Transcript Surface-enhanced Raman Scattering for Metabolomic Studies

Surface-enhanced Raman Scattering for Metabolomics
Roger Jarvis & Roy Goodacre
Contact: [email protected]
School of Chemistry &
Manchester Interdisciplinary
Biocentre, The University of
Manchester
Levels of functional genomics
• Metabolomics
• Metabolomics Technology
Development
– E. coli stress (BBSRC & AZ);
Metabolomics
Genomics
recombinant
mammalian
– Gene
LDI-MS (UK
EPSRC/RSC); The analysis
of metabolites
(typically low
Genome
cancer
molecular cells
weight(BBSRC);
molecules)Oral
in a
biological
SERS (UK
BBSRC)
Transcriptomics
mRNA
organism (EPSRC);
at a given Psoriasis
time, with (Stiefel
the aim of
• Imaging Transcriptome
elucidatingLabs);
gene META-PHOR
function and
(EUdefining
Proteomics (Shimadzu);
Bioinformatics
– Protein
MALDI imaging
biochemical
pathways.
FP6);
Biotrace IP (EU FP6);
Proteome
Integration
Raman, FT-IR imaging
The Metabolome
Plants (BBSRC)
Metabolomics
Metabolite
(ORS); SIMS
(UK
BBSRC)
Metabolome
“The total biochemical composition of a cell,
• Systems Biology
tissue
or organisms at any given time (Oliver et
Phenomics
• Bacterial
Identification
Phenotype
Phenome
– STREPTOMICS (EU FP6);
al., 1998).”
et al. (2002)
– Holtorf
SERS
(UK HOSDB)
SYSMO (EU/BBSRC)
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
Why study the metabolome?
Current
Need
Knowledge of (most)
fundamental metabolic
processes
Mainly E. coli,
S. cerevisieae
Develop understanding to
investigate metabolic
network regulation
Measure cell
components with
MS, FTIR, GCMS
Develop understanding of
responses to genetic or
environmental influences
Ultimate
Goal
Determine gene function
(including Bioinformatics)
Grow mutant & WT cells
under different conditions
Functional genomics
Functional Genomics aims to assign (new) functions to (uncharacterised) genes.
“Genomics and proteomics tell you what might happen, but metabolomics
tells you what actually did happen.”
Bill Lasley, University of California, Davis.
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
Metabolite analysis.
Metabolic Fingerprinting
Crude metabolite
mixtures for
SER(R)S
classification. (FT-IR/Raman/DIMS)
Metabolite target
analysis
SER(R)S
??
Analysis of specific
metabolites.
Particular interest in
low molecular weight
compounds – the
substrates and
products in pathways.
Four
Approaches
Selection of
technology is a
compromise
between speed,
selectivity and
sensitivity.
Metabolic profiling
Quantification of
SER(R)S
pre-defined
targets.
(GC-MS, LC-MS, NMR,
HPLC, LC/MS/MS)
Metabolomics
Unbiased identification of
all metabolites in sample.
(Fiehn, 2001)
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
SERRS Reproducibilty
• We want to use SERRS as a metabolic
profiling and fingerprinting tool
• We know that there is a question mark
over reproducibilty
• Metabolomics requires quantitatively
accurate data
• Therefore we have been looking at
strategies for assessing objectively, the
reproducibility of our SERRS experiments
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
Colloidal Batch-Batch Reproducibility
Extinction
3
• 3 replicate
absorbance
measurements
•  (absorption) max. larger value equates to
a larger particle size
• FWHH (full width at
half height), a larger
FWHH indicates wider
particle size
distribution.
• Extinction - lower
value for the extinction
indicates greater
aggregation
2
FWHH
1
150
100
 max.
50
500
450
400
Ag
citrate
Au
citrate
EDTA
Fructose Glucose
Oleylamine
PVP
Thiol
Colloids prepped by Emma Oleme and Arunkumar Paneerrselvam
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
1230
1167
1186
1102
1038
1049
1077
877
*
949
964
810
*
*
906
Raman photon count (a.u.)
778
EDTA
846
985
1277
SERRS spectra of Cresyl Violet
*
*
*
PVP
*
*
*
*
Ag citrate
* *
*
*
Au citrate
800
850
*
*
900
950
1000 1050 1100 1150 1200 1250 1300
Raman shift (cm -1)
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
Mean SERRS
spectra of cresyl
violet acquired
using the four
colloidal
substrates that
were found to be
SERRS active.
Signal-to-noise ratios (S/N) observed in the
median SERRS spectra of cresyl violet
Substrate
Raman shift (cm-1)
Mean
Au citrate
877
1.24
1049
1.20
1186
1.58
1277
1.88
Ag citrate
846
1.23
877
1.28
985
1.81
1277
2.10
1.60
EDTA
1.09
1.11
1.44
1.72
1.34
PVP
1.29
1.28
1.93
2.03
1.63
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
1.47
MANOVA on the S/N ratios calculated from the SERRS
bands identified in spectra of cresyl violet, from four
active substrates
Ag citrate
Raw SERRS spectra
EDTA
PVP
Wilks' L[a]
0..429
0.061
~ F[b]
1.187
6.890
P[c]
NS
0.000
Row normalised SERRS spectra
0.122
4.187
0.006
0.300
1.856
NS
Wilks' L[a]
~ F[b]
P[c]
0.577
0.712
NS
0.560
0.757
NS
0.543
0.803
NS
Au citrate
0.141
3.749
0.009
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
Quantification of Cresyl Violet using
SERRS
• Bootstrapped
correlation analysis
for the log-log
relationship to area
under the cresyl violet
SERRS band at 930
cm-1
• Dilution series from
5 x 10-6 M to 5 x 10-2
M, using the
• PVP capped
colloidal silver
substrate.
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
Next question – we can find colloids that give
statistically reproducible batch to batch SERS – but
what happens when we start playing with chemistry?
1200
Potassium
choride
1000
I732cm-1
800
2500
Sodium
chloride
2000
600
400
200
0
5
10
15
20
40
% colloidal silver
55
60
70
3000
Potassium
nitrate
2500
2000
1000
I732cm-1
I732cm-1
1500
1500
1000
500
500
0
5
0
10
15
20
40
% colloidal silver
55
60
70
3000
10
15
20
40
% colloidal silver
55
60
70
2500
Optimisation of cytosine SERS
Sodium
nitrate
2000
I732cm-1
5
1500
1000
500
0
5
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
10
15
20
40
% colloidal silver
55
60
70
Cytosine
Power fit
0.15
Batch 1
Batch 2
0.1
0.05
0
R = 0.79295
0.2
log10 S/N 599 cm -1
log10 S/N 599 cm -1
0.2
Power fit
0.15
0.1
0.05
0
-7
-6.5
-6
log10 Concentration (M)
Power fit
1.5
R = 0.86377
Batch 1
Batch 2
log10 Area under 599 cm-1
log10 Area under 599 cm-1
2.5
2
0.5
-7
-6.5
-6
log10 Concentration (M)
Power fit
2.5
1
R = 0.79295
-7
-6.5
-6
log10 Concentration (M)
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
2
1.5
1
0.5
R = 0.86377
-7
-6.5
-6
log10 Concentration (M)
Optimisation of surfaceenhanced Raman scattering
(SERS) experiments
Roger Jarvis, William Rowe, Nicola
Yaffe, Sven Evans, Joshua Knowles,
Ewan Blanch & Roy Goodacre
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
Experimental
Pseudo Full-Factorial Experiment
• 3 colloidal silver preps at 25, 50 & 75% v/v
–
•
6 aggregating agents at 1, 10 & 100 mM
–
•
•
•
hydroxylamine, citrate, borohydride
NaCl, KCl, Na2SO4, K2SO4, NaNO3, KNO3
785 nm NIR Raman probe, 3 s integrations with ~
(Goodness knows what!!) mW power a source, spectral
range (150 - 2900 cm-1)
Single analyte – L-cysteine (100 mM)
Total of 162 experiments,5 replicate measurements for
each giving 810 SERS spectra
This allows us to determine the “optimal” experimental
conditions
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
Cont…
Multiobjective optimisation
• Questions
1.Can we use this experiment to determine the utility of an
directed search algorithm for optimising these conditions
more rapidly?
2.Could some form of interpolation be used to derive further
experiments that yield superior results?
• Objective functions
1.Reproducibility: standard deviation of the Mahalanobis
distance between principal component scores recovered
from replicate spectra
2.Signal intensity: peaks areas calculated for 4 major bands
and meaned across replicates
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
Published results: GC-TOF mass
spectrometer optimization via PESA-II
O’Hagan,S., Dunn, W.B.,
Brown, M., Knowles, J.D. and
Kell, D.B. (2005) Closed-loop,
multiobjective optimization of
analytical instrumentation:
gas chromatography/time-offlight mass spectrometry of
the metabolomes of human
serum and of yeast
fermentations. Analytical
Chemistry 77(1): 290-303.
PESA-II used to optimize
the settings of a massspectrometer to improve the
chromatograms.
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
Optimized:
- Number of true peaks
- Signal-to-noise ratio
- Sample analysis time - throughput
647
Typical SERS spectrum of L-cysteine and Raman bands for which peak
areas were calculated
700
400
300
200
100
400
C-S
Red –
shifted due
to binding at
silver
surface
600
1034
911
500
795
Raman photon counts
600
800
1000
Raman shift (cm-1)
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
1200
1400
Summary of metrics calculated to quantify signal reproducibility and
intensity of enhancement
14
120
12
100
80
Frequency
Frequency
10
8
6
40
4
20
2
0
0.2
60
0.4
0.6
0.8
1
1.2
 Mahalanobis distance
1.4
1.6
Homogeneous distribution
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
0
0
100
200
300
400
500
 peak area
600
700
Skewed Distribution
800
Experiment #45
2000
800
Raman photon count
Summary of Results
 45
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 54
 36
1500
1000
500
 51
500
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300
200
100
0
0.2
Exp.
45
36
54
0
400
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23

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15
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9
155
 63 
Pareto
24
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
27
front
 69
 32
 99  60
 72
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8190  96
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3987
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66
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3
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161
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0.4
0.6
0.8
1
1.2
 Mahalanobis distance
1.4
600
800
1000
1200
-1
Raman shift (cm )
1400
Experiment #54
1500
Raman photon count
 Area under peaks
600
1000
500
1.6
0
400
600
800
1000
1200
Raman shift (cm-1)
1400
Colloid
Amount (% v/v) Agg. Agent Conc. (mM) Enhancement M. dist.
Hydroxylamine
75
K2SO4
100
662.0311
0.8539
Hydroxylamine
75
NaNO3
100
779.4253
0.7642
Hydroxylamine
75
KNO3
100
675.0239
0.6618
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
Multiobjective Pareto optimisation
using the PESA II algorithm
800
• Find solutions which give
best trade-off between 2
objectives
• PESA II is a region based
Pareto selection
algorithm
 45
700
 54
 36
 Area under peaks
600
400
300
200
100
0
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500
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 15
 9  63 
155
24
6

27
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 69
 32
 99  60
 72
8190  96
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 20  4839
 87
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 57
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158  75
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Pareto
front
0.4
0.6
0.8
1
1.2
1.4
– Select a region or
hypercube
– Randomly select individual
from this subset
1.6
 Mahalanobis distance
Analysis to be completed, however:
• Directed search optimises
experimental conditions in 60
iterations
• Interpolation attempted but
hasn’t improved SERS
• Problem!! Our solution
space is quite sparse and
disperse!!
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)
www.biospec.net
Group Leader: Professor Roy Goodacre
Postdocs: Dr Will Allwood, Dr Robert Cormell, Dr Elon Correa,
Dr Roger Jarvis, Dr Yankuba Kassama, Dr Iggi Shadi,
Dr Catherine Winder, Dr Yun Xu.
With Collabs: SERS (4), Metabolomics (2), ToF-SIMS (2)
Research Technicians: Steffi Schuler, Richard O’Connor
PhD Students: Felicity Currie, Katherine Hollywood, Nicoletta
Nicolaou, Soyab Patel, Ketan Patel, Emma Wharfe, Nicola Wood,
Dong Hyun Kim, Will Cheung, Robert Coe.
Laboratory for Bioanalytical Spectroscopy (http://biospec.net/)