Article

Not peer-reviewed version

The Confluence Analysis Program: A

User-Friendly Tool for Automated Cell

Confluence Measurement and

Visualization

Abraham Lobsenz and Paul Seidler

Posted Date: 12 August 2024

doi: 10.20944/preprints202408.0786.v1

Keywords: confluency; bioinformatics; cell segmentation; high-throughput screening

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Article

The Confluence Analysis Program: A User-Friendly

Tool for Automated Cell Confluence Measurement

and Visualization

Abraham Lobsenz

and Paul Seidler

Bridge Undergraduate Science Program, Bridge Institute, USC Michelson Center for Convergent

Bioscience, University of Southern California, Los Angeles, CA 90089

Department of Pharmacology and Pharmaceutical Sciences, USC Mann School of Pharmacy and

Pharmaceutical Sciences, University of Southern California, Los Angeles, CA 90089

* Correspondence: pseidler@usc.edu

Abstract: Measuring cell confluence in high-throughput screening of pharmaceutics, cell culture monitoring,

cancer research, and industrial applications requires quantitative and standardizable measures to minimize

subjective observations and increase throughput [[1–12]

[

. Over the past half-century, non-invasive specialized

programs have been created to quantitatively assess cell confluence, and some software tools exist to meet

these needs[1,4–9]

. However, many of these tools are expensive, computationally-demanding, or inaccessible

to non-experts[1,6]. With the assistance of generative artificial intelligence, we have developed the Confluence

Analysis Program (CAP), a modern tool designed to be precise, interactive, and user-friendly. CAP is written

entirely in Python and runs in any command-line interface (CLI), including Terminal (OS) and Command

Prompt (Windows). In addition to fetching all necessary external libraries automatically and prompting drag-

and-drop image retrieval, CAP features live visual comparison of cell mass identification to the original image

assay. Our results demonstrate robust performance of CAP on fluorescent micrographs of varying confluences

ranging from low to high. CAP is provided as a free resource, accessible in the Supplementary Materials.

Keywords: confluency; bioinformatics; cell segmentation; high-throughput screening

Introduction

Cell confluence assays are critical to a wide range of scientific applications, including

pharmaceutics, cell culture monitoring, cancer research, and bioreactor maintance[2–4,8,10,11]. These

assays measure the percentage of the surface area covered by adherent cells, providing valuable

insights into cell growth, proliferation, and behavior[1–12].

Few modern applications to measure cell confluence are open-source, interactive, accessible to

non-experts, and have limited computational demands. Addressing this gap, we have developed the

Confluence Analysis Program (CAP). Written entirely in Python, CAP operates seamlessly in any

command-line interface (CLI), including Terminal on macOS and Command Prompt on Windows.

CAP allows users to easily retrieve images through a drag-and-drop interface. Additionally, it

features live visual comparisons of cell mass identification to the original image assay, enhancing the

user’s ability to verify and adjust measurements in real-time. All necessary external libraries are

automatically installed if not found on your computer at runtime. The only manual installation steps

are downloading Python3 at Python.org (make sure to download the appropriate version for your

system), and perhaps the Pip package if not already installed (see Methods section for additional

installation and run details).

In this study, we aimed to develop and test CAP. Our work demonstrates that CAP is an effective

tool with exceptional ease of use and widespread research applications for cell confluence

measurements, which are commonplace in academic and industrial science laboratories.

Disclaimer/Publisher’s Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and

contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting

from any ideas, methods, instructions, or products referred to in the content.

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Methodological Development

CAP performs cell segmentation analysis to estimate cell confluency percentage as the surface

area occupied by detected cell bodies, in alignment with standard image processing techniques for

this application[1,9]. Of note, several features were implemented in CAP with the intention of

optimizing runtime.

CAP first reads images within an input folder and converts them to grayscale for further

processing. To enhance processing speed and efficiency, the images are first compressed using a

specialized function before any segmentation analysis. The first crucial step involves adaptive

thresholding, a technique used to convert grayscale images into binary images. In this process, each

pixel is compared to the mean or weighted mean of its neighborhood, and a threshold is applied to

determine whether the pixel belongs to the foreground (cell) or background. The Block Size

parameter controls the size of the neighborhood area used for this comparison. Smaller block sizes

make the thresholding more sensitive to local variations in intensity, which is useful for images with

uneven lighting or varying cell densities. Conversely, larger block sizes provide a more global

threshold, suitable for images with more uniform intensity distribution. The program uses adaptive

Gaussian thresholding, which considers the weighted sum of neighborhood values minus a constant

to determine the threshold for each pixel.

After thresholding, the binary image often contains noise and small gaps within cell regions. To

address this, morphological operations are applied to improve the accuracy of cell detection. The

primary morphological operation used is closing, which involves dilation followed by erosion.

Dilation adds pixels to the boundaries of cell regions, helping to close small gaps and connect

disjointed cell parts. Erosion, on the other hand, removes pixels from the boundaries, refining the

overall shape and removing small noise particles. The size of the kernel used in these operations is

controlled by the Mask Fill parameter, where a larger kernel results in more connected and filled-in

cell areas, representing higher confluence. Additionally, the Mask Iterations parameter specifies the

number of times these morphological operations are applied. More iterations result in a smoother

and cleaner binary mask, but excessive iterations can cause over-merging of close objects. Therefore,

adjusting this parameter allows users to balance between connecting cell regions and maintaining

individual cell boundaries.

To further improve efficiency, CAP uses numba, a just-in-time compiler, to accelerate the

removal of small objects based on area. This optimization significantly speeds up the processing time

for large images or datasets. Following morphological processing, CAP performs connected

components analysis to identify individual cell regions. During this step, each connected component

(potential cell) is labeled, and its area is calculated. To filter out noise and small artifacts, a threshold

based on the Minimum Area parameter is applied. Any objects below this area threshold are removed

from the binary mask, ensuring that only significant cell regions are considered in the final confluence

measurement. If cell confluence is calculated to be above 3%, CAP includes an additional post-

processing step where pixels are filled or removed based on local density, further enhancing the

accuracy of cell detection. Figure 1 shows a flowchart describing CAP’s processing steps.

For handling multiple images, CAP leverages parallel computing through the

ProcessPoolExecutor. This approach allows concurrent processing of images, drastically reducing the

overall time required for analysis in high-throughput scenarios. The total area covered by cells is then

calculated by summing the pixels in the binary mask, and this value is expressed as a percentage of

the total image area, providing the cell confluence measurement. This comprehensive and efficient

pipeline ensures users are easily and rapidly able to select optimized parameters in CAP and execute

batch confluence measurements for a wide range of cell types and applications.

Preprints.org (www.preprints.org) | NOT PEER-REVIEWED | Posted: 12 August 2024 doi:10.20944/preprints202408.0786.v1

Figure 1. Flowchart of CAP processing steps.

Results

In order to validate the functionality of CAP, we empirically evaluated its measurements of

various confluences of cultured HEK293 cells and fluorescent biosensor cells. Eight different two-fold

dilutions ranging from 1:500 to 1:4 of HEK93 were prepared using Dulbecco’s Modified Eagle

Medium (DMEM) supplemented with 10% Fetal Bovine Serum (FBS), 1x GlutaMAX, and 1x

Penicillin-Streptomycin. Two-fold dilutions were pipetted to each row of a 96-well plate. After 3 days,

nuclei were stained with Hoechst 33342 and analyzed using a fluorescence microscope. As an

additional test, fluorescent biosensor cells expressing yellow fluorescent protein (YFP) were also

analyzed. Figure 2 shows the sample wells along with the cell mask and estimated confluence

generated by CAP.

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Figure 2. (A) Plated HEK293 cells stained with Hoechst 33342. The analysis parameters were a mask

fill of 3, minimum area of 0, block size of 5, and mask iterations of 3. (B) Plated fluorescent biosensor

cells analyzed with a mask Fill of 2, minimum area of 1000, block size of 7, and mask iterations of 3.

As evidenced in results shown in Figure 2, CAP will estimate cell confluency for a variety of

staining methods and cell confluences. Using a laptop, CAP analyzed 147 cell images in 4 minutes

and 57 seconds, corresponding to an average image processing rate of 2 seconds per image,

significantly improved from the rate of 13 seconds per image in other modern algorithms[4].

Discussion

CAP provides a practical and efficient solution for measuring cell confluence, an essential metric

in various biological research domains[2–4,8,10,11]. CAP’s user-friendly interactive design, limited

computation demands, and robust functionality make it a valuable tool for researchers in these

domains.

In pharmaceutics, precise confluence measurements are crucial for evaluating the efficacy of

drug candidates on cultured cells. In high-throughput drug screening, where thousands of

compounds are tested for cytotoxicity or proliferation effects, accurate confluence data ensures

reliable assessment of cell health and response to treatments[12]. CAP’s computationally optimized

processing allows researchers to quickly assess cell confluence for large quantities of images,

enhancing the efficiency of the screening process. Additionally, live image updates responding to

parameter changes boost user confidence.

In bioreactor research and manufacturing, effective confluence measures are crucial to

maintaining optimal production levels[8]. Accurate confluence measurements ensure that cell

cultures are at the right stage for harvesting or further processing.

In cancer research, confluence measurements are used to study tumor cell growth behavior and

the effects of potential therapeutics. Lobo et al. (2024) investigated Argonaute (AGO) protein, an RNA

silencing protein crucial in regulating cell growth, within a variety of cell lines, demonstrating the

effect of cell confluence on AGO2 expression[2]. CAP’s ability to provide precise confluence data

Preprints.org (www.preprints.org) | NOT PEER-REVIEWED | Posted: 12 August 2024 doi:10.20944/preprints202408.0786.v1

finds through real-time parameter adjustments enhances researchers’ ability to evaluate the efficacy

and validity of such studies.

Cell culture monitoring, in general, benefits from accurate confluence measurements to maintain

optimal growth conditions and ensure experimental consistency. Freshney (2010) emphasizes the

importance of routine confluence monitoring to prevent overgrowth and ensure reproducibility

across experiments[11]. CAP’s live visual comparison feature and adjustable parameters facilitate

accurate monitoring, helping researchers maintain healthy cell cultures and avoid common issues

such as overconfluence or insufficient growth.

Conclusions

While CAP is not revolutionary, it fills a crucial gap by providing a free, modern,

computationally-ideal, and user-friendly tool for cell confluence measurement. Its application spans

multiple research domains, improving the accuracy and efficiency of experiments in pharmaceutics,

cancer research, regenerative medicine, and general cell culture monitoring. By simplifying the

confluence measurement process, CAP supports the advancement of research and contributes to the

reliability of experimental outcomes.

Instructions

Download Python:

Go to python.org and download the appropriate version of Python3.

Install/Check Installation of Pip:

Run one of the following commands in your Command-Line Interface (CLI):

OS: python -m ensurepip --upgrade

Windows: py -m ensurepip --upgrade

Downloading Script:

Download the ConfluenceAnalysisProgram.txt file linked in supplementary material, change the file

extension to .py, and place it in the desired file directory.

Running the Script:

Launch your preferred CLI such as Terminal for OS, or Command Prompt for Windows. Within your

CLI, navigate to the file directory containing ConfluenceAnalysisProgram.py. Then, run the script.

File Provision:

The program will check that all necessary external libraries are installed (and install them if not).

Then, it will prompt you to enter a FOLDER with images of type .jpg, .jpeg, .png, .bmp, .tiff, .tif, or

.gif.

Pop-up Preview Window:

After supplying the path to the relevant folder, a pop-up window will emerge displaying an image

within the folder on the left, and the cell mask on the right. To the left are four adjustable parameters

that should help you align the cell mask with perceived cell presence in the original image. They are

each described below:

Mask Fill: This parameter controls the size of the kernel used in morphological operations

(such as closing) applied to the binary image after thresholding. It affects how much the detected cell

areas are filled in. A higher value for Mask Fill means a larger kernel, which results in more connected

and filled-in areas, representing higher confluence. This value is initialized to three on runtime.

Preprints.org (www.preprints.org) | NOT PEER-REVIEWED | Posted: 12 August 2024 doi:10.20944/preprints202408.0786.v1

Minimum Area: Sets the threshold for the smallest area that a detected object (in this case, a

cell) must have to be considered in the mask. Any objects below this area will be removed. This helps

to filter out noise and small artifacts that are not cells. This value is initialized to 1,000 on runtime.

Block Size: Controls the size of the neighborhood area used for adaptive thresholding. It

determines how local the thresholding will be. Smaller block sizes make the thresholding more

sensitive to local variations in intensity, while larger block sizes make it more global. This value is

initialized to seven on runtime.

Mask Iterations: Specifies the number of iterations for morphological operations applied to

the thresholded image. Morphological operations such as dilation and erosion are used to close gaps

and remove noise. More iterations result in a more smoothed and cleaned-up mask, but can also

cause more merging of close objects. This value is initialized to three on runtime.

Figure 1. Example preview window with fluorescent biosensor cell image describing CAP

functionalities.

Saving Results:

After finding suitable parameters, click the ‘save’ button in the bottom right corner to process

every image in the folder and save the calculated confluences to a tab-delimited text file within the

input folder called ‘ConfluenceResults.txt’. For larger input folders (100+ images), this saving process

may take a few minutes. Within your CLI window, there is a progress bar that will measure time

elapsed, time expected until completion, number of images processed, and the average processing

time for each image. Don’t be alarmed if the progress bar initially shows very high expected

completion times. We have found that it tends to update non-linearly.

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Acknowledgments: We would like to thank the USC Bridge Undergraduate Science (BUGS) Program for

funding this work as part of a summer research project. Additionally, we would like to acknowledge the role of

generative AI, ChatGPT particularly, in code development and troubleshooting.

References

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measurements of confluence in cultured adherent cell lines. MethodsX, 2, 8-13.

https://doi.org/10.1016/j.mex.2014.11.002

2. Lobo, V., Shcherbinina, E., Westholm, J.O. et al. Integrative transcriptomic and proteomic profiling of the

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Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those

of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s)

disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or

products referred to in the content.

Preprints.org (www.preprints.org) | NOT PEER-REVIEWED | Posted: 12 August 2024 doi:10.20944/preprints202408.0786.v1