Live-cell visualization of histone modification using bimolecular complementation

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Epigenetic modifications of histones in human, animal, and other eukaryotic cells play a crucial role in regulating gene expression. Histones can undergo a variety of post-translational modifications in different combinations, including methylation, acetylation, phosphorylation, and others at various amino acid residues, which determine the functional state of a given chromatin locus. Changes in epigenetic modifications accompany all normal and pathological cellular processes, including proliferation, differentiation, cancer transformation, and more. Currently, the development and application of new methods for analyzing the epigenome at the single-cell level, including in live cells, are particularly relevant. In this study, new sensor systems were developed for visualizing epigenetic modifications H3K9me3 (trimethylated Lys9), H3K9ac (acetylated Lys9), and the spatial colocalization of H3K9me3 with H3K9ac, based on fluorogenic dyes. The creation of these sensors involved the use of splitFAST system as well as the histone natural reader domains MPP8 and AF9. Adding the fluorogens HMBR and N871b to the cell medium allowed for the detection of clearly distinguishable fluorescence patterns in the green and red channels, respectively. We also performed the analysis of the obtained fluorescent images using the LiveMIEL (Live-cell Microscopic Imaging of Epigenetic Landscape) computational method. Clustering of the resulting data showed agreement with the expected class labels corresponding to the presence of H3K9me3, H3K9ac, and the spatial colocalization of H3K9me3 and H3K9ac in the nucleus. The developed sensors can be effectively used to study histone modifications in various cellular processes, as well as in investigating disease development mechanisms.

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作者简介

A. Stepanov

Skolkovo Institute of Science and Technology; Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry

编辑信件的主要联系方式.
Email: gurskayanadya@gmail.com
俄罗斯联邦, Moscow; Moscow

L. Putlyaeva

Skolkovo Institute of Science and Technology; Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry

Email: gurskayanadya@gmail.com
俄罗斯联邦, Moscow; Moscow

A. Shuvaeva

Moscow Institute of Physics and Technology

Email: gurskayanadya@gmail.com
俄罗斯联邦, Moscow

M. Andrushkin

Pirogov Russian National Research Medical University

Email: gurskayanadya@gmail.com
俄罗斯联邦, Moscow

M. Baranov

Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry; Pirogov Russian National Research Medical University

Email: gurskayanadya@gmail.com
俄罗斯联邦, Moscow; Moscow

N. Gurskaya

Skolkovo Institute of Science and Technology; Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry; Pirogov Russian National Research Medical University

Email: gurskayanadya@gmail.com
俄罗斯联邦, Moscow; Moscow; Moscow

K. Lukyanov

Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry

Email: gurskayanadya@gmail.com
俄罗斯联邦, Moscow

参考

  1. Millán-Zambrano G., Burton A., Bannister A.J., Schneider R. // Nat. Rev. Genet. 2022. V. 23. P. 563– 580. https://doi.org/10.1038/s41576-022-00468-7
  2. Yun M., Wu J., Workman J.L., Li B. // Cell Res. 2011. V. 21. P. 564–578. https://doi.org/10.1038/cr.2011.42
  3. Kungulovski G., Kycia I., Tamas R., Jurkowska R.Z., Kudithipudi S., Henry C., Reinhardt R., Labhart P., Jeltsch A. // Genome Res. 2014. V. 24. P. 1842–1853. https://doi.org/10.1101/gr.170985.113
  4. Mauser R., Kungulovski G., Keup C., Reinhardt R., Jeltsch A. // Epigenetics Chromatin. 2017. V. 10. P. 45. https://doi.org/10.1186/s13072-017-0153-1
  5. Delachat A.M.-F., Guidotti N., Bachmann A.L., Meireles-Filho A.C.A., Pick H., Lechner C.C., Deluz C., Deplancke B., Suter D.M., Fierz B. // Cell Chem. Biol. 2018. V. 25. P. 51–56.e6. https://doi.org/10.1016/j.chembiol.2017.10.008
  6. Villaseñor R., Pfaendler R., Ambrosi C., Butz S., Giuliani S., Bryan E., Sheahan T.W., Hélène D. // Nat. Biotechnol. 2020. V. 38. P. 728–736. https://doi.org/10.1038/s41587-020-0434-2
  7. Stepanov A.I., Shuvaeva A.A., Putlyaeva L.V., Lukyanov D.K., Galiakberova A.A., Gorbachev D.A., Maltsev D.I., Pronina V., Dylov D.V., Terskikh A.V., Lukyanov K.A., Gurskaya N.G. // Cell Mol. Life Sci. 2024. V. 81. P. 381. https://doi.org/10.1007/s00018-024-05359-0
  8. Müller I., Moroni A.S., Shlyueva D., Sahadevan S., Schoof E.M., Radzisheuskaya A., Højfeldt J.W., Pedersen D.S., Trifonov M., Mikkelsen J.G. // Nat. Commun. 2021. V. 12. P. 3034. https://doi.org/10.1038/s41467-021-23308-4
  9. Ng A.H.M., Khoshakhlagh P., Rojo Arias J.E., Pasquini G., Wang K., Swiersy A., Shipman S.L., Tian C., Miller D.M. // Nat. Biotechnol. 2021. V. 39. P. 510–519. https://doi.org/10.1038/s41587-020-0742-6
  10. Tebo A.G., Gautier A. // Nat. Commun. 2019. V. 10. P. 2822. https://doi.org/10.1038/s41467-019-10855-0
  11. Plamont M.-A., Billon-Denis E., Maurin S., Gauron C., Pimenta F.M., Specht C.G., Shi J., Lamberti A., Carlier A. // Proc. Natl. Acad. Sci. USA. 2016. V. 113. P. 497–502. https://doi.org/10.1073/pnas.1513094113
  12. Povarova N.V., Zaitseva S.O., Baleeva N.S., Smirnov A.Y., Myasnyanko I.N., Zagudaylova M.B., Bozhanova N.G., Yegorov A.Y., Mikhaylov A.D. // Chemistry. 2019. V. 25. P. 9592–9596. https://doi.org/10.1002/chem.201901151
  13. Benaissa H., Ounoughi K., Aujard I., Fischer E., Goïame R., Nguyen J., Tebo A.G., Pimentel C., Nicolas C. // Nat. Commun. 2021. V. 12. P. 6989. https://doi.org/10.1038/s41467-021-27334-0
  14. Padeken J., Methot S.P., Gasser S.M. // Nat. Rev. Mol. Cell Biol. 2022. V. 23. P. 623–640. https://doi.org/10.1038/s41580-022-00483-w
  15. Gates L.A., Shi J., Rohira A.D., Feng Q., Zhu B., Bedford M.T., Sagum C.A., Hewitt M.J., Cai Z. // J. Biol. Chem. 2017. V. 292. P. 14456–14472. https://doi.org/10.1074/jbc.m117.802074
  16. Kokura K., Sun L., Bedford M.T., Fang J. // EMBO J. 2010. V. 29. P. 3673–3687. https://doi.org/10.1038/emboj.2010.239
  17. Li Y., Wen H., Xi Y., Tanaka K., Wang H., Peng D., Ren Y., Li Z., Liu C. // Cell. 2014. V. 159. P. 558–571. https://doi.org/10.1016/j.cell.2014.09.049
  18. Stepanov A.I., Besedovskaia Z.V., Moshareva M.A., Lukyanov K.A., Putlyaeva L.V. // Int. J. Mol. Sci. 2022. V. 23. P. 8988. https://doi.org/10.3390/ijms23168988
  19. Stepanov A.I., Zhurlova P.A., Shuvaeva A.A., Sokolinskaya E.L., Gurskaya N.G., Lukyanov K.A., Putlyaeva L.V. // Biochem. Biophys. Res. Commun. 2023. V. 687. P. 149174. https://doi.org/10.1016/j.bbrc.2023.149174
  20. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Linh H., Poggio T. // J. Machine Learning Res. 2012. V. 12. P. 2825–2830. https://doi.org/10.48550/arXiv.1201.0490

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2. Fig. 1. Fluorescence microscopy of HEK293T cells transfected with MPP8-FAST-NLS-MPP8 and AF9-FAST-NLS-AF9 sensors (NLS is removed from the figure legends for convenience). Fluorogen – HMBR.

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3. Fig. 2. (a) – List of the generated plasmids divided into groups. The asterisk indicates the introduction of the MPP8 W80A and AF9 Y78A mutations into the corresponding DNA sequences; (b) – fluorescence microscopy of plasmids cotransfected with different variants into HEK293T cells. Fluorogen – HMBR; (c) – fluorescence microscopy of cotransfected plasmids in HEK293T cells in two channels, the fluorogens used are indicated above the images; NLS has been removed from the figure legends for convenience.

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4. Fig. 3. (a) – Fluorescence microscopy of HEK293T cells expressing MPP8-FAST-NLS-AF9. Fluorogen – HMBR; (b) – Fluorescence microscopy of HEK293T cells expressing different combinations of splitFAST system plasmids. Fluorogen – HMBR. The asterisk indicates that the domains have the mutation MPP8 W80A and AF9 Y78A; (c) – Fluorescence microscopy of HEK293 AF9-NFAST-NLS and MPP8-CFAST11-NLS in two channels, the fluorogens used are indicated above the images. NLS has been removed from the figure legends for convenience.

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5. Fig. 4. (a) LiveMIEL analysis of H3K9me3, H3K9ac, and H3K9me3-H3K9ac epigenetic modifications. Segmented nuclei of HEK293T cells expressing MPP8-NFAST + MPP8-CFAST11, AF9-NFAST + AF9-CFAST11, and AF9-NFAST + MPP8-CFAST11; (b) principal component analysis (PCA) analysis of texture features extracted from single nuclear images (feature values ​​were averaged over n = 40 nuclei); (c) silhouette coefficient si values ​​for EM clustering of PCA data (n = 3). The red line corresponds to the average silhouette coefficient s = 0.31 in the dataset; (d) visualization of EM clustering for n = 3 clusters. Dots represent textural features derived from images visualizing the abundance of H3K9me3, H3K9ac, and H3K9me3-H3K9ac in nuclei. NLS has been omitted from figure legends for clarity.

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