Polar)–10-m x 320-μm i.d. –0.25-μm poly(5% diphenyl/ 95% dimethyl siloxane) –35°C initial, 120°C/min program, 25.5 psi H2

Column 2 (Polar)–2-m x 250-μm i.d. –0.2-μm cyanopropyl polysiloxane –100°C, 25.0 psi H2

Comprehensive Two-Dimensional Gas Chromatography with Time-of-Flight Mass

Spectral Detection (GC x GC - TOFMS)

• Complete mass spectra ⇒peak identification

• Fast ⇒ 500 spectra / secondPeak widths on column two ~ 50 ms

• Adds another selective dimension ⇒ 3rd - order technique, benefit by

using chemometric software

Ion

Cou

nts

Ion

Cou

nts

Ion

Cou

nts

Extracted Ion Chromatograms

m/z 217

m/z 128

m/z 73

m/z

Time 1

Data Cube

3rd Order Data• column 1 retention time • column 2 retention time • full mass spectrum at each point

We analyze the RAW data!

Metabolomics

“Metabolomics is the study of the small molecules that are an integral facet of cell biology, where the metabolitesfound in a given sample are inextricably connected to protein expression as manifested by gene regulation.”

“Metabolomics is emerging as possibly the most important of the “-omics” fields, providing complementary information in relation to the genomics and proteomics fields.”

- Up in derepressed cells

- Up in repressed cells

Gene Expression

- Up in repressed cells

- Up in derepressed cells

Metabolite Concentration

PathwaysFructose- 1,6-P

Young, Elton T., et.al. J Biol. Chem. (2003), 278, 26146-26158.

Yeast Cell Studies –

Genetic and Metabolic Pathways

Typical R sample

GC x GC –TOFMS of Repressed Yeast Cell Extract, m/z = 73,Metabolites have been derivatized: m/z = TMS group is a “selective” channel

Over 590 peaks at this m/z alone - Complex !!

DISCOVERY-BASED DATA ANALYSIS TO FIND“INTERESTING” METABOLITES:

GC x GC-TOFMS TIC Chromatogram

TIC

30

Sum

of F

ishe

r rat

ios

Fisher ratios plot reduces data down to differentiating chromatographic peak

locations of interest

Analyze locations of interest in GC x GC-TOFMS data using PARAFAC GUI

KM Pierce, JC Hoggard, JL Hope, RE Synovec et. al., Anal Chem 2006, 78, 5068-5075.

Fisher Ratio = Class-to-Class VarianceΣ(Within-Class Variance)

Yeast Metabolic Cycle (YMC)

Our Research: Study the effects of ultradian cycle on the yeast metabolome. How does the metabolome relate to the genome?

B. P. Tu, et. al. Science 310 (2005) 1152.

In 2005, Ben Tu, Steve McKnight and coworkers reported an ultradian cycle in yeast cells* where the molecular oxygen (dO2) exhibited robust oscillation with a period of 5 hours. ~57% of yeast genes were shown to experience periodic behavior. *(Saccharomyces cerevisiae -prototrophic yeast strain CEN.PK)

*Collaboration with the Steve McKnight group at the U. Texas Southwestern Medical Center and Ted Young group, U. Washington

Yeast Metabolic Cycle (YMC) and GC x GC - TOFMSProduction and Consumption of Molecular Oxygen

Saccharomyces cerevisiae - prototrophic yeast strain CEN.PK

Samples collected every 25 minutes over 10 hours = 24 samples

Rel

ativ

e S

igna

l

0Time (hours)

2 4 6 8

dO2

10

metabolite

B. Tu, R. E. Mohler, J. C. Liu, K. M. Dombek, E. T. Young, R. E. Synovec and S. L. McNight, Proc. Nat. Acad. Sci., 2007, 104, 16886 – 16891.

Can cycling metabolitepatterns be readilyobserved?

What do they look like and how do they relate to dO2?

5 10 15 200

1

2

3

4

5x 10

4

Time IntervalPA

RA

FAC

vol

ume

Cystathionine

5 10 15 200

2

4

6

8

10

12x 10

5

Time Interval

PAR

AFA

C v

olum

e

Myo-Inositol

Similar cycling patterns with different phase:

Methyl Citrate

5 10 15 200

5

10

15x 10

5

Time IntervalPA

RA

FAC

vol

ume

Glucose-6-Phosphate

Spiking pattern: Overlapped cycling pattern:

5 10 15 200

1

2

3

4

5

x 105

Time Interval

PAR

AFA

C v

olum

e

Heatmap based on PCA ordering

serinecystathionine

lysineornithine

2-amino adipinic acidpyruvate

homoserinesuccinate

arginine2-methyl malic acid

leucineglycine

ribulose5Pproline

ribose5Pglucose

NAD fragmentglucose6P

alaninethiamin diphosphate

threoninepseudo uridine

mannitolUDP and G1P

valinefumarate

glycerate3Pphenylalanine

trehaloseisoleucine

myo-inositol3OH butanoic acid

threonic acidaspartic acid

norvaline2OHisovaleric acid

sucroseglucopyranose

lactic acid6-phospho-d-gluconate

methyl citrateglycerol3P

fructoseglycerol

Time Interval5 10 15 20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

44 Identified Cycling MetaboliteShown to the right

41 Unidentified Cycling Metabolites

Three-Dimensional Separations ? !

1D

One-Dimensional Chromatography……..How many analytes?

Time, Column 1

2D

Early 1990’sPhillips and Jorgenson

Comprehensive Two-Dimensional Chromatography

Time, Column 1

Tim

e, C

olum

n 2

2D

Time, Column 1

Tim

e, C

olum

n 2

Are there just 5 analytes ?

3D

Tim

eC

olum

n 3

Comprehensive Three-Dimensional Chromatography

Comprehensive Three-Dimensional Gas Chromatography

GC x GC x GC ….. or GC3

DetectorPrimaryInjector

SecondaryInjector

Column 1

Column 2

SampleIntroduction

First Modulator

TertiaryInjector

Column 3

SecondModulator

N. E. Watson, W. C. Siegler, J. C. Hoggard and R. E. Synovec, Anal. Chem., 2007, 79, 8270-8280

GC3: Columns 2 and 3Good Peak Capacity in 3 s with Complementary Selectivity

Tim

e (m

illis

econ

ds),

Col

umn

3

Time (seconds), Column 20 1 2 30

20

40

60

80

100

120

140

160

180

200

Using Valve-BasedModulators

Peak capacity ~ 20 in 3 seconds !

Columns for Good, Complementary Selectivity**

• Column 1: DB-5 (5% phenyl)-methyl polysiloxane

• Column 2: Rtx-200 triflouropropylmethyl polysiloxane

• Column 3: DB-Wax polyethylene glycol

**Bueno, P. A.; Seeley, J. V. J. Chromatogr. A 2004, 1027, 3-10.

(1) Ethanol(2) Acetone(3) Pentane(4) 1-Propanol(5) Hexane(6) Benzene(7) 1-Heptene(8) Heptane(9) 2-Pentanol(10) 1-Heptyne(11) Toluene(12) 1-Pentanol(13) Octane

(14) Cl-benzene(15) 1-Cl-hexane(16) Ethyl-benzene(17) 2-Heptanone(18) Nonane(19) Br-benzene(20) 1-Br-hexane(21) 3-Octanone(22) Decane(23) Di-ethyl-methyl-phosphonate (24) 1-Br-heptane(25) Undecane(26) 1-Br-octane

Initial Test Mixture – Many Classes of Compounds

Time (minutes), Column 1, DB-5

Tim

e (s

), C

olum

n 2,

Rtx

-200

1

2

3

4

5 6

7

8, 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

5 10 15 20 25 30 35 40 45 50

1

2

3

4

5

CONTOUR PLOT of initial test mix separation - along Columns 1 and 2 -

Time (minutes), Column 1, DB-5

Tim

e (s

), C

olum

n 2,

Rtx

-200

1

2

3

4

5 6

7

8, 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

5 10 15 20 25 30 35 40 45 50

1

2

3

4

5

Sections A, B and C to be viewed on theColumn 2 vs. Column 3 dimensions

A B C

GC3 2D contour plot for Column 1 = 18 - 24 min summed

Time (s), Column 2, Rtx-200

Tim

e (m

s), C

olum

n 3,

DB

-Wax

1 2 3 4 5

40

80

120

160

200

11

12

13

13A

Time (s), Column 2, Rtx-200

Tim

e (m

s), C

olum

n 3,

DB

-Wax

1 2 3 4 5

40

80

120

160

200

16

15

14

B

GC3 2D contour plot for Column 1 = 24 - 30 min summed

Time (s), Column 2, Rtx-200

Tim

e (m

s), C

olum

n 3,

DB

-Wax

1 2 3 4 5

40

80

120

160

200

20

19

17

18

18

GC3 2D contour plot for Column 1 = 30 - 36 min summed

C

Recent study with Triflate (ionic liquid)** stationary phase – Column 2

• Column 1: DB-5• Column 2: Triflate ionic liquid• Column 3: DB-Wax

• Temperature Program: 150 °C to 240 °C at 7°C/min held at 240 °C for 7.14 min

** Column provided by J. A. Crank and D. W. Armstrong, University of Texas, Arlington, TX, U.S.A.

Two samples that both appear to be Diesel fuel……..generally need TOFMS to gain further insight

Tim

e (m

s), C

olum

n 3,

DB

-Wax

Time (minutes), Column 1, DB-5

Tim

e (m

s), C

olum

n 3,

DB

-Wax

Time (minutes), Column 1, DB-5

Diesel Fuel #1 Diesel Fuel #2

“Traditional non-polar (1) vs. polar (3)”

Two samples that both appear to be Diesel fuel……..generally need TOFMS to gain further insight

Tim

e (m

s), C

olum

n 3,

DB

-Wax

Time (minutes), Column 1, DB-5

Tim

e (m

s), C

olum

n 3,

DB

-Wax

Time (minutes), Column 1, DB-5

Diesel Fuel #1 Diesel Fuel #2

“Traditional non-polar (1) vs. polar (3)”

?

Tim

e (s

), C

olum

n 2,

Trif

late

Ioni

c Li

quid

Time (min), Column 1, DB-5

DMMP

DEMP

DIMP

Time (min), Column 1, DB-5

Tim

e (s

), C

olum

n 2,

Trif

late

Ioni

c Li

quid

Diesel Fuel #1 Diesel Fuel #2

The added dimension, in this case the triflate Column 2, provides considerable insight that these two samples have substantial differences…

“Traditional non-polar (1) vs. the other selective polar (2)”

……Very selective for P-O containing compounds

GC – MS AlignmentGoal – Rapid, accurate alignment of all m/z

How to ensure that alignment is robust to different chemical functional groups and/or column degradation, while providing a fastalignment?

Heading toward on-line……..

OUTLINE

GC – MS Alignment Algorithms

- TIC Shift Function Vector - Diagnostic m/z’s- Quantification of aligned GC-MS data

0 5 10 150

1

2

3

4

5

6

7

0 5 10 150

1

2

3

4

5

6

7

Nor

mal

ized

Sig

nal (

x10-

3 )

Nor

mal

ized

Sig

nal (

x10-

3 )

Retention Time (min) Retention Time (min)

(A) TIC of one gasoline sample run using two different programs (to purposely inflict severe retention time shifts) before alignment.

(B) The same gasoline sample run using two different programs after alignment.

A B Program 1Program 2

Program 1Program 2

Total Ion Current TIC Alignment

TIC Shift Vector Functionfor Program 1 vs. Program 2

“Current alignment method for GC data is ~ 10-fold faster than last yeardue to applying a recently developed vectorized approach”

0 5 10 15-30

-20

-10

0

10

Retention Time (min)

Shi

ft (s

ec)

Positive shift requiredfor correction at the startof the separation

Negative shift requiredfor correction at the endof the separation

2.4 2.6 2.80

1

2

3

4

5

6

2.4 2.6 2.80

1

2

3

4

5

6

Nor

mal

ized

Sig

nal (

x10-

3 )

Nor

mal

ized

Sig

nal (

x10-

3 )

Retention Time (min) Retention Time (min)

A B

(A) Expanded region of the previous graph showing the shifting in the regionbetween 2.4 and 3 minutes.

(B) After alignment, the retention time precision has improved drastically.

Applying TIC Shift Vector Function (Early Section)

Applying TIC Shift Vector Function (Late Section)

10.6 10.8 11 11.2 11.40

0.2

0.4

0.6

0.8

10.6 10.8 11 11.2 11.40

0.2

0.4

0.6

0.8

Nor

mal

ized

Sig

nal (

x10-

3 )

Nor

mal

ized

Sig

nal (

x10-

3 )

Retention Time (min) Retention Time (min)

Retention time alignment as a function of m/z was studied to see if there was functional group dependence on shifting.

The top 45 most intense ions were chosen by sorting the total signal from the individual ion chromatograms from GC-MS data.

50 100 150 200 250 300-1

0

1

2

3

4

Tota

l Sig

nal,

arb.

uni

ts

Mass Scan (m/z)

5% of Highest Signal

…..use as “diagnostic” ions?!

0 5 10 15

-20

-10

0

10Region with Signal

Shift

, s

Retention Time, min

Shift Vectors for all 45 ions and the TIF Shift Vectors

The Shift Vectors align one chromatogram to anotherfrom separation Programs 1 and 2

2 4 6 8 10-2

-1

0

1

2

Retention Time, min

Diff

eren

ce in

Shi

ft, s

Difference from Shift Vector for a given m/z relativeto the Shift Vector for the TIC.

If there is no functional group dependence, the Difference plotsshould all be essentially zero, or within the selected limits.

User-selectedcontrol limits

using “diagnostic ions”

alkanesaromatics

etc.

Using Alignment for Quantification

7.1 7.15 7.2 7.250

0.5

1

1.5

2

2.5

3

Sample FromMethod 1

Isobutylbenzene spikedStandards Run withMethod 2

Interference

Nor

mal

ized

Sig

nal (

x10-

3 )

Nor

mal

ized

Sig

nal (

x10-

3 )

Retention Time (min) Retention Time (min)

BA

(A) GRAM was used to obtain quantitative information between the two temperature programming methods (Gasoline samples). Isobutylbenzene was spiked into the Sample at three different concentrations. The spiked samples became the standards. The sample from method 1 was combined with the standards run on method 2.

(B) After alignment, the retention time precision has improved drastically and itis now possible to use GRAM (chemometric deconvolution) with the data set.

Aligned

7.08 7.1 7.120

0.5

1

1.5

2

2.5

3

GRAM Deconvolution of Overlapping Peaks

Sample FromMethod 1

0.2% StandardFrom Method 2

0.4% StandardFrom Method 2

0.8% StandardFrom Method 2

Retention Time (min) Retention Time (min)

Nor

mal

ized

Sig

nal (

x10-

3 )

Sample and Standards Sent to GRAMDeconvolution Results from GRAM

with Relative Concentrations

Nor

mal

ized

Sig

nal (

x10-

3 )

TIC shown, but full GC-MS data were used with GRAM

Accurate and

Precise

Conclusions for GC-MS alignment

• Use of the TIC Shift Vector is acceptable anddesirable if the Shift Vectors for selected diagnostic m/z are “in control”.

• Needing only the TIC Shift Vector saves time when carefully applied to all m/z.

• The TIC shift vector alignment method makes possibleand/or improves GRAM quantification (and PCA).

Software Evaluation, Refinement & Development

• Evaluation and refinement of retention time alignment for process GC

• Understanding and evaluating classification and identification / quantification tools

• Evaluating NeSSITM / GC for gas-phase and liquid-phase analysis – a bridge between process and analysis

• Developing software for automated analysis for on-line process GC, GC x GC and GC x GC-TOFMS (discovery-based analyses)

THANK YOU !