quantification pipeline for evaluating protein distribution with varied nuclear organization patterns in human t lymphocytes

Analyseworkflow van nucleaire eiwitkleuring in menselijke primaire cellen

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.

Auteur in de schijnwerpers: uitgebreide epigenetische analyse voor het onderzoeken van menselijke cellulaire plasticiteit en aanpassing aan het milieu met behulp van immunofluorescentietesten

Een veelzijdige pijplijn voor het analyseren van dynamische veranderingen in kernlichamen in verschillende celtypen

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.

Kwantificeringspijplijn voor het evalueren van eiwitdistributie met gevarieerde nucleaire organisatiepatronen in menselijke T-lymfocyten

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Research

JoVE Journal

Genetics

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

37 Views. 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 ...