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Discovering New Drugs for Disease: Advancing Imaging in the WellDiscovering New Drugs for Disease: Advancing Imaging in the WellDiscovering New Drugs for Disease: Advancing Imaging in the WellDiscovering New Drugs for Disease: Advancing Imaging in the Well

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Webinar SeriesWebinar SeriesWebinar SeriesWebinar SeriesScienceScienceScienceScienceDiscovering New Drugs for Disease :Discovering New Drugs for Disease :

Brought to you by the Science/AAAS Custom Publishing Office

14 September, 201114 September, 2011

Discovering New Drugs for Disease :Discovering New Drugs for Disease :Advancing Imaging in the WellAdvancing Imaging in the Well

Participating Experts:

Brought to you by the Science/AAAS Custom Publishing Office

Jeremy Simpson, Ph.D.University College DublinDublin, Ireland

David W. Andrews, Ph.D.McMaster UniversityHamilton, Ontario, Canada

Sponsored by:

Di i N D f DiDiscovering New Drugs for Disease:Advancing Imaging in the Well

Jeremy C. Simpson

Professor of Cell Biology,School of Biology and Environmental ScienceSchool of Biology and Environmental Science,Conway Institute of Biomolecular and Biomedical Research,University College Dublin,Ireland

AAAS Science webinar, 14th September 2011

Cells contain spatial and temporal information

spatial informationspatial information

spatial and temporal information

The need for high content screening and analysis

Number of genes / compounds studied

Experimental detail obtained

Human genome sequencing

- comprehensive cDNA libraries- genome-wide RNAi reagents

- novel compound librariesnovel compound libraries

Imaging approaches are central to Systems Biology

Imaging modalities

Experimental consistencyp y

Quantification

Information

Pepperkok & Ellenberg (2006) Nature Rev. Mol. Cell Biol. 7:690‐696‘High‐throughput fluorescence microscopy for systems biology’

Cells Primary cells ‘conventional’ cells specialised cells stable cell lines

HCS experiments - so many decisions to make!

Cells

Plate format

Plating density

Primary cells,  conventional  cells, specialised cells, stable cell lines

Multi‐well plates (24/96/384 etc.), chambered slides

Sufficient for quantitative analysis but not impinging segmentationPlating density

Transfection

Reagents

Sufficient for quantitative analysis, but not impinging segmentation

Liquid‐based transfection, reverse transfection, viral mediated

Compound number concentration rangeReagents

Incubation time

Assay format

Compound number, concentration range

Sufficient to see phenotype, but minimising toxicity

Fixed cells (end‐point) or live cells (time‐lapse)Assay format

Assay readout

Replicates

Fixed cells (end point) or live cells (time lapse)

Fluorescence based ‐ number of channels required

Numbers of samples per plate, numbers of replicate platesReplicates

Controls

QC

Numbers of samples per plate, numbers of replicate plates

Positive controls (strong and weak), negative controls, plate position

Plating abnormalities, high toxicity, apoptosisQC

Analysis

Plating abnormalities, high toxicity, apoptosis

Cell segmentation and analysis, statistics, variance  across screen

HCS experiments - so many decisions to make!

Basicassay

Primaryscreen

Secondaryscreen

Identifycandidates

Characterisecandidates

primary screen Primary screen

High sample number: eg: 80,000 siRNAses g p g ,High image number: eg: >1m images

High cell number: eg: >10m cellsLow parameter number: eg: 3 values/cell

er o

f sam

ple

secondary screen Secondary screen

Low sample number: eg: 2,000 siRNAsLow image number: eg: 20,000 images

Low cell number: eg: 0 5m cells

num

be

Low cell number: eg: 0.5m cellsHigh parameter number: eg: >50 values/cellnumber of parameters / cell

What can HCS / HCA tell us?

How much / how many ? Where ? What effects ?

Intact organelleNot internalisedNone

I t li d DisruptedInternalisedMany

Automated quantitative image analysis

Image Processing

• background correction and subtraction• border objects (to keep or not to keep)

• object identification and counting• object segmentation

• object dilation

Object intensity

• mean intensity of fluorescence

Object texture

• Granularity

Object morphology

• area y• variation of intensity (stdev)

• object distribution• radial intensity distribution

• o lo alisation

y• Haralick texture features• Gabor texture features• SER (Spot‐edge‐ridge)

• width• length

• roundness / circularity• idth / len th ratio • co‐localisation• width / length ratio

raw data mask generation

Automated quantitative image analysis

raw data mask generation

subtractive mask segment cells

‘good cells’

‘bad cells’

extract cellular features- morphology- intensity

t t

classify and analyse

- texture

Cell segmentation

fibroblast astrocyte neuron

Physiological relevance of y gcells for particular assay

Ease of segmentation and quantification during analysis

Cell segmentation

Primary cilia Nuclear segmentation and dilation

Bacterial infection Cytoplasm segmentationBacterial infection Cytoplasm segmentation

Analysis of internalisation

Pathogenslow

Plasma membrane

PathogensToxins

NanoparticlesDrugs

low

Early Recycling

medium

yendosome

y gendosome

LatehighTGN

Lateendosome

Golgi

high

Lysosome

Golgi

ens

/ cel

l

Endoplasmic reticulum

path

oge

pathogen load

Analysis of internalisation0 mins

Plasma membrane

Earlyendosome

Recyclingendosome

5 mins

TGNLate

endosome

Lysosome

Golgi

30 mins

Endoplasmic reticulum

Image texture analysis

Intensity

Spot Hole

Ridge Valley

Saddle Edge

Bright Dark

SER texture analysis Image courtesy of Perkin Elmer Columbus / Acapella software)

Image texture analysis

HomogeneityCorrelation

Spearman correlationSubcellular localisations

IntensityGabor minGabor maxSum varianceHoleHoleDarkValleySpotEdgeBright RidgeSaddleContrast

y y x e e k y t e t e e t

Hom

ogen

eity

Cor

rela

tion

Inte

nsit y

Gab

or m

inG

abor

ma x

Sum

var

ianc

eH

ole

Dar

kVa

lley

Spo

tE

dge

Brig

htR

idge

Sad

dle

Con

trast

High differenceHigh similarity

H S

Image texture analysis

nt 2

All featuresSubcellular localisations

Com

pone

Component 1O f t

ent 2

One feature removed

Com

pone

Component 1

0 i 5 i

Analysis of internalisation

Plasma membrane0 mins 5 mins 30 mins

Earlyendosome

Recyclingendosome

Control RNAi

TGNLate

endosome

0.4

0.6

0.8

1

GolgiEndosomes and Golgi

E d

Lysosome

Golgi0

0.2

RNAi

Endosomes

5 15 30 60 90 120 min

Endoplasmic reticulum

0.4

0.6

0.8

1

RNAi

GolgiEndosomes and Golgi

0

0.2

0.4Endosomes

5 15 30 60 90 120 min

Acknowledgements

Collaborators

Oliver Blacque (UCD, Dublin)Kenneth Dawson (UCD, Dublin

Wim Meijer (UCD, Dublin)Rainer Pepperkok (EMBL, Heidelberg)

John Presley (McGill, Montreal)

UCD-Dublin

S j A h fIrish Research Council for Science,

Engineering & Technology

Sanju AshrafMariana BexigaBrendan BuryGeorge GaleaKenan Handzic

Nora LieggiElaine O’Neill

Angela PanarellaVasanth Singan

Juan Varela

Science Foundation Ireland

Perkin ElmerJuan Varela

Webinar SeriesWebinar SeriesWebinar SeriesWebinar SeriesScienceScienceScienceScienceDiscovering New Drugs for Disease :Discovering New Drugs for Disease :

14 September, 201114 September, 2011

Discovering New Drugs for Disease :Discovering New Drugs for Disease :Advancing Imaging in the WellAdvancing Imaging in the Well

Brought to you by the Science/AAAS Custom Publishing Office

Participating Experts:

Brought to you by the Science/AAAS Custom Publishing Office

Jeremy Simpson, Ph.D.University College DublinDublin, Ireland

David W. Andrews, Ph.D.McMaster UniversityHamilton, Ontario, Canada

Sponsored by:

Live Cell imaging: Measuring apoptosis and other cellular responses to drugsusing automated image analysis and machine learning

Drugs

Cellular responses to drugs: Apoptosis, Stress, Autophagy

Damaged Cell

One potential outcome:

Bcl‐2 Proteins Regulate Programmed Cell Death

Bax tBid

Bcl-XL tBid

Caspases execute the cell

Caspases 3 & 7

Using Annexin V to measure apoptosis

Measures externalization of phosphatidylserineve

0.4

0.6nn

V positiv

0.2

entage An

0

CDMSO 0.1

0.3 1 3 10 0.1

0.3 1 3 10 0.1

0.3 1 3 10 0.1

0.3 1 3 10

Perce

TNF Thaps BFA DTTp

Using Annexin V to measure apoptosis

Measures externalization of phosphatidylserineve

0.4

0.6nn

V positiv

0.2

entage An

0

CDMSO 0.1

0.3 1 3 10 0.1

0.3 1 3 10 0.1

0.3 1 3 10 0.1

0.3 1 3 10

Perce

TNF Thaps BFA DTT

Expensive, slow, not very sensitive

p

Control 100 mM NAO 10x DTT

Measuring apoptosis usingNonylacridine Orange (NAO)

Control 100 mM NAO 10x DTT

10x BFA 10x TNFa +CHX

Tony Collins, Fei Geng, Sudeepa Dixit

Measuring apoptosis usingNonylacridine Orange (NAO)

Control 100 mM NAO 10x TG

Analysis of Cell textures

Measuring apoptosis usingNonylacridine Orange (NAO)

Control – 100 nM NAO 10x DTT

Tony Collins, Fei Geng, Sudeepa Dixit, Sean Cianflone

Measuring apoptosis usingNonylacridine Orange (NAO)

Control – 100 nM NAO 10x DTT

Tony Collins, Fei Geng, Sudeepa Dixit, Sean Cianflone

Measuring apoptosis usingNonylacridine Orange (NAO)

Control – 100 nM NAO 10x DTT

Tony Collins, Fei Geng, Sudeepa Dixit, Sean Cianflone

Measuring apoptosis usingNonylacridine Orange (NAO)

Control – 100 nM NAO 10x DTT

010x BFA

Tony Collins, Fei Geng, Sudeepa Dixit, Sean Cianflone

Texture analysis M hi l i d l ifi i d l i i l iMachine learning, automated classification and multiparametric clustering

Support Vector Machine

Sean Cianflone

Using texture analysis to measure apoptosis

Measures changes in subcellular membranes

0.75

1fe

d tr

eate

d Treated’

0.25

0.5

ctio

n cl

assi

fFraction

 ‘T

0

CD

MSO

0.1x

TNF

0.3x

TNF

1xTN

F3x

TNF

10xT

NF

0.1x

Tg0.

3xTg

1xTg

3xTg

10xT

g

0.1x

BFA

0.3x

BFA

1xB

FA3x

BFA

10xB

FA

0.1x

DTT

0.3x

DTT

1xD

TT3x

DTT

10xD

TT

Frac

CDMSO 0.1

0.3 1 3 10 0.1

0.3 1 3 10 0.1

0.3 1 3 10 0.1

0.3 1 3 10

TNF Thaps BFA DTT

F

p

Tony Collins, Fei Geng, Sudeepa Dixit

Using texture analysis to measure apoptosis

Measures changes in subcellular membranes

0.75

1fe

d tr

eate

d Treated’

0.25

0.5

ctio

n cl

assi

fFraction

 ‘T

0

CD

MSO

0.1x

TNF

0.3x

TNF

1xTN

F3x

TNF

10xT

NF

0.1x

Tg0.

3xTg

1xTg

3xTg

10xT

g

0.1x

BFA

0.3x

BFA

1xB

FA3x

BFA

10xB

FA

0.1x

DTT

0.3x

DTT

1xD

TT3x

DTT

10xD

TT

Frac

CDMSO 0.1

0.3 1 3 10 0.1

0.3 1 3 10 0.1

0.3 1 3 10 0.1

0.3 1 3 10

TNF Thaps BFA DTT

F

Tony Collins, Fei Geng, Sudeepa Dixit

Using texture analysis of cellular unhappiness

% SVM classified as treated highest TNF as positive control training group.

Dose response curves

Ctr

highest TNF as positive control training group.

E t i i A t iST

ActDTN

TNF

Intrinsic Apoptosis

Extrinsic Apoptosis

ER Stress/UPR TNTAMCtrCtr

ER Stress/UPRAutophagic cell death

Ctr

Increasing dose of compounds

Tony Collins, Fei Geng, Sudeepa Dixit

But can we tell them apart???

% SVM classified as treated highest TNF as positive control training group.

Dose response curves

Ctr

highest TNF as positive control training group.

E t i i A t iST

ActDTN

TNF

Intrinsic Apoptosis

Extrinsic Apoptosis

ER Stress/UPR TNTAMCtrCtr

ER Stress/UPRAutophagic cell death

Ctr

Increasing dose of compounds

Tony Collins, Fei Geng, Sudeepa Dixit

Dose Response Curves 3 way classification

90100 Ctr

TAMTN

60708090

cells

(%)

reated

’

405060

Trea

ted

cPe

rcen

t ‘Tr

102030P

-2 -1 0 1 2 3 4 50

TN log(conc.)

Tony Collins, Fei Geng, Sudeepa Dixit

Dose Response Curves 3 way classification

90

100 CtrTAMTN

6070

8090

cells

(%)

reated

’

3040

5060

Trea

ted

cPe

rcen

t ‘Tr

1020

30P

-1 0 1 2 3 4 5 60

TAM log(conc.)

Tony Collins, Fei Geng, Sudeepa Dixit

Measuring cellular functions in un-engineered live cellse.g. primary cells, stem cells, cell lines etc.

Use a single assay system to measure: ApoptosisER StressAutophagyAutophagyIntoxication____________________________________Differentiaion of stem cellsDifferentiaion of stem cellsRadiation damage Membrane biogenesisInsulin secretionProtein traffickingEtc.

WWW.ANDREWSLAB.CA

Canada Research Chairswww.chairs-chaires.gc.ca

Webinar SeriesWebinar SeriesWebinar SeriesWebinar SeriesScienceScienceScienceScienceDiscovering New Drugs for Disease :Discovering New Drugs for Disease :

14 September, 201114 September, 2011

Discovering New Drugs for Disease :Discovering New Drugs for Disease :Advancing Imaging in the WellAdvancing Imaging in the Well

Brought to you by the Science/AAAS Custom Publishing Office

Participating Experts:

Brought to you by the Science/AAAS Custom Publishing Office

Jeremy Simpson, Ph.D.University College DublinDublin, Ireland

David W. Andrews, Ph.D.McMaster UniversityHamilton, Ontario, Canada

Sponsored by:

Webinar SeriesWebinar SeriesWebinar SeriesWebinar SeriesScienceScienceScienceScienceDiscovering New Drugs for Disease :Discovering New Drugs for Disease :

14 September, 201114 September, 2011

Discovering New Drugs for Disease :Discovering New Drugs for Disease :Advancing Imaging in the WellAdvancing Imaging in the Well

Brought to you by the Science/AAAS Custom Publishing OfficeBrought to you by the Science/AAAS Custom Publishing Office

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