This repository contains the code used the analysis of spatial proteomics screen in the manuscript "Subcellular mapping of the protein landscape of SARS-CoV-2 infected cells for target-centric drug repurposing".
Step 1. Convert file names and max-projection
python generate_covid_dataset-max-projection.py
Paths need to be updated:
ACQUIRED_DATA_PATH
: Raw data acquisition path with Images folder (input folder)FORMATTED_DATA_PATH
: Images with formatted names (intermediate folder)IMG_FOLDER
: Images with max-projection (output folder)ANNOTATION_FOLDER
: Folder forannotation-tool
with Imjoy (output folder, only used when manual annotation is later needed)
Step 2. Segmentation with HPACellSegmentator and convert mask to geojson file
python generate_covid_dataset-max-projection_segmentation.py
python mask_to_json.py
Paths need to be updated:
IMG_FOLDER
: Images with max-projectionSEGMENTATION_FOLDER
: path to save segmentation masksNUC_MODEL
: nuclei model pathCELL_MODEL
: cell model path
If signals are too low in a plate, they need to be magnified for the segmentation algirthm to work. Note: The increased intensity is only used to create masks, subsequent analysis rely on raw intensity.
python generate_covid_dataset-max-projection.py
python generate_covid_dataset-max-projection_segmentation.py
If the experiment needs to be uploaded to annotation tool later, change SEGMENTATION_FOLDER
and run
python mask_to_json.py
to create annotation.json
files for each FOV.
Step 3. Covid and protein quantification, Violin plot
python s1_covid_quantify_well.py
python s2_combine_quantification_df.py
python s3_define_cellsize_and_virus_threshold.py
python s4_violinplot_adjustment.py
Paths need to be updated:
IMG_FOLDER
: Images with max-projectionSAMPLE_DEST
: sample destination- (s4_)
NONINFECTED
: Wells or well pattern with non-infected cells - (s4_)
INFECTED
: Wells or well pattern with non-infected cells
Step 4. Fold change, volcano and circos plot
python covid19plots.py
Volcano and circos in R: covid19plots_<cellline>.R
.