Name: Using Computer Vision Libraries to Streamline Nuclei Quantification (Video) | JoVE Journal
Uploaded: 2025-06-06T13:50:00.000+00:00
Duration: 6 min 25 s
Description: Learn automated nuclei quantification for skeletal muscle research using computer vision libraries. This open-source method reduces observer bias and improves accuracy for normalizing metabolic data in cell biology studies.

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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&#160;Michael P. Massett for graciously sharing their laboratory space and equipment.

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The authors declare no conflicts of interest.

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...

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&#8242;,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.

<table><tbody><tr><td>Computer with access to results file from method 2 or 3</td><td>-</td><td>-</td><td>See step 2.6 (for Method 2) or step 3.3.9 or 3.4.9 (for Method 3)</td></tr><tr><td>Computer with internet access, modern browser</td><td>-</td><td>-</td><td>e.g., Google Chrome</td></tr><tr><td>Computer with internet access, modern browser, and Windows OS</td><td>Varies</td><td>Varies</td><td>For Mac, Linux, or other OS, use Method 3</td></tr><tr><td>Computer with software for image capture</td><td>Zeiss</td><td>AxioVision</td><td>Other software is acceptable; must be compatible with the fluorescence microscope</td></tr><tr><td>File location for output (results spreadsheet and image contours)</td><td>-</td><td>-</td><td>Can be a new, empty folder</td></tr><tr><td>Fluorescence microscope</td><td>Zeiss</td><td>Axiovert 200M</td><td>Other fluorescence microscopes are acceptable; must be equipped with appropriate filter cubes, desired objective, and camera&amp;nbsp;</td></tr><tr><td>Folder containing all images to be quantified</td><td>-</td><td>-</td><td>See step 1.12</td></tr><tr><td>Python version 3.10 or higher</td><td>Python</td><td>-</td><td>Available for free download and installation at https://www.python.org/downloads/&amp;nbsp;</td></tr><tr><td>Samples to be imaged</td><td>-</td><td>-</td><td>Fixed or live, stained or counterstained with fluorescent DNA dyes</td></tr><tr><td>Spreadsheet software</td><td>Microsoft</td><td>Excel</td><td>Similar spreadsheet software is also acceptable</td></tr></tbody></table>

using computer vision libraries to streamline nuclei quantification

Using Computer Vision Libraries to Streamline Nuclei Quantification

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&#160;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.

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 ...

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.

Live cell assays and image-based cell analyses require data normalization for accurate interpretation. A commonly used method is to stain and quantify ...

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JoVE Journal

Biology

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To distinguish cell division from cell cycle variations in cardiomyocytes, we present protocols using two transgenic mouse lines: Myh6-H2B-mCh transgenic mice, for the unequivocal identification of cardiomyocyte nuclei, and CAG-eGFP-anillin mice, for distinguishing cell division from cell cycle variations.

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This method describes an immunofluorescence protocol and quantification pipeline for evaluating protein distribution with varied nuclear organization patterns in human T lymphocytes. This protocol provides step-by-step guidance, starting from sample preparation and continuing through the execution of semi-automated analysis in Fiji, concluding with data handling by a Google Colab notebook.

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Accurate analysis of multidimensional microscopy data requires complex workflows. This article demonstrates how to use the software Cell-ACDC. It leverages state-of-the-art AI-driven models for segmentation, tracking, cell pedigree analysis, and quantification of microscopy data. Crucially, it complements these models with an innovative framework for semi-automated correction of the models' output.

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Learn automated nuclei quantification for skeletal muscle research using computer vision libraries. This open-source method reduces observer bias and improves accuracy for normalizing metabolic data in cell biology studies.

Using Computer Vision Libraries to Streamline Nuclei Quantification

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