rapid, high-content genome-wide assays using cell microarrays
TRANSCRIPT
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Rapid, high-content genome-wide assays using cell microarrays
Anne E. Carpenter, Ph.D.David M. Sabatini Lab
Whitehead Institute for Biomedical Research
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Apoptosis
Nuclear size & morphology
Cell cycle distribution
(DNA content)
Protein content
Phosphorylation of signaling proteins
(S6, S6K, Akt)
Actin content/ morphology
Nucleolar size, count & morphology
Cell size, count& morphology
Localization ofsignaling proteins
Upregulation of translationUpregulation of
transcription
Amount & distribution of signaling lipids
Mitochondrial size & morphology
What are all the genes doing?
Nature Reviews Genetics5:11-22 (2004)
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Technologies to quickly determine gene function
Create spots of cells, each treated
with a different RNAi reagent
Create a set of RNA interference reagents, one for
each gene Automated image collection
Determine the phenotypic effects of knocking down every gene in the genome
Stain cells for a specific phenotype
Data analysis
Automated image analysis with CellProfiler
“Living cell microarray”every cluster of cells has a different gene knocked-down
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Living cell microarray technology
top view
FastCheapRequires little reagent/ few cellsUniform - can see subtle effectsSynthetic genetic interactions easyHigh content screening/western blots
gain of function
loss of function
chemical genetics/
drug discovery
cDNA expression
RNA interference
small molecules
Ziauddin & Sabatini: Nature, 411:107-110 (2001)Bailey, et al. Nature Methods (2006)
5,600 spots per slide
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Drosophila:RNAi effective, reliable, and convenientNo interferon responseLess redundancy vs. mammalsGenome-wide libraries available
Genome-wide screens in Drosophila
One glass slide: 5600 spots of dsRNA, each knocking down a different gene.
ActinDNA
DNA: dark spot = lack of cells
technique described in Wheeler…Sabatini, Nature Methods 2004reviewed in Wheeler, Carpenter, Sabatini, Nature Genetics suppl., June 2005
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PPV dTOR PP2A CatSub
CG9006 String rpL12Cyclin A
GFP
How can we measure cells automatically?
…plus ~20,000 more images
We want to know quantitatively and automatically: size, shape, intensity, texture, overlap of colors, etc. for every cell in every image.
- less tedious, less biased, quantitative
Result: hundreds of thousands of cell images
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Sophisticated algorithms needed
DNA(nuclei)
Actin(cell edges)
Drosophila Kc167 cells
Jones, Carpenter & Golland (2005) ICCV Workshop on Computer Vision for Biomedical Image Applications
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The CellProfiler project
Allows quantitative analysis of various cell phenotypes in thousands of images (high-
throughput experiments, time lapse, etc.)
Usable by cell biologists without programming
knowledge
Modular design allows custom image analysis modules to be
added
Thouis R. JonesMIT Computer Sciences/ Artificial Intelligence Laboratory: Laboratory of Polina Golland MIT
Anne E. CarpenterWhitehead Institute for Biomedical Research:Laboratory of David Sabatini
Runs on Mac/PC/Unix, plugs into Matlab, can make use of cluster
computing
Image file types: tif, jpg, bmp, gif, cur, dib, hdf,
ico, pbm, pcx, pgm, png, ppm, ras, stk, xwd, avi
Free!
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Measurable cell features
• Location: X,Y• Cell count• Object count within cells (e.g. speckles within nucleus)• Neighbors• Size• Shape• Intensity (of entire object and of the edge of the object)• Texture• Correlation between different colors
For all colors
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Cell count validation
Human HT29 Drosophila
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Cell area validation
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Validation for DNA content (cell cycle)-Drosophila
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Slide scale normalization
DNA content >N
umbe
r of
cel
ls >
Num
ber
of c
ells
>
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Field-of-view illumination correction
image from Steve Bailey, Sabatini lab
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Field of view illumination correction
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Validation for DNA content
images from Andrew Baltus, Page lab, Whitehead Institute
num
ber
of c
ells
DNA content
wild-type knockout
Human HT29mouse
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Validation for DNA content
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Time lapse movies of Drosophila embryos
movie from Victoria Foe, Univ. Washington
Goal: identify nuclei & measure morphology & GFP content
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QuickTime™ and a decompressor
are needed to see this picture.
Time lapse movies of Drosophila embryos
movie from Victoria Foe, Univ. Washington
Goal: identify nuclei & measure morphology & GFP content
GFPcontent
Area
Shape
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Time lapse movies of Drosophila embryos
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Antibody staining intensity - Mouse tissueGoal: score cells as positive or negative for the red-stained Mvh
image from Andrew Baltus, Page lab, Whitehead Institute
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Membrane localizationGoal: quantify the localization of proteins
Thy1
70%
80%
90%
100%
110%
120%
130%
Nucle
usEd
ge o
f Nuc
leus
Cyto
plas
mMem
bran
e
p = 10-9
p = 10-8
p = 10-7
p = 10-9
RFP
70%
80%
90%
100%
110%
120%
130%
Nucle
us
Edge
of N
ucle
usCy
topl
asm
Membr
ane
GFP
70%
80%
90%
100%
110%
120%
130%
Nucle
usEd
ge o
f Nuc
leus
Cyto
plas
mMem
bran
e
Bailey, Ali, Carpenter, Higgins, Sabatini, Nature Methods (2006)
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Thresholded Cytoplasm/Nucleus IntensityDose Response
00.10.20.30.40.50.60.70.80.9
1
0 00.9
7656
251.9
5312
5
3.906
25
7.812
5
15.62
5
31.25 62
.5 125
250
150
Dose of Wortmannin (nM)
Cytoplasm-nucleus translocation assay
error bars = SEM
Goal: quantify the localization of proteins
CellProfiler results V-factor
Z-factor Wortmannin LY294002
0.91 0.86 0.83
images from BioImage
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Time
Speckle-counting assay
0
5
10
15
20
25
30
35
40
45
0 4 8 16
Time (Hours
Goal: count and measure phospho-Histone2AX speckles
images from Scott Floyd, MIT
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First genome-wide screen: in progress
DNA staining: cell countcell cycle distributionchromatin texturenuclear sizenuclear morphology
Actin staining: cell sizecell morphologyactin contentactin texture
phospho-H3: p-H3 amountp-H3 localization
Every gene can be screened in a single experiment using four microscope slides!
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Data analysis: Population measures
Cell count
Average cell area
Correlation between actin and phospho-Akt staining
black = low values, white = high values
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Data analysis: Population measures
True high-content data set produced by multi-parameter phenotypic analysis
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Discovering the function of undescribed genes
Phenotypes
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In progress: data exploration with CellVisualizer
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Apoptosis
Nuclear size & morphology
Cell cycle distribution
(DNA content)
Protein content
Phosphorylation of signaling proteins
(S6, S6K, Akt)
Actin content/ morphology
Nucleolar size, count & morphology
Cell size, count& morphology
Localization ofsignaling proteins
Upregulation of translationUpregulation of
transcription
Amount & distribution of signaling lipids
Mitochondrial size & morphology
What are all the genes doing?
Nature Reviews Genetics5:11-22 (2004)
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David M. Sabatini LaboratoryAnne CarpenterColin ClarkeXana (Maria) FriasDavid GuertinKalyani GunturThouis R. JonesMike LamprechtWyman LiSusan MaJason MoffatKathleen OttinaTim PetersonDos Sarbassov Yasemin SancakShomit SenguptaJoon-Ho Sheen Carson Thoreen Doug Wheeler
www.cellprofiler.org
Thanks to…
Created by: Anne E. Carpenter and Thouis R. Jones
In the laboratories of:David M. Sabatini and Polina Golland
at:the Whitehead Institute and MIT
with help from:Michael Lamprecht, Colin Clarke, In Han Kang, Ola Friman, Steve Lowe, Joo Han
Chang, Susan MaCellProfiler has been supported by funding from:•Novartis postdoc fellowship from the Life Sciences Research Foundation•Merck/MIT Computational & Systems Biology postdoc fellowship•Society for Biomolecular Screening Small Grant Award•DOD Tuberous Sclerosis Complex Grant•Sabatini Laboratory