Application of pigment Application of pigment analysis and CHEMTAX to analysis and CHEMTAX to field studies of field studies of phytoplankton communities phytoplankton communities Simon Wright Simon Wright Australian Antarctic Division Australian Antarctic Division
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Application of pigment analysis and CHEMTAX to field studies of phytoplankton communities Simon Wright Australian Antarctic Division.
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Application of pigment analysis Application of pigment analysis and CHEMTAX to field studies of and CHEMTAX to field studies of
Simon WrightSimon WrightAustralian Antarctic DivisionAustralian Antarctic Division
This powerpoint presentation has been cut back considerably to reduce its size from 29MB to somewhat closer to the 2MB requested.
In doing so, I have had to exclude all of my antarctic and shipboard photos (not a great scientific loss), but also a photo sequence on exactly how we filter and extract our samples.
I am placing these separately on a ‘Pigment HPLC’ web site via the Australian Antarctic Division. I will forward the address to the PICODIV site.
I have also annotated some of the slides to make them more stand-alone.
“Find a simple chemical technique for determining the
abundance of phytoplankton”
This talk will consider how far we have come toward that goal.
The application of pigment analysis to biological oceanography was largely pioneered by Shirley Jeffrey. In one of her first post-docs with George Humphrey, she was given the challenge to:
Outline
Historical perspective – development of CHEMTAX
BROKE 1996 – CHEMTAX at work
Optimising pigment analysis and data
CHEMTAX problems
‘unusual’ algae
choice of inputs
variability of algal pigment content
Modelling pigments in the underwater light field
Current directions
Conclusions
CLOUD
UV VIS
ICEDMS
UV Pig
Carotenoids Chlorophyll a
Photosynth
Faeces
ZooplanktonBirds Fish
Whales etc
Grazing Food Chain
Marine Snow
Phytoplankton
Viruses Bacteria
Protozoa
Aggregation
Nutrients N, P, Si, Fe
SINKING
DOM
MIXINGMixed Layer
PYCNOCLINE
CO2 O2
WIND DRIVES MIXING
ADVECTION
LIGHT
How do we measure the abundance of phytoplankton in the presence of protozoa, bacteria, detritus and viruses?
Many species can be identified by electron microscopy but cannot be identified by light microscopy [photos omitted]
Even if they could be identified by light microscopy, the statistics of enumeration means that 10000 cells of each type must be counted to ensure 1% precision.
And….“Die numerische Erfassung von Phytoplankton-Arten gleicht einer Danaiden-Arbeit die mit einer Zerstoerung von Koerper und Seele einhergeht” Haeckel, 1890
(roughly =)…….Plankton counting is a task that cannot be achieved without ruin of body and soul
Chlorophylls and carotenoids are useful chemical markers that, in the open ocean, are only found in living phytoplankton. By chromatographically separating them, we can determine the composition and abundance of phytoplankton populations.
Chl a Astaxanthin
Diadinoxanthin
Fucoxanthin
Neofucoxanthin
Phaeophytin a
Carotenes
Chl b
PeridininNeoperidinin
Neoxanthin
Chlorophyllide aChl c
Phaeophorbide aOrigin
TLCJeffrey 1974
Jeffrey 1974
Pigments Algal types or biological processes indicatedChl aChl c Diatoms and / or chrysomonadsFucoxanthinDiadinoxanthin
Chl b Green algaeNeoxanthin
Peridinin DinoflagellatesChlorophyllide a Senescent diatoms (due to chlorophyllase)Phaeophorbide a Faecal pellets of copepodsPhaeophytin a Us. Trace amounts on all c’gramsAstaxanthin Copepods presentHigh chl c:a ratios Senescent phytoplankton or detritus
In earlier times, we thought in terms of individual marker pigments indicating particular algal types or processes.
Abaychi and Riley 1979
Mantoura and Llewellyn 1983
Wright and Shearer 1984
Zapata et al. 1987
Wright et al. 1991
Kraay et al. 1992
Goericke and Repeta 1993
van Heukelem et al. 1994
Barlow et al. 1997
Zapata et al. 2000
HPLC systems development
Steady improvement in HPLC techniques led to recognition of many more pigment markers
Major marker pigments
Ubiquitous Chl a
Unambiguous Alloxanthin Peridinin Prasinoxanthin
Jeffrey and Vesk (1997)
Major marker pigments
Ubiquitous Chl a
Unambiguous Alloxanthin Peridinin Prasinoxanthin
Shared e.g. Fucoxanthin Chl b Zeaxanthin Violaxanthin
Major marker pigments
We can no longer talk in terms of individual marker pigments. Instead we talk of “SUITES” of pigments that may cross conventional taxonomic boundaries.
By the late 80’s it became very apparent that normal interpretation of pigment data amounted to little more than guesswork.
There was an urgent need for objective computational methods for determining the phytoplankton community composition from pigment data.
Each ratio is iteratively modified to minimize the difference between observed and calculated total pigment concentration
Matrix factorization
(Half of table only)
Currently based on a MATLAB platform
Can distinguish algal groups with qualitatively
identical pigment compositions using differences
in pigment ratios (Wright et al, 1996)
Requires the user to enter the expected mix of
algal components which the software then
optimises
Microscopic examination of the samples is thus
essential
CHEMTAX software
Changes in pigment ratios with depth
It is essential to split samples into a series of depth strata that are computed independently (Mackey et al., 1998, Higgins and Mackey, 2000, Wright and van den Enden, 2000)
Hapto4 Pigment Ratios vs Depth
0
0.5
1
1.5
2
2.5
0 100 200
Depth (m)
Pig
men
t:C
hl
a R
atio
chlc3/chla
19butfu/chla
fucox/chla
19hexfu/chla
diadinox/chla
1004 Samples were split into 8 depth layers. Samples from each layer were computed independently. Graph at left shows the computed ratios for type 4 haptophytes (e.g. Phaeocystis spp.) vs. depth.The smooth change with depth suggests that CHEMTAX is measuring something real.
Phytoplankton community structure and stocks in the East Antarctic marginal ice
zone (BROKE survey, January - March 1996) determined by CHEMTAX analysis of HPLC
pigment signatures
S. W. Wright and R. L. van den Enden (2000) Deep-Sea Research II, 47, 2363 - 2400
Does CHEMTAX work?
An example where it worked well to map phytoplankton communities in the Southern Ocean
2. ‘Hole’ in algal distribution at the ice edge, except for Cryptophytes
3. Generally uniform to pycnocline under ice
4. Importance of frontal features - downwelling tongue from Tmin layer
These observations could not have been obtained using microscopy or any other method currently available.
Optimizing pigment data
Aim: Sensitivity – maximum peak height
Accuracy
Integrity – lack of pigment degradation
Reproducibility of retention times
Data reliability
These aims require care at each step of the process
Optimising pigment data
Field sampling
It is important to realise that the pigment composition of the sample starts changing from the moment it is enclosed in a dark Niskin bottle.
For maximum reproducibility of pigment ratios, all samples should be subjected to the same time delay from collection to end of filtration, and all should be handled in the same light regime (preferably very dim).
For example, in our cruises, it is normally 40 minutes before we can sample the Niskin bottles after the physical and chemical oceanographers have collected their samples. Thus we never see diatoxanthin. It has all been converted to diadinoxanthin in the dark.
Sample filtration
Dim light, cool lab
Fluorescence check (each sample is measured in a Turner fluorometer - double checks HPLC result)
Small filter (13mm GF/F, extractable in 1.5 ml solvent)
Removal of water from filter reproducibly
Double label cryotubes (black pen & engraver)
Freeze in liq. N2 – directly into 45 L Dewar
Sonication in methanol
Small volumes (1.5ml)
Internal standard
140 ng β–apo-8’-carotenal (Fluka)
Precision
Accounts for volume changes
Checks injection status
Straight to refrigerated (-10°C) autoinjector stage
Extraction
More on the internal standard and data reliability
As well as improving analytical precision, the internal standard provides data reliability.
Thus if you have a sample with no chlorophyll but a good internal standard peak, then you know that the injection and the chromatogram are OK.
The filtration may have been faulty (e.g. holed filter). This is where you go back to check the fluorescence measurement you made while filtering.
The fluorescence check has also saved us when fatigued shipboard workers have labelled two sets of cryotubes with the same numbers!
A photographic sequence of pigment extraction has been omitted here, to reduce the size of the file. It will be posted on the Australian Antarctic Division web site. The address will be forwarded to the PICODIV site.
Our extraction procedure
Extraction is performed in 2.5ml plastic syringes, with a leur lock tap.Add 1.5 ml cold methanolAdd 25 ul internal standard solution with ~140ng apo-8’-carotenal (Fluka)(add these first to avoid delays once filter is thawed)Remove frozen filter from cryotubeWhile still frozen, cut 1 x lengthways, 4 x sideways into small pieces, with small scissors. Pieces fall into the syringe and thaw.Extract with a probe sonicator (4mm diameter, 50 W, 60 seconds), moving the syringe up and down to ensure that no pieces of filter avoid the sonic beam.The filter is completely disrupted into a slurry. It gets quite warm.Immediately put a plunger into the syringe, attach a 3 mm dia. leur lock filter (0.45 um, nylon, Advantec MFS Inc) and a needle, and squirt the extract into an amber autosampler vial.Place the autosampler vial immediately into a refrigerated (-10°C) autosampler rack.This process averages 1 min 40 sec from starting cutting to completion.
All syringe parts are washed with ethanol and dried before reassembly
Reproducibility
Make up solvents by weight
Column thermostatted in a water bath (more stable than air oven)
Autoinjection
Tubing minimum length
HPLC Analysis
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
0 5 10 15 20 25 30
c2
pdn
ddx
dnx
dtx
apo8
a
BB
Graph showing reproducibility of retention times through the day for a series of dinoflagellate samples
RT
Sample #
Chl c2
peridinin
diaddinoxdiatoxInt Std
Chl aBB carot
Peak identification
Mixed standard every batch
RT table
Check column performance
Reference spectral library
HPLC Analysis
The SCOR/UNESCO method • separated 52 pigments in 20 minutes C18 monomeric column Ternary gradient
Main technique in our lab Good resolution of marker carotenoids, esp fucoxanthin derivs Excellent resolution of polar chlorophylls Divinyl chlorophylls resolved from chlorophylls Resolution order differs from Wright et al. (1991) Complementary techniques
Prasinophytes TYPE 1 (+prasino):Pseudoscourfeldiamarina
TYPE 2 (- prasino):Pyramimonasdisomata
TYPE 1:
5 species/genera
TYPE 2:
7 Pyramimonas,
1 Tetraselmis,
1 Nephroselmis
Chlorophytes Brachiomonas sp. Brachiomonas sp.
Euglenophytes Eutreptiellagymnastica
Eutreptiellagymnastica
Cyanobacteria Synechococcus sp. Synechococcus sp.
In another expt they compared microscopically estimated C biomass (itself selective) with CHEMTAX attribute of chl a, starting from 2 different ratio files (above)
Århus Bight 1997-99Dinoflagellates
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20
40
60
80
100
Co
ntr
ibu
tio
n t
o c
hlo
ro
ph
yll a
/ C
bio
mass (
%)
Rat_1
Rat_2
C biomasse
Gymnodinium chlorophorum
Århus Bight 1997-99Diatoms
0
20
40
60
80
100
Co
ntr
ibu
tio
n t
o c
hlo
rop
hy
ll a
/ C
bio
ma
ss
(%
)
Rat_1
Rat_2
C biomasse
Århus Bight 1997-99Haptophytes
0
10
20
30
40
50
Co
ntrib
utio
n t
o c
hlo
ro
ph
yll a
/ C
bio
mass (
%)
Rat_1
Rat_2
C biomasse
I believe these correlations were so bad because a whole year’s data (summer and winter) was included in the analysis.
The pigment ratios would have changed between seasons, contravening CHEMTAX’s assumption that pigment ratios are constant through the data set.
Taxon specific subsurface chlorophyll Taxon specific subsurface chlorophyll maxima in the Southern Oceanmaxima in the Southern Ocean
south of Tasmania south of TasmaniaMarch 1998March 1998
S.W.Wright, R. L. van den Enden, F. B. Griffiths, A. C. Crossley
The microscopic data had neither the sampling density nor the statistical precision to determine whether these patterns were real (hence the need for CHEMTAX in the first place).
The only comparable data were flow cytometric counts of cyanobacteria for four stations in the SAZ.
Comparing cyanobacterial counts with CHEMTAX estimates of cyanobacterial chlorophyll showed that CHEMTAX consistently underestimated cyanobacterial abundance near the surface and overestimated at depth (data for 4 stations follows)
0 40 80 120 160 200 240
Depth (m )
0E+000
2E+007
4E+007
6E+007
8E+007
Cells / L
0
0.02
0.04
0.06
0.08
Chla (ug/L)
SAZ CTD 004
CyanobacteriaFlow cytom eter vs. CHEM TAX
0 40 80 120
Depth (m )
0E+000
1E+007
2E+007
3E+007
4E+007
5E+007
Cells / L
0
0.02
0.04
0.06
0.08
Chla (ug/L)
SAZ CTD 019
CyanobacteriaFlow cytom eter vs. CHEM TAX
0 50 100 150 200 250
Depth (m )
0E+000
2E+007
4E+007
6E+007
8E+007
1E+008
Cells / L
0
0.02
0.04
0.06
0.08
0.1
Chla (ug/L)
SAZ CTD 029
CyanobacteriaFlow cytom eter vs. CHEM TAX
0 40 80 120
Depth (m )
0E+000
2E+006
4E+006
6E+006
8E+006
Cells / L
0
0.004
0.008
0.012
0.016
Chla (ug/L)
SAZ CTD 041
CyanobacteriaFlow cytom eter vs. CHEM TAX
If you calculate the amount of cyanobacterial chlorophyll per cell vs depth (CHEMTAX cyano chl / flow cytometer counts), you find that it is relatively constant near the surface, then increases dramatically at depth.
Four stations follow with cyano chl per cell and cell counts vs depth.
NB. Ignore noisy data where cell counts approach zero at depth.
0 40 80 120 160 200 240Depth (m )
0E+000
2E+007
4E+007
6E+007
8E+007
Cells / L
0
2
4
6
8
10
Chla/cell (fg)
SAZ CTD 004
CyanobacteriaFlow cytom eter vs. Chl a / cell
0 40 80 120
Depth (m )
0E+000
1E+007
2E+007
3E+007
4E+007
5E+007
Cells / L
0
2
4
6
8
10
Chla/cell (fg)
SAZ CTD 019
CyanobacteriaFlow cytom eter vs. Chl a / cell
0 50 100 150 200 250Depth (m )
0E+000
2E+007
4E+007
6E+007
8E+007
1E+008
Cells / L
0
4
8
12
16
Chla/cell (fg)
SAZ CTD 029
CyanobacteriaFlow cytom eter vs. Chl a / cell
0 40 80 120Depth (m )
0E+000
2E+006
4E+006
6E+006
8E+006
Cells / L
0
1
2
3
4
Chla/cell (fg)
SAZ CTD 041
CyanobacteriaFlow cytom eter vs. Chl a / cell
0 40 80 120
Depth (m )
0
2
4
6
8
10
Chla/cell (fg)
SAZ CTD 041
CyanobacteriaCHEM TAX Chl a / cell
vs. Depth
SAZ CTD 004SAZ CTD 019SAZ CTD 029
Summary
How does cellular pigment content respond to irradiance?
Is this real?
We compared calculated pigment per cell with data obtained in our lab over the last 2 years measuring :
Variation of pigment content in response to irradiance
Several species cultured under a range of irradiances • 10 – 888 uE m-2 s-1
• ‘marine blue’ filtered light• log phase cultures used
• Homo sapiens• Lana Pirrone - University of Tasmania• Suzanne Roy - Université de Québec, Canada• Peter Henriksen - Danish Environmental Research Inst.
Inlet water flow
Insulation tank
Air flow
Air flowAir flow
Fan
Fan
Fan
Light regime
Powermeter
DC
DC
DC
Light sourceLight source
AC
Outlet water flow
AC
Powermeter
AC
AC
Insulation box
Powersource
Thermo-regulator
Powersource
Light gradient apparatus
0 200 400 600 800 1000
Irradiance (uE)
0
0.2
0.4
0.6
0.8
fg /
ce
ll
0
0.1
0.2
0.3
0.4
0.5
0.6
fg/c
ell
Pigment content per cellvs Irradiance
Pavlova gyrans
Chlorophyll a Fucoxanthin
0 200 400 600 800 1000
Irradiance (uE)
0
0.04
0.08
0.12
0.16
fg /
ce
ll
0
0.04
0.08
0.12
0.16
fg/c
ell
Pigment content per cellvs Irradiance
Pavlova gyrans
Chl c1Chl c2
0 200 400 600 800 1000
Irradiance (uE)
0
0.1
0.2
0.3
fg /
ce
ll
0
0.01
0.02
0.03
0.04
fg/c
ell
Pigment content per cellvs Irradiance
Pavlova gyrans
Diadinoxanthin Diatoxanthin
0 200 400 600 800 1000
Irradiance (uE)
0
0.2
0.4
0.6
0.8P
igm
en
t / c
hl a
ra
tio
0 . 1
0 . 2
0 . 3
0 . 4
Pig
me
nt
/ Ch
l a r
ati
o
Pigment / Chl a ratio vs Irradiance
Pavlova gyransDiadinox / Chl a Diatox / Chl a
0 200 400 600 800 1000
Irradiance (uE)
0
0.04
0.08
0.12
0.16
0.2
Pig
me
nt
/ ch
l a r
ati
o
0 .05
0.1
0.15
0.2
0.25
Pig
me
nt
/ Ch
l a r
ati
o
Pigment / Chl a ratio vs Irradiance
Pavlova gyransChl c1 / Chl a Chl c2 / Chl a
0 200 400 600 800 1000
Irradiance (uE)
0
0.2
0.4
0.6
0.8P
igm
en
t / c
hl a
ra
tio
0 .04
0.08
0.12
0.16
Pig
me
nt
/ Ch
l a r
ati
o
Pigment / Chl a ratio vs Irradiance
Pavlova gyransFuco / Chl a B,B-car / Chl a
We obtained equations for these lines so that we could model the pigment /cell vs irradiance, then put those equations into an underwater light field to model pigment /cell vs depth.
Modelled Pavlova Pigment vs Irradiance
0
0.1
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0.7
0.8
0 200 400 600 800 1000 1200
Irradiance (uE)
Pig
men
t /
cell
(p
g)
Fuc
Ddx
chl a
c2
c1
Modelled Dunaliella Pigment vs Irradiance
0
0.5
1
1.5
2
2.5
3
3.5
0 200 400 600 800 1000 1200
Irradiance (uE/m-2/s)
Pig
men
t /
cell
(p
g)
Zea
Lutein
Chlb
Chla
Modelled Synechococcus Pigment vs Irradiance
0
2
4
6
8
10
12
14
16
18
20
0 200 400 600 800 1000 1200
Irradiance (uE /m-2/ s)
Pig
men
t /
cell
(fg
)
Zea
Chl a
Modelled Irradiance vs Depth
0
200
400
600
800
1000
1200
0 50 100 150 200
Depth (m)
Irra
dia
nc
e (
uE
/ m
-2 /
s)
Light
Vertical Atten. Coeff. = 0.046
Combining these models showed that pigment / cell varied with depth and produced a subsurface chl maximum in all species.
Modelled Dunaliella Pigment vs Depth
0
0.5
1
1.5
2
2.5
3
3.5
0 50 100 150 200
Depth (m)
Pig
men
t /
cell
(p
g)
Zea
Lutein
Chlb
Chla
Modelled Pavlova Pigment vs Depth
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100 150
Depth (m)
Pig
men
t (f
g/c
ell)
Fuc
Ddx
chl a
c2
c1
Modelled Synechococcus Pigment vs Depth
0
2
4
6
8
10
12
14
16
18
20
0 50 100 150 200
Depth (m)
Pig
men
t /
cell
(fg
)
Zea
Chl a
Compare the last graph with the only real data we have:
0 40 80 120
Depth (m )
0
2
4
6
8
10
Chla/cell (fg)
SAZ CTD 041
CyanobacteriaCHEM TAX Chl a / cell
vs. Depth
SAZ CTD 004SAZ CTD 019SAZ CTD 029
Changes in pigment ratios with depth
It seems that CHEMTAX accurately calculated the pigment response of cyanobacteria and hence presumably the other categories.
More data required on pigment content vs light fields
Need to model algal responses to depth
Methods to convert chlorophyll estimates to cells / L or total carbon?
What about variable light climates?
Message
• Lack of data on pigment composition of
phytoplankton
• Few species done
• Ideally should know characteristics of major species
• But usually don’t know what proportion the species are
in, so can’t calculate average
• Refine the limits range
• Normally have to let CHEMTAX calculate average ratio
• Nutrient effects?
How do we model pigment content?
Thought:
Maybe we can use cells such as cyanobacteria and cryptophytes (each readily distinguishable in both flow cytometry and CHEMTAX) to determine their pigment / cell and use them as proxies for the underwater light field.
Current directions
New pigmentsChl c derivsNon polar chl csGyroxanthin diester4 keto-acyl fucoxanthinsUn421
Tracking particular species e.g toxic dinos
Differentiate between nanoplankton and microplankton
• ecologically meaningful • remove contribution of large diatoms from pools
of fucoxanthin etc.• Simplifies CHEMTAX interpretation
Use of size fractionation
Current workup software (Excel, Surfer macros)
Translate CHEMTAX to Excel?
Software should incorporate changing pigment ratios with depth or (preferably) light field.
Software should identify changes of oceanic region and/or gross species composition within a sample set.(following)
After CHEMTAX has finished optimising ratios, it should look at how each sample responds to a change in ratio - e.g. increasing diatom fuco/chl_a ratio may increase total chl_a in some samples (pink) and decrease others (yellow). By looking at how individual samples respond to ratio changes, CHEMTAX may decide the data set can be spilt and optimized separately.
What are the overall conclusions?
first step in discriminating algal types
Power of pigment analysis
first step in discriminating algal types allows hundreds of samples to be analysed
Power of pigment analysis
first step in discriminating algal types allows hundreds of samples to be analysed
BUT
Power of pigment analysis
first step in discriminating algal types allows hundreds of samples to be analysed
BUT lack of pigment ratios
Power of pigment analysis
first step in discriminating algal types allows hundreds of samples to be analysed
BUT lack of pigment ratios more algal cultures must be analysed by the
best methods
Power of pigment analysis
first step in discriminating algal types allows hundreds of samples to be analysed
BUT lack of pigment ratios more algal cultures must be analysed by the
best methods pigment:chl a ratios needed to validate
CHEMTAX in response to:
Power of pigment analysis
first step in discriminating algal types allows hundreds of samples to be analysed
BUT lack of pigment ratios more algal cultures must be analysed by the
best methods pigment:chl a ratios needed to validate
CHEMTAX in response to: light
Power of pigment analysis
first step in discriminating algal types allows hundreds of samples to be analysed
BUT lack of pigment ratios more algal cultures must be analysed by the
best methods pigment:chl a ratios needed to validate
CHEMTAX in response to: lightnutrient regimes
Power of pigment analysis
first step in discriminating algal types allows hundreds of samples to be analysed
BUT lack of pigment ratios more algal cultures must be analysed by the
best methods pigment:chl a ratios needed to validate
CHEMTAX in response to: lightnutrient regimes
need pigment / cell w.r.t. to environment
Power of pigment analysis
first step in discriminating algal types allows hundreds of samples to be analysed
BUT lack of pigment ratios more algal cultures must be analysed by the
best methods pigment:chl a ratios needed to validate
CHEMTAX in response to: lightnutrient regimes
need pigment / cell w.r.t. to environment improved computational methods needed