Trait phenotyping: About asking the right questions to harness phenomics' progress
Australian Plant Phenomics Facility Mark Tester
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Transcript of Australian Plant Phenomics Facility Mark Tester
Australian Plant Phenomics Facility
Mark Tester
Phenotyping – the new bottleneck in plant science
Genomics is accelerating gene discovery but how do we capitalise on these resources to establish gene function and development of new genotypes?
Physiological characterization of plants is still time consuming and labor intensive
High throughput phenotyping
Phenotyping is essential for– functional analysis of specific genes– forward and reverse genetic analyses– production of new plants with beneficial characteristics
High throughput is essential for phenotyping– in different growth conditions (e.g. watering regimes)– of many different lines
• mutant populations• mapping populations• breeding populations• germplasm collections
The technological opportunity
Relieve phenotyping bottleneck with robotics, noninvasive imaging and analysis using powerful computing
Provide “whole of lifecycle”, quantitative measurements of plant performance from the growth cabinet to the field
Help deliver genomics advances to all plant science - e.g. model systems, cereals, grapevines, natural ecosystems
Accelerate transfer of IP from gene discovery to trait discovery and release of innovative new varieties
Australian Plant Phenomics Facility
Established with NCRIS award of $15.2m to relieve the phenotyping bottleneckTotal package = $53m
Aim: To provide infrastructure based on automated image analysis to enable the phenotypic characterisation of plants- National facility, at the international forefront- Robotics, non-invasive imaging, analysis using powerful computing- ‘Whole lifecycle’ quantitative measurements of plant performance
from the growth cabinet to the field- Ontology-based storage of phenomics data
- Research collaborations, international profile and engagement
Australian Plant Phenomics Facility – two nodes
The Plant Accelerator™ Adelaide
Mark Tester ([email protected])
High Resolution Plant Phenomics CentreCanberra
Bob Furbank ([email protected])
$21 m$32 m
Australian Plant Phenomics Facility – two nodes
Australian Plant Phenomics Facility
The Plant Accelerator™
Mark Tester
ACPFG
The Plant Accelerator™
The Plant AcceleratorTM
High throughput phenotyping of plant populations 4,485 m2 building, 2,340 m2 of greenhouses, 250 m2 for growth chambers Grow >100,000 plants annually in a range of conditions 4 x 140 m2 fully automated ‘Smarthouses’
– Plants delivered on 1.2 km of conveyors to five sets of cameras– High capacity state-of-the-art image capture and analysis equipment– Regular, non-destructive measurements of growth, development, physiology
First public sector facility of this type and scale in the world– Owned by University of Adelaide, opened 29 Jan 2010– National facility to support Australian plant research– Full GM and quarantine status
UniSA and ACPFG established a Chair and Assoc Prof in Plant Phenomics and Bioinformatics ($1.5m)
Measuring techniques relevant for drought 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, control of drought conditions
Image acquisition modes
Top View Side View Side View 90°
Technical Details:
Camera: 1280 x 960 PixelOptic: 17 mm technical optic
Plant skeleton analysisKey to growth dynamics and
morphology
• separation of stem and leaves• information about nodes, length of leaves• morphology• plant growth phase
Color classification of leaves
User defined color classification e.g. to characterise plant fitness under optimum or draught conditions or to distinguish herbicide/genetically modified from other plants
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Pot position
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yellow areadark green arealight green area
Quantitative morphology to characterise plants
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area top
area wide side
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heightarea top
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Angle 5Angle 6
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area top
area w ide side
area small side
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area w ide side
area small side
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Dist 2-3Dist 3-4Dist 4-5
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Fingerprinting of morphological data
•Areas
•Node distances
•Leaf-stem angle
•Height
•width
Plant colour classificationKey to plant health
LemnaTec 2004
Identification of active male flower
But wheat is not as neat….
Growth measurements – counting pixels
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Berkut Krichauff
Time post transplant [days]
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Estimation of shoot biomass
The projected shoot area of the RBG images gives a good correlation with shoot biomass
Tested for various plant species– wheat, barley– rice– cotton– Arabidopsis …
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f(x) = 154154.220319601 x + 19065.3925892172R² = 0.920516039741044
Dry weight [g]
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5wk old barley plants, 8 cultivars
Estimation of shoot biomass
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f(x) = 167806.785972392 xR² = 0.993976565757912
f(x) = 216362.953029618 xR² = 0.995820614282722
control
Dry weight [g]
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20d old barley
But control and salt stressed plants have different area-weight ratios
Measured shoot dry weight [g]
Pred
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Golzarian et al. (2010) IEEE Proceedings Signal Processing, in review
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)
Colour classified image
Line Green area Necrosis 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 informatione.g. boron toxicity screen
Original image
B toxicity - leaf symptoms 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 (1999)
Salinity tolerance - trait dissection
Breeding for overall salt tolerance difficult due to low heritability
Dissection into individual traits suitable for forward genetics approach
Use of The Plant AcceleratorTM to perform high throughput phenotyping
Na+ exclusion
tissue tolerance
osmotictolerance
Munns & Tester (2008) Annu Rev Plant Biol 59: 651-681
Osmotic tolerance screen in bread 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 (day-1)
Berkut
Krichauff
Karthika Rajendran
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
Hardware purchased
• Currently– IBM BladeCenter Chassis– 3 x HS21 blade servers– 6 x HS22 blade servers– 2 x DS4700 storage controller– 8 x DS4000 storage expansion units– 140 x 1TB hard drives– $510K (2008 & 2009)
• Virtualisation with VMware
• Expansion, room for– 5 additional servers– 20 additional hard disks
• TPA acknowledges– Lachlan Tailby (ACPFG)
• Picked up by IBM’s Smarter Planet campaign
LemnaTec Data System
FLUO
1392 x 1040
RGB
2056 x 2454
IR
320 x 256 320 x 256
NIRSnapshot
Smarthousedatabase
Imaging configurations
Conveyor tasks
Watering tasks
Smarthouse operations
• Single database stores acquired data, SmartHouse operation configurations and tasks and analysis results
• No project level data management– Backup, archive, delete– Access control
• Around 30MB per snapshot– 72 GB per day, 0.5 TB per week Analysis results
James Eddes
Data flow / management
SH1
Smarthouse 1 (South)
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OCE
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Smarthouse 2 (North)
Plant Accelerator Project DBs
LemnaTec Production DB
SH2
SH2SH1
Project 1 Project 2 Project 3 Project 4 Project 5
buffe
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Project 6
MID
DLE
DaemonDaemonLemnaLauncher
LemnaLauncher LemnaLauncher
LemnaMiner
Plant Accelerator servers
James Eddes
Building databases, managing export of data from LemnaTec, returning data to LemnaTec for further analyses
Image analyses – LemnaTec image processing grids, quality control, basic statistics
Data service – image directories, processing, analysis spreadsheets, metadata, PODD
Data dissemination Embargo Offsite back-up
Data management issues
James Eddes
• Data acquisition• Data management• Image analysis
• Counting pixels• 3-D modelling – computer vision, machine intelligence
• Statistical analyses• Modeling and biological interpretation
• Plugging numbers back in to the plant• Genetics – aligning phenomics data with genomics data to allow
quantitative genetics
Wider computational issues
• Raise money, hire people, collaborate• NCRIS ALA (Bogdan!) - Systems manager, feeding PODD• NCRIS ANDS - 2 data architects for 1 yr, feeding PODD• EIF programming - Image analysis• - Computer vision (Anton Vandenhengel)• ARC Linkage (LemnaTec) - Image analysis, computer vision• HFSP - Computer vision• - Machine intelligence• Collaboration with - PODD, ALA, etc within IBS• - UniSA node of ACPFG • - Desmond Lun• - Computer vision group of UniAdl• - Anton Vandenhengel
Plans to address issues
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