Experimental design part 3 controls

Controls
Experimental
design: part 3

What controls do you
need?
Your experiment is only
as good as your controls
You should “control” for
anything that could
influence your
interpretation of your data
“Controls” are additional
components of your
experiment that help you
interpret your data in the
way that you want to

Think about what the harshest critic
of your work would think and try
and design ways so that your
experiment stands up to that
criticism

Controls have different roles
in your experiment.
We’ll talk about each of
them in turn.
Experimental
Biological
Interpretation
Calibration

Experimental Experimental controls help
you with troubleshooting
problems with your
experiment
They are extra samples that you
process that you already know
“work” in this experiment.
Including a sample like this
at each stage will allow you
to work out where problems
have come up.
Examples please

Experimental
The mRNA for protein A is decreased and the mRNA for protein
B is decreased In RNA extracted from squamous cell carcinoma
tissue compared to RNA isolated normal skin
In this example
experiment we want
to study mRNA
abundance
To decide which
experimental controls
are needed, I need to
think what could go
wrong
Basically, every stage
of the experiment
RNA isolation
Cancer tissue
Reverse
transcription
Quantitative PCR

Experimental
Reverse
transcription
Quantitative PCR
RNA isolation
Cancer tissue
Now think, what
sample can I use
that will
definitely work at
each step
Tissue that I have
used successfully
before
RNA that I have
already used
and know is OK
cDNA that I have
used previously
with these
primers
It won’t always be
possible to have
everything.
Especially with a
new experiment.
Or primers that
you have used
before on the
new cDNA
But the more you
can include, the
better!

Experimental
RNA isolation
Reverse
transcription
Quantitative PCR
Cancer tissue In addition you will
want negative
controls
Samples that will
give a negative
result at every stage
of the experiment
No tissue
Water only as
template in RT
reaction
Water instead of
cDNA as template
for qPCR

Biological
Whereas
experimental
controls are to help
you troubleshoot
problems
Biological controls are
used to show that your
data are really what you
think they are
Let’s move on

Biological
You can’t prove a negative without a positive!
You can’t prove a positive without a negative!
This is big!
Remember this
throughout your
planning

Biological
Let’s talk about
positive controls
First consider what are
you measuring and
what a positive result
would look like
This could be the
presence of
something new
Or an increase or
decrease in
something

Biological
Your positive control
should give you a
similar response
The control could be
a treatment that has
been published
before
A cell line / tissue or
whatever known to
express your protein
or mRNA of interest

Biological
Positive controls
increase your
confidence that if there
is a difference you
would be able to see it

Biological OK, let’s move on to
negative controls
I can feel an
example coming!
No surprises here,
negative controls are
treatments that you are
confident won’t produce
the response you are
looking at

Biological In this experiment we are
using an antibody to detect
our protein of interest
But how do we know
that the signal we have
imaged is real?
What else could it
be?
Well, no antibody is
perfect. So you will
always get some signal
that is non-specific
Using controls can help
us distinguish between
what is real versus what
is non-specific

Biological
sample
The process of
preparing a sample
involves two key steps
First you probe the
sample with the
primary antibody
Then you detect the
primary antibody by
probing with a secondary
antibody with coloured
tag
We need to think about what
could go “wrong” and design
ways to identify them
Let’s look at ways we
can check that the
signal we get is ”real”

Biological
sample
First the simplest one. If the
secondary antibody bound to
the sample without the
primary being present, we
would get a false positive
signal
So, we control for that by
having a sample probed
with just the secondary
antibody alone.Secondary
only
A secondary only control
doesn’t help us identify if our
antibody has bound
specifically to our protein of
interest or to other proteins
So we add a second
negative control: a sample
that doesn’t express our
protein of interest but is
otherwise as similar as
possible as our real sample
Sample that
doesn’t express
our protein

Biological
sample
Let’s add a positive
experimental control too: a
sample that definitely does
express our protein
Secondary
only
Sample that
doesn’t express
our protein
Sample that
definitely does
express your
protein
(experimental
control)
And a biological control: a
treatment that does
cause the effect your are
expecting to see
Treatment that
causes the change
you are looking for

Biological
sample
It’s not unusual to have more
controls than samples!
Secondary
only
Sample that
doesn’t express
our protein
Sample that
definitely does
express your
protein
(experimental
control)
Some of these can serve multiple
roles, so you may not need
everything. But the more robust
your controls are, the more
confident you will be in your data
Treatment that
causes the change
you are looking for
That’s a lot

Interpretation Biological and experimental
controls help you identify what
is real, but controls can also
help you interpret your data,
rule out confounding variables
As you are thinking about the
design, try to consider every other
possible reason you might be
obtain the effect you are looking
for and try to think of a way to rule
out those alternative mechanisms

Interpretation Let’s say you are studying the
effect of altitude training on
VO2 max.
You have two groups of
people; group 1 training at
sea level, group 2 training up
a mountain.
Other than the location of
their training, what else
could influence the results?
Why am I
in the
altitude
group!

Smoking
Previous altitude
training
Starting fitness
levels
Interpretation
Age
Weight
BMI
Gender
You could control for some or
all of these during the group
selection process
Depending on the sample size,
you might also be able to
determine the effect the
confounder has on the
outcome (as a secondary
metric)
Your pilot study might also help
you to determine which of your
potential confounders are likely
to have the biggest effect, this
will help you identify which
ones are most important to
control for

B
+ Reading 2
A
+ Reading 1
OK, let’s look at an
experiment comparing
a new drug to the
current best drug
In your pilot
experiments you
found that the
response varied a lot
Paired analyses
And you think the
variation is due to
differences in
confounding variables
Example time!

A
+ Treatment 1
Measurement
One way to control
for these variables is
to pool the study
population and do a
paired analysis
In this experiment, the
same people will get
both treatments
Baseline
Reading 1
Baseline
Reading 2
B
+ Treatment 2
Measurement
Paired analyses
And you can compare
the same individual’s
response to the different
drugs

A
+ Treatment 1
Measurement
B
+
By reducing variation in
your data, they can make
your stats stronger
Baseline
Reading 1
Baseline
Reading 2
Treatment 2
Measurement
Paired analyses
You can often use more
powerful tests
When you have a limited
sample size, and can’t
control for confounders
by grouping, a paired
design can really help!

A
+ Treatment 1
Measurement
B
+
Baseline
Reading 1
Baseline
Reading 2
Treatment 2
Measurement
Hopefully by this
point you can spot
the potential problem
here?
And a pretty clear
potential solution
Paired analyses

Break the
population in 2 and
reverse the
treatment order
between groups
A+
B+
Baseline
Reading 1
Baseline
Reading 2
B+
A+
Baseline
Reading 1
Baseline
Reading 2
When you do your data
analysis the first thing
you do is determine if
treatment order matters
Yes, this way you can
control for order
effects

Calibration
One more quick point on
controls
Often you will use known
samples to calibrate your
system
concentration
absorbance
This can involve using
measurements to set up
a standard curve
However, in other
situations you might use
relative quantification….
But relative to what?
In these cases the
calibration might be
really obvious
Feels like you are
setting up
another example!

Note that this approach
isn’t very common for
this type of application
The mRNA for protein A is decreased and mRNA for protein B is
decreased In RNA extracted from squamous cell carcinoma
Let’s use a study
question from before
We could calibrate
this experiment in
two ways
mRNA amount
Cycle
number
We could set up a
standard curve using
mRNA that we have
synthesised
Our readout would then
be mg of protein B mRNA
per mg of total RNA or
per mg of tissue

A second option
would be to compare
protein B mRNA
abundance to one or
more reference
mRNAs
Tumour Sample
Control Sample
Protein B mRNA
150
140
Reference mRNA
400
500
150
400
140
500
Relative abundance
But how do you
choose what to
use as your
reference

Good question!
Sometimes your
understanding of the system
will help you identify
something that will stay
constant within your study
But, you should still
test that assumption!
Again, identifying
appropriate reference
points is something that
your pilot experiments
should be designed to do
As with other aspects of
your data you should be
able to justify the
decisions you have made
along the way

Time for you to do
something!
Squamous cell carcinoma cells induced to
overexpress protein B display increased
invasion compared with control treated cells.
Squamous cell carcinoma cells will be induced
to express protein B or not treated then
seeded onto a skin substitute After 48 hours
the distance migrated into each substrate will
be measured
Remember this
experiment? Can you
identify what controls
you might need and
what they would be?
Experimental?
Biological?
Interpretation?
Calibration?
Skin substitute
Normal
cells
+ protein
B

Experimental controls
-ve: Use a non-invasive cell line, should see no invasion
Treat with an invasion inhibiting drug
+ve: Use a well-established highly invasive cell line
Treat with an invasion promoting drug
Biological controls
-ve: Introduce a protein that is known to not affect invasion
Treat cells with all the reagents needed to induce expression
of the protein (i.e. everything but the protein)
+ve: Use the same expression system to introduce a protein already
established as increasing or decreasing invasion
Interpretation controls
A lot of overlap
here! That’s OK!
Include cell proliferation inhibiting drugs so that measurements are solely due
to invasion
Use multiple different cell donors, to control for donor specific responses
Did you think of
any more?

Controlling for
human bias
Let’s talk about a
way we can control
for human bias;
“blinding”

Controlling for
human bias
You’ve probably heard
about clinical studies
being described as
”double blind”.
This means that person in
the trial doesn’t know if they
have received the placebo
(control) treatment or the
actual treatment
In addition, the clinicians
do not know what the
participants have
received.

Controlling for
human bias
By not knowing, both parties
won’t introduce
sub-conscious
bias.
In lab studies, it can be
harder to introduce blinding
but if it is possible then you
should.
The most obvious times
would be when the data will
be subjectively analysed,
such as scoring of images

Controlling for
human bias
Another option, when you
have subjective data, is for
multiple observer to be used
and some consensus scoring
system developed

Part 3 Recap.
Every experiment needs a positive and negative
control that can tell you whether it worked or
not
You can’t prove a positive without a negative.
You can’t prove a negative without a positive.
Try to control for every variable or confounder that could
affect your interpretation of your data
Think about what the harshest critic of your work would
think and try and design ways so that your experiment
stands up to that criticism

Experimental design part 3 controls

Recommandé

Recommandé

Contenu connexe

Similaire à Experimental design part 3 controls

Similaire à Experimental design part 3 controls (20)

Dernier

Dernier (20)

Experimental design part 3 controls