Name: 作者聚焦：使用免疫荧光检测研究人类细胞可塑性和环境适应性的综合表观遗传学分析
Uploaded: 2024-06-28T14:25:00
Description: Various nuclear processes, such as transcriptional control, occur within discrete structures known as foci that are discernable through the ...

我们感谢 INGM 成像设施的科学和技术援助，特别是 C. Cordiglieri 和 A. Fasciani，以及 INGM FACS 分拣设施，特别是 M.C Crosti（意大利米兰 'Romeo ed Enrica Invernizzi' （INGM） 国家遗传学研究所）。感谢 M. Giannaccari 提供的信息技术支持。这项工作由以下赠款资助：Fondazione Cariplo（Bando Giovani，赠款 nr 2018-0321）和 Fondazione AIRC（赠款 nr MFAG 29165）给 FM Ricerca Finalizzata（赠款 nr GR-2018-12365280）、Fondazione AIRC（赠款 nr 2022 27066）、Fondazione Cariplo（赠款 nr 2019-3416）、Fondazione Regionale per la Ricerca Biomedica （FRRB CP2_12/2018）、Piano Nazionale di Ripresa e Resilienza （PNRR）（赠款 nr G43C22002620007）和 Progetti di Rilevante Interesse Nazionale （PRIN）（赠款 nr 2022PKF9S）给 B。B.

将人类样本用于研究目的已获得 Fondazione Istituto di Ricovero e Cura a Carattere Scientifico （IRCCS） Cà Granda Ospedale Maggiore Policlinico（米兰）伦理委员会的批准，并获得了所有受试者的知情同意（授权号：708_2020）。该方案分为三个主要部分：免疫荧光执行、图像采集和图像分析。平均而言，需要 4 个工作日才能完成（图 1）。
1. 免疫荧光制备
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人原代细胞中核蛋白染色的分析工作流程

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在这项研究中，我们提出了一种对人 T 淋巴细胞中的核蛋白进行免疫荧光实验的方法。如前所述，该方法通过对固定和透化步骤的微小修改，为各种细胞类型提供了灵活性30,31。
我们的成像工作流程建立在文献中概述的既定技术之上，特别是 FindFoci 和 3D Suite 22,23。与以前的出版物不同，?...

真核生物基因组的组织由多层表观遗传修饰1 控制，协调可能发生在称为核体或凝聚物2 的特殊隔室中的几种核功能。在这些结构中，发生转录起始3、RNA 加工 4,5,6、DNA 修复 7,8、核糖体生物发生9、10、11 和异染色质调节12,13 等过程。在相分离原理的指导下，核体的调节在空间和时间维度上进行调整，以适应细胞需求14,15。因此，这些物体充当了瞬态工厂的功能，功能组件在这里组装和拆卸，大小和空间分布发生变化。因此，通过显微镜了解核蛋白的特性，包括它们形成体的倾向及其在不同细胞条件下的空间排列，为了解它们的功能作用提供了有价值的见解。荧光显微镜检查是一种广泛用于研究核蛋白的方法，可通过荧光抗体检测核蛋白，或使用荧光蛋白报告基因直接表达靶标16,17。
在这种情况下，核体表现为明亮的焦点或点状，具有显着的球形度，使其很容易与周围环境区分开来 16,18。STORM 和 PALM 等超分辨率技术通过提供更高的分辨率（高达 10 nm）19，能够更精确地表征特定凝聚物的结构和组成20。然而，它们的可访问性受到设备费用和数据分析所需专业技能的限制。因此，共聚焦显微镜仍然很受欢迎，因为它在分辨率和更广泛的使用之间取得了良好的平衡。这种流行是由于固有的离焦光去除，这减少了对精确分割的广泛后处理程序的要求、它在研究机构中的广泛可用性、有效的采集时间以及通常高效的样品制备。然而，使用免疫荧光检测在不同细胞条件下准确测量蛋白质分布、组装或扩散带来了挑战，因为许多现有方法缺乏为具有不同分布模式的蛋白质选择合适的参数的指导21。此外，对于数据分析经验有限的用户来说，处理产生的大量数据可能会令人生畏，这可能会损害结果的生物学意义。
为了应对这些挑战，我们引入了免疫荧光制备和数据分析的详细分步方案，旨在提供一种无偏的方法来量化具有各种组织模式的蛋白质染色（图 1）。此半自动管道专为计算和成像分析专业知识有限的用户而设计。它结合了两个成熟的斐济插件的功能：FindFoci22 和 3D suite23。通过将 FindFoci 的精确焦点识别功能与 3D 套件提供的 3D 空间中的对象识别和分割功能集成，我们的方法为每个采集区域的每个通道生成两个 CSV 文件。这些文件包含补充信息，有助于计算适用于各种类型信号分布的指标，例如每个细胞的病灶计数、病灶与核质心的距离以及我们引入的用于弥散蛋白染色的不均匀系数 （IC）。此外，我们承认，对于数据处理技能有限的用户来说，数据外推可能非常耗时。为了简化此过程，我们提供了一个 Python 脚本，该脚本可自动将每个实验的所有收集的测量值编译到一个文件中。用户无需安装任何编程语言软件即可执行此脚本。我们在 Google Colab 上提供了可执行代码，这是一个基于云的平台，允许直接在浏览器中编写 Python 脚本。这确保了我们的方法直观且易于立即使用。
我们证明了我们的方案在分析和量化两种核蛋白信号分布改变方面的有效性：含溴结构域的蛋白 4 （BRD4） 和 zeste-12 抑制因子 （SUZ12）。BRD4 是 Mediator 复合体中一种有据可查的共激活因子蛋白，已知可形成与聚合酶 II 依赖性转录起始相关的缩合物24,25。SUZ12 是多梳抑制复合物 2 （PRC2） 的蛋白质组分，负责调节 H3K27me3 组蛋白修饰的沉积26,27。这些蛋白质在两种不同的细胞类型中表现出不同的模式：新鲜分离的人 CD4+ 幼稚 T 细胞，它们是静止的，转录活性速度较慢，以及体外分化的 TH1 CD4+ 细胞，它们是特化的增殖效应细胞，显示转录增加28。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>1.5 mL Safe-Lock Tubes</td>
			<td>Eppendord</td>
			<td>#0030121503</td>
			<td>Protocol section 1</td>
		</tr>
		<tr>
			<td>10 mL Serological pipettes</td>
			<td>VWR</td>
			<td>#612-3700</td>
			<td>Protocol section 1</td>
		</tr>
		<tr>
			<td>20 &#181;L barrier pipette tip</td>
			<td>Thermo Scientific</td>
			<td>#2149P-HR</td>
			<td>Protocol section 1</td>
		</tr>
		<tr>
			<td>50 mL Polypropylene Conical Tube</td>
			<td>Falcon</td>
			<td>#352070</td>
			<td>Protocol section 1</td>
		</tr>
		<tr>
			<td>200 &#181;L barrier pipette tip</td>
			<td>Thermo Scientific</td>
			<td>#2069-HR</td>
			<td>Protocol section 1</td>
		</tr>
		<tr>
			<td>antifade solution - ProlongGlass - mountingmedia</td>
			<td>Invitrogen</td>
			<td>#P36984</td>
			<td>Step 1.3.12</td>
		</tr>
		<tr>
			<td>BSA (Bovine Serum Albumin)</td>
			<td>Sigma</td>
			<td>#A7030</td>
			<td>Step 1.3.6., 1.3.8.</td>
		</tr>
		<tr>
			<td>CD4+ T Cell Isolation Kit</td>
			<td>Miltenyi Biotec</td>
			<td>#130-096-533</td>
			<td>Step 1.1.2.</td>
		</tr>
		<tr>
			<td>DAPI (4,6-diamidino-2-phenylindole)</td>
			<td>Invitrogen</td>
			<td>Cat#D1306</td>
			<td>Step 1.3.10.</td>
		</tr>
		<tr>
			<td>Dry ice</td>
			<td></td>
			<td></td>
			<td>Step 1.3.1.</td>
		</tr>
		<tr>
			<td>Dynabeads Human T-activator anti-CD3/anti-CD28 bead</td>
			<td>Life Technologies</td>
			<td>#1131D</td>
			<td>magnetic beads step 1.1.4.</td>
		</tr>
		<tr>
			<td>EtOH</td>
			<td>Carlo Erba</td>
			<td>#4146320</td>
			<td>Step 1.2.1.1.</td>
		</tr>
		<tr>
			<td>FACSAria SORP</td>
			<td>BD Bioscences</td>
			<td></td>
			<td>Step 1.1.3. Equipped with BD FACSDiva Software version 8.0.3</td>
		</tr>
		<tr>
			<td>FBS (Fetal Bovine Serum)</td>
			<td>Life Technologies</td>
			<td>#10270106</td>
			<td>Step 1.1.4</td>
		</tr>
		<tr>
			<td>FICOLL PAQUE PLUS</td>
			<td>Euroclone</td>
			<td>GEH17144003F32</td>
			<td>Step 1.1.1.</td>
		</tr>
		<tr>
			<td>FIJI Version 2.14.0</td>
			<td>-</td>
			<td>-</td>
			<td>Protocol section 3</td>
		</tr>
		<tr>
			<td>Glass coverslip (10 mm, thickness 1.5 H)</td>
			<td>Electron Microscopy Sciences</td>
			<td>#72298-13</td>
			<td>Step 1.2.1.</td>
		</tr>
		<tr>
			<td>Glycerol</td>
			<td>Sigma</td>
			<td>#G5516</td>
			<td>Step 1.2.7-1.3.1.</td>
		</tr>
		<tr>
			<td>Goat anti-Rabbit AF568 secondary antibody</td>
			<td>Invitrogen</td>
			<td>A11036</td>
			<td>Step 1.3.8.</td>
		</tr>
		<tr>
			<td>HCl</td>
			<td>Sigma</td>
			<td>#320331</td>
			<td>Step 1.3.4.</td>
		</tr>
		<tr>
			<td>human neutralizing anti-IL-4</td>
			<td>Miltenyi Biotec</td>
			<td>Cat#130-095-753</td>
			<td>Step 1.1.4.</td>
		</tr>
		<tr>
			<td>human recombinant IL-12</td>
			<td>Miltenyi Biotec</td>
			<td>Cat#130-096-704</td>
			<td>Step 1.1.4.</td>
		</tr>
		<tr>
			<td>human recombinant IL-2</td>
			<td>Miltenyi Biotec</td>
			<td>Cat#130-097-744</td>
			<td>Step 1.1.4.</td>
		</tr>
		<tr>
			<td>Leica TCS SP5 Confocal microscope</td>
			<td>Leica Microsystems</td>
			<td>-</td>
			<td>Protocol section 2, Equipped with HCX PL APO 63x, 1.40 NA oil immersion objective, with an additional 3x zoom. Pinhole size : 0.8 AU. Line average 2&#215;. Frame size 1024&#215;1024 pixel.</td>
		</tr>
		<tr>
			<td>MEM Non-Essential Amino Acids Solution</td>
			<td>Life Technologies</td>
			<td>#11140035</td>
			<td>Step 1.1.4.</td>
		</tr>
		<tr>
			<td>Microscope Slides</td>
			<td>VWR</td>
			<td>#631-1552</td>
			<td>Step 1.3.12.</td>
		</tr>
		<tr>
			<td>Mouse monoclonal anti-Human CD4 APC-Cy7 (RPA-T4 clone)</td>
			<td>BD Bioscience</td>
			<td>#557871</td>
			<td>Step 1.1.3.</td>
		</tr>
		<tr>
			<td>Mouse monoclonal anti-Human CD45RA PECy5 (5H9 clone)</td>
			<td>BD Bioscience</td>
			<td>#552888</td>
			<td>Step 1.1.3.</td>
		</tr>
		<tr>
			<td>Mouse monoclonal anti-Human CD45RO APC (UCHL1 clone)</td>
			<td>Miltenyi Biotec</td>
			<td>#130-113-546</td>
			<td>Step 1.1.3.</td>
		</tr>
		<tr>
			<td>Multiwell 24 well</td>
			<td>Falcon</td>
			<td>#353047</td>
			<td>Protocol section 1</td>
		</tr>
		<tr>
			<td>Normal Goat Serum</td>
			<td>Invitrogen</td>
			<td>PCN5000</td>
			<td>Step 1.3.6., 1.3.8.</td>
		</tr>
		<tr>
			<td>PBS</td>
			<td>Life Technologies</td>
			<td>#14190094</td>
			<td>Protocol section 1</td>
		</tr>
		<tr>
			<td>Penicillin/Streptomycin solution</td>
			<td>Life Technologies</td>
			<td>#15070063</td>
			<td>Step 1.1.4.</td>
		</tr>
		<tr>
			<td>PFA</td>
			<td>Sigma</td>
			<td>#P6148</td>
			<td>Step 1.2.4.</td>
		</tr>
		<tr>
			<td>poly-L-lysine</td>
			<td>Sigma</td>
			<td>#P8920</td>
			<td>1.2.1.</td>
		</tr>
		<tr>
			<td>Primary antibody - BRD4</td>
			<td>Abcam</td>
			<td>#ab128874</td>
			<td>Step 1.3.6.</td>
		</tr>
		<tr>
			<td>Primary antibody - SUZ12</td>
			<td>Cel Signalling</td>
			<td>mAb #3737</td>
			<td>Step 1.3.6.</td>
		</tr>
		<tr>
			<td>RPMI 1640 W/GLUTAMAX-I</td>
			<td>Life Technologies</td>
			<td>#61870010</td>
			<td>Step 1.1.4.</td>
		</tr>
		<tr>
			<td>Sodium Pyruvate</td>
			<td>Life Technologies</td>
			<td>#11360039</td>
			<td>Step 1.1.4.</td>
		</tr>
		<tr>
			<td>Triton X-100</td>
			<td>Sigma</td>
			<td>#T8787</td>
			<td>Step 1.2., 1.3.</td>
		</tr>
		<tr>
			<td>TWEEN 20</td>
			<td>Sigma</td>
			<td>#P9416</td>
			<td>Step 1.3.</td>
		</tr>
		<tr>
			<td>Tweezers</td>
			<td>-</td>
			<td>-</td>
			<td>Protocol section 1</td>
		</tr>
	</tbody>
</table>

a versatile pipeline for analyzing dynamic changes in nuclear bodies in a variety of cell types

各种核过程，例如转录控制，发生在称为病灶的离散结构中，可通过免疫荧光技术进行识别。通过显微镜研究这些病灶在不同细胞条件下的动力学，可以深入了解控制细胞身份和功能的分子机制。然而，对不同细胞类型进行免疫荧光检测并评估这些病灶的组装、扩散和分布的改变存在许多挑战。这些挑战包括样品制备、分析成像数据的参数确定以及大量数据管理方面的复杂性。此外，现有的成像工作流程通常是为熟练用户量身定制的，因此限制了更广泛的受众的可访问性。
在这项研究中，我们引入了一种优化的免疫荧光方案，该方案专为研究不同人类原代 T 细胞类型中的核蛋白而量身定制，该方案可以针对任何感兴趣的蛋白质和细胞类型进行定制。此外，我们提出了一种无偏量化蛋白质染色的方法，无论它们是否形成不同的病灶或表现出弥漫的核分布。
我们提出的方法提供了一个全面的指南，从细胞染色到分析，利用在 Jython 中开发并在斐济执行的半自动化管道。此外，我们还提供了一个用户友好的 Python 脚本来简化数据管理，可在 Google Colab 笔记本上公开访问。我们的方法已证明对在不同环境中具有不同核组织模式的蛋白质进行信息丰富的免疫荧光分析的有效性。

The organization of the eukaryotic genome is governed by multiple layers of epigenetic modifications1, coordinating several nuclear functions that can occur within specialized compartments called nuclear bodies or condensates2. Within these structures, processes such as transcription initiation3, RNA processing4,5,6, DNA repair7,8, ribosome biogenesis9,10,11, and heterochromatin regulation12,13 take place. The regulation of nuclear bodies adjusts over both spatial and temporal dimensions to accommodate cellular requirements, guided by principles of phase separation14,15. Consequently, these bodies function as transient factories where functional components assemble and disassemble, undergoing changes in size and spatial distribution. Hence, understanding the characteristics of nuclear proteins by microscopy, including their propensity to form bodies and their spatial arrangement in different cellular conditions, offers valuable insights into their functional roles. Fluorescence microscopy is a widely used method for studying nuclear proteins, allowing their detection through fluorescent antibodies or directly expressing targets with a fluorescent protein reporter16,17.
In this context, nuclear bodies appear as bright foci or puncta, with a notable degree of sphericity, making them easily distinguishable from the surrounding environment16,18. Super-resolution techniques like STORM and PALM, by providing improved resolution (up to 10 nm)19, enable more precise characterization of the structure and composition of specific condensates20. However, their accessibility is limited by equipment expenses and the specialized skills needed for data analysis. Therefore, confocal microscopy remains popular due to its favorable balance between resolution and wider usage. Such popularity is facilitated by the inherent removal of out-of-focus light, which diminishes the requirement for extensive post-processing procedures for accurate segmentation, its widespread availability in research institutes, its effective acquisition time, and sample preparation that is typically efficient. However, accurately measuring protein distribution, assembly, or diffusion using immunofluorescence assays across diverse cellular conditions poses challenges, as many existing methods lack guidance on selecting suitable parameters for proteins with varying distribution patterns21. Moreover, handling the resulting large data volume can be daunting for users with limited experience in data analysis, potentially compromising the biological significance of the results.
To address these challenges, we introduce a detailed step-by-step protocol for immunofluorescence preparation and data analysis, aiming to provide an unbiased method for quantifying protein staining with various organization patterns (Figure 1). This semi-automated pipeline is designed for users with limited expertise in computational and imaging analysis. It combines the functionalities of two established Fiji plugins: FindFoci22 and 3D suite23. By integrating the precise foci identification capability of FindFoci with the object identification and segmentation features in 3D space offered by 3D suite, our approach generates two CSV files per channel for each field of acquisition. These files contain complementary information that facilitates the calculation of metrics suitable for various types of signal distribution, such as the count of foci per cell, the distance of foci from the nuclear centroid, and the inhomogeneity coefficient (IC), which we have introduced for diffuse protein staining. In addition, we acknowledge that data extrapolation can be time-consuming for users with limited data handling skills. To streamline this process, we provide a Python script that automatically compiles all collected measurements into a single file for each experiment. Users can execute this script without the need to install any programming language software. We provide an executable code on Google Colab, a cloud-based platform that allows the writing of Python scripts directly in the browser. This ensures that our method is intuitive and readily accessible for immediate use.
We demonstrate the effectiveness of our protocol in analyzing and quantifying alterations in signal distribution of two nuclear proteins: Bromodomain-containing protein 4 (BRD4) and Suppressor of zeste-12 (SUZ12). BRD4 is a well-documented coactivator protein within the Mediator complex known to form condensates associated with polymerase II-dependent transcriptional initiation24,25. SUZ12 is a protein component of the Polycomb Repressive Complex 2 (PRC2) responsible for regulating the deposition of H3K27me3 histone modification26,27. These proteins exhibit different patterns within two distinct cell types: freshly isolated human CD4+ na&#239;ve T cells, which are quiescent and exhibit slow rates of transcriptional activity, and in vitro differentiated TH1 CD4+ cells, which are specialized, proliferating effector cells showing increased transcription28.

作者聚焦：使用免疫荧光检测研究人类细胞可塑性和环境适应性的综合表观遗传学分析

用于分析各种细胞类型中核体动态变化的多功能管道

Our main research interest is an epigenetic mechanism that regulates human cellular elasticity and environmental adaptation. I am currently focusing on studying how changes in chromatization within a nuclear condensate three pathological cellular conditions. I believe that studying the biophysical properties of chromatization opens a new field and offers unique opportunities for developing novel therapeutic approaches.Immunofluorescence assays are valuable for screening protein organization in nuclear condensates. However, human genetics can be challenging due to parameter selection issues and large data volume when studying multiple conditions. We offer a comprehensive with guidance on how to use the right parameters and automated analysis pipeline to streamline the process.Our protocol guides user from sample preparation to the analysis. We provided a simulated pipeline design through Doza with limited computation and image analysis expertise. The workflow is accessible to everyone at no cost, and leveraging widely adoptable software like Imogene and Python and the free cloud based service Google Colab.

该方法中概述的方案有助于可视化和量化人原代 T 细胞内核蛋白染色的改变，并且可以针对不同的细胞类型和蛋白质靶标进行定制。作为案例研究，我们进行并分析了幼稚细胞和 TH1 CD4 + 细胞中 BRD4 和 SUZ12 的染色。
BRD4 在静止幼稚和分化的 TH1 CD4 + 细胞中均显示出良好的点状染色模式，因此能够使用设计的参数对两种细胞类型进行...

该方法描述了一种免疫荧光方案和定量管道，用于评估人 T 淋巴细胞中具有不同核组织模式的蛋白质分布。该协议提供了分步指导，从样品制备开始，一直到斐济的半自动分析执行，最后通过 Google Colab 笔记本进行数据处理。

Various nuclear processes, such as transcriptional control, occur within discrete structures known as foci that are discernable through the ...

a-versatile-pipeline-for-analyzing-dynamic-changes-nuclear-bodies

Research

JoVE Journal

Genetics

A Versatile Pipeline for Analyzing Dynamic Changes in Nuclear Bodies in a Variety of Cell Types

Various nuclear processes, such as transcriptional control, occur within discrete structures known as foci that are discernable through the immunofluorescence technique. Investigating the dynamics of these foci under diverse cellular conditions via microscopy yields valuable insights into the molecular mechanisms governing cellular identity and functions. However, performing immunofluorescence assays across different cell types and assessing alterations in the assembly, diffusion, and distribution of these foci present numerous challenges. These challenges encompass complexities in sample preparation, determination of parameters for analyzing imaging data, and management of substantial data volumes. Moreover, existing imaging workflows are often tailored for proficient users, thereby limiting accessibility to a broader audience.
In this study, we introduce an optimized immunofluorescence protocol tailored for investigating nuclear proteins in different human primary T cell types that can be customized for any protein of interest and cell type. Furthermore, we present a method for unbiasedly quantifying protein staining, whether they form distinct foci or exhibit a diffuse nuclear distribution.
Our proposed method offers a comprehensive guide, from cellular staining to analysis, leveraging a semi-automated pipeline developed in Jython and executable in Fiji. Furthermore, we provide a user-friendly Python script to streamline data management, publicly accessible on a Google Colab notebook. Our approach has demonstrated efficacy in yielding highly informative immunofluorescence analyses for proteins with diverse patterns of nuclear organization across different&#160;contexts.

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.

科研

遗传学

Quantification Pipeline for Evaluating Protein Distribution with Varied Nuclear Organization Patterns in Human T Lymphocytes

To begin, download and install the latest version of Fiji on a computer. After downloading the convert_to_TIFF. py script, drag and drop the script on Fiji and run the code.Browse to the path where the experiment is stored. The converted TIFF files are created within the same experimental folder. Click on plugins, then 3D Suite, and open the 3D manager options.Within the 3D manager window, select the check boxes corresponding to the volume in unit, mean gray value, bounding box in pixel, standard deviation gray value, centroid in pixel, and centroid in unit. Tick the exclude objects on edges XY, and exclude objects on edges Z.Then, click okay. Open the fluorescent image of human T-lymphocytes.Click Control Shift D to duplicate the protein channel, including the stack. Check the hyper stack checkbox. Specify the appropriate channel number within the channels box and rename it accordingly.To launch the Fiji macro recorder, navigate to the plugins, then macros, and select record. Then, to open the FindFoci plugin, sequentially click on plugins GDSC, FindFoci, and FindFoci GUI. From the image dropdown menu, select the image to be analyzed.Set the Gaussian blur to 1.5, background method to standard deviation above mean, background parameter to nine, search method to fraction of peak background, search parameter to 0.7, peak method to relative above background, peak parameter to 0.2, and minimum size to five. Set the max peaks to high numbers to include all foci in the image. To enhance foci identification, adjust the Gaussian blur close to the foci diameter, and increase the values of the background parameter to impose more stringent thresholds.Then, decrease the values of the search parameter to include areas farther away from the fluorescence peak and decrease the values of the peak parameter for the separation of peaks. Run FindFoci and copy the string that appears in the recorder window. The string contains the selected parameters, excluding the quotation marks.After downloading the nuclear_prod_q. py script, drag and drop the script on Fiji, and click run to execute the code. In the nucleus channel, enter the number corresponding to the channel of DAPI.For nucleus Gaussian blur, enter the value of sigma needed to blur the image for segmentation. Then, enter the number corresponding to the channel of the staining of interest for the protein channel. In the FindFoci parameter, paste the string representing the macro recording of the selected parameters.Tick the visual check box and click browse to select the directory where the images are stored. Once all boxes are compiled, click okay to continue the execution. From the ROI list in the ROI manager 3D window, select the nuclei channel, and click live ROI to on.Select the ROI belonging to the same nucleus and press merge. And for undesired nuclei, press delete. Again, click live ROI to on, select all, and confirm that all nuclei are correctly segmented.From the quantification folder, open the TXT file with records of the parameters used for the analysis. Open Google Colab in a browser. Then, from the file, select open notebook, and upload the final nuclear protein metrics.ipynb script. Upload all the folders containing the CSV files of each image field into a folder of preference in Google Drive. In the notebook, indicate the path of the folder where the results sub-folders are stored and run all cells.When the code has finished compiling, open the final spreadsheet file containing all the compiled data.