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Biology
使用计算机视觉库简化细胞核定量
使用计算机视觉库简化细胞核定量
JoVE Journal
Biology
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JoVE Journal Biology
Using Computer Vision Libraries to Streamline Nuclei Quantification

使用计算机视觉库简化细胞核定量

Full Text
637 Views
06:25 min
June 6, 2025

DOI: 10.3791/67945-v

Danielle E. Levitt1, Alexandra L. Khartabil1, Rylea E. Hall1, Matthew R. DiLeo1, Connor J. Mills1, Ashley K. Williams1, Casey R. Appell2, Ronald G. Budnar, Jr.1, Hui-Ying Luk2

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

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Please note that some of the translations on this page are AI generated. Click here for the English version.

Overview

This study presents a method for automating the quantification of nuclei in images, which aids in normalizing metabolic data in skeletal muscle research. The automated program, validated across varying cell densities, addresses challenges inherent to manual counting, such as bias and variability.

Key Study Components

Research Area

  • Cell biology
  • Metabolic health
  • Heat therapy effects on muscle

Background

  • Manual quantification of nuclei is time-consuming and biased.
  • Automated methods can improve accuracy and efficiency.
  • Understanding the effects of heat therapy on muscle adaptations is crucial for metabolic health.

Methods Used

  • Automated nuclei counting using an open-source program
  • Cell line models for skeletal muscle research
  • Image processing and analysis

Main Results

  • The automated program showed excellent reliability with an intra-class correlation coefficient of 0.993.
  • Quantification accuracy was high across varying cell densities.
  • Problems with clustering nuclei and artifacts were identified for further improvement.

Conclusions

  • The study demonstrates an objective approach to nuclei quantification that enhances experimental normalization.
  • This method is relevant for advancing research in metabolic health and muscle adaptations.

Frequently Asked Questions

What is the significance of nuclei quantification in this study?
Nuclei quantification is essential for normalizing metabolic data in skeletal muscle research.
How does the automated program compare to manual counting?
The automated program significantly reduces observer bias and variability, showing high reliability against manual counts.
What challenges does manual nuclei counting present?
Manual counting is prone to biases and inconsistencies, making it less reliable than automated methods.
What improvements can be made to the automated method?
Further enhancements can focus on better handling of clustered nuclei and image artifacts.
Is the program user-friendly for those with limited technical skills?
Yes, the program is designed to be accessible for scientists with varying levels of coding experience.
What was the primary validation for the automated program?
The program was validated with high intra-class correlation coefficients across all tested cell densities.
How does this research relate to metabolic health?
It aims to uncover mechanisms of heat therapy in improving muscle and mitochondrial health, particularly in pre-diabetic individuals.

本文介绍了使用开源可执行程序自动进行基于图像的细胞核定量的分步方法,该程序在一系列细胞密度中进行了验证。该计划提供了一种替代方案,可以解决与成本相关的障碍、技术技能有限的用户的可访问性以及可能限制现有技术效用的特定应用程序验证。

我们开发了这种方法来标准化来自细胞模型的代谢数据,以帮助确定机制,强调热疗诱导的骨骼肌适应,并最终改善糖尿病前期患者的代谢健康。

我们必须计算原子核以进行实验归一化。手动定量细胞核带来了挑战,包括观察者偏差、时间以及遇到不同样品或条件的变异性。

我们的程序是开源的,确保具有不同编码相关技术技能水平的科学家的可用性,并针对快速准确量化原子核的特定任务进行了验证。

这项技术使我们能够从我们最近 NIA 资助的临床研究中客观地验证热疗对肌肉和线粒体健康益处的潜在影响的机制。

[旁白]首先,在计算机系统上启动网络浏览器,导航到 github.com 和原子核计数器版本。下载名为 Count nuclei.zip 的最新版本的文件。在“下载”文件夹中,右键单击 zip 文件并选择“全部解压”以将文件解压到本地计算机上的所需位置。接下来,在搜索栏中搜索 CMD 或命令提示符以打开命令提示符。使用 CD 命令将目录更改为可执行文件的文件路径,即刚刚从下载文件夹中提取的应用程序文件。然后按 Enter 确认目录更改。在下一个命令行中,将图像路径替换为包含要分析的图像的文件夹的文件路径。输出路径,其中包含应保存.csv文件的文件夹的文件路径,并results.csv输出所需的文件名。屏幕上显示了一个示例代码,并且可以插入图像和输出的文件路径,如引号所示。使用 results.csv 作为结果文件名或指定另一个。然后,按 Enter。当出现下一个命令行时,确认处理已完成。验证等高线和结果电子表格在指定的输出目录中是否可用。在数据归一化之前,目视检查轮廓并与计数进行比较,以验证计数质量。打开浏览器并导航到 github.com 上的原子核计数器。单击绿色代码按钮,然后选择下载 ZIP 以下载代码存储库。对于 Mac OS,单击“下载”文件夹中的文件菜单,然后选择“打开”将文件解压到本地计算机。导航到名为 nuclei_counter main 的提取文件夹,其中包含代码存储库。将文件夹保存在可访问的位置,并记下文本文档中的文件路径。接下来,按 Command + 空格键打开 Spotlight。然后在 Spotlight 中键入终端并选择终端应用程序。使用 CD 命令通过从文本文档中复制并粘贴文件路径并将目录更改为代码存储库路径,然后按 Enter。在下一个命令行中,确保美元符号后面有一个空格。然后键入给定的命令并按 Enter 安装所需的库并启用可编辑模式。在 pip 之后立即包含适当的 Python 版本,如图所示,不带空格。在下一个命令行上键入屏幕命令,将目录更改为主源代码目录,即屏幕上显示的 CD 核计数器。然后,键入屏幕命令以根据需要替换文件路径,然后按 Enter。当出现下一个命令行时,确认处理已完成。验证等高线和结果电子表格在指定的输出目录中是否可用。在数据归一化之前,目视检查轮廓并与计数进行比较,以验证计数质量。自动化程序生成的图像中的所有细胞核都用纯绿色轮廓勾勒出轮廓,表明细胞核已成功计数。两次手动计数之间的评分者间可靠性非常好,组内相关系数大于 0.999,P 值小于 0.0001。与平均手动计数相比,自动化程序表现出优异的可靠性,类内相关系数为 0.993,P 值小于 0.0001。在所有细胞密度四分位数中观察到极好的可靠性,类内相关系数范围为 0.986 至 0.998,所有 P 值均小于 0.0001。自动化程序无法准确计算多个原子核聚集在一起的区域或具有光晕等伪影的区域。屏幕上的表格中列出了这些潜在问题以及可能的原因和故障排除步骤,以提高图像质量和自动原子核定量工作流程的准确性。

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