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

Abiotic Stress Tolerance in Australian Cereals

• Drought

• Salinity

• Frost

• Hostile soils

– nutrients at toxic levels

– nutrients at deficient levels.

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)

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

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)

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.

High Resolution Plant Phenomics Centre

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

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.

LemnaTec System

Image capture

Side View Side View 90° Top view

Bettina Berger Barley cv. Sahara

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]

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

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?

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

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

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)

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

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

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

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

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

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