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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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Presentation Transcript
slide2
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

slide3
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
slide4
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

slide5
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

slide6

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)

slide7

Australian Plant Phenomics Facility

The Plant Accelerator™

Mark Tester

slide10
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)

slide14
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
slide15

Image acquisition modes

Top View Side View Side View 90°

TechnicalDetails:

Camera: 1280 x 960 Pixel

Optic: 17 mm technical optic

slide16

Plant skeleton analysis

Key to growth dynamics and morphology

  • separation of stem and leaves
  • information about nodes, length of leaves
  • morphology
  • plant growth phase
slide17

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

slide18

Quantitative morphology to characterise plants

  • Areas
  • Node distances
  • Leaf-stem angle
  • Height
  • width

Fingerprinting of morphological data

slide19

Plant colour classification

Key to plant health

slide23
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

slide24
Estimation of shoot biomass
  • But control and salt stressed plants have differentarea-weight ratios

20d old barley

slide25
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

slide26

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

slide27

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

slide28
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

slide29
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

slide30
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

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
lemnatec data system
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

data flow management
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

data management issues
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

wider computational issues
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
plans to address issues
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

slide37
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