High-throughput cell-based assays Wolfgang Huber European Molecular Biology Laboratory European Bioinformatics Institute
Jan 11, 2016
High-throughput cell-based assays
Wolfgang HuberEuropean Molecular Biology Laboratory
European Bioinformatics Institute
Signaling pathways
Drosophila antibacterial signalling
Drosophila Toll/antifungalsignaling
Small compounds
Full-length cDNA (over-)expression
RNAi
Interference/Perturbation tools
He and Hannon, 2004
Initiation
Execution
RNAi
RNAi as a loss of function perturbator
gene-sequence specific
reagents (eg siRNAs)
easy to make for any gene (there are caveats...)
protein
living cells
mRNA
gene
degradation
translation
transcription
RNAi as a loss of function perturbator
gene-sequence specific
reagents (eg siRNAs)
easy to make for any gene (there are caveats...)
protein
living cells
mRNA
gene
degradation
transcription
RNAi as a loss of function perturbator
gene-sequence specific
reagents (eg siRNAs)
easy to make for any gene (there are caveats...)
living cells
mRNA
gene
transcription
T7
Precursor dsRNA
siRNAs
Degradation of target message
C. elegans Drosophila Mammals
Injection and soaking
Feeding bacteria
WormsCell-
culture
Bathing Transfection
> 200bp> 200bp 21bp
Cell-culture
dsRNA dsRNA siRNAE. coli
RNAi experiments in different organisms
Dicer
Differential Expression vs Signaling Function
RNAiphenotypes
Differentially regulatedgenes
~ 70 280 genes
RIP/IMDpathways
RIP
Tak1
IKK
Rel
R
Targets
Michael Boutros
Most pathway targets are not required for pathway function
RNAiphenotypes
Differentially regulatedgenes
~ 70 280 genes
3
RIP/IMDpathways
RIP
Tak1
IKK
Rel
R
Targets
Michael Boutros
Any cellular process can be probed.- (de-)activation of a signaling pathway- cell differentiation- changes in the cell cycle dynamics- morphological changes- activation of apoptosisSimilarly, for organisms (e.g. fly embryos, worms)
Phenotypes can be registered at various levels of detail- yes/no alternative- single quantitative variable- tuple of quantitative variables- image- time course
What is a phenotype: it all depends on the assay
Plate reader 96 or 384 well plates, 1…4 measurements per well
FACS; Acumen Explorer ca. 2000 x 4…8 measurements per well
Automated Microscopy practically unlimited. many MB
Monitoring tools for automated phenotyping
RNAi by reverse-transfection
Cells are seeded on-top of pre-aliquoted siRNA pools
Computational analysisSecondary assays
72h
2x 68 384-well plates
Cell viability(‘CellTiterGlo’ Assay)
+/- Compound treatment (48h pt)
Plate reader Assays
cellHTS2
workflows for analysing a cell-based assay experiment
NChannelSetassayData can contain N=0, 1, 2, ..., matrices of the same size
feat
ure
Dat
a (A
nn
ota
ted
Dat
afra
me)
D
Target gene ID
Sequence
Physical coordinates
Phy
sica
lco
ordi
nate
sS
eque
nce
Tar
get g
ene
ID
“ph
eno
”Dat
a (A
nn
ota
ted
Dat
afra
me)
Sam
ple-
ID r
edS
ampl
e-ID
gre
en
Arr
ay ID
BSample-ID blue
_ALL_
G
R
Array-ID
Sample-ID green
Sample-ID red
Sam
ple-
ID b
lue
varMetaData
labelDescriptio
n labelDescriptio
n
channelDescriptio
n
The dataNumeric values x
ijk
i = wells (e.g. 20,000)
j = different reporters (e.g. 2)
k = different assays (e.g. 5)
Metadata about wells
pi = plate in which is well i
ri = row (within plate) of well i
ci = column (within plate) of well I
siRNA sequence, target gene, ....
Metadata about reporters
Fluc, Rluc, ...
Metadata about assays k
replicate number
different variants of the assay
date it was done
Between plate effects
kth wellith plate
packagesplots
"Normalisation"x
ijki = wells (e.g. 20,000)
j = different reporters, dyes
k = different assays
medianijk
ijk
hjk h i
xx
x | p = p
Plate median normalisation
- can use other estimators of location, e.g. mean, midpoint of shorth;
or shift and scale according to values of positive and negative
controls
- maintains the dimensions of x
Normalization: Plate effects
Percent of control
Normalized percent
inhibition
z-score
k-th welli-th plate100' ki
ki posi
xx =
μ
100pos
' i kiki pos neg
i i
μ xx =
μ μ
' ki iki
i
xx =
σ
Spatial normalization
B-score:
two-way medianpolish
rth rowcth columnith plate
ˆˆˆrci i ri ci'
rcii
x μ +R +Cx =
MAD
after
fitted row and column effects
before
Malo et al., Nat. Biotech. 2006
How to estimate the normalization parameters?
From which data points:
• Based on the intensities of the controls
if they work uniformly well across all plates
• Based on the intensities of the samples
invoke assumptions such as "most genes have no effect", or "same distribution of effect sizes"
Which estimator:
mean vs median vs shorth
standard deviation vs MAD vs IQR
In the best case, it doesn't matter.No universally optimal answer, it depends on the data.
Estimators of location
Histogram of x
x
Fre
qu
en
cy
-2 0 2 4 6 8 10
02
04
06
08
01
00
12
0
meanmedianshorthhalf.range.mode
mean
Estimators of scale
2
1
75 25
1standard deviation : ( )
1
median absolute deviation: median | median{ }|
interquartile range: Q ({ }) Q ({ })
shorth
{ }
n
ii
i j
i i
x xn
x x
x x
Channel summarisationx
ijki = wells (e.g. 20,000)
j = different reporters, dyes
k = different assays
i2ki1k i2k
i1k
xx ,x
x
(log-)ratio
collapses the second dimension of x
Replicate Summarisationx
ijki = wells (e.g. 20,000)
j = different reporters, dyes
k = different assays
...ijk ijk ijr1 nx , ,x y
replicate summarization
changes third dimension of x
Processing steps
xijk
i = wells (e.g. 20,000)
j = different reporters, dyes
k = different assays
normalisation of whole plate effects dim unchanged
normalisation of within plate effects dim unchanged
summarization of channels change 2nd dim
replicate summarization change 3rd dim
scoring (transformation into z-scores) can change 1st dim
contrasts (as in linear models) change 3rd dim
NchannelSet provides a robust and powerful infrastructure for these
operations (and keeping the metadata aligned and intact)
' 1 3 p n
p n
Z
Zhang JH, Chung TD, Oldenburg KR, "A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays." J Biomol Screen. 1999;4(2):67-73.
2 2p n NB: would be more efficient
show example report
Thanks
Ligia Bras
Florian Hahne
Michael Boutros
Thomas Horn, Tina Büchling, Dorothee Nickles, Dierk Ingelfinger
Elin Axelsson
Gregoire Pau
Martin Morgan