hi all thank you for clicking and joining on this video much love and thanks to all of you that have
subscribed if you haven't subscribed you can go ahead and hit the subscribe button so in this
video we're going to look at where you've detected an interaction an underlying antigen interaction
hormone interaction cytokine interaction in a biological setting system experiment and
you want to be able to determine what that response is by using standard curves and so
where you introduce a chromogenic detection system light absorbance of the visibly colored
substrate is used to detect the interaction and you use something called a spectral photometer
the principle here is that every compound colored or not absorbs light characteristically so they
have a specific way that they absorb light and so by measuring how much light passes through the
colored product produced by the enzyme in response to the underlying antigen antibody interaction
you can analyze the sample or specimen so you can use this principle to decide if there has been an
antibody produced against an antigen just as a yes or no or you can even if you fine tune it
and put standard so a known amount of the antigen in there against which you can read your unknown
concentrations then you can even decide how much of the antibody antigen ah interactions
are there in a sample of amongst individuals in this scenario you would need a standard curve
you would take a known amount of the antibody or antigen that you're trying to detect you'll do
serial dilutions i have a video up here on how to desire dilution is a critical technique for
bioscientists for anyone working in the laboratory in biological sciences so take a look at that so
you take a known amount you do a serial dilution and the serial dilution allows you to just
to incrementally decide how much protein is in each well that's going to act as something
called a standard curve a standard curve in the sensor is going to be a reference point for all
the samples which you are going to analyze from sources that you don't know how much there is so
the standard curve will be used to reference that and you read off the standard curve compare the
scanner curve with your reading from the unknown patients or samples or experimental groups to see
how they're comparing from that can't you know estimate how much protein or how much antibody
or antigen you have in the unknown samples or specimens in a spectrophotometer the absorption
of light through that solution and depending on how much color there is there will be different
absorption or optical densities as is reported the detection system which you can think of as the
reporter of the underlying interaction can also be a fluorescently based detector or a luminescence
based detector it's not always absorbance that you measure in the lab with these kind of assays
and your optical densities is then the y variable because the response and then your independent
variable is the known concentration so you put on the x-axis and then from that you'll
be able to infer the unknown concentrations and so you draw a line and the equation of the line
is just turns out to be y equals mx plus b but you know you can't use a straight line for biological
responses it turns out it's actually a sigmoidal response in a case where there's sort of the
binding is different at different concentrations so it's not a linear scale so that's why you can't
use a straight line when you draw the graph you have the concentrations and then you have their
responses and so if the points are close to a straight line that's when you can use
a linear regression that's when you can use the equation y equals mx plus b and y equals mx plus
b means the response the equals or depends on m times x plus b and m it turns out is the slope
so the angle of the line it's the angle of the line times your concentration which is x plus
your y-intercept and your y-intercept is giving the symbol b and your it just means the intercept
of the line on y which is your response and so it turns out that unless you're using very limited
range of concentrations you're not really going to have your responses being fitting
around a straight line they're more likely to fit on in a shape that's called the sigmoidal
shape the that s shape for like a dose response and so that s shape is analyzed in a different way
because you don't just have these two constants they're m and b the slope and the y-intercept
being the critical factor there are other factors and it turns out there are four so in the straight
line you just have two constants or two parameters that describe that slope or that y is exactly y
equals so the y is the response but when is the sigmoidal shape you actually have four
constants that describe it and so that's the four p or the four parameter logistics
it turns out that the four parameters you need are really just to fit the shape so when it was
linear you only needed two you just needed this slope and you needed the intercept of the of b
which is the y-intercept so you just need a slope and the y-intercept when it's a
straight line but when it's sigmoidal you need to know the minimum signal they have maximum signal
and then you just have this inflection point where it sort of turns and starts to go up to plateau
and then you also need to need a maximum and so those are the four parameters and that's why you
assess these sigmoidal shaped fits with a four parameter fit rather than a straight line which
is just two parameters it just captures more accurately so that you can read the unknown values
the minimum signal is the point where there isn't much of a signal it's very close to your
negative control the maximum signal is the point where even though you have a lot of interactions
between your ligand and your antibody or your antigen and your antibody you just won't be able
to detect any more signal it's saturated it's the maximum output you're going to get from the system
from the assay that you set up then there's your inflection point your inflection point is giving
the alphabet b or the symbol b it's halfway off your slope halfway up the slope and it's called
another name is called the heal coefficient finally there's the fourth parameter which
is the concentration where you get half of your maximum signal so there's the maximum
signal where it's saturated and so half way of that is your lC50 so that's your ligand
concentration when your ligand concentration is at half of its maximal saturation and so in
this video we're really going to get into how you can go ahead and draw your standard curve
putting a line of best fit get an equation of the line so that you can interpolate or infer
from that plot what the concentrations are in your unknown sample there is actually uh an online
tool where you don't even have to download it called Arigo where you can go ahead and put your
concentrations in there and it will also tell you what your unknown concentrations are without you
having access to a four parameter logistic software with a four parameter logistic fit
capability whose you don't even have to download it you just simply enter
your concentrations for your unknowns your concentrations for your standard curve and it's
going to be able to allow you to determine what your concentrations of your unknowns
are and it needs it to be side by side so what i did was just to copy it and then i use the paste
transpose to have it side by side so for standard two for instance i just go ahead and copy that
and then if i come here to paste i right click and this is the transpose option so it places
it side by side rather than on top of each other as it appears here and that's the right format for
placing it into the data gain analyzer software that we're going to use so in the gain data
Arigo's elisa calculator you're going to go ahead and place the raw optical densities that
we've obtained for the standards here and then it will show us where it's fitting inside the well
so as you can see it's placed it correctly we've got our standards right here we will get to assign
which ones are standards over here you then assign which ones are the unknowns and in each of these
circumstances you can indicate the replicas that you have so you did if you did them in
duplicates then you can change it if you did it in triplicates or quadruplicates you can also
let the software you can also adjust it on this part of the Arigo data interface
and so you go ahead and once you've entered your experiment name you select the regression model
you want to use so whether you want to use a four parameter logistics five parameter logistics or a
linear regression model in for analyzer data you definitely use a4 parameter logistics
if it was just approaching determination you would select a linear concentration because
you've got set responses that you are going to obtain for normalisation you can either say you
don't want it normalised at all or you want to set the maximum response as a hundred percent
or maybe you want to set the maximum response as a hundred percent and then the minimum
as a zero percent and they will try and fit all the values within that so everything is normalised
against your maximum concentration response and your minimum concentration response so that's the
idea of normalisation this in order for our rego for the software to give you a line of best fit
i had to go ahead and subtract the minimum in the other settings you can't leave it blank i needed
to subtract the minimum from all other optical densities and the minimum was my zero reading so i
went ahead and ticked that and you also need to go ahead and set the blank as standard data so that
within that fitting of the curve the software knows what a zero point it looks like or where
this real point is and so once you've got both selected in the other settings you can go ahead
and fill in the other parameters and then in terms of your plot settings the scale that you want to
use you can use normal scale or you could maybe perhaps you want to use a semi log or you want
to use a log log scale then you go ahead and hit analyse and in analyse you what you see is that
it's going because the four parameter logistics is going to apply four parameters in determining the
equation of the line where your 'a' equates to the theoretical response you would have received at
zero concentration so the concentration where you did not have any of the hormone present
then 'b' is the slope of the curve c is called the inflection point or and this may equate to your in
effective concentration 50 or your inhibitory concentration 50 and d is your theoretical
response at infinite in concentration and here we go now i have a fit a line of best fit for this
we're using the four parameter logistics so you can see that was the equation and it's gone ahead
and uh computed the values is telling me my line of best fit is one is perfect fit as you can see
it's really going through all the points or the values and so based on that it's been able to
spit out an unknown value for me so i'm able to obtain concentrations now based on which it's
based on this standard curve which was based if you remember on the concentrations that i have
told it to calculate and that's how easy it can be with these softwares rather than using something
like excel you can go ahead and export it export it to excel export it to a pdf but essentially
we've now got all our unknown concentrations it even tells you the standard deviations for each
of the reads and so that's how you determine the unknown concentrations in your sample i whether
it's a protein determination or whether it's a particular hormone or essay that you're doing
elisa that you're doing and you need to work out what the unknown is the unknown concentrations are
so now you can go ahead and plot it in excel to see the concentrations that you've obtained
and beautify your graph as i've shown in the other video if you want to refer to it
so i'm gonna leave it here i'll catch you in the next video God bless bye