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Pipeline logic

  1. Roughly recognize what is a signal coming from cardiomyocytes and use it for mask (Primary object: cells)
  2. Identify nuclei in the sarcomere signal masked image (Primary object: CM_nuclei)
  3. Merge close-by nuclei as one object (SplitOrMergeObjects: CM_nuclei_merged)
  4. Segment sarcomere signal channel into cells based on the merged nuclei (Secondary object: cells_final)
  5. Now there are 3 different objects
  • nucleus (Primary object)
  • cytoplasm (Tertiary object)
  • cell (Secondary object)
  1. Measure YAP1 signal in nucleus, cytoplasm, and cell
  2. Save image for checking cell segmentation:
  • Left: Sarcomeric signal overlayed with CM_merged_nucleicells_final` outlines
  • Right: YAP1 signal overlayed with CM_merged_nuclei and cells_final outlines
  • object numbers
  1. Save files with measurements, set that all measurements are exported

Reproducible structure of the project

  1. source folder contains all the images
  2. output_<group-name> folders where output from different pipelines go
  3. CP-<Pipeline-ID>_<group-specification> one or more pipelines with clearly indicated what group was which pipeline used on

Pipeline Quality control

  1. Go throgh the exported overlays and check for segmentation
  2. If there are some really badly segmented cells (more then 5% per image) remove them from the measurment files
  • create a filter file:
    • first column for image number, second column for object number to be removed
    • in case of all to be removed write all in second column
    • save and run the filterCP script on the files to remove the files
  1. Add QC-passed to all output folders which were checked
  2. Check the number of columns
  • cells_final.csv (BJ last column)
  • CM_nuclei_merged.csv (BL last column)

Post processing

  1. Stack all the cells_final.csv and CM_nuclei_merged.csv
  • csvstack -g *cells*final*.csv > cells_final.csv
  • csvstack -g *CM*merged*.csv > CM_nuclei_merged.csv

  1. Select the columns for intensities and ratios in the CM_nuclei_merged.csv and add them to the end of cells_final.csv selectR -c 1,40,47,63 CM_nuclei_merged.csv

  2. Rename the columns to:

    • YAP_nucleus_Int-Int
    • YAP_nucleus_Mean-Int
    • YAP_ratio_Int-Int
    • YAP_ratio_Mean-Int
    • scale_umperpx (fill in 0.55 for all)
    • Area_Scaled (0.550.55 AreaShape_Area)

  3. Create the columns for the experimental conditions and groups (new columns 2-4, manually fill in the values)

  • ExpNo
  • ExpGroup
  • Coating
  1. save as data_final_all-cols.csv

Data analysis

  1. Select only the relevant columns from this file
selectR -c 1-4,7,6,42,49,69-72 data_final_all-cols.csv

  1. rename to data_final_selected-cols.csv

  2. Run descriptive statistics

summaryR -c 2-4 data_final_selected-cols.csv
  1. Weed out the non-necessary columns
selectR -c 1-4,10-12,15-17,20-22,25-27,30-32,35-37,45-47 summary_*.csv

Save as summary_statistics.csv

  1. In excel sort the files by ExpGroup, Coating, ExpNo, this way you can copy into the grouped graph
  • select grouped graph
  • replicates = 3
  • Group A -fill FN1, Group B fill FN10
  • In rows put WT, mock, full-YAP,dPDZ
  • use ctrl+shift+T to transpose the values when copying

Example files

Pipeline (works on version 4.2.7)

pipeline

Output files

example-output-image_01

Output cells_final.csv file with measurment data

Post-Processing

Output cells_final.csv file with all images and objects

“Example: cells_final.csv file”

Example Filter file removes first object in image “1” and all objects in image “2”

Example: filter.csv file

Filter command

Example: filter command

Cells_final.csv file after filtering

This filter removes first object in image 1 and all objects in image 2

Example: cells_final before separating image number

Split the name of the file into an experimental condition and image number

The script for separating image number from image name

Output of the reformatting script

Example: cells_final.csv with new column with image number separated

Add a new column name to the Image number split from the name

Get the names of columns to select only the relevant ones using csvcut -n <file>

Decide which columns are relevant for further analysis (highlighted in red)

Save the relevant columns into a new file using select.R -c N,M-O <file>

Check the columns got selected

To get the final descriptive statistics (aka summary), select what columns describe the experimental condition (here it is the the column number 3)

First check the number of columns using csvcut -n <file>

Make a summary statistics using the summaryR -c <file> targeting the third columns

The output file contains statistics for each numerical column

In first, last, mean, sd and N is calculated for each group

Acquisition setting

Resources

Cell Profiler Versions

Pipeline location on Leica computer

Tutorial on cell profiler