MarkToBlue: Illuminating the -difference
-Alejandro Gombau García
-2024-12-23
- -Introduction.
-Immunocytochemistry (IHC) and Immnunocytochemistry (ICC) are robust -techniques widely used for visualizing antigen locations in tissue -samples. While offering unique insights, it is often limited in its -quantification capabilities. This project presents a computational -solution to complement experimental workflows.
-Objectives
-The objective of this analysis
--
-
Explain and document the approach used by MarkToBlue
-To support the results provided by the program in a detailed -way.
-Explore the possible limitations and propose -improvements.
-
Analitical approach
-When using IHC and ICC, we notice that is well-suited for comparative -assessment but is of limited value for absolute quantification. Cell -samples with different confluence, fluorescence fading, subtle -differences in antibody concentration, can make the comparison -unreliable.
-The proposed method is a ratio creation used for normalize the marker -(green) parameters to the blue (base or nuclei) parameters.
-The reason for doing so is because we assume that the nuclei marker -(DAPI or Hoescht) is a good proxy for total tissue area and brightness -baseline.
-\[ -\text{MarkToBlue Ratio} = \frac{A_{\text{blue}} \cdot -B_{\text{blue}}}{A_{\text{green}} \cdot B_{\text{green}}} -\]
-The marker to blue approach has precedents in the literature. Further -improvements are needed, however, it may be useful as a first step in -marker quantification.
-Camp RL, Chung GG, Rimm DL. Automated subcellular localization and -quantification of protein expression in tissue microarrays. Nat Med. -2002 Nov;8(11):1323-7. doi: 10.1038/nm791. Epub 2002 Oct 21. PMID: -12389040.
-Carpenter, A.E., Jones, T.R., Lamprecht, M.R. et al. -CellProfiler: image analysis software for identifying and quantifying -cell phenotypes. Genome Biol 7, R100 (2006). -https://doi.org/10.1186/gb-2006-7-10-r100
-Exploratory Analysis (EA)
-We will start the analysis by loading the sample image.
-path <- "/home/alex/Documentos/Proyectos/marktoblue/MarkToBlue/documentation/sample.png"
-# Load the image
-image <- readImage(path)
-# Display the original image
-display(image, method = "raster")
After uploading we process the image and plot histograms:
-# Separate color channels
-blue_channel <- channel(image, "blue")
-green_channel <- channel(image, "green")
-
-# Plot histograms for color channels
-par(mfrow = c(1, 2)) # Arrange plots in a 1x2 grid
-
-# Histogram for the blue channel
-hist(as.vector(blue_channel), breaks = 50, main = "Blue Channel Histogram",
- xlab = "Intensity", col = "blue", border = "white")
-
-# Histogram for the green channel
-hist(as.vector(green_channel), breaks = 50, main = "Green Channel Histogram",
- xlab = "Intensity", col = "green", border = "white")
# Thresholding to create masks
-threshold_blue <- 0.5 # Adjust as needed
-threshold_green <- 0.5 # Adjust as needed
-mask_blue <- blue_channel > threshold_blue
-mask_green <- green_channel > threshold_green
-
-# Visualize masks
-par(mfrow = c(1, 2)) # Arrange plots in a 1x2 grid for masks
-display(mask_blue, method = "raster", title = "Blue Mask")
-display(mask_green, method = "raster", title = "Green Mask")
Calculate area and brightness. But first, the MarkToBlue Shiny App -approach is presented.
-# MarkToBlue App
-# Compute metrics
- metrics <- reactive({
- req(processed_image())
- channels <- processed_image()
-
- if (!is.null(channels$green) && !is.null(channels$blue)) {
- # Convert channels to RGB
- green_rgb <- image_convert(channels$green, colorspace = "RGB")
- blue_rgb <- image_convert(channels$blue, colorspace = "RGB")
-
- # Extract pixel data
- green_matrix <- as.numeric(image_data(green_rgb))
- blue_matrix <- as.numeric(image_data(blue_rgb))
-
- # Calculate area and brightness
- green_area <- sum(green_matrix > 0)
- blue_area <- sum(blue_matrix > 0)
- # Calculate brightness and multiply by 1000 for legibility
- green_bri <- mean(green_matrix)*1000
- blue_bri <- mean(blue_matrix)*1000
-
- # Compute MarkToBlue ratio
- marktoblue_ratio <- (green_area * green_bri) / (blue_area * blue_bri)Now, we calculate the ratio using the same approach as the Shiny App.
-We have two variables, blue_channeland
-green_channel, each containing pixel values in the range of
-[0,1]. Each entry correspond to the intensity od the pixel in that
-channel.
# DOCUMENTED R SCRIPT
-# Suppose 'blue_channel' and 'green_channel' are numeric vectors or matrices
-# that contain pixel values in the [0, 1] range (as returned by image_data())
-
-# 1) Define a "mask" as all non-zero pixels (just like sum(green_matrix > 0))
-mask_blue <- (blue_channel > 0)
-mask_green <- (green_channel > 0)
-
-# 2) Calculate area (number of non-zero pixels)
-area_blue <- sum(mask_blue)
-area_green <- sum(mask_green)
-
-# 3) Check for zero area to avoid division by zero
-if (area_blue == 0 || area_green == 0) {
- stop("One of the masks has an area of zero. Check your thresholds or input data.")
-}
-
-# 4) Calculate mean brightness of only non-zero pixels
-brightness_blue <- mean(blue_channel[mask_blue], na.rm = TRUE)
-brightness_green <- mean(green_channel[mask_green], na.rm = TRUE)
-
-# 5) Multiply by 1000 for legibility (same approach as R Shiny app)
-brightness_blue <- brightness_blue * 1000
-brightness_green <- brightness_green * 1000
-
-# 6) Check for zero brightness to avoid invalid results
-if (brightness_blue == 0 || brightness_green == 0) {
- stop("One of the channels has a brightness of zero. Check your image processing steps.")
-}
-
-# 7) Calculate the Green to Blue ratio (same formula)
-gb_ratio <- round((area_green * brightness_green) / (area_blue * brightness_blue),2)
-
-# 8) Output the ratio
-gb_ratio## [1] 0.89Below is the result of the same image using MarkToBlue App.
-
We see that the results differ by ± 0.2. Probably due to the R rounds -results or intermediate variables.
-To test whether the app is able to capture experimental differences, -two cell culture images are compared. These are brown wistar rat lung -cells treated with collagenase and seeded in monolayer. They were then -stained with anti Rat -CD31/PECAM-1 Antibody(R&D Systems) and Secondary -Antibody, Alexa Fluor™ 488(ThermoFisher)
-Before seeding, endothelial cells were isolated using the MACS® -Columns for magnetic cell isolation (Miltenyi Biotech) system. CD31+ -cells were selected.
-Isolated cells:
-
Not isolated cells:
-