Sarcomere Length

How to process sarcomere length images in standardized way

Overview of the Scripts

`rmv_long file1 file2 [file..]`

`analysis_prepare folder [folder...]`

requires folders with ome-tiffs Experiment/image1..N.ome.tiff

`analysis_make_panels folder [folder...]`

2KB

analysis_make_panels

Open

requires Experiment/Condition_1..N/image_folder_1..N structure

`analysis_make_cells_panels folder [folder...]`

2KB

analysis_make_cells_panel

Open

requires Experiment/image_folder_1..N structure

Application to measure the distance of sarcomere peaks

Application link: https://vinarsky.shinyapps.io/sarcomere/

Detailed description

0. Remove redundant parts of the name

Use the rmv_long script to remove the redundant parts of the names of ome.tiff files (such as Maximum Intensity Projection etc.) before you start the analysis

1. Create the analysis structure

First, the ome.tiff images exported from czi format need to be created.

Use the analysis_prepare script applied to a folder (folders) which containg ome.tiff images we want to measure

The created analysis folder contains one .csv file for results of the measurements and one folder per each image containg following files:

one ome.tiff image which is used for measurments
four .csv files which is used to save the line graphs from measurments
four .png with dummy files to be used to save the measurments

The .png and .csv files are empty and serve to be used as a name for saving the screenshot of the region measured (.png) and the graph of intensity of the measurment (.csv)

2. Analysis in FiJi and the App

During the measurement in image J/FiJi it is recomendded just save the screenshots and csv files, and then count the number of the Z-discs from the .csv files using the online app.

To gather only the .csv files to be analyzed by the app use the following line in the gitbash when you are in the Experiment/image_folder_1..N structure, go into the Experiment folder, start gitbash and paste following line.

It will throw some errors, but will copy all the .csvfiles into the Experiment/csvs-for-app folder

mkdir csvs-for-app; for folder in */; do cp ${folder}*.csv ./csvs-for-app/; done

The resulting .csv file with results is supposed to replace the original .csv file created by the script

986KB

sarcomere-app_manual.docx

Open

3. When the analysis is done - check it

Once the analysis is done the selection of sarcomeres should be checked and verified by another person and the faulty selection measurments need to be removed from the analysis csv.

To do this we need to go through all the regions used for measurment and check them. Therefore the following scripts are used to make this more straightforward in following way:

Create panels of the whole image and the selections for checking
Check the quality of sarcomere selection
Rescale the intenstiy of individual cell images to be easily visible
Make panels out of these normalized images to give a quick overview of the images.

1. Create panels of the regions of interest

Script analysis_make_panels is used to get images of whole cell and the regions into panels in and save them in panels_<date-time> folder. However there is a catch, that the script expects a folder structure as shown below:

Experiment_folder
- Condition1_folder
  - Image1_folder
  - Image2_folder
- Condition2_folder
  - Image1_folder
  - Image2_folder

In case the structure is missing the condition layer and looks like this:

Experimet_folder
- Image1_folder
- Image2_folder

The script would fail and it is necessary to recreate the structure by copying the experiment folder into another "helper" folder

Helper_folder
- Experiment_folder
  - Image1_folder
  - Image2_folder

Once this is done the script can be used

The output of this folder is panels_<date-time> folder which contains the panels for checking

2. Check the quality of selection

Open the results .csv file and the first panel of cell+regions of interest side by side in windows photo or somethng which enables drawing and go through the panels one by one. The ones which are not good are to be crossed out on the panel (and the modified image saved to replace the old panel) and the corresponding line in the results should be deleted.

Save the .csv file with -DONE at the end

3&4 rescale intensity of normal images and make them into panels

To generate an overview of the cells the last script is applied to the Experiment folder of the following structure:

Experimet_folder
- Image1_folder
- Image2_folder

In case there are conditions, do them individually

4. Make it neat

Once the analysis and checking is done, its time to put it together with other results.

In order to avoid nesting try to keep it as flat as possible and put each condition separately at all levels

The structure is as follows:

01_SOURCE_IMAGES (minimal nesting to be able to grab a subset of images which you need for something else)
- Exp01_Condition01
- Exp01_Condition02
- Exp02_Condition01

02_ANALYSIS (here the nesting doesnt make sense)
- Exp01
  - Condition01
  - COndition02

03_RESULTS

03_Results
- Exp01
  - Condition01
    CELLS (normalized images of individual cells)
    ROIS (panels of regions of interest used for checking)
    multiple .png panels of normalized cells
  - Condition02
    CELLS (normalized images of individual cells)
    ROIS (panels of regions of interest used for checking)
    multiple .png panels of normalized cells
  - multiple exp_condition_results.csv files of the checked measurements
  - graphpad results
  - powerpoint results
- Exp02 ...