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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 ?

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Australian Plant Phenomics Facility Mark Tester

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  1. Australian Plant Phenomics Facility Mark Tester

  2. 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

  3. 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

  4. The technological opportunity Relieve phenotyping bottleneckwith 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

  5. Australian Plant Phenomics Facility Established with NCRIS award of $15.2m to relieve the phenotyping bottleneck Total 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

  6. Australian Plant Phenomics Facility – two nodes Australian Plant Phenomics Facility – two nodes $21 m $32 m High Resolution Plant Phenomics Centre Canberra Bob Furbank (robert.furbank@csiro.au) The Plant Accelerator™ Adelaide Mark Tester (mark.tester@acpfg.com.au)

  7. Australian Plant Phenomics Facility The Plant Accelerator™ Mark Tester

  8. The Plant Accelerator™ ACPFG

  9. 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)

  10. 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

  11. Image acquisition modes Top View Side View Side View 90° TechnicalDetails: Camera: 1280 x 960 Pixel Optic: 17 mm technical optic

  12. Plant skeleton analysis Key to growth dynamics and morphology • separation of stem and leaves • information about nodes, length of leaves • morphology • plant growth phase

  13. 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

  14. Quantitative morphology to characterise plants • Areas • Node distances • Leaf-stem angle • Height • width Fingerprinting of morphological data

  15. Plant colour classification Key to plant health

  16. Identification of active male flower

  17. But wheat is not as neat….

  18. Growth measurements – counting pixels mean±SE;n=8

  19. 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 … 5wk old barley plants, 8 cultivars

  20. Estimation of shoot biomass • But control and salt stressed plants have differentarea-weight ratios 20d old barley

  21. 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) Predicted shoot dry weight [g] Measured shoot dry weight [g] Golzarian et al. (2010) IEEE Proceedings Signal Processing, in review

  22. Use of colour information e.g. boron toxicity screen Original image Colour classified image Treated with 100 mM GeO2, 8 d Julie Hayes, Margie Pallotta and Tim Sutton, ACPFG

  23. QTL for Ge tolerance identified using colour imaging overlaps QTL for B tolerance (1999) B toxicity - leaf symptoms Ge toxicity - leaf symptoms Jefferies et al. 1999. TAG98, 1293-1303 Hayes et al., unpubl., using LemnaTec

  24. 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 osmotic tolerance Na+ exclusion tissue tolerance Munns & Tester (2008) Annu Rev Plant Biol59: 651-681

  25. 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 Berkut Krichauff (day-1) Karthika Rajendran

  26. 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

  27. 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 Hardware purchased • Expansion, room for • 5 additional servers • 20 additional hard disks • TPA acknowledges • Lachlan Tailby (ACPFG) • Picked up by IBM’s Smarter Planet campaign

  28. Single database stores acquired data, SmartHouseoperation 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 IR FLUO NIR RGB Snapshot 1392 x 1040 320 x 256 320 x 256 2056 x 2454 LemnaTec Data System Smarthouse operations Imaging configurations Conveyor tasks Watering tasks Smarthouse database Analysis results James Eddes

  29. Data flow / management Plant Accelerator servers LemnaMiner LemnaLauncher Daemon Daemon DATA PROCESSING & MINING Plant Accelerator Project DBs Project 1 Project 2 Project 3 Project 4 Project 5 Project 6 MIDDLE LemnaTec Production DB buffered transfer buffered transfer SH1 SH2 OPERATION & ACQUISITION Smarthouse 1 (South) Smarthouse 2 (North) SH1 SH2 LemnaLauncher LemnaLauncher James Eddes

  30. Data management issues 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 James Eddes

  31. Wider computational issues 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

  32. Plans to address 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

  33. The Plant Accelerator™ team to date Mark Tester Geoff Fincher Helli Meinecke – business manager Bettina Berger – postdoctoral scientist James Eddes, BogdanMasznicz, Jianfeng Li – computer programmers Robin Hosking – horticulturalist Richard Norrish – electricalengineer 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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