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High-throughput phenotyping – taking crop biotechnology to the next level Michael H. Malone, Ph.D. PhenoDays 2011 Wageningen, The Netherlands October 12-14, 2011

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Outline 1. Monsanto’s mission: Increasing crop yields to support growing food demand 2. Fusing automation and imaging technologies to build a high-throughput plant physiology screening platform 3. Converting a wealth of phenotypic data into actionable information 4. Assuring quality at 100,000 images per day

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Monsanto’s mission: Increasing crop yields to support growing food demand Ž 3


The demand for food is rapidly outpacing supply Factors driving demand include:

Increasing Grain Demand

Increasing World Population Over 9 billion people by 2050

Shrinking Arable Land 1.0 Per Capita ACRES

Increasing Protein Demand

0.75

0.5

0.25

1961

1980

2000

2020

2030F

ARABLE LAND PER CAPITA WORLDWIDE

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Biotechnology can improve crop yield with genes that improve resource utilization and stress tolerance

Nitrogen Utilization Efficiency Water Utilization Efficiency

Intrinsic Yield Potential

Improved yield over elite varieties

Stress Tolerance

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High-throughput screening is key to delivering a strong product pipeline

1This

is the estimated average probability that the traits will ultimately become products based on Monsanto experience. These probabilities may change over time. Commercialization is dependent on many factors, including successful conclusion of the regulatory process.

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Automation and imaging merge to enable indepth phenotyping without sacrificing throughput Discovery

Phase 1

Phase 2

Phase 3

Phase 4

Field testing Gene Discovery

Monsanto’s Yield & Stress Gene Discovery Engine

Gene transformation and seed production

High Throughput Physiology Screening

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Fusing automation and imaging technologies to build a highthroughput plant physiology screening platform 速 8


The Automated Greenhouse @ Monsanto’s Research Triangle Park Plant Physiology Center

Youtube - Monsanto Greenhouse 9


True high-throughput phenotyping requires scaling more than just the measurements Automated Sample Handling

• Automated plant handling • Anticipatory environmental controls

High-Throughput Phenotyping

• Image-based characterization of plant growth, structure, and composition • Daily data acquisition and analysis for pipeline screening

Precisely Controlled Treatments

Corn Soy Cotton • Automation enables flexible growth conditions to elicit and measure stress responses • Assays customized for each crop and product concept

Robust Data Analysis And Distribution • High computing capacity for image analysis • Automated statistical analysis and quality control • Report integration with corporate data systems

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Physiological data is acquired via color and hyperspectral imaging The Hyperspectral Advantage Color Data

RGB Spectral Resolution

• Biomass • Plant Height • Growth Rates • Canopy Area • Morphology Hyperspectral Data

Hyperspectral Resolution

• Chlorophyll • Anthocyanin 1660 950 720 680 570 490

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Grand Challenge: Converting a wealth of phenotypic data into actionable information 速 12


Observed phenotypes are a function of the environment and instrumental and biological responses

Is the number an accurate representation of the biological response?

Observed Phenotype = f (Instrument, Environment, Biology)

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Observed phenotypes are a function of the environment and the instrument as well as the biological response Observed Phenotype = f (Instrument, Environment, Biology) Instrumental influences include: • Wavelength calibration

Illumination intensity changes following system-wide bulb replacement

• Detector response • Regression model accuracy • Illumination source intensity

Reflectance

Standard #1

Standard #2

Standard #3

Standard #4

Mar 1

May 1

Jul 1

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Observed phenotypes are a function of the environment and the instrument as well as the biological response Observed Phenotype = f (Instrument, Environment, Biology) Outdoor Temperature

Greenhouse Temperature

100

Degrees Fahrenheit

Environmental influences include: • Daily light integral • Humidity • Nursery source • Temperature

80

60

40

20 September

January

May

September

January

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Observed phenotypes are a function of the environment and the instrument as well as the biological response Observed Phenotype = f (Instrument, Environment, Biology) Biological influences include: • Transgene effect • Germplasm background • Response to treatment • Plant-to-plant variability

Frequency

Optimal Growth Conditions

27

23 18

Biomass (g)

Age (days)

14

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Challenge: Parsing biological response from everything else

Foliar Chlorophyll Content by Sow Date (Control Seedline – Nitrogen Deficit Assay) [Chl]

• Plants grown under nitrogen deficit conditions generally lose chlorophyll over time • Kinetic patterns are not associated with a particular camera or instrumentation line but with sow date Foliar Chlorophyll Content by Hybrid Line (Corn Hybrid Lines – Nitrogen Deficit Assay) [Chl]

Consistency across diverse hybrid lines suggests system is detecting subtle (<10 ppm) changes in chlorophyll in response to daily environmental fluctuations.

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System-wide quality control detects system errors – and one-in-a-million oddities QC checks at all points of process help detect abnormalities when they occur – Inputs (soil, water, seed) – Image acquisition – Data analysis – Result publication

Plant biomass = 830 g Is the number an accurate representation of the biological response? 18


HT quality control â&#x20AC;&#x201C; Images make terrible liars Stray light misidentified as plant

Primary descriptors of image data can be used to evaluate image quality

X Centroid of Detected Plant Material

Age of Plant

Lighting and Detection Algorithm Corrected

Left

Center

Right 19


HT Phenotyping provides high dimension data for classifying biological responses Anthocyanin Content Nitrogen Deficit Optimal Water Deficit

Canopy Area

Water Applied

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Characterizing growth under different conditions identifies a gene with increased water utilization efficiency during drought Drought Conditions % delta, compared to control

Water Utilization Efficiency = Biomass Water Applied

Event 1

2

3

1

1

2

3

1

2

3

1

3

1

2

3

Reduced Nitrogen

2

3

% delta, compared to control

% delta, compared to control

Optimal Growth Conditions

2

1

2

3

1

2

3

1

2

3

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Conclusions

• Automated imaging provides a means to in-depth phenotyping without sacrificing throughput • True high-throughput phenotyping requires scaling the whole experiment • Majority of work lies in converting rich datasets of phenotypic responses into actionable information

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High-throughput phenotyping - Taking crop biotechnology to the next level