Bioengineering
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Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
Chapters
Summary February 15th, 2022
We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.
Transcript
This method can be used to help researchers analyze and count co-cultures of cells using a simple and effective area-based analysis. This technique is relatively easy to implement using widely available software and offers a reliable and accurate means of identifying various cell types within a co-culture setting. This method can provide insight into how specific co-cultures of cells synergize to aid in tissue regeneration.
This method can also be employed to validate monoculture biomarker studies. To begin, culture raw 264.7 macrophages at 37 degrees Celsius and 5%carbon dioxide for monoculture imaging in one milliliter of DMEM supplemented with FBS, penicillin-streptomycin, sodium bicarbonate, and beta-mercaptoethanol in a five milliliter cell culture flask at a density of 25, 000 cells per centimeter square. Culture NIH/3T3 cells in DMEM supplemented with 10%FBS and 1%penicillin-streptomycin.
For co-culture imaging, culture raw 264.7 macrophages and NIH/3T3 fibroblasts using co-culture medium containing one part raw 264.7 medium and one part NIH/3T3 medium together at varied ratios and total density of 25, 000 cells per centimeter square. Following seeding, incubate the cells at 37 degrees Celsius and 5%carbon dioxide to reach a viable cell density of 80%cell confluence. To acquire cell images using an inverted microscope equipped with the 40X objective, determine the capacity of the algorithm to accurately evaluate images with a range of image qualities.
Acquire images in gray scale with varying foci producing both non-bulbus and bulbus images and export them in the raw czi file. To obtain images of cells using the area-based method, copy and paste the image file for analysis into the bin and enter the filename by executing the command. Press Run to start the program.
Analyze the images by opening by reconstruction, followed by closing by reconstruction using source functions to magnify the foreground from the background by executing the respective command. To binarize the reconstructed images utilizing a percentile-based identification system, distinguish cells from the background by using a percentile difference from the maximum relevant pixel with the largest pixel value comprising at least 0.5%of a given image. Analyze and evaluate the pixel values for raw 264.7 macrophages, ensuring that the values are within 4.5%of the maximum relevant pixel.
Then mark the pixel as cellular. For images containing bulbus cell profiles, implement an iterative procedure to correct for erroneous binarization at the centers of the cells. Determine an initial guess for the total cell coverage.
Run the algorithm and analyze images using initial estimates of alpha and kappa to fill a portion of the islands. Then use the post-analysis cell counts and coverage to recalculate kappa. To determine the average cell area after binarization of the image, obtain a vector of all center locations and radii of circles found within the image by executing the command.
Use the radii outputs to calculate the average cell area by averaging and analyze at least 10 cells to ensure accurate area identification. Perform the image analysis using the commands as described previously. Then conduct an analysis of co-cultures containing raw 264.7 and NIH/3T3 cells by experimentally determining a parameter phi using images of raw 264.7 and NIH/3T3 cells.
Guess an initial phi value and iterate until cell counts and coverage match closely with the manual counts specific to the raw 264.7 and NIH/3T3 co-culture. Determine raw 264.7 cell counts in total cell coverage as described previously for the monoculture images. Analyze the watershed transformed image again without the phi parameter, detecting both macrophages and fibroblasts.
Acquire NIH/3T3 fibroblast data by selectively subtracting raw 264.7 cell pixels obtained using the standard thresholding and area-based quantification methods described previously. Analysis of non-bulbus raw 264.7 macrophages was performed and algorithm outputs were recorded. Average area calculations of the image using algorithm counted 226 cells, while a manual count identified 241 cells with an approximate error value of 6%Analysis of bulbus raw 264.7 macrophages was also carried out.
The average area calculations of the image using algorithm counted 221 cells, verified by a manual count of 252 cells with an approximate error value of 12%Analysis of co-cultures containing both raw 264.7 macrophages and NIH/3T3 fibroblasts was conducted. Algorithm outputs for the raw 264.7 macrophage count were 137, while a manual count identified 155 cells with an approximate error value of 11%To determine the robustness of the cell counting algorithm, five raw 264.7 macrophages images were counted by automatic cell identification and manual user counts. The Dice coefficient was obtained with an average parameter of 0.85 across five images.
The critical step within this protocol is the use of percentile-based parameters which allows for robust cell detection in both monoculture and co-culture images. This protocol can be used to identify cells of interest for further analysis using molecular biology techniques to assess biomarkers that define specific cell populations and biomarker development in co-culture systems.
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