fundamental studies of separation science principles and metrics instrumentation and sensor...

Fundamental Studies of Separation Science Principles and Metrics

Instrumentation and Sensor Development

Data Analysis – Chemometrics – Software

Methodology Design and Optimization

Advances in Separation Science Knowledge and Technology:

10 nm

Synovec Research Group

Robert E. Synovec U. Washington, Chemistry

………from high-speed analysis of simple mixtures to the analysis of complex samples

• Discovery-stage fundamental studies

• Real-time analytical technology

• Process optimization and control

Our research focus:

• Metabolomics

- Food quality and safety - Bacteria - Yeast - Mice - Human disease profiling - Primates, related to human health

• Fuel characterization (Bio and Fossil)

• High Speed GC-on-a-chip

• On-line, Real-time Chromatographic Retention Time Alignment

• Chemometric Software Development

Current research projects:

Metabolomics

“Metabolomics is the study of the small molecules that are an integral facet of cell biology, where the metabolites found 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.”

…at the Discovery Stage ofthe process analysis effort….

Need to learn what to control !

- Up in derepressed cells

- Up in repressed cells

Gene Expression

- Up in repressed cells

- Up in derepressed cells

Metabolite Concentration

Yeast cell studies with different growth conditions

Validation Study Analytical Goal:

Measure metabolite concentration ratio, in different growth conditions i.e., the [DR] / [R], to link control of gene expression to metabolic changes that occur in response to glucose limitation.

Glycerol

Glycerol-3-P Glyceraldehyde-3-P

Glucose-6-P

Dihydroxy-acetone-P

Pyruvate

L-Lactate

AcetaldehydeEthanol

Acetate Acetyl-CoA

Propionate

Propionyl-CoA

2-methyl-citrate

2-methyl-isocitrate

Threonine

Isocitrate

Formate

2H

CO2

2H

2H

2H

2H2H

2H

Oxaloacetate

Phosphoenol-pyruvate

Citrate

-KetoglutarateSuccinyl-CoA

Succinate

Fumarate

Malate

2H

2H

2H

Glyoxylate

ADH1

ADH2

GUT119

GUT27.8

FDH135

ALD4,68.6, 3.4

CYB28.1

PDC5,-2.6

ACH15.2

-2.1

LSC2

FUM1

PYK1

PYC1,2

PDA1,2etc.

2H2H

PGI1

2H

TPI1-2.0?

ACS143

,2

GPM1

PGK1

TDH1,2,3

PFK1,2

FBA1

CIT350

KGD1,2LPD1

IDH1,2

IDP221

ICL128

SDH3,43.1, 3.0

,1,2

ENO1,2-2.4, -2.1

ACO13.2

ACO13.2or

Pyruvate

ICL221

MLS111

BAT1-5.0

,2

128

1,6

92

6.9

ACS143

,215

2.2

PDH143

5.8, 4.7, 50

PathwaysFructose- 1,6-P

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

Comprehensive Two-Dimensional Gas Chromatography (GC x GC)

Column 1 Time, seconds

Co

lum

n 2

Tim

e,

seco

nd

s

FID

Sig

nal

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

Column 1 (Non-Polar)–10-m x 320-m i.d. –0.25-m poly(5% diphenyl/ 95% dimethyl siloxane)

–35C initial, 120C/min program, 25.5 psi H2

Column 2 (Polar)–2-m x 250-m i.d. –0.2-m cyanopropyl polysiloxane

–100C, 25.0 psi H2

15 20 25 30 35 40 45

0.3

0.4

0.5

0.6

Column 1 Time, Seconds

Co

lum

n 2

Tim

e,

Se

co

nd

s

Alcohols

Ketones

Alkyl Benzenes

Alkanes

15 20 25 30 35 40 45

0.3

0.4

0.5

0.6

Column 1 Time, Seconds

Co

lum

n 2

Tim

e,

Se

co

nd

s

Alcohols

Ketones

Alkyl Benzenes

Alkanes

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

Spectral Detection (GC x GC - TOFMS)

• Complete mass spectra peak identification

• Fast 500 spectra / second Peak widths on column two ~ 50 ms

• Adds another selective dimension 3rd - order technique, benefit by using chemometric software

Time 1, minutes

Time 1, minutes

Time 1, minutes

Time 2, seconds

Time 2, seconds

Time 2, seconds

Ion

Co

un

tsIo

n C

ou

nts

Ion

Co

un

ts

Extracted Ion Chromatograms

m/z 217

m/z 128

m/z 73

m/z

Time 1 Tim

e 2

Data Cube

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

We analyze the RAW data!

Typical data, yeast grown in glucose conditions

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 !•Many data runs…a huge amount of data to process !

ISSUES:

Repressed Yeast Sample: subsection shows excellent chromatographic separation efficiency in two dimensions !

m/z 73

But not all of the 590+ peaks are important……..need high-throughput data reduction !

Chemometric data analysis tools: utilize 3rd order data structure

(1) Discover sample-class distinguishing locations in 2D separation space – Data reduction by a 3D Fisher ratio method, Signal ratio method

(2) Targeted metabolite analysis: 3D mathematical resolution, confirmed mass spectral identification and quantification – PARAFAC GUI ….state-of-the-art software tools to apply powerful Linear Algebra concepts

Discovery-Based Approach:Discovery-Based Approach: comprehensively explore the comprehensively explore the data using chemometric classification/data reduction data using chemometric classification/data reduction methods to methods to “discover” “discover” the sample-distinguishing metabolitesthe sample-distinguishing metabolites

From high throughput data reduction and analysis to valuable information !

TCA Cycle

R- glucose DR- ethanol

Glucose

Ethanol Acetyl CoA

glycolysis

Study Protein Function with Metabolomics (Snf1 mutant study)

• Study this mutant strain at metabolome level• Wild type (R & DR)• Mutant (R & DR)

• In the absence of specific proteins (Snf1 Protein Complex) cells are unable to switch from using glucose to ethanol

~ 160 metabolites analyzed

TCA Cycle

Glucose

Ethanol Acetyl CoA

glycolysis

Study Protein Function with Metabolomics (Snf1 mutant study)

XX

ΔSnf1 cannot

complete the shift

• Study this mutant strain at metabolome level• Wild type (R & DR)• Mutant (R & DR)

• In the absence of specific proteins (Snf1 Protein Complex) cells are unable to switch from using glucose to ethanol

~ 160 metabolites analyzedR- glucose DR- ethanol

0

0.00002

0.00004

0.00006

0.00008

0.0001

0.00012

0.5 2 4 6Time (hours)

No

rmal

ized

(T

IC)

PA

RA

FA

C v

olu

me

Fumarate

• TCA Cycle is active in DR conditions• Snf1 protein complex needed to make shift from R

to DR conditions

TCA Cycle

Glucose

Ethanol Acetyl CoA

glycolysis

Cacao Beans and the Chocolate Industry

Organic, Fair Trade, Bean-to-BarChocolate Factory, Seattle, WA

Differences can be IdentifiedDifferences can be Identified

UNMOLDED

MOLDED

UnmoldedMolded

Analyte 3

0

1E7

2E7

3E7

4E7

5E7

6E7

1 2 3 4 5 6 7 8 9 10

Pea

k A

rea

Bean Number

Analyte 4

1 2 3 4 5 6 7 8 9 100

5.0E5

1.0E6

1.5E6

2.0E6

2.5E6

Bean Number

Others analytes are elevated in Molded Samples:

Some analytes are elevated in Unmolded Samples:

Pea

k A

rea

Analyte 1

0

2.0E7

4.0E7

6.0E7

8.0E7

1.0E8

1.2E8

1.4E8

1.6E8

1.8E8

1 2 3 4 5 6 7 8 9 10Bean Number

Analyte 2

0

5.0E6

1.0E7

1.5E7

2.0E7

2.5E7

3.0E7

1 2 3 4 5 6 7 8 9 10Bean Number

Separation conditions must be fully optimized

Fully Integrated Micro-GC:•Injection

•Separation•Detection

Minimal Dead VolumesPotential for Large

Dead Volumes

Standard GC:•Injection

•Separation•Detection

Miniaturization of Instrument Components

GC-on-a-chip:Instrumentation Challenges of High-Speed GC

• 50 sq. micron channels x 30 cm

• 30 sec CNT growth time

• Integrated thin film resistive heating:

5 nm Ti 100 nm Pt

Microfabricated GC-on-a ChipMicrofabricated GC-on-a Chip

Reid, V.R., Stadermann, M., Bakajin, O., Synovec, R.E. Talanta, 2009, 77, 1420-1425.

1 μm

SEM image Back of Chip

HydrogenCarrier

Gas

Commercial GC Injector

Diaphragm Valve Injection

Voltage/Grounding

Leads

VariableAC Power

Supply(0 -120 V)

V1 V2

HydrogenCarrier

Gas

Commercial GC Injector

Diaphragm Valve Injection

Voltage/Grounding

Leads

VariableAC Power

Supply(0 -120 V)

Vent FID

V

DeactivatedSilica Capillary

Leads

HydrogenCarrier

Gas

Commercial GC Injector

Diaphragm Valve Injection

Microfabricated SWCNT Column 30 cm, 50 μm x 50 μm

Voltage/Grounding

Leads

VariableAC Power

Supply(0 -120 V)

FID

V

DeactivatedSilica Capillary

Leads

FID

V

Top of Chip

with Carbon Nanotube (CNT) Stationary Phase and High-Speed Resistive Heating with Carbon Nanotube (CNT) Stationary Phase and High-Speed Resistive Heating collaboration with Lawrence Livermore National Lab (LLNL)collaboration with Lawrence Livermore National Lab (LLNL)

Solution to General Elution Problem: Rapid Temperature Solution to General Elution Problem: Rapid Temperature Programming via Resistive Heating ~ 1500 °C/minProgramming via Resistive Heating ~ 1500 °C/min

(Hexane, Octane, Nonane, Decane and Undecane)Ti = 50 ºC, H2 carrier gas at 10 psi, 15 ms injection pulse

Application of 36 V yields 1560 ºC/min

0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.40

0.2

0.4

0.6

0.8

1

1.2

Time, seconds

FID

Sig

nal,

volts

50 °C 115 °C

C11

C6

C8C9

C10

Recently Joined:

Angie MadridMahmoud Al-Shaer Ryan WilsonTom Dearing (post doc)

Pictured:

Rachel Mohler (PhD)Chris SieglerVanessa Reid (PhD)Jeremy NadeauLiz HumstonNate Watson (MS)Matthew VanWingerden (UG) Jamin Hoggard (PhD, post doc)Thomas Skov (PhD, R. Bro)

Synovec Research Group

Funding and Support: NIH, WTC, Theo Chocolate, LECO, PNNL, LECO, LLNL, CPAC and various sponsors

After Today’s Webinar• Please go to the CPAC

web site (www.cpac.washington.edu) for the program and registration details of the CPAC Spring Meeting, May 4-7, 2009

• We would like you to respond to a short questionnaire regarding the topics of this webinar – please provide your e-mail address to nan@cpac.washington.edu