Deconvolution IRF from fluorescence decay curves

In summary: But I will try again. Thanks a ton!In summary, the conversation is about fitting exponential decay data using multiple exponentials in Origin 8 without taking the instrument response function (IRF) into account. The use of non-linear least square fitting (NLSF) method is discussed and it is suggested to use the right function for fitting the data. There is also a discussion on the deconvolution of the IRF and the possibility of using software such as Matlab or Mathematica for this purpose. The use of deconvolution in Origin 8 is also mentioned, with a tutorial available on the website. The conversation ends with a suggestion to try the FFT deconvolution method again.
  • #1
sunny bob
5
0
Hi There,

I have been working on fluorescence decays of the fluorophores whose lifetimes are comparable to the instrument response function(IRF) of my device. The technique I use is based on Time-correlated single photon Counting(TCSPC). I have been fitting the decay curves using multiple exponentials in Origin 8, which fits it on the basis of Non-linear least square fitting(NLSF) method. However, the fit is independent of any information about IRF.

My perception was that though the NLSF method doesn't take IRF into account, but because it fits only to the exponential forms, and indirectly it implies that it would not fit the non-exponential forms of IRF. Thereby fitting the data along with deconvolution, in which case it would never need to know the IRF.

I was just wondering if Iam right in thinking so or do I have to go for deconvolution first followed by fitting. Any source of code for deconvolution and fitting would be very much appreciable.

Thanks all,
Sunny.
 
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  • #2
Hi,
In fitting to an experimental curve one normally use NLSF or Marquardt-Levenberg (ML is also called least-squares menthod) procedure. These two methods are just fitting..
If your curve is Lorentzian or Gaussian or exponential decay or any other function...you can always use any of these two methods. That means method used for fitting is never dependent on any theory. But you should use the right function to fit. For example, if you know that your experimental data is a Lorentzian function, then you should use the Lorentzian function to fit (you should not use Gaussian or exponential). But the fitting method can be NLSF or ML. NLSF or ML is just a tool to fit experimental data with some theory (You should choose the right theory). Got it?
 
  • #3
Hi Rajini,

Thanks for responding. I got your point. But I think I did not frame my question well. Let me try to put it again. I could be able to fit the exponential decay data with exponential function. However the concern is the deconvolution of the Instrument response function(IRF, due to device) from the experimental decay data before fitting it. The NLSF technique(used Origin 8 package) fits the curve but it doesn't ask for any input of IRF. So obviously it appears like it doesn't do any deconvolution thing.
However, my perception is that because i have chosen an exponential curve to fit an exponentially decaying data points, the fit should obviously be circumventing the non-exponential form of IRF(assuming it is some kinda gaussian). So even if the fitting procedure doesn't ask for IRF data input, it is (unknowingly) doing the deconvolution procedure too while fitting.
Do you think that is not right?

Thank you,
Sunny.
 
  • #4
Hi Sunny,

First you deconvolute the experimental data with a instrument function (i guess you know the instrument function, Lorentzian or Gaussian ?)
And then fit the deconvoluted data by exponential function.
Fitting is just a fitting using NLSF or ML. So in the fitting routine the convolution or deconvolution are not considered.
I am not a expert in Origin (I use it only for fitting especially using peak analysis). I assume deconvolution can also be performed using Origin.
Hope this helps.
cheers
 
  • #5
Thanks Rajini! That was a helpful discussion. Thanks again.
 
  • #6
Hi Rajini,

By any chance, do you have or know any free source of program written for deconvolution using NLSF? I have seen a code in O' Connors's book on "Time-correlated single photon counting" but as Iam a beginner in programming, I fear if it might take ages to finish it. Do you have any recommendation?

Thanks,
Sunny.
 
  • #7
Hi Sunny,

I don't have any idea for deconvolution software.
But you can try to find some information in Matlab or Mathematica (both are not free).
I think if you have origin you can easily perform deconvolution.
But may be you can find ready made c codes or fortran in the book: Numerical recipes The Art of Scientific Computing.
 
  • #9
Origin is not free. But for few days you can use the evaluation version.
 
  • #10
Thanks you guys! I tried the FFT deconvolution method, in Origin, before. However, it didn't give satisfying result.
 

1. What is deconvolution IRF from fluorescence decay curves?

Deconvolution IRF (Instrument Response Function) from fluorescence decay curves is a mathematical process used to separate the contribution of the instrument from the actual fluorescence decay curve. This allows for a more accurate measurement of fluorescence lifetimes, which are important in various scientific fields such as biochemistry and biomedical imaging.

2. Why is deconvolution IRF from fluorescence decay curves important?

Deconvolution IRF is important because it allows for a more precise measurement of fluorescence lifetimes, which can provide valuable information about the structure and interactions of molecules. It also helps to eliminate any distortions or errors caused by the instrument's response, resulting in more accurate and reliable data.

3. How does deconvolution IRF from fluorescence decay curves work?

The deconvolution process involves mathematically reversing the instrument's response function from the measured fluorescence decay curve. This is achieved by using mathematical algorithms and fitting techniques to separate the instrument response from the actual fluorescence signal, resulting in a more accurate representation of the fluorescence decay curve.

4. What are some applications of deconvolution IRF from fluorescence decay curves?

Deconvolution IRF is commonly used in the fields of biochemistry, biomedical imaging, and fluorescence spectroscopy. It is used to study the kinetics and dynamics of chemical reactions, as well as the structure and interactions of biomolecules. It is also used in medical imaging techniques such as fluorescence lifetime imaging microscopy (FLIM).

5. Are there any limitations or challenges with deconvolution IRF from fluorescence decay curves?

One potential limitation of deconvolution IRF is that it requires precise knowledge of the instrument's response function, which may vary over time. This can lead to errors if the instrument's response changes during the measurement. Additionally, deconvolution can be computationally intensive and may require specialized software and expertise to perform accurately.

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