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CalciumFluxAnalysis

by Martin Rausch

The plugin provides tools to perform analysis of different types of calcium measurments in cells:

a) Calculation of DeltaF/F for time laps imaging using non-ratiometric dyes such as Fluo-4 (e.g. muscle or neuron)
b) Quantrification of calcium concentration with Fura-2.
c) Calculation of DeltaF/Ffor linescan-imaging experiments.

Publication Id
ICY-U8Z3K8
See technical details
View complete changelog
Tags: Calcium - Muscle

Documentation

The plugin provides tools to perform analysis of different types of calcium measurments in cells:

  • Calculation of DeltaF/F for time laps imaging using non-ratiometric dyes such as Fluo-4 (e.g. muscle or neuron)
  • Quantrification of calcium concentration with Fura-2.
  • Calculation of DeltaF/F for linescan-imaging experiments.

 

Sample types

Data processing is optimized for

a) Mytubes: rather slow increase of the signal (can be several 100ms according to the length of the EPS pulse train) and even longer signal decay

b) Myofibers: fast increase of the signal (<2ms), fast return to baseline (around 50ms). In combination with low afinity calcium dyes it is also possible to visualize single peaks from a high frequency EPS pulsed train.


Type of stimulation

The tool will adapt processing according to the different types of stimulation:

a) Electrical pulse stimulation using different type of stimulation patterns

b) Chemical stimulation

Calculation of DeltaF/F for linescan-imaging eand time-laps xperiments

The linescan image should use the x direction to enclode the position along the line and the y direction to encode time. The scan interval needs to be set manually in the tool. Only for Olympus OIF files it will be derived posthoc from the parameter file.

The first processing step comprises the calculation of the linescan profile for the selected channel or or signal time course for a specified ROI. Secondly, the profile can be plottet as raw data or converted to a DeltaF/F curve.

Modeling of the signal decay by a mono- or bi-exponential function

In addition, relevant parameters for the curve will be quantified and shown in the left part of the panel. Depending on the cell culture type (myofiber or myotube) the descending part of the curve will be modeled by a mono- or bi-exponential function:

Cell culture Dye Function
Myofiber Low afinity Bi-exponential
Myofiber High afinity Mono-exponential
Myotube High afinity Mono-exponential

Calculation of the AUC

The tool will calculate the AUC for the whole stimulation period. The length of the integration period is predifined by the tool:

 

Stimulation/sample type

Integration time

  Chemical 40sec
  EPS / myotube 4sec
  EPS / myofiber 400msec

For multi-pulse EPS stimulation, the AUC will also be calculated for the last peak of the pulse train.

 

Output: Instead of plotting the results, it is also possible to save them as a txt-file.

 

Example 1: Stimulation of a myotube by a 300msec EPS pulse train.

 Example 2: Stimulation of a FDB fiber by single pulse EPS pulse train

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