Sep 28, 2022
Version 2
Data Processing of Technologica Chlorophyll Fluorescence Imager Data for Photoprotection and NPQ Relaxation V.2
This protocol is a draft, published without a DOI.
- 1Realizing Increased Photosynthetic Efficiency (RIPE)
Protocol Citation: Lynn Doran 2022. Data Processing of Technologica Chlorophyll Fluorescence Imager Data for Photoprotection and NPQ Relaxation. protocols.io https://protocols.io/view/data-processing-of-technologica-chlorophyll-fluore-cg8ftztnVersion created by Lynn Doran
Manuscript citation:
Gotarkar, D., Doran, L., Burns, M., Hinkle, A., Kromdijk, J., Burgess, S. J. High-throughput Analysis of Non-Photochemical Quenching in Crops using Pulse Amplitude Modulated Chlorophyll Fluorometry. J. Vis. Exp. (185), e63485, doi:10.3791/63485 (2022).
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: September 28, 2022
Last Modified: July 11, 2023
Protocol Integer ID: 70631
Keywords: Chlorophyll Fluorescence, CF Imaging, Technologica CF Imaging, Photoprotection, NPQ Relaxation, Non-Photochemical Quenching, Data, Data processing
Abstract
Data Processing of Technologica Chlorophyll Fluorescence Imager Data for Photoprotection and NPQ Relaxation using Matlab and Microsoft Excel to generate graphs of NPQ over time.
To use R studio to collate Discdata.csv and MedianNPQ.csv and additional Matlab scripts to calculate coefficients representing NPQ relaxation parameters by fitting a bi-exponential function please refer to Gotarkar, D., Doran, L., Burns, M., Hinkle, A., Kromdijk, J., Burgess, S. J. High-throughput Analysis of Non-Photochemical Quenching in Crops using Pulse Amplitude Modulated Chlorophyll Fluorometry. J. Vis. Exp. (185), e63485, doi:10.3791/63485 (2022).
Image Attribution
Lynn Doran
Before start
Download the following software:
FluorImagerSetupLiteV2_305.exe Open the .igr file generated from the CF Imager using the FluorImager software.
If you are using a computer removed from the instrumentation for data processing, click OK and ignore the warning about camera calibration. The camera is not necessary for data processing.
If initial screen that comes up, has "AutoSave" in the top name bar and all images are solid blue. Close this screen using the red X in the top right corner of the window.
The actual file should be open behind the AutoSave file. Export these files to a folder by clicking on "File" > "Export to Folder ...".
Return to the folder where the original .igr file was opened from. There should now be a new folder with the same name.
Copy the following three script files into the new folder.
A1_MapAndLabelDiscs.m A2_ProcessFoFm.m A3_ProcessNPQdata.m *These files must be copied directly into each folder that you want to process.
Open A1_MapAndLabelDiscs.m. This will open the first script in MATLAB.
Adjust the threshold = #. The larger this number is made the more items from the original image that will be filtered out. The original value of 10 is sufficient if imagers were thoroughly isolated during CF Imaging. 100 is a good default number if some pixels remain outside the desired objects.
Press the green run arrow at the top.
A black and white image will be generated that identifies and enumerates each object to be processed.
On the image, click "File" > "Save". Save the image as a .fig file in the current folder.
It is also advisable to "save as" a version of this image as a .jpeg. All processed data will be assigned to objects as identified. When screening seedlings, saving this image as a .jpeg allows you to select individual seedlings for additional analysis (i.e. genotyping) or transplanting.
Return to the sample folder. Open A2_ProcessFoFm. This will open the second script in MATLAB. Click the green arrow at the top to run the A2_ProcessFoFm Script.
A heat map of your identified objects will be generated and Fv/Fm data will automatically be exported to an Excel sheet named "DiscData" in the sample folder.
Click "File" > "Save". Save the image as a .fig file in the same folder.
Return to the sample folder. Open A3_ProcessNPQdata. This will open the third script in MATLAB. Click the green arrow at the top to run the A3_ProcessNPQdata.
When the green status bar is complete, data will automatically be exported to two Excel sheets named "meanNPQperDisc" and "medianNPQperDisc" in the sample folder.
Open "DiscData" from step 14 in Excel. Insert a row above row 1. Add the header "Plant/Sample" to column A, "Mean Fv/Fm" to column B, and "Median Fv/Fm" to column C. Save the file.
Open either "meanNPQperDisc" and "medianNPQperDisc", whichever is the desired data set, from step 17 in Excel.
*We typically use the "medianNPQperDisc" file for AgSynBio Lab.
Insert a row above row 1. The header represents time points. Start A1 with 0 and continue sequentially across the row.
Return to the file "DiscData". Copy columns A through C.
Return to file "medianNPQperDisc". Insert columns A-C copied from "DiscData" into "medianNPQperDisc" before column A.
Because we are working in "medianNPQperDisc", we only want Median Fv/Fm values. Delete the column of Mean Fv/Fm values.
*If you are working in "meanNPQperDisc", then delete Median Fv/Fm values and keep Mean Fv/Fm values instead.
Evaluate Median Fv/Fm values for plant stress. A properly adapted, healthy plant should give a Fv/Fm value of ~0.8. This has been shown to be highly stable between species. Significant deviation from this (e.g. <0.7) either suggests incomplete dark adaptation prior to imaging or stressed plant material.
Stressed plants with poor Median Fv/Fm
Healthy plants with good Median Fv/Fm
To graph, switch Fv/Fm to column A and Plant/Sample to column B. "Cut" column B, "insert cut cells" in front of column A. Highlight all cells with data, except for column A. Select "Insert", "Charts", "Scatter".
Note
f you are having a difficult time getting Excel to plot the coordinates correctly, start by only highlighting row 1 and 2 and inserting a scatter plot.
Once you've verified that it is plotting one data set correctly, you can expand the plotting range by right clicking on the graph and selecting "Select Data".
Change the last number in the "Chart data range:" to the last row of data. In this example the highlighted 2 below would be changed to 16.
The correct figure should now be available.
Add the desired chart elements (i.e. title, legend, horizontal and vertical axis labels). The data is ready for interpretation.