A subscription to JoVE is required to view this content. Sign in or start your free trial.

Method Article

Using Computer Vision Libraries to Streamline Nuclei Quantification

907 views

⸱

DOI:

10.3791/67945

⸱

June 6th, 2025

 ,  ,  ,  ,  ,  ,  ,  , 
1Metabolic Health & Muscle Physiology Laboratory, Department of Kinesiology and Sport Management, Texas Tech University, 2Applied Exercise Physiology Laboratory, Department of Kinesiology and Sport Management, Texas Tech University

In This Article

Summary

This article describes step-by-step methods to automate image-based nuclei quantification using an open-source executable program validated across a range of cell densities. This program provides an alternative that addresses barriers related to cost, accessibility for users with limited technological skillsets, and application-specific validation that may limit utility of existing technologies.

Abstract

Live cell assays and image-based cell analyses require data normalization for accurate interpretation. A commonly used method is to stain and quantify nuclei, followed by data normalization to nuclei count. This nuclei count is often expressed as cell count for uninucleate cells. While manual quantification can be laborious and time-consuming, available automated methods may not be preferred by all users, may lack validation for this specific application, or may be cost-prohibitive. Here, we provide step-by-step instructions for capturing quantifiable images of nuclei stained with fluorescent DNA stains and subsequently quantifying the nuclei using an automated object counting software program developed using Python computer vision libraries. We also validate this program across a range of cell densities. Although the exact time for program execution varies based on the number of images and computer hardware, this program consolidates hours of work counting nuclei into seconds for the program to run. While this protocol was developed using images of fixed, stained cells, images of stained nuclei in live cells and immunofluorescence applications can also be quantified using this program. Ultimately, this program provides an option that does not require a high degree of technological skill and is a validated, open-source alternative to aid cell and molecular biologists in streamlining their workflows, automating the tedious and time-consuming task of nuclei quantification.

Introduction

Functional and image-based experiments are critical for understanding the impacts of experimental treatments on whole-cell biochemistry and physiology. Valid interpretation of data from cell biology experiments depends on the accuracy and reproducibility of the experimental protocol, including data normalization. For example, analyses of oxygen consumption and extracellular acidification rates in live cells at baseline and after treatment with specific drugs allow for the assessment of various aspects of energy metabolism1,2. Measuring the activity of enzymes such as lactate dehydrogenase in the supernatant of cell cultures can aid in quantifying cell membrane integrity3. Staining cultured cells with annexin V and propidium iodide before fixation allows for the assessment of apoptotic and necrotic cells4. However, differences in cell density across wells affect the results of each of these assays. Relying solely on seeding density could provide misleading results due to errors in counting cells for seeding, variations in cell density in media while plating, or different rates of cell growth between samples or treatments throughout experiments. Therefore, normalization of experimental results is required.

Current normalization methods for functional and image-based cell data include protein concentration5 or nuclei or cell count6. To normalize data to protein concentration, cells must be extracted in equal volumes of lysis buffer with protease inhibitor after the assay is run, and an additional assay (e.g., bicinchoninic acid assay, Bradford assay, etc.) must be performed for protein quantification. The accuracy of this method relies on complete cell extraction; leaving any protein behind contributes to errors in quantification. Counting nuclei or cells provides an alternative where no cells need to be removed from the surface on which they are grown. Instead, live or fixed cells are stained or counter-stained using colorimetric or fluorescent stains, and counting can be performed based on images of whole cells or only nuclei. Quantifying nuclei is preferred over whole cells for fluorescence microscopy applications and to normalize data in fused cells (e.g., myotubes) or tissues. For quantification of nuclei using a fluorescence approach, fluorescent DNA dyes (e.g., 4′,6-diamidino-2-phenylindole dichloride (DAPI), Hoechst 33342, Hoechst 33358, DRAQ5, etc.) are applied to live or fixed cells and imaged using fluorescence microscopy. DAPI, Hoechst, and DRAQ5 preferentially bind to adenine-thymine-rich regions of DNA7,8,9, providing a means for detecting nuclei. Optimization of dye concentration prevents off-target binding (i.e., to mitochondrial DNA, causing punctate cytosolic fluorescence)10. The optimization process can be streamlined by using commercially available mounting media containing DAPI for fixed cells (e.g., VECTASHIELD Antifade Mounting Medium with DAPI) and published concentrations of stains for live cells (e.g., Hoechst)6,11,12. Once stained, nuclei in live or fixed cells are visualized using the appropriate excitation filter (e.g., ultraviolet [DAPI and Hoechst] or far-red [DRAQ5]). Excitation maxima for DAPI, Hoechst, and DRAQ5 are at either end of the spectrum for fluorescence microscopy. This attribute, plus the DNA specificity of these dyes and their use at optimal concentrations, minimizes off-target fluorescence. The resulting images show bright nuclei against a black background, and these nuclei are quantified as a measure of cell number for uninucleate cells. While other accurate stain-based methods of nuclei quantification exist using histological stains and brightfield microscopy13, automation is more difficult, albeit possible, using those approaches.

While it remains the gold standard, manual nuclei quantification is laborious, time-consuming, and susceptible to potential human error with prolonged counting. Although automated cell counting programs exist, they are not preferred by all users, may be cost-prohibitive, and validation for specific applications may be minimal. Python has become a valuable and accessible resource for biologists in recent years. Python computer vision libraries are particularly useful for image analysis14. Here, we provide step-by-step instructions for using our executable program developed using Python code to quantify nuclei stained with fluorescent dyes and imaged using fluorescence microscopy. Coding skills are not necessary to use the program described here. This workflow is specific to cells or tissues with fluorescent DNA dyes applied. It is not intended for use with brightfield images. Section 1 describes how to capture and save images collected using fluorescence microscopy in a manner compatible with this workflow. Section 2 provides instructions for running the nuclei quantification program as an executable and fetching output. The executable program can be run directly from the .exe file provided and requires no modifications to quantify nuclei in quality images. This executable requires a Windows-based PC. Section 3 provides instructions for running the program as a Python script rather than an executable (required for Mac- or Linux-based systems, optional for Windows-based systems) and allows for tuning the code if desired. Section 4 describes how to use the results for normalization. These protocols are followed by representative results, including validation data from our laboratory.

Access restricted. Please log in or start a trial to view this content.

Protocol

NOTE: Supplemental files can be found at the following link https://osf.io/a2s4d/?view_only=2d1042eb8f7c4c4a84579fe4e84fb03c

1. Capturing and saving images using fluorescence microscopy

  1. Prepare cell or tissue samples to be imaged, including staining with desired DNA dye. To obtain the images used here, C2C12 myoblasts (CRL-1772, American Type Culture Collection) were grown in 6-well plates for 48-72 h under standard culture conditions (5% CO2, 37 °C, humidified), with or without 50 mM EtOH6, and fixed in ice-cold methanol as previously described13. Fixed cells were mounted using a DAPI-containing mounting medium15 and dried in the dark before imaging.
  2. Turn on all components of the fluorescence microscope and computer according to established standard operating procedures. Open the software associated with the microscope.
  3. Set the microscope to visualize samples through the eyepiece. Check the objective. If needed, turn the nosepiece to the desired objective. Ensure the software program has the desired objective selected for an accurate indication of the image area. Here, images were captured using a 10x objective.
  4. With the lights off, place the sample (slide, plate, or dish) on the stage. Turn on live imaging with the appropriate filter and set exposure time either to a pre-determined duration or to automatic exposure. Here, DAPI filter and exposure time of 61-62 ms was used. See the troubleshooting table for guidance on setting exposure time, especially for images with high cell densities.
  5. Using the eyepiece slowly adjust the focus using the gross adjustment knob until stained nuclei are visible, then use the fine adjustment knob to slowly adjust the focus until nuclei appear clear and crisp.
    NOTE: Examples of appropriate images are shown in Figure 1.
    1. If excessive background staining appears or if nuclei appear very dim, adjust exposure time manually. In a single image, there may be visible differences in signal intensity for some nuclei; however, the round shape should be visible for all nuclei with minimal signal interference.
      NOTE: The program used in downstream applications is robust to different intensities. Raw images used for the development and validation of the nuclei quantification program include images with a range of signal intensities and are available through the Open Science Framework repository (see link mentioned above).

Fluorescent microscopy image showing blue-stained cells in three stages of proliferation.
Figure 1: Example images of DAPI-stained nuclei. These images depict nuclei at (A) low, (B) medium, or (C) high cell densities with minimal background. Note the varying signal intensity of nuclei across image C; this did not interfere with quantification. Scale bar (C) indicates 100 µm; scale bars should be omitted from images used for nuclei quantification. Please click here to view a larger version of this figure.

  1. Once the nuclei are in focus through the eyepiece, switch the detection mode to the camera. Tune the focus using the fine adjustment knob.
  2. Adjust exposure time if needed. Note the final exposure time for use with subsequent samples and images.
    NOTE: Although using the same exposure time across all images is ideal, variations in exposure time are acceptable for this application because fluorescence intensity is not quantified or compared across samples.
  3. Capture the image using the appropriate image capture button on the software. If samples have multiple stains applied, switch the filter (e.g., to FITC, TRITC, or Cy5) and image the same area for merging purposes.
  4. Apply the shutter to minimize sample exposure to light. Without burning in a scale bar, save the image in a universal figure file format (.tif, .png, or .jpg) in a dedicated folder. Ensure that each image file name is appropriately descriptive or that any coding used for file names has appropriate linked documentation.
    NOTE: This will become the Image Directory for the quantification program. If multicolor imaging was used, copy only images of nuclei to a separate, dedicated folder. This can also be done at the end of the imaging session. Images exported using default export settings on most imaging software are typically of sufficient resolution for use with this program, provided that quality images have been captured. However, the images should be visually checked after export to ensure that the morphology of nuclei are clear. If images are saved in .jpg format, care should be taken to ensure that images are not excessively compressed, and quality is maintained.
  5. Open shutter, return to live imaging, and scan across the sample to another location for imaging. Repeat steps 1.8-1.9 as needed. For images of cells seeded in 6-well plates captured at 10x magnification, capture 10-15 images per well (depending on the consistency of cell density across the sample area).
  6. After all images for a single sample are acquired, perform one of the following options.
    1. Move on to the next sample and repeat steps 1.4-1.10.
    2. Pause the protocol and return at another time. Samples should remain protected from light. This option is not recommended if working with live cells.
  7. Once imaging is complete, ensure all samples are light-protected before turning the room lights on. If the folder containing images is saved locally, transfer files to a thumb drive, cloud storage location, or other network drive accessible to the computer where the quantification program will be run. Save in a dedicated folder.
  8. Shut off all parts of the microscope and computer according to established standard operating procedures.
  9. Imaging is now complete. Analyze the files using the automated quantification program as described in Section 2 or, alternatively, by running the program as a Python script as described in Section 3.

2. Running the automated nuclei quantification program as an executable

NOTE: This method, running the program as an executable, is compatible with Windows-based PCs and is the recommended method for Windows OS users. Mac OS or other non-Windows users should run the program as a Python script (see Section 3).

  1. Ensure Python is installed on the computer. If needed, install Python from python.org/downloads (preferred) or by using Anaconda or Miniconda. As of the date of publication, this program has been tested with Python versions 3.10-3.12.
  2. Open the browser and navigate to https://github.com/rbudnar/nuclei_counter/releases. Download the latest version of the file named count_nuclei.zip.
  3. From the Downloads folder, right-click on the Zip file and Extract All files to the desired location on the local computer (see Figure 2A). Keep this file location open; this file path will be used in step 2.5 (see Figure 2B).
    NOTE: Count_nuclei should now appear as an application file. This is the executable file.
  4. Open a command prompt by searching CMD or Command Prompt in the search bar. Use the cd command to change the directory to the file path for the executable file (see Figure 2C) and press Enter. Example command line: C:\Users\janedoe>cd [space after cd, then copy/paste file path]
  5. On the next command line, type the following and press Enter.
    count_nuclei.exe --img_dir "path/to/images" --output_dir "path/to/output" --save_images --output_count_filename "results.csv"
    where path/to/images is the file path to the folder containing images to be analyzed (see step 1.14); path/to/output is the file path to the folder where the .csv file should be saved (defaults to current working directory when not provided); and results.csv is the file name that the resulting counts will be saved to (if not provided, the output file name defaults to count_results.csv).
    NOTE: Quotation marks are needed in the command line. Ensure there are no spaces after quotation marks before the file path (see Figure 2C).
  6. When the next command line appears, the processing is complete. The contours and the results spreadsheet are now available in the location specified as the output directory in step 2.5 (i.e., path/to/output; see Figure 2C).
  7. Visually inspect the contours to verify count quality. Results can now be used to normalize data as described in Section 4.
    NOTE: If contours appear to have missed nuclei (e.g., nuclei appear too faint) or cluster groups of nuclei into a single item (e.g., over-exposed image, too much background, artifacts), the user should refer to the troubleshooting table and may need to return to Section 1 to optimize image acquisition before moving on to Section 4.

File extraction and command line process for nuclei quantification using count_nuclei tool setup.
Figure 2: Examples of key parts of the workflow for section 2. (A) Extract files from the Downloads folder, taking care to click on the correct folder as indicated by the arrow to enable extraction (B) to the desired destination in step 2.3. (C) Examples of commands for steps 2.4 and 2.5 and the next command line indicating completion in step 2.6. Note that the file path in step 2.4 (C) matches the location for extraction in step 2.3 (B). Please click here to view a larger version of this figure.

3. Running the automated nuclei quantification program as Python script

  1. Ensure Python is installed on the computer. If needed, install Python from python.org/downloads (preferred) or by using Anaconda or Miniconda. As of the date of publication, this program has been tested with Python versions 3.10-3.12.
  2. Open the browser and navigate to https://github.com/rbudnar/nuclei_counter. Click the Green Code button, then click Download ZIP to download the code repository (see Figure 3A).
  3. Use the following instructions for Windows OS. See step 3.4 for Mac OS-based machines.
    1. From the Downloads folder, right-click on the File and select Extract All to extract the files to a local computer (see Figure 3B-C).
    2. In the extracted file location, open the folder named nuclei_counter-main. This is the code repository.
      NOTE: This only needs to be downloaded the first time this program is used; simply move the extracted files to an accessible network location and make note of that location.
    3. Open a command prompt by searching CMD or Command Prompt in the search bar. If Python has been installed by Anaconda or Miniconda, open the Anaconda Prompt instead of a standard command prompt. This can be found by typing Anaconda Prompt in the search bar.
    4. Use the cd command to change the directory to the code repository and press Enter. See the line of code below, which is case-sensitive. Ensure that the folder named nuclei_counter-main is open when copying the file path; the file named pyproject or pyproject.toml should be visible (see Figure 3D). Example command line: C:\Users\janedoe>cd [space after cd, then copy/paste file path]
    5. On the next command line, type the following, including the space and period (see Figure 3D:
      pip install -e .
      NOTE: Steps 3.3.5 and 3.3.6 only need to be completed the first time this program is run on any computer. If this has already been completed, proceed to step 3.3.7. The installation process may take a few moments to initialize.
    6. Press Enter. Running this command installs required libraries to run this script and installs the repository in an editable mode in the event code adjustments are needed (see Figure 3D).
    7. Type the following into the next command line to change the directory to the main source code directory (see Figure 3E):
      cd "nuclei_counter"
    8. On the next command line, type the following and press Enter (see Figure 3E):
      python count_nuclei.py --img_dir "path/to/images" --output_dir "path/to/output" --save_images --output_count_filename "results.csv"
      Where path/to/images is the file path to the folder containing images to be analyzed (step 1.14); path/to/output is the file path to the folder where the .csv file should be saved (defaults to current working directory when not provided); and results.csv is the file name that the resulting counts will be saved to (if not provided, the output file name defaults to count_results.csv).
      NOTE: Quotation marks are needed in the command line. Ensure there are no spaces after quotation marks before the file path. The python script will read the files in the provided file path and execute the nuclei counting program for all the images in the directory (see Figure 3E). The resulting counts will be saved to the results .csv file in the location specified.
    9. When the next command line appears, the processing is complete (see Figure 3E). The contours and the results spreadsheet are now available in the location specified as the output directory in step 3.3.8 (i.e., path/to/output).

File extraction and command prompt process for nuclei counter installation and usage steps.
Figure 3: Examples of key parts of the workflow for section 3 using Windows OS. To access the code, click the green button labeled Code and (A) download the Zip file (indicated by arrows). (B) Extract by right-clicking on the Zip file (shown in blue) and (C) setting the desired destination. (D) After opening the command prompt, examples of commands for steps 3.3.4 and 3.3.5. Note that the command for step 3.3.5 is all on one line; the text beginning "Obtaining file" on the next line appears after pressing enter (step 3.3.6; i.e., running the command). (E) Examples of commands for steps 3.3.7 and 3.3.8, a progress bar following execution of step 3.3.8, and the command line showing completion in step 3.3.9. Please click here to view a larger version of this figure.

  1. Use the following instructions for Mac OS.
    1. From the Downloads folder, click on the File menu and select Open to extract the files to local computer (see Figure 4A).
    2. In the extracted file location, open the folder named nuclei_counter-main. This is the code repository.
      NOTE: This only needs to be downloaded the first time this program is used; simply move the extracted files to an accessible network location and make note of that location.
    3. Open a terminal by pressing command+space bar which opens spotlight; then, search "terminal".
    4. Use the cd command to change the directory to the code repository and press Enter. See the line of code below, which is case-sensitive. Type the file path out in a text document and copy it into the terminal. Ensure the file path begins with the text immediately preceding cd (e.g., /Users/userid in Figure 4B). Example command line: /Users/janedoe›cd /Users/janedoe/Desktop/nuclei_counter-main
    5. On the next command line, a dollar sign will appear after the User ID (see Figure 4B). Ensure that there is a space after the dollar sign, and type the following, including the space and period at the end of the command. Include the appropriate Python version immediately after pip without a space: pip3 install -e .
      NOTE: Steps 3.4.5 and 3.4.6 only need to be completed the first time this program is run on any computer. If this has already been completed, proceed to step 3.4.7. The installation process may take a few moments to initialize.
    6. Press Enter. Running this command installs required libraries to run this script and installs the repository in an editable mode in the event code adjustments are needed.
    7. On the next command line, ensure that there is a space after the dollar sign, then type the following to change the directory to the main source code directory (see Figure 4C):
      cd "nuclei_counter"
    8. On the next command line, type the command line below. Replace the number after Python with the correct version number if using a version other than 3. Then, press Enter (see Figure 4C):
      python3 count_nuclei.py --img_dir "path/to/images" --output_dir " path/to/output" --save_images --output_count_filename "results.csv"
      where path/to/images is the file path to the folder containing images to be analyzed (step 1.14); path/to/output is the file path to the folder where the .csv file should be saved (defaults to current working directory when not provided); and results.csv is the file name that the resulting counts will be saved to (if not provided, the output file name defaults to count_results.csv).
      NOTE: Quotation marks are needed in the command line. Ensure there are no spaces after quotation marks before the file path. The Python script will read the files in the provided file path and execute the nuclei-counting program for all the images in the directory (see Figure 4C). The resulting counts will be saved to the results .csv file in the specified location.
    9. When the next command line appears, the processing is complete (see Figure 4C). The contours and the results spreadsheet are now available in the location specified as the output directory in step 3.4.8 (i.e., path/to/output).
  2. Visually inspect the contours to verify count quality. Results can now be used to normalize data as described in Section 4.
    NOTE: If contours appear to have missed nuclei (e.g., nuclei appear too faint) or cluster groups of nuclei into a single item (e.g., over-exposed image, too much background, artifacts), the user should refer to Table 1 and may need to return to Section 1 to optimize image acquisition before moving on to Section 4.

File extraction and Python execution method, terminal commands, software setup steps outlined.
Figure 4: Examples of key parts of the workflow for section 3 using Mac OS. After downloading the code (see Figure 3A), (A) extract by clicking on the Zip file (shown in blue) and setting the desired destination. (B) After opening the terminal, examples of commands for steps 3.4.4 (changing the directory to the code repository) and 3.4.5 (installing pip). (C) Examples of commands for steps 3.4.7 and 3.4.8, a progress bar following execution of step 3.4.8, and the command line showing completion in step 3.4.9. Please click here to view a larger version of this figure.

4. Using counts to normalize experimental data

  1. Open the results.csv file linking each image with nuclei quantification determined using the automated program.
  2. Decide on the method of normalization: 1) image-by-image basis (e.g., if multicolor fluorescence microscopy), 2) normalize data as a ratio of the average nuclei count per sample versus a reference sample (or condition), 3) normalize to nuclei count per sample (e.g., extrapolate total nuclei count to sample area). Recommendations for the method of normalization are in the Discussion section.
    1. If normalization will be performed on an image-by-image basis, simply divide experimental data for each image by the number of nuclei for that image.
    2. To normalize data as a ratio, follow the steps described below.
      1. For any samples where multiple images were collected for a single replicate (e.g., 10-15 images captured from a single well in a 6-well plate), calculate the average number of nuclei per image. These nuclei counts are considered as the cell count assuming uninucleate cells or a number of cells fused together in multinucleate cells (such as with myotubes).
      2. Select either a reference sample or a reference condition (e.g., average count across all samples from the control condition) and note the count.
      3. Divide the count for all samples by that reference count, resulting in a normalization value. If a reference sample was used, use the normalization value for the reference sample as 1.
        NOTE: If a reference condition was used, the average normalization value across the condition would now be 1. Samples with fewer nuclei than the reference (i.e., less dense) will have normalization values less than 1, and samples with more nuclei than the reference (i.e., denser) will have normalization values greater than 1.
      4. Divide the experimental data for each well by the corresponding calculated normalization value.
    3. To normalize data to nuclei count per sample, follow the steps described below.
      1. For any samples where multiple images were collected for a single replicate (e.g., 10-15 images captured from a single well in a 6-well plate), calculate the average number of nuclei per image. These nuclei counts are considered as the cell count assuming uninucleate cells, or number of cells fused together in multinucleate cells (such as with myotubes).
      2. Determine the surface area of the slide (coverslip), well, or dish from which the data were derived. This information can be found in the specifications section on product websites.
      3. Determine the area of each captured image. This information is often available as metadata in the imaging or analysis software.
      4. Ensure the sample surface area and the image area are expressed in the same units (e.g., mm2). If not, convert to the desired unit.
      5. Calculate the total count for each sample using the following equation:
        TC = AC • (SA/IA)
        where TC is total count, AC is average count, SA is surface area, IA is image area.
      6. Divide experimental data by corresponding TC for the sample.
  3. Normalize experimental data and use for statistical analyses as appropriate.

Access restricted. Please log in or start a trial to view this content.

Results

Each batch image run produces: 1) a set of image files with contours applied showing the outlines of identified nuclei (Figure 5), and 2) a .csv file (spreadsheet) linking image file names and the associated counts. Viewing the contours will allow the user to visually assess the count quality. Specifically, images obtained according to section 1 should have all (or nearly all) nuclei surrounded by a solid green line indicating that the nucleus was counted by ...

Access restricted. Please log in or start a trial to view this content.

Discussion

Our nuclei quantification program has several advantages over existing options: it requires only minimal technological skills, is validated for the specific task of nuclei quantification, and is open-source; the latter overcomes cost-related barriers. Ultimately, this program provides cell and molecular biologists with an additional option to quickly and accurately quantify nuclei in images captured using fluorescence microscopy. Currently available automated nuclei or cell counting programs are not preferred by all user...

Access restricted. Please log in or start a trial to view this content.

Disclosures

The authors declare no conflicts of interest.

Acknowledgements

Funding for this work was provided by the National Institutes of Health/National Institute on Aging (R01AG084597; DEL and HYL) and by start-up funds from Texas Tech University (DEL). The authors would like to thank the Texas Tech University Undergraduate Research Scholars and TrUE Scholars programs for providing financial support to the undergraduate researchers who contributed to this work (REH, MRD, CJM, AKW). We also thank Drs. Lauren S. Gollahon and Michael P. Massett for graciously sharing their laboratory space and equipment.

Access restricted. Please log in or start a trial to view this content.

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Computer with access to results file from method 2 or 3--See step 2.6 (for Method 2) or step 3.3.9 or 3.4.9 (for Method 3)
Computer with internet access, modern browser--e.g., Google Chrome
Computer with internet access, modern browser, and Windows OSVariesVariesFor Mac, Linux, or other OS, use Method 3
Computer with software for image captureZeissAxioVisionOther software is acceptable; must be compatible with the fluorescence microscope
File location for output (results spreadsheet and image contours)--Can be a new, empty folder
Fluorescence microscopeZeissAxiovert 200MOther fluorescence microscopes are acceptable; must be equipped with appropriate filter cubes, desired objective, and camera 
Folder containing all images to be quantified--See step 1.12
Python version 3.10 or higherPython-Available for free download and installation at https://www.python.org/downloads/ 
Samples to be imaged--Fixed or live, stained or counterstained with fluorescent DNA dyes
Spreadsheet softwareMicrosoftExcelSimilar spreadsheet software is also acceptable

References

  1. Chacko, B. K., et al. The bioenergetic health index: A new concept in mitochondrial translational research. Clin Sci. 127 (6), 367-373 (2014).
  2. Desousa, B. R., et al. Calculation of ATP production rates using the Seahorse XF Analyzer. EMBO Rep. 24 (10), e56380(2023).
  3. Rayamajhi, M., Zhang, Y., Miao, E. A. detection of pyroptosis by measuring released lactate dehydrogenase activity. Inflammasome. 1040, 85-90 (2013).
  4. Baskic, D., Popovic, S., Ristic, P., Arsenijevic, N. Analysis of cycloheximide-induced apoptosis in human leukocytes: Fluorescence microscopy using annexin V/propidium iodide versus acridin orange/ethidium bromide. Cell Biol Int. 30 (11), 924-932 (2006).
  5. Duplanty, A. A., Siggins, R. W., Allerton, T., Simon, L., Molina, P. E. Myoblast mitochondrial respiration is decreased in chronic binge alcohol administered simian immunodeficiency virus-infected antiretroviral-treated rhesus macaques. Physiol Reps. 6 (5), e13625(2018).
  6. Levitt, D. E., Bourgeois, B. L., Rodríguez-Graciani, K. M., Molina, P. E., Simon, L. Alcohol impairs bioenergetics and differentiation capacity of myoblasts from Simian immunodeficiency virus-infected female Macaques. Int J Mol Sci. 25 (4), 2448(2024).
  7. Larsen, T. A., Goodsell, D. S., Cascio, D., Grzeskowiak, K., Dickerson, R. E. The structure of DAPI bound to DNA. J Biomol Str Dyn. 7 (3), 477-491 (1989).
  8. Bazhulina, N. P., et al. Binding of Hoechst 33258 and its derivatives to DNA. J Biomol Str Dyn. 26 (6), 701-718 (2009).
  9. Njoh, K. L., et al. Spectral analysis of the DNA targeting bisalkylaminoanthraquinone DRAQ5 in intact living cells. Cytomet A. 69A (8), 805-814 (2006).
  10. Hamada, S., Fujita, S. DAPI staining improved for quantitative cytofluorometry. Histochemistry. 79 (2), 219-226 (1983).
  11. Levitt, D. E., Chalapati, N., Prendergast, M. J., Simon, L., Molina, P. E. Ethanol-impaired myogenic differentiation is associated with decreased myoblast glycolytic function. Alcohol Clin Exp Res. 44 (11), 2166-2176 (2020).
  12. Levitt, D. E., et al. Skeletal muscle bioenergetic health and function in people living with HIV: association with glucose tolerance and alcohol use. Am J Physiol Regul, Integ Comp Physiol. 321 (5), R781-R790 (2021).
  13. Levitt, D. E., Adler, K. A., Simon, L. HEMA 3 staining: A simple alternative for the assessment of myoblast differentiation. Curr Prot Stem Cell Biol. 51 (1), e101(2019).
  14. Solem, J. E. Programming computer vision with Python. , O'Reilly. Sebastopol, CA. (2012).
  15. Delery, E. C., Levitt, D. E., Amedee, A. M., Molina, P. E., Simon, L. High fat, high sucrose diet promotes increased expression of ACE2 receptor in the SIV-infected host: implications for SARS-CoV-2 infection. Front Nutrition. 11, 1458106(2024).
  16. Corrà, M. F., et al. Advantages of an automated method compared with manual methods for the quantification of intraepidermal nerve fiber in skin biopsy. J Neuropathol Exp Neurol. 80 (7), 685-694 (2021).
  17. Neves, K., et al. The reliability of the isotropic fractionator method for counting total cells and neurons. J Neurosci Meth. 326, 108392(2019).
  18. Tiong, K. H., et al. QuickCount: a novel automated software for rapid cell detection and quantification. BioTechniques. 65 (6), 322-330 (2018).
  19. Koo, T. K., Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropractic Med. 15 (2), 155-163 (2016).
  20. Kam, Y., Jastromb, N., Clayton, J., Held, P., Dranka, B. P. Normalization of Agilent Seahorse XF Data by in situ. cell counting using a BioTek Cytation 5. , https://www.agilent.com/cs/library/applications/5991-7908EN.pdf 5991-7908 (2017).
  21. Schneider, C. A., Rasband, W. S., Eliceiri, K. W. NIH image to imageJ: 25 years of image analysis. Nat Meth. 9 (7), 671-675 (2012).
  22. O'Brien, J., Hayder, H., Peng, C. Automated quantification and analysis of cell counting procedures using ImageJ plugins. J Vis Exp. (117), e54719(2016).
  23. Grishagin, I. V. Automatic cell counting with ImageJ. Anal Biochem. 473, 63-65 (2015).
  24. Carpenter, A. E., et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7 (10), R100(2006).
  25. Tollemar, V., et al. Quantitative chromogenic immunohistochemical image analysis in cellprofiler software. Cytometry A. 93 (10), 1051-1059 (2018).
  26. Chazotte, B. Labeling nuclear DNA using DAPI. Cold Spring Harbor. 2011 (1), (2011).
  27. Cole, R. Live-cell imaging. Cell Adhesion Migrat. 8 (5), 452-459 (2014).
  28. Rieger, A. M., Nelson, K. L., Konowalchuk, J. D., Barreda, D. R. Modified Annexin V/Propidium Iodide apoptosis assay for accurate assessment of cell death. J Vis Exp. (50), e2597(2011).
  29. Hernandez, J. J., Beaty, D. E., Fruhwirth, L. L., Lopes Chaves, A. P., Riordan, N. H. Dodging COVID-19 infection: low expression and localization of ACE2 and TMPRSS2 in multiple donor-derived lines of human umbilical cord-derived mesenchymal stem cells. J Transl Med. 19 (1), 149(2021).
  30. Bourgeois, B. L., et al. Extracellular vesicle miR-206 improves chronic binge alcohol-mediated decreased myoblast differentiation in SIV-infected female macaques. Am J Physiol Cell Physiol. 327 (6), C1626-C1637 (2024).
  31. Kumar, P., Nagarajan, A., Uchil, P. D. Analysis of cell viability by the lactate dehydrogenase assay. Cold Spring Harbor. 2018 (6), (2018).
  32. Hsu, S., et al. Effects of everolimus on macrophage-derived foam cell behavior. Cardiovasc Revasc Med Including Mol Intervent. 15 (5), 269-277 (2014).

Access restricted. Please log in or start a trial to view this content.

Reprints and Permissions

Tags

Automated Object CountingFluorescent DNA StainsImage-Based Cell AnalysisData NormalizationPython Computer VisionCell AssaysOpen Source WorkflowCell Count
Using Computer Vision Libraries to Streamline Nuclei Quantification
Contact Us
Recommend to Library

Privacy

Terms of Use

Policies

Copyright © 2026 MyJoVE Corporation. All rights reserved.