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##
## Analysis of RNAScope points in Iba1-positive single cells
##
## Cristina de Dios - Marta Valeri
## Microscopy Platform - High Technology Unit - Vall d'Hebron Research Institute (UAT-VHIR)
## Contact: microscopia@vhir.org
##
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## Requirements: MorpholibJ should be pre-installed before running the script. To install MorpholibJ, go to
## Help > Update > Manage Update Sites and select IJBP-Plugins. Restart ImageJ after installation.
## Adjustable parameters
pos_condition = newArray("Control", "AD", "MS", "PD"); ## Possible groups#conditions on your experiment
red_prom = 1000; ## Prominence to use for red points count. Modify sensibly considering channel fluorescence and differences in signal-background fluorescence
far_red_prom = 1000; ## Prominence to use for red points count. Modify sensibly considering channel fluorescence and differences in signal-background fluorescence
filename_end_regex = "Focus.czi"; ## Regular expression used for recognition of Z-projected images. Filenames must include Z-projection tag (e.g. "Focus" in "Extended Depth of Focus" for Zeiss LSM980 microscope) and file extension at the end of the filename
autom_nuc_prominence = 750; ## Prominence to use in the nuclei channel for automatic cell selection. Note this value is set after applying Gaussian filter (sigma=10) to the nuclei channel
## Inicialization
close("*");
close("ROI Manager");
run("Set Measurements...", "area min redirect=None decimal=3");
## Essential global variables
title_array = newArray();
condition_array = newArray();
redpos_cells_array = newArray();
farpos_cells_array = newArray();
doublepos_cells_array = newArray();
avg_redpoints_percell_array = newArray();
avg_farpoints_percell_array = newArray();
avg_skelmax_array = newArray();
avg_skelmean_array = newArray();
avg_skelbranch_array = newArray();
##setBatchMode(true);
## Selecting projected images
dir = getDir("Choose image folder");
results_dir = dir + "#Results#";
selected_dir = dir + "#Selection#";
File.makeDirectory(results_dir);
File.makeDirectory(selected_dir);
filelist = getFileList(dir);
## Selecting region segmentation method
Dialog.create("Nucleated Regions Selection");
Dialog.addMessage("Select how you want to select nucleated regions:\n \n- Automatically (nuclei maxima overlap)\n- Semi-automatically (point selection overlap)\n \nPlease, take into account that if nucleus does not overlap selection will not be properly done\n \n");
Dialog.addChoice("Select option", newArray("Semi-automatic selection (recommended)", "Automatic Selection"), "Semi-automatic selection (recommended)");
Dialog.show();
choice = Dialog.getChoice();
## Iteration for every image
for (i = 0; i < lengthOf(filelist); i++) {
if (endsWith(filelist[i], filename_end_regex)) {
## Essential local variables (reset for every image)
label_array = newArray();
redpoints_percell_array = newArray();
farpoints_percell_array = newArray();
nuc_percell_array = newArray();
relat_redpoints_percell_array = newArray();
relat_farpoints_percell_array = newArray();
skel_numbranch_percell_array = newArray();
skel_meanlength_percell_array = newArray();
skel_maxlength_percell_array = newArray();
red_counter = 0;
far_red_counter = 0;
double_counter = 0;
classification_array = newArray();
run("Bio-Formats Importer", "open=[" + dir + filelist[i] + "] autoscale color_mode=Default rois_import=[ROI manager] view=Hyperstack stack_order=XYCZT");
img_title = filelist[i];
title_array = Array.concat(title_array, img_title);
for (j = 0; j < lengthOf(pos_condition); j++) {
if (matches(img_title.toUpperCase, ".*" + pos_condition[j].toUpperCase + ".*")) {
condition_array = Array.concat(condition_array, pos_condition[j]);
}
}
run("Split Channels");
selectImage("C3-" + filelist[i]);
run("Duplicate...", "title=iba1_original");
selectImage("C4-" + filelist[i]);
run("Duplicate...", "title=nuclei_master");
selectImage("C3-" + filelist[i]);
## Binarization
setAutoThreshold("Huang dark");
##run("Threshold...");
setOption("BlackBackground", true);
run("Convert to Mask");
## Mask perfectioning
run("Fill Holes");
run("Options...", "iterations=10 count=4 black do=Erode");
run("Analyze Particles...", "size=5-Infinity show=Masks");
selectImage("Mask of C3-" + filelist[i]);
rename("iba1");
##Skeletonization for microglial morphology
run("Duplicate...", "title=iba1_skel");
run("Invert LUT");
run("Skeletonize");
rename("iba1_skel");
## Retrieval of selection coordinates
nucleated_cells = "";
if (choice == "Automatic Selection") {
selectImage("C4-" + filelist[i]);
run("Duplicate...", "title=nuclei");
selectImage("C4-" + filelist[i]);
run("Gaussian Blur...", "sigma=10");
run("Find Maxima...", "prominence="+d2s(autom_nuc_prominence, 0)+" output=List");
selectWindow("Results");
y_coords = Table.getColumn("Y");
x_coords = Table.getColumn("X");
close("Results");
for (k = 0; k < lengthOf(y_coords); k++) {
x_coords_def = newArray();
y_coords_def = newArray();
selectImage("iba1");
coord_label = getPixel(x_coords[k], y_coords[k]);
if (coord_label != 0) {
x_coords_def = Array.concat(x_coords_def, x_coords[k]);
y_coords_def = Array.concat(y_coords_def, y_coords[k]);
}
}
if (nucleated_cells == "") {
x_coords_def = newArray();
y_coords_def = newArray();
waitForUser("No Iba1-positive regions overlapping with nuclei maxima were found. Moving on to semi-automatic selection");
run("Merge Channels...", "c1=iba1 c2=nuclei c3=iba1_original c4=[C1-" + filelist[i] + "] c5=[C2-" + filelist[i] + "] create keep");
setTool(7);
setBatchMode("exit and display");
waitForUser("- Select cells with nucleus. When done, click OK");
getSelectionCoordinates(xpoints, ypoints);
x_coords_def = Array.concat(x_coords_def, xpoints);
y_coords_def = Array.concat(y_coords_def, ypoints);
selectImage("iba1");
setBatchMode(true);
}
}
if (choice == "Semi-automatic selection (recommended)") {
x_coords_def = newArray();
y_coords_def = newArray();
selectImage("iba1");
run("16-bit");
run("Red");
run("Merge Channels...", "c1=iba1 c2=[C4-" + filelist[i] + "] c3=iba1_original c4=[C1-" + filelist[i] + "] c5=[C2-" + filelist[i] + "] create keep");
setTool(7);
setBatchMode("exit and display");
waitForUser("- Select cells with nucleus. When done, click OK");
getSelectionCoordinates(xpoints, ypoints);
x_coords_def = Array.concat(x_coords_def, xpoints);
y_coords_def = Array.concat(y_coords_def, ypoints);
selectImage("iba1");
setBatchMode(true);
}
## Creation of green + nuclei mask
selectImage("nuclei_master");
##run("Threshold...");
setAutoThreshold("MaxEntropy dark no-reset");
setOption("BlackBackground", true);
run("Convert to Mask");
run("Fill Holes");
run("Options...", "iterations=10 count=4 black do=Erode");
run("Analyze Particles...", "size=20.00-Infinity show=Masks");
rename("nuclei_binary");
imageCalculator("Add create", "nuclei_binary", "iba1");
rename("combined");
close("iba1");
selectImage("combined");
run("Connected Components Labeling", "connectivity=4 type=[16 bits]");
close("combined");
selectImage("combined-lbl");
rename("combined");
## Retrieval of selection masks and inclusion in ROI Manager for analysis
for(j = 0; j < x_coords_def.length; j++){
nucleated_cells = nucleated_cells + d2s(getPixel(x_coords_def[j], y_coords_def[j]),0)+", ";
}
run("Select Label(s)", "label(s)=[" +nucleated_cells + "]");
run("Remap Labels");
close("combined");
selectImage("combined-keepLabels");
rename("combined");
run("Measure");
setBatchMode(true);
max_value = getResult("Max");
close("Results");
run("ROI Manager...");
for (l = 0; l < max_value; l++) {
setThreshold(l+1, l+1, "raw");
run("Create Selection");
roiManager("Add");
}
## Nuclei count per cell (needed for relativization in multinucleated masks)
selectImage("nuclei_binary");
for (m = 0; m < roiManager("count"); m++) {
roiManager("select", m);
run("Find Maxima...", "prominence=250 output=Count");
nuc = getResult("Count");
if (nuc == 0) {
nuc_percell_array = Array.concat(nuc_percell_array, 1);
}
else {
nuc_percell_array = Array.concat(nuc_percell_array, nuc);
}
close("Results");
}
## FarRed points count
selectImage("C1-" + filelist[i]);
for (m = 0; m < roiManager("count"); m++) {
label_array = Array.concat(label_array, m+1);
roiManager("select", m);
run("Find Maxima...", "prominence="+d2s(far_red_prom, 0)+" output=Count");
far_red = getResult("Count");
farpoints_percell_array = Array.concat(farpoints_percell_array, far_red);
relat_farpoints_percell_array = Array.concat(relat_farpoints_percell_array, far_red#nuc_percell_array[m]);
close("Results");
}
## Red points count
selectImage("C2-" + filelist[i]);
for (m = 0; m < roiManager("count"); m++) {
roiManager("select", m);
run("Find Maxima...", "prominence="+d2s(red_prom, 0)+" output=Count");
red = getResult("Count");
redpoints_percell_array = Array.concat(redpoints_percell_array, red);
relat_redpoints_percell_array = Array.concat(relat_redpoints_percell_array, red#nuc_percell_array[m]);
close("Results");
}
## Skeleton analysis (for individual label)
selectImage("iba1_skel");
for (m = 0; m < roiManager("count"); m++) {
roiManager("select", m);
run("Duplicate...", "title=iba1_skelroi");
run("Analyze Skeleton (2D#3D)", "prune=none");
selectWindow("Results");
mean_branch_len = getResult("Average Branch Length");
max_branch_len = getResult("Maximum Branch Length");
branch_number = getResult("# Branches");
skel_numbranch_percell_array = Array.concat(skel_numbranch_percell_array, branch_number);
skel_meanlength_percell_array = Array.concat(skel_meanlength_percell_array,mean_branch_len);
skel_maxlength_percell_array = Array.concat(skel_maxlength_percell_array, max_branch_len);
close("Tagged skeleton");
close("iba1_skelroi");
}
## Getting means for summary
Array.getStatistics(relat_redpoints_percell_array, min, max, red_mean, stdDev);
Array.getStatistics(relat_farpoints_percell_array, min, max, far_mean, stdDev);
Array.getStatistics(skel_numbranch_percell_array, min, max, num_mean, stdDev);
Array.getStatistics(skel_meanlength_percell_array, min, max, mn_mean, stdDev);
Array.getStatistics(skel_maxlength_percell_array, min, max, mx_mean, stdDev);
## Cleanup
close("Results");
setBatchMode("exit and display");
close("ROI Manager");
setBatchMode(true);
## Label classification criteria
for (p = 0; p < redpoints_percell_array.length; p++) {
class = "";
if (redpoints_percell_array[p] > 0 && farpoints_percell_array[p] == 0) {
class = "Red";
red_counter += 1;
}
else if (redpoints_percell_array[p] == 0 && farpoints_percell_array[p] > 0) {
class = "FarRed";
far_red_counter += 1;
}
else if (redpoints_percell_array[p] > 0 && farpoints_percell_array[p] > 0) {
class = "Both";
double_counter += 1;
}
else {
class = "None";
}
classification_array = Array.concat(classification_array, class);
}
## Create and save table for image
Table.create("Single Microglia Analysis of RNAScope Expression of Image " + filelist[i]);
Table.setColumn("Cell Label", label_array);
Table.setColumn("# Red points", redpoints_percell_array);
Table.setColumn("# FarRed points", farpoints_percell_array);
Table.setColumn("# Nuclei", farpoints_percell_array);
Table.setColumn("Positive for", classification_array);
saveAs("Results", results_dir + filelist[i] + ".csv");
close(filelist[i] + ".csv");
## Save label image
selectImage("combined");
run("Select None");
saveAs("Tiff", selected_dir + filelist[i] + ".tif");
close("*");
redpos_cells_array = Array.concat(redpos_cells_array, red_counter);
farpos_cells_array = Array.concat(farpos_cells_array, far_red_counter);
avg_redpoints_percell_array = Array.concat(avg_redpoints_percell_array, red_mean);
avg_farpoints_percell_array = Array.concat(avg_farpoints_percell_array, far_mean);
doublepos_cells_array = Array.concat(doublepos_cells_array, double_counter);
avg_skelbranch_array = Array.concat(avg_skelbranch_array, num_mean);
avg_skelmax_array = Array.concat(avg_skelmax_array, mx_mean);
avg_skelmean_array = Array.concat(avg_skelmean_array, mn_mean);
}
}
## Creating global summary
Table.create("Summary of Single Microglia Analysis of RNAScope Expression");
Table.setColumn("Image_Title", title_array);
Table.setColumn("Group", condition_array);
Table.setColumn("Average # of Red Points per Cell", avg_redpoints_percell_array);
Table.setColumn("Average # of FarRed Points per Cell", avg_farpoints_percell_array);
Table.setColumn("# Red-Only Positive Cells", redpos_cells_array);
Table.setColumn("# FarRed-Only Positive Cells", farpos_cells_array);
Table.setColumn("# Double Positive cells", doublepos_cells_array);
Table.setColumn("Average # of Branches", avg_skelbranch_array);
Table.setColumn("Average Mean Branch Length", avg_skelmean_array);
Table.setColumn("Average Maximum Branch Length", avg_skelmax_array);RNAscope
RNAscope quantification
The following script depicts the quantification of P2RY12 and GPNMB RNA positive dots within IBA1+ cells, as well as the number, mean and max length of branches per IBA1+ cells. The script was run on Fiji (ImageJ 1.54f).