evaluation of next generation phenotyping: the australian plant phenomics facility
DESCRIPTION
Plant Phenomics at PAG 2011TRANSCRIPT
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Evaluation of Next Generation Phenotyping:
The Australian Plant Phenomics Facility
Geoff Fincher, Mark Tester, Bob Furbank,
Murray Badger
PAG 2011 LemnaTec Workshop San Diego; 18 January 2011
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Abiotic Stress Tolerance in Australian Cereals
• Drought
• Salinity
• Frost
• Hostile soils
– nutrients at toxic levels
– nutrients at deficient levels.
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The key research strategies to enhanced drought and salinity tolerance
Reverse genetics Nominate candidate genes from -omics approaches, bioinformatics; measure effects of
altering levels and patterns of expression in crop and model plants Forward genetics Discover and exploit natural variation • germplasm collections, mapping populations, association panels, mutant populations,
breeding populations • positional cloning of responsible alleles • introgression into commercial lines Useful approach for complex, multi-genic traits (drought, salinity tolerance)
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Phenotyping – overcoming
the bottleneck
Genotyping is relatively fast; genome sequencing advancing!
Phenotyping is still time consuming and labor intensive
Technological advances essential for high throughput phenotyping – robotics, non-destructive image analysis, powerful computers
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Australian Plant Phenomics
Facility – two nodes
The Plant Accelerator™
Adelaide
Mark Tester and Geoff Fincher
High Resolution Plant Phenomics Centre
Canberra
Bob Furbank and Murray Badger
$21 m
$32 m
Australian Plant Phenomics Facility
(Commissioned January 2010)
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High Resolution Plant Phenomics Centre
(Canberra): Phenotyping technology
• Infra-red imaging of transpiration
• Hyperspectral imaging of C, N, phenolics
• FTIR imaging at cellular level
• Chlorophyll fluorescence imaging of photosynthesis
• Hyperspectral sensing of stress tolerance
• Validation and deployment.
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High Resolution Plant Phenomics Centre
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Australian Plant Phenomics
Facility – two nodes
The Plant Accelerator™
Adelaide
Mark Tester and Geoff Fincher
High Resolution Plant Phenomics Centre
Canberra
Bob Furbank and Murray Badger
$21 m
$32 m
Australian Plant Phenomics Facility
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The Plant Accelerator
4,485 m2 building, 2,340 m2 of greenhouses, 250 m2 for growth chambers
4 x 140 m2 fully automated ‘Smart-houses’
Plants delivered on 1.2 km of conveyors to four sets of cameras
High capacity image capture and analysis equipment
50% containment/quarantine - 50% standard glasshouse; 2x imaging stations in each
handle >100,000 plants annually in a range of conditions, automated watering
variable room/compartment sizes and independent environmental control for each room
water purification and re-cycling system.
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LemnaTec System
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Image capture
Side View Side View 90° Top view
Bettina Berger Barley cv. Sahara
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Image analysis data match with
measured phenotypic data in wheat
Rajendran et al. (2009) Plant Cell Environ 32, 237-249
• The projected shoot
area of the RBG
image gives a good
correlation with
shoot biomass
• Tested for various
plant species
– wheat, barley
– rice
– cotton
– Arabidopsis …
y = 154154x + 19065
R² = 0.9205
0
50000
100000
150000
200000
250000
300000
0 0,5 1 1,5
Pro
jecte
d s
ho
ot
are
a [
pix
el]
Dry weight [g]
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Measuring techniques
relevant for drought and
salinity research
Colour imaging – biomass, structure, phenology
– leaf health (chlorosis, necrosis)
Near infrared imaging – tissue water content
– soil water content
Far infrared imaging – canopy/leaf temperature
Fluorescence imaging – physiological state of photosynthetic machinery
Automated weighing and watering – water usage, imposing drought/salinity conditions
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Colour classified image
Line Green area Necrotic area % Necrosis
Sahara 30739 4232 12%
Clipper 11640 15321 57%
Treated with 100 mM GeO2, 8 d
Julie Hayes, Margie Pallotta and Tim Sutton, ACPFG
Use of colour information e.g. Ge/B toxicity screen in barley
Original image
Germanium can alleviate B toxicity: same transporter?
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B toxicity - leaf symptom score Ge toxicity - leaf symptoms
Jefferies et al. 1999. TAG 98, 1293-1303 Hayes et al., unpubl., using LemnaTec
QTL for Ge tolerance identified using colour imaging overlaps QTL for B tolerance
Barley Chromosome 2H
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Clipper Vlamingh
Object properties
• minimum enclosing
rectangle
• minimum enclosing
circle
• convex hull
• compactness
e.g. wilting:
- Alters rectangle
parameters
- Increases area below
top of pot
- Increases the
rotational moment
System can quantify
morphometric parameters
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Measured shoot dry weight [g]
Pre
dic
ted s
hoot
dry
weig
ht
[g]
Golzarian et al. (2010) Plant Methods, submitted
Estimation of shoot biomass
Improved estimate of biomass
when age of the plant is
taken into account
Y = a0 + a1×(G+B+Y)+
a2×(G+B+Y)×H
(H = number of days after seed
preparation date)
(Correction for leaf colour did
not greatly improve weight
estimates)
(Cross validation run 10x)
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Osmotic tolerance in wheat
Mapping population of Berkut x Krichauff – Berkut – CIMMYT
– Krichauff – Australian cultivar
– Berkut higher overall tolerance despite higher tissue [Na+]
Parents – Berkut – 0.65
– Krichauff – 0.33
Range of progeny – 0.13 to 0.96
Mapped significant (21%) QTL to chromosome 1D
(day-1)
Berkut
Krichauff
Karthika Rajendran
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QTL mapping of osmotic tolerance
Significant QTL on chromosome 1D
QTL1D.9 explains 21% of phenotypic variation in the population
Favourable allele comes from Berkut
Chromosome 1D
Karthika Rajendran
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Data acquisition
Data management
Image analysis
Statistical analyses
Modeling and biological interpretation
aligning phenomics data with genomics data
ontologies development.
Offsite back-up
UniSA and ACPFG established a Chair and Assoc Prof in Plant Phenomics and Bioinformatics ($1.5m)
LemnaTec Data System
FLUO
1392 x 1040
RGB
2056 x 2454
IR
320 x 256 320 x 256
NIR
Snapshot
Smarthouse database
Imaging configurations
Conveyor tasks
Watering tasks
Smarthouse operations
Around 30MB per snapshot – 72 GB per day, 0.5 TB per week
Analysis results
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The Plant Accelerator™ team to date
Mark Tester
Geoff Fincher
Helli Meinecke – business manager
Bettina Berger – postdoctoral scientist
James Eddes, Bogdan Masznicz, Jianfeng Li – computer programmers
Robin Hosking – horticulturalist
Richard Norrish – electrical engineer
Lidia Mischis, A.N. Other – technicians
Karthika Rajendran – PhD student
Brett Harris – Honours student
Desmond Lun, Irene Hudson, Mahmood Golzarian
– UniSA /ACPFG maths, stats
Anton van den Hengel – UA computer vision
+ three programmers in UQ to construct the database repository
www.plantaccelerator.org.au www.plantphenomics.org.au
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Acknowledgement of funding
Adelaide Canberra
NCRIS $10 m NCRIS $5.24 m
NCRIS - ALA $0.25 m NCRIS - ALA
$0.25 m
Federal government (stimulus package)
$5 m Federal government
$5 m
South Australian government
$10 m ACT government $1.1 m
University of Adelaide $5.9 m CSIRO $5.8 m
Interest (est.) $0.41 m ANU $3.5 m
Total $31.56 m Total $20.89 m
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Evaluation of Next Generation Phenotyping:
The Australian Plant Phenomics Facility
Geoff Fincher, Mark Tester, Bob Furbank,
Murray Badger
PAG 2011 LemnaTec Workshop San Diego; 18 January 2011