A Versatile Pipeline for Analyzing Dynamic Changes in Nuclear Bodies in a Variety of Cell Types
Summary
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.
Abstract
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 contexts.
Introduction
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+ naïve 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.
Protocol
Representative Results
Discussion
In this study, we present a method for performing immunofluorescence experiments on nuclear proteins in human T lymphocytes. This method offers flexibility for use with various cell types through minor modifications in fixation and permeabilization steps, as described previously30,31.
Our imaging workflow builds upon established techniques outlined in the literature, specifically FindFoci and 3D Suite22,</…
Offenlegungen
The authors have nothing to disclose.
Acknowledgements
We acknowledge the scientific and technical assistance of the INGM Imaging Facility, in particular, C. Cordiglieri and A. Fasciani, and the INGM FACS sorting facility in particular M.C Crosti (Istituto Nazionale di Genetica Molecolare 'Romeo ed Enrica Invernizzi' (INGM), Milan, Italy). We acknowledge M. Giannaccari for his technical informatic support. This work was funded by the following grants: Fondazione Cariplo (Bando Giovani, grant nr 2018-0321) and Fondazione AIRC (grant nr MFAG 29165) to F.M. Ricerca Finalizzata, (grant nr GR-2018-12365280), Fondazione AIRC (grant nr 2022 27066), Fondazione Cariplo (grant nr 2019-3416), Fondazione Regionale per la Ricerca Biomedica (FRRB CP2_12/2018,) Piano Nazionale di Ripresa e Resilienza (PNRR) (grant nr G43C22002620007) and Progetti di Rilevante Interesse Nazionale (PRIN) (grant nr 2022PKF9S) to B.B.
Materials
| 1.5 mL Safe-Lock Tubes | Eppendord | #0030121503 | Protocol section 1 |
| 10 mL Serological pipettes | VWR | #612-3700 | Protocol section 1 |
| 20 µL barrier pipette tip | Thermo Scientific | #2149P-HR | Protocol section 1 |
| 50 mL Polypropylene Conical Tube | Falcon | #352070 | Protocol section 1 |
| 200 µL barrier pipette tip | Thermo Scientific | #2069-HR | Protocol section 1 |
| antifade solution – ProlongGlass – mountingmedia | Invitrogen | #P36984 | Step 1.3.12 |
| BSA (Bovine Serum Albumin) | Sigma | #A7030 | Step 1.3.6., 1.3.8. |
| CD4+ T Cell Isolation Kit | Miltenyi Biotec | #130-096-533 | Step 1.1.2. |
| DAPI (4,6-diamidino-2-phenylindole) | Invitrogen | Cat#D1306 | Step 1.3.10. |
| Dry ice | Step 1.3.1. | ||
| Dynabeads Human T-activator anti-CD3/anti-CD28 bead | Life Technologies | #1131D | magnetic beads step 1.1.4. |
| EtOH | Carlo Erba | #4146320 | Step 1.2.1.1. |
| FACSAria SORP | BD Bioscences | Step 1.1.3. Equipped with BD FACSDiva Software version 8.0.3 | |
| FBS (Fetal Bovine Serum) | Life Technologies | #10270106 | Step 1.1.4 |
| FICOLL PAQUE PLUS | Euroclone | GEH17144003F32 | Step 1.1.1. |
| FIJI Version 2.14.0 | – | – | Protocol section 3 |
| Glass coverslip (10 mm, thickness 1.5 H) | Electron Microscopy Sciences | #72298-13 | Step 1.2.1. |
| Glycerol | Sigma | #G5516 | Step 1.2.7-1.3.1. |
| Goat anti-Rabbit AF568 secondary antibody | Invitrogen | A11036 | Step 1.3.8. |
| HCl | Sigma | #320331 | Step 1.3.4. |
| human neutralizing anti-IL-4 | Miltenyi Biotec | Cat#130-095-753 | Step 1.1.4. |
| human recombinant IL-12 | Miltenyi Biotec | Cat#130-096-704 | Step 1.1.4. |
| human recombinant IL-2 | Miltenyi Biotec | Cat#130-097-744 | Step 1.1.4. |
| Leica TCS SP5 Confocal microscope | Leica Microsystems | – | 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×. Frame size 1024×1024 pixel. |
| MEM Non-Essential Amino Acids Solution | Life Technologies | #11140035 | Step 1.1.4. |
| Microscope Slides | VWR | #631-1552 | Step 1.3.12. |
| Mouse monoclonal anti-Human CD4 APC-Cy7 (RPA-T4 clone) | BD Bioscience | #557871 | Step 1.1.3. |
| Mouse monoclonal anti-Human CD45RA PECy5 (5H9 clone) | BD Bioscience | #552888 | Step 1.1.3. |
| Mouse monoclonal anti-Human CD45RO APC (UCHL1 clone) | Miltenyi Biotec | #130-113-546 | Step 1.1.3. |
| Multiwell 24 well | Falcon | #353047 | Protocol section 1 |
| Normal Goat Serum | Invitrogen | PCN5000 | Step 1.3.6., 1.3.8. |
| PBS | Life Technologies | #14190094 | Protocol section 1 |
| Penicillin/Streptomycin solution | Life Technologies | #15070063 | Step 1.1.4. |
| PFA | Sigma | #P6148 | Step 1.2.4. |
| poly-L-lysine | Sigma | #P8920 | 1.2.1. |
| Primary antibody – BRD4 | Abcam | #ab128874 | Step 1.3.6. |
| Primary antibody – SUZ12 | Cel Signalling | mAb #3737 | Step 1.3.6. |
| RPMI 1640 W/GLUTAMAX-I | Life Technologies | #61870010 | Step 1.1.4. |
| Sodium Pyruvate | Life Technologies | #11360039 | Step 1.1.4. |
| Triton X-100 | Sigma | #T8787 | Step 1.2., 1.3. |
| TWEEN 20 | Sigma | #P9416 | Step 1.3. |
| Tweezers | – | – | Protocol section 1 |
Referenzen
- Aboelnour, E., Bonev, B. Decoding the organization, dynamics, and function of the 4D genome. Dev Cell. 56 (11), 1562-1573 (2021).
- Erdel, F., Rippe, K. Formation of chromatin subcompartments by phase separation. Biophys J. 114 (10), 2262-2270 (2018).
- Henninger, J. E., et al. RNA-mediated feedback control of transcriptional condensates. Cell. 184 (1), 207-225.e24 (2021).
- Guo, Y. E., et al. Pol II phosphorylation regulates a switch between transcriptional and splicing condensates. Nature. 572 (7770), 543-548 (2019).
- Bhat, P., et al. 3D genome organization around nuclear speckles drives mRNA splicing efficiency. bioRxiv. , (2023).
- Spector, D. L., Lamond, A. I. Nuclear speckles. Cold Spring Harb Perspect Biol. 3 (2), a000646 (2011).
- Kilic, S., et al. Phase separation of 53BP1 determines liquid-like behavior of DNA repair compartments. EMBO J. 38 (16), e101379 (2019).
- Parker, M. W., et al. A new class of disordered elements controls DNA replication through initiator self-assembly. Elife. 8, e48562 (2019).
- Feric, M., et al. Coexisting liquid phases underlie nucleolar subcompartments. Cell. 165 (7), 1686-1697 (2016).
- Yoneda, M., Nakagawa, T., Hattori, N., Ito, T. The nucleolus from a liquid droplet perspective. J Biochem. 170 (2), 153-162 (2021).
- Alberti, S., Carra, S. Nucleolus: A liquid droplet compartment for misbehaving proteins. Curr Biol. 29 (19), R930-R932 (2019).
- Strom, A. R., et al. Phase separation drives heterochromatin domain formation. Nature. 547 (7662), 241-245 (2017).
- Sanulli, S., et al. HP1 reshapes nucleosome core to promote phase separation of heterochromatin. Nature. 575 (7782), 390-394 (2019).
- Hyman, A. A., Weber, C. A., Julicher, F. Liquid-liquid phase separation in biology. Annu Rev Cell Dev Biol. 30, 39-58 (2014).
- Feric, M., Misteli, T. Phase separation in genome organization across evolution. Trends Cell Biol. 31 (8), 671-685 (2021).
- Mitrea, D. M., et al. Methods for physical characterization of phase-separated bodies and membrane-less organelles. J Mol Biol. 430 (23), 4773-4805 (2018).
- Fetter, J., et al. Endogenous gene tagging with fluorescent proteins. Methods Mol Biol. 1239, 231-240 (2015).
- Alberti, S., Gladfelter, A., Mittag, T. Considerations and challenges in studying liquid-liquid phase separation and biomolecular condensates. Cell. 176 (3), 419-434 (2019).
- Galbraith, C. G., Galbraith, J. A. Super-resolution microscopy at a glance. J Cell Sci. 124 (10), 1607-1611 (2011).
- Scalisi, S., Ahmad, A., D’Annunzio, S., Rousseau, D., Zippo, A. Quantitative analysis of PcG-associated condensates by stochastic optical reconstruction microscopy (STORM). Methods Mol Biol. 2655, 183-200 (2023).
- Shihan, M. H., Novo, S. G., Le Marchand, S. J., Wang, Y., Duncan, M. K. A simple method for quantitating confocal fluorescent images. Biochem Biophys Rep. 25, 100916 (2021).
- Herbert, A. D., Carr, A. M., Hoffmann, E. FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis. PLoS One. 9 (12), e114749 (2014).
- Ollion, J., Cochennec, J., Loll, F., Escude, C., Boudier, T. TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization. Bioinformatics. 29 (14), 1840-1841 (2013).
- Sabari, B. R., et al. Coactivator condensation at super-enhancers links phase separation and gene control. Science. 361 (6400), eaar3958 (2018).
- Jang, M. K., et al. The bromodomain protein Brd4 is a positive regulatory component of P-TEFb and stimulates RNA polymerase II-dependent transcription. Mol Cell. 19 (4), 523-534 (2005).
- Pasini, D., Bracken, A. P., Jensen, M. R., Denchi, E. L., Helin, K. Suz12 is essential for mouse development and for EZH2 histone methyltransferase activity. EMBO J. 23 (20), 4061-4071 (2004).
- Margueron, R., Reinberg, D. The Polycomb complex PRC2 and its mark in life. Nature. 469 (7330), 343-349 (2011).
- Peng, Z., et al. Brd4 regulates the homeostasis of CD8(+) T-lymphocytes and their proliferation in response to antigen stimulation. Front Immunol. 12, 728082 (2021).
- Marasca, F., et al. LINE1 are spliced in non-canonical transcript variants to regulate T cell quiescence and exhaustion. Nat Genet. 54 (2), 180-193 (2022).
- Marasca, F., Cortesi, A., Bodega, B. 3D COMBO chrRNA-DNA-ImmunoFISH. Methods Mol Biol. 2157, 281-297 (2021).
- Marasca, F., Cortesi, A., Manganaro, L., Bodega, B. 3D Multicolor DNA FISH tool to study nuclear architecture in human primary cells. J Vis Exp. (155), (2020).
- Jakob, B., Splinter, J., Durante, M., Taucher-Scholz, G. Live cell microscopy analysis of radiation-induced DNA double-strand break motion. Proc Natl Acad Sci U S A. 106 (9), 3172-3177 (2009).
.