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Evidence for widespread cytoplasmic structuring into mesoscale condensates

Nature Cell Biology volume 26, pages 346–352 (2024)Cite this article

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Abstract

Compartmentalization is an essential feature of eukaryotic life and is achieved both via membrane-bound organelles, such as mitochondria, and membrane-less biomolecular condensates, such as the nucleolus. Known biomolecular condensates typically exhibit liquid-like properties and are visualized by microscopy on the scale of ~1 µm (refs. 1,2). They have been studied mostly by microscopy, examining select individual proteins. So far, several dozen biomolecular condensates are known, serving a multitude of functions, for example, in the regulation of transcription3, RNA processing4 or signalling5,6, and their malfunction can cause diseases7,8. However, it remains unclear to what extent biomolecular condensates are utilized in cellular organization and at what length scale they typically form. Here we examine native cytoplasm from Xenopus egg extract on a global scale with quantitative proteomics, filtration, size exclusion and dilution experiments. These assays reveal that at least 18% of the proteome is organized into mesoscale biomolecular condensates at the scale of ~100 nm and appear to be stabilized by RNA or gelation. We confirmed mesoscale sizes via imaging below the diffraction limit by investigating protein permeation into porous substrates with defined pore sizes. Our results show that eukaryotic cytoplasm organizes extensively via biomolecular condensates, but at surprisingly short length scales.

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Fig. 1: Proteomics of filtrated cytoplasm reveals organization in liquid-like assemblies.
Fig. 2: Liquid-like assemblies are sensitive to moderate dilution.
Fig. 3: Proteomics and imaging assays indicate that BMCs predominantly organize at the ~100 nm length scale.
Fig. 4: Integrating proteomics experiments enhances LLPS prediction and classifies at least 18% of the cytoplasm as BMC.

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Data availability

Assignments of MS spectra were searched against the X. laevis v9.2 genome assembly (Xenbase (RRID:SCR_003280))72. The LLPS database was sourced from PhasePro22, PhaSepDB25, DrLLPS24 and LLPSDB26, and the references from the PSAP predictor23, and protein complex data from CORUM63. Protein nucleic acid binding was sourced from Uniprot43, QuickGO44 and Castello et. al.42. Organelle data were sourced from DrLLPS24, Uniprot43 and RNPgranuleDB64. Predictions of catGRANULE45, Pscore46 or PSAP23 were re-evaluated and plotted. IDRs, transmembrane helix annotations and enrichment analyses were generated from Espritz41, Krogh et.al.65 and STRING81, respectively. Housekeeping gene and L-body annotations were derived from Eisenberg et. al.31 and Neil et. al.33. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD029879. The raw sequencing data and gene expression matrices have been deposited to the National Center for Biotechnology Information’s GEO with the GEO series accession number GSE232651. All other data are available from the corresponding authors upon reasonable request. Source data are provided with this study in Source data and Supplementary Table 3. Source data are provided with this paper.

Code availability

Custom code is available from the corresponding authors upon reasonable request. Code for the analysis of TMTproC data is available on GitHub74.

References

  1. Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Shin, Y. & Brangwynne, C. P. Liquid phase condensation in cell physiology and disease. Science 357, aaf4382 (2017).

    Article  Google Scholar 

  3. Hnisz, D., Shrinivas, K., Young, R. A., Chakraborty, A. K. & Sharp, P. A. A phase separation model for transcriptional control. Cell 169, 13–23 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ivanov, P., Kedersha, N. & Anderson, P. Stress granules and processing bodies in translational control. Cold Spring Harb. Perspect. Biol. 11, a032813 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wippich, F. et al. Dual specificity kinase DYRK3 couples stress granule condensation/dissolution to mTORC1 signaling. Cell 152, 791–805 (2013).

    Article  CAS  PubMed  Google Scholar 

  6. Chong, P. A. & Forman-Kay, J. D. Liquid–liquid phase separation in cellular signaling systems. Curr. Opin. Struct. Biol. 41, 180–186 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Alberti, S. & Dormann, D. Liquid–liquid phase separation in disease. Annu. Rev. Genet 53, 171–194 (2019).

    Article  CAS  PubMed  Google Scholar 

  8. Tsang, B., Pritisanac, I., Scherer, S. W., Moses, A. M. & Forman-Kay, J. D. Phase separation as a missing mechanism for interpretation of disease mutations. Cell 183, 1742–1756 (2020).

    Article  CAS  PubMed  Google Scholar 

  9. Fei, J. et al. Quantitative analysis of multilayer organization of proteins and RNA in nuclear speckles at super resolution. J. Cell Sci. 130, 4180–4192 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Go, C. D. et al. A proximity-dependent biotinylation map of a human cell. Nature 595, 120–124 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Hubstenberger, A. et al. P-body purification reveals the condensation of repressed mRNA regulons. Mol. Cell 68, 144–157 (2017).

  12. Jain, S. et al. ATPase-modulated stress granules contain a diverse proteome and substructure. Cell 164, 487–498 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Markmiller, S. et al. Context-dependent and disease-specific diversity in protein interactions within stress granules. Cell 172, 590–604 (2018).

  14. Youn, J. Y. et al. High-density proximity mapping reveals the subcellular organization of mRNA-associated granules and bodies. Mol. Cell 69, 517–532 (2018).

  15. Pancsa, R., Vranken, W. & Meszaros, B. Computational resources for identifying and describing proteins driving liquid–liquid phase separation. Brief. Bioinform. 22, bbaa408 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Vernon, R. M. & Forman-Kay, J. D. First-generation predictors of biological protein phase separation. Curr. Opin. Struct. Biol. 58, 88–96 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Lohka, M. J. & Maller, J. L. Induction of nuclear envelope breakdown, chromosome condensation, and spindle formation in cell-free extracts. J. Cell Biol. 101, 518–523 (1985).

    Article  CAS  PubMed  Google Scholar 

  18. Hannak, E. & Heald, R. Investigating mitotic spindle assembly and function in vitro using Xenopus laevis egg extracts. Nat. Protoc. 1, 2305–2314 (2006).

    Article  CAS  PubMed  Google Scholar 

  19. Sonnett, M., Yeung, E. & Wuhr, M. Accurate, sensitive, and precise multiplexed proteomics using the complement reporter ion cluster. Anal. Chem. 90, 5032–5039 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Johnson, A., Stadlmeier, M. & Wuhr, M. TMTpro complementary ion quantification increases plexing and sensitivity for accurate multiplexed proteomics at the MS2 level. J. Proteome Res. 20, 3043–3052 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. McAlister, G. C. et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86, 7150–7158 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Meszaros, B. et al. PhaSePro: the database of proteins driving liquid–liquid phase separation. Nucleic Acids Res. 48, D360–D367 (2020).

    CAS  PubMed  Google Scholar 

  23. van Mierlo, G. et al. Predicting protein condensate formation using machine learning. Cell Rep. 34, 108705 (2021).

    Article  PubMed  Google Scholar 

  24. Ning, W. et al. DrLLPS: a data resource of liquid–liquid phase separation in eukaryotes. Nucleic Acids Res. 48, D288–D295 (2020).

    Article  CAS  PubMed  Google Scholar 

  25. You, K. et al. PhaSepDB: a database of liquid–liquid phase separation related proteins. Nucleic Acids Res. 48, D354–D359 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Li, Q. et al. LLPSDB: a database of proteins undergoing liquid–liquid phase separation in vitro. Nucleic Acids Res. 48, D320–D327 (2019).

    Article  PubMed Central  Google Scholar 

  27. Dignon, G. L., Best, R. B. & Mittal, J. Biomolecular phase separation: from molecular driving forces to macroscopic properties. Annu. Rev. Phys. Chem. 71, 53–75 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Brangwynne, C. P., Tompa, P. & Pappu, R. V. Polymer physics of intracellular phase transitions. Nat. Phys. 11, 899–904 (2015).

    Article  CAS  Google Scholar 

  29. Kar, M. et al. Phase-separating RNA-binding proteins form heterogeneous distributions of clusters in subsaturated solutions. Proc. Natl Acad. Sci. USA 119, e2202222119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Mittag, T. & Pappu, R. V. A conceptual framework for understanding phase separation and addressing open questions and challenges. Mol. Cell 82, 2201–2214 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Eisenberg, E. & Levanon, E. Y. Human housekeeping genes, revisited. Trends Genet. 29, 569–574 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. Thomas, L., Putnam, A. & Folkmann, A. Germ granules in development. Development 150, dev.201037 (2023).

    Article  Google Scholar 

  33. Neil, C. R. et al. L-bodies are RNA–protein condensates driving RNA localization in Xenopus oocytes. Mol. Biol. Cell 32, ar37 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ma, W. & Mayr, C. A membraneless organelle associated with the endoplasmic reticulum enables 3′ UTR-mediated protein–protein interactions. Cell 175, 1492–1506 (2018).

  35. Lee, D. S. et al. Size distributions of intracellular condensates reflect competition between coalescence and nucleation. Nat. Phys. 19, 586–596 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Molliex, A. et al. Phase separation by low complexity domains promotes stress granule assembly and drives pathological fibrillization. Cell 163, 123–133 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kato, M. et al. Cell-free formation of RNA granules: low complexity sequence domains form dynamic fibers within hydrogels. Cell 149, 753–767 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  39. Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).

    Article  Google Scholar 

  40. Mardia, K. V. Fisher’s pioneering work on discriminant analysis and its impact on AI. Preprint at arXiv 2309.04774 (2023).

  41. Walsh, I., Martin, A. J., Di Domenico, T. & Tosatto, S. C. ESpritz: accurate and fast prediction of protein disorder. Bioinformatics 28, 503–509 (2012).

    Article  CAS  PubMed  Google Scholar 

  42. Castello, A. et al. Comprehensive identification of RNA-binding domains in human cells. Mol. Cell 63, 696–710 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. UniProt, C. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2021).

    Article  Google Scholar 

  44. Huntley, R. P. et al. The GOA database: gene ontology annotation updates for 2015. Nucleic Acids Res. 43, D1057–1063 (2015).

  45. Bolognesi, B. et al. A concentration-dependent liquid phase separation can cause toxicity upon increased protein expression. Cell Rep. 16, 222–231 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Vernon, R. M. et al. Pi–Pi contacts are an overlooked protein feature relevant to phase separation. eLife 7, e31486 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Zarin, T. et al. Proteome-wide signatures of function in highly diverged intrinsically disordered regions. eLife 8, e46883 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Zwicker, D., Seyboldt, R., Weber, C. A., Hyman, A. A. & Jülicher, F. Growth and division of active droplets provides a model for protocells. Nat. Phys. 13, 408–413 (2017).

    Article  CAS  Google Scholar 

  49. Honerkamp-Smith, A. R., Veatch, S. L. & Keller, S. L. An introduction to critical points for biophysicists; observations of compositional heterogeneity in lipid membranes. Biochim. Biophys. Acta 1788, 53–63 (2009).

    Article  CAS  PubMed  Google Scholar 

  50. Wlizla, M., McNamara, S. & Horb, M. E. Generation and care of Xenopus laevis and Xenopus tropicalis embryos. Methods Mol. Biol. 1865, 19–32 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ubbels, G. A., Hara, K., Koster, C. H. & Kirschner, M. W. Evidence for a functional role of the cytoskeleton in determination of the dorsoventral axis in Xenopus laevis eggs. J. Embryol. Exp. Morphol. 77, 15–37 (1983).

    CAS  PubMed  Google Scholar 

  52. Good, M. C. & Heald, R. Preparation of cellular extracts from xenopus eggs and embryos. Cold Spring Harb. Protoc. 2018, 097055 (2018).

    Article  Google Scholar 

  53. Sawin, K. E. & Mitchison, T. J. Mitotic spindle assembly by two different pathways in vitro. J. Cell Biol. 112, 925–940 (1991).

    Article  CAS  PubMed  Google Scholar 

  54. Nguyen, T. et al. Differential nuclear import sets the timing of protein access to the embryonic genome. Nat. Commun. 13, 5887 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  55. 3DFilterHolderDesigns. GitHub https://github.com/wuhrlab/3DFilterHolderDesigns (2021).

  56. Grant, I. M. et al. The Xenopus ORFeome: a resource that enables functional genomics. Dev. Biol. 408, 345–357 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  58. Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophoton. Int. 11, 36–42 (2004).

    Google Scholar 

  59. Gupta, M., Sonnett, M., Ryazanova, L., Presler, M. & Wühr, M. Quantitative proteomics of Xenopus embryos I, sample preparation. Methods Mol. Biol. 1865, 175–194 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).

    Article  CAS  PubMed  Google Scholar 

  61. Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896–1906 (2007).

    Article  CAS  PubMed  Google Scholar 

  62. Edwards, A. & Haas, W. Multiplexed quantitative proteomics for high-throughput comprehensive proteome comparisons of human cell lines. Methods Mol. Biol. 1394, 1–13 (2016).

    Article  CAS  PubMed  Google Scholar 

  63. Ruepp, A. et al. CORUM: the comprehensive resource of mammalian protein complexes—2009. Nucleic Acids Res. 38, D497–D501 (2010).

    Article  CAS  PubMed  Google Scholar 

  64. Youn, J. Y. et al. Properties of stress granule and p-body proteomes. Mol. Cell 76, 286–294 (2019).

    Article  CAS  PubMed  Google Scholar 

  65. Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).

    Article  CAS  PubMed  Google Scholar 

  66. Sonnhammer, E. L., von Heijne, G. & Krogh, A. A hidden Markov model for predicting transmembrane helices in protein sequences. Proc. Int. Conf. Intell. Syst. Mol. Biol. 6, 175–182 (1998).

  67. Sonnett, M., Gupta, M., Nguyen, T. & Wühr, M. Quantitative proteomics for Xenopus embryos II, data analysis. Methods Mol. Biol. 1865, 195–215 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Rad, R. et al. Improved monoisotopic mass estimation for deeper proteome coverage. J. Proteome Res. 20, 591–598 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  70. Sashimi. Source Forge http://sashimi.svn.sourceforge.net/viewvc/sashimi/ (2023).

  71. Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass. Spectrom. 5, 976–989 (1994).

    Article  CAS  PubMed  Google Scholar 

  72. Fortriede, J. D. et al. Xenbase: deep integration of GEO and SRA RNA-seq and ChIP-seq data in a model organism database. Nucleic Acids Res. 48, D776–D782 (2020).

    CAS  PubMed  Google Scholar 

  73. Elias, J. E. & Gygi, S. P. Target–decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).

    Article  CAS  PubMed  Google Scholar 

  74. TMTProC. GitHub https://github.com/wuhrlab/TMTProC (2021).

  75. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2018).

    Article  PubMed Central  Google Scholar 

  76. Andrews, S. FastQC. Babraham Bioinformatics, Babraham Institute http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

  77. Krueger, F. Trim galore. A wrapper tool around Cutadapt and FastQC to consistently apply quality and adapter trimming to FastQ files Babraham Bioinformatics, Babraham Institute http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ (2015).

  78. http://www.xenbase.org/entry/

  79. Dobin, A. & Gingeras, T. R. Optimizing RNA-seq mapping with STAR. Methods Mol. Biol. 1415, 245–262 (2016).

    Article  CAS  PubMed  Google Scholar 

  80. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).

    Article  Google Scholar 

  81. Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638–d646 (2023).

    Article  CAS  PubMed  Google Scholar 

  82. Liu, Y., Zhou, J. & White, K. P. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30, 301–304 (2014).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank L. Ryazanova for help with sample preparation and members of the Wühr and the Brangwynne laboratories for useful discussions. We thank D. Hill for access to the Xenopus ORFeome and J. Pelletier for help with designing the filter holders. This work was supported by European Molecular Biology Organization ALTF 601-2018 (F.C.K.), the Lewis–Sigler Institute (M.W. and C.P.B.), National Institutes of Health grant R35GM128813 (M.W.), American Heart Association predoctoral fellowship 20PRE35220061 (T.N.), Princeton Catalysis Initiative (A.M. and M.W.), Eric and Wendy Schmidt Transformative Technology Fund (M.W. and C.P.B.), the Howard Hughes Medical Institute (C.P.B.), the Princeton Biomolecular Condensate Program (C.P.B.), the Princeton Center for Complex Materials, the Materials Research Science and Engineering Center (NSF DMR-2011750) (C.P.B.) and the Air Force Office of Scientific Research Multi-University Research Initiative (FA9550-20-1-0241) (C.P.B.).

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Authors and Affiliations

  1. Department of Molecular Biology, Princeton University, Princeton, NJ, USA

    Felix C. Keber, Andrea Mariossi & Martin Wühr

  2. Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA

    Felix C. Keber, Thao Nguyen, Clifford P. Brangwynne & Martin Wühr

  3. Lewis–Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Felix C. Keber, Thao Nguyen, Andrea Mariossi & Martin Wühr

  4. Howard Hughes Medical Institute, Princeton University, Princeton, NJ, USA

    Clifford P. Brangwynne

  5. Omenn–Darling Bioengineering Institute, Princeton University, Princeton, NJ, USA

    Clifford P. Brangwynne

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Contributions

F.C.K., C.P.B. and M.W. designed the research. F.C.K. conducted the experiments and analysed the data. F.C.K. and T.N. developed and performed in vitro protein expression. F.C.K. and A.M. analysed the RNA-seq data. M.W. and C.P.B. provided funding and supervised the study. F.C.K., C.P.B. and M.W. wrote the manuscript, and all authors helped edit the manuscript.

Corresponding authors

Correspondence to Clifford P. Brangwynne or Martin Wühr.

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Competing interests

C.P.B. is a founder and consultant of Nereid Therapeutics. F.C.K., T.N., C.P.B. and M.W. are inventors and applicants in the provisional patent application US 63/433,243 on the filtration chromatography method. A.M. declares no competing interests.

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Extended data

Extended Data Fig. 1 Sedimentation in extract preparation spin is negligible.

a, Schematic illustrating the sample collection. After 16 min of the preparation spin (14400 krcf), the tube is shock frozen in liquid nitrogen and subsequently cut into a top and a bottom section with a razor blade. b, Comparison of the two halves of the extract. While mitochondrial proteins (orange) are shifted to the bottom, indicating sedimentation, the bulk part of the proteome, including both ribosomal (magenta) and LLPS proteins (green), stays unchanged. Scatter plot of raw TMT signals. N = 1 biological sample. c, Receiver operator characteristics on the signal ratio top to bottom of the data in (b). Source numerical data and proteomics data are provided in Source Data and Table 3.

Source data

Extended Data Fig. 2 Filtration at pore size 100 nm.

a, Executing the experiment presented in Fig. 1c with a larger pore size (dpore = 100 nm) yields qualitatively similar results. However, there are quantitative changes, and, as expected, the overall permeation is higher. FCs were normalized separately for each experiment to their 0.95 quantile, representing freely passing proteins. N = 1 biological sample. b, The receiver operating characteristic for LLPS proteins is best for the 30 nm filtration (AUC = 0.81). Notably, the separation of the LLPS and complexes groups is weaker in the 100 nm condition. Source numerical data and proteomics data are provided in Source Data and Supplementary Table 3.

Source data

Extended Data Fig. 3 Sedimentation of diluted cytoplasm suggests partial dissolution of condensates.

a, Schematic of sedimentation assay. Extract is diluted by a factor f and centrifuged in a hard spin; the top and bottom part are analyzed by mass-spectrometry. For an unbound protein, the top and bottom part have equal concentrations independent of dilution, \({c}_{top}={c}_{bot}=\frac{{c}_{0}}{f}:={c}_{eq}\). By contrast, phase separated proteins sediment in assemblies and the concentration in the bottom part is higher unless they fully dissolve upon dilution. b, Scatter plots of sedimentation data for different dilution conditions (f = 1.2/1.4/2.0) against the undiluted case (f=1). Concentrations are normalized to \({c}_{{eq}}\), derived from the 20% least sedimenting proteins. At the chosen timepoint (200 krcf, 15 min), the ribosome is fully sedimented (magenta, bottom left corner), while the proteasome (magenta cluster, middle) is not. The proteasome indicates facilitated sedimentation for the diluted conditions, however LLPS proteins (green) still exhibit similar ctop/ceq. N = 1 biological sample. c, Box-plot representation of the data in (b). In all tested dilutions, most LLPS proteins are observed to sediment. While their median starting point suggests sedimentation similar to the proteasome, ctop/ceq remains on a similar level, whereas the proteasome sediments further. This (together with (d)) may be explained by the counteracting effects of sedimentation and dissolution. N = 1 biological sample, number of proteins per group: 788 (proteome), 35 (proteasome), 11 (LLPS). d, At longer centrifugation times (200 krcf, 30 min), an overall higher sedimentation is observed, along with a trend towards equilibration upon higher dilution. Importantly, LLPS proteins are sedimenting even up to 8-fold dilution. N = 1 biological sample, number of proteins per group: 844 (proteome), 19 (proteasome), 11 (LLPS). Boxplots (c, d) display data distribution with the center as the median, box limits as quartiles, and whiskers to non-outlier extremes. Source numerical data and proteomics data are provided in Source Data and Supplementary Table 3.

Source data

Extended Data Fig. 4 RNA binding proteins elute later, especially when part of liquid assemblies.

Comparison of the flowthrough behavior of the proteome (gray) with RBPs (purple), grouped by their LLPS annotation (w: aqua, w/o: pink). Left column depicts which metric is used in the cumulative histograms for 30 nm (middle) and 100 nm (right) pore diameters. The insets list the p-values (two-sided ks-test) between the groups color-coded by the marker. a, In the filtration experiment (see Fig. 1), RBPs exhibit higher retention than the bulk proteome. Notably, only few RBPs pass the filters unhindered. These observations are more pronounced for the LLPS subgroup. FCs were normalized separately for each experiment to their 0.95 quantile. N = 1 biological sample. Proteins per group 3922 (proteome), 941 (RNA-binding), 116 (RNA-b. and LLPS), 825 (RNA-b. w/o LLPS). b, The squeezing behavior of most RBPs is slightly greater than that of the average protein. However, the LLPS subgroup shows a much greater shift. N = 1 biological sample. Proteins per group 3922 (proteome), 941 (RNA-binding), 116 (RNA-b. and LLPS), 825 (RNA-b. w/o LLPS). c, Filtration of RNase-treated cytoplasm facilitated the overall flowthrough of RBPs. Again, the effect was much stronger on the LLPS subgroup, suggesting disruption of their assemblies. N = 1 biological sample. Proteins per group 1802 (proteome), 489 (RNA-binding), 69 (RNA-b. and LLPS), 420 (RNA-b. w/o LLPS). Source numerical data and proteomics data are provided in Source Data and Supplementary Table 3.

Source data

Extended Data Fig. 5 Transcriptomics of filtrated cytoplasm suggests organization for accessibility.

a, Scatter plot of the transcriptome for a filtration experiment as in Fig. 1c, comparing the foldchanges to the input of an early and a late time T1 and T2. The majority of RNAs exhibit lightly enhanced flowthrough later on, while about 15% of RNAs are strongly retained at T1 but become much more abundant at T2. The contour denotes the marker density, each line marking a two-fold increase. FC is the ratio between RNA abundance in transcripts per million reads at T1 and T2 normalized by the unfiltered condition. N = 3 biologically independent replicates. b, c, Enrichment analysis of gene database terms (STRING81) for the FC at T1. The selection of top UniProt Keywords (b) suggests that constitutively translated mRNAs pass the pores easily, while mRNAs for transiently translated are more retained. Highlighting development-related terms in the volcano plot of all enriched terms (c) supports this picture. False discovery rates (FDR) calculated by two sided ks-test81. N = 3 biologically independent replicates. d, Histogram of filtration retention values for the transcriptome. Housekeeping gene transcripts31 are shifted towards easier flowthrough (p = 3.3e-78), while the cumulative of all development-related terms from (c) have mRNAs in the retained cluster (p = 3.9e-29). Similarly enriched are transcripts which are contained in L-bodies33 (p = 2.4e-11). P-values determined by two-sided t-test. N = 3 biologically independent replicates. Source numerical data and transcriptomics data are provided in Source Data and Supplementary Table 3.

Source data

Extended Data Fig. 6 Pore size dependence of filtration.

a, Schematics of the spin filtration setup, using polyethersulfone (PES) membranes and the alternative setup gravity flow setup, using cellulose acetate (CA)-mesh filters. The large open area of the CA mesh enables flow through at 1 g force. b, Scatter plots of the fold-changes FC = c/c0 in CA gravity flow experiments. Larger meshes (3 µm, 1.2 µm, 0.8 µm; left, mid, right panel on y-axis) can only resolve few structures compared to a 0.22 µm mesh (x-axis). LLPS proteins are shifted to less permeation. N = 1 biological sample. c, Permeation histograms of the PES filters at the early elution (from main text) (left) and the CA mesh filters (right). The retention increases with smaller pores or meshes, suggesting assemblies on the sub-micrometer length scale. This behavior is pronounced for LLPS proteins. Note that pore and mesh sizes dpore and dmesh are stated as the filter cutoff, i.e., particles larger than this size are confidently retained, and thus most pores or meshes are smaller than this size. However, due to the squeezing behavior of assemblies, we cannot determine a precise size from the cutoff. Red lines in the violin plots denote the 0.25, 0.5, 0.75 quantiles. FCs were normalized separately for each experiment to their 0.95 quantile. N = 1 biological sample. Source numerical data and proteomics data are provided in Source Data and Supplementary Table 3.

Source data

Extended Data Fig. 7 Exemplary proteins organize below the micrometer scale.

a, Confocal micrographs of GFP-fused proteins expressed from mRNA in the cell extract. Solutions appear relatively homogenous, and we did not detect structures on the micrometer scale. Lookup tables adjusted individually to 0.35% saturated pixels to enhance contrast. b, Diffusion constants D of GFP-fused proteins in extract measured by fluorescence correlation spectroscopy. A 70 kDa dextran-rhodamine serves as a reference. Phase separating proteins (ILF3, G3BP2, DDX3X, HNRNPA1, CIRBP) exhibit low diffusion constants, suggesting the presence of assemblies. The measured values would correspond to assemblies with at least tens to hundreds of monomers, based on a rough estimation by the Einstein-Stokes equation and the dextran reference. Boxplots display data distribution with the center as the median, box limits as quartiles, gray dots as outliers, and whiskers to non-outlier extremes. Dex70 (N = 2, n = 36), ILF3 (N = 5, n = 36), G3BP2 (N = 1, n = 12), HNRNPA1 (N = 4, n = 26), CIRBP (N = 4, n = 68), GID8 (N = 4, n = 28), WDR1 (N = 4, n = 23), AP2S1 (N = 3, n = 13), PCF11 (N = 4, n = 23), CCS (N = 3, n = 33); where N denotes the number of biological samples, and n denotes the number of FCS measurements. Source numerical data are provided in Source Data and Supplementary Table 3.

Source data

Extended Data Fig. 8 Size exclusion assay.

a, Confocal micrographs of the assay described in the main text illustrating the measurement. Chromatography beads of different cutoff sizes are placed in extracts with GFP-labeled proteins (WDR1 and CIRBP) and Dextran 70 kDa-rhodamine solution for the calibration measurement. Intensities are normalized to the outside solution. White rectangles indicate regions of the line plots in panel (b). Intensities are normalized to the outside solutions Iout for comparability. b, Density comparison of the solutions above. Left y-axis displays the normalized, background corrected fluorescence intensity I. Right y-axis displays the intensity normalized to the dextran density I/ρ0, as done to correct for accessible volume. c, Measurement of dextran intensity ratios that serve as calibration of the assay. The bright, homogenous solutions allow for a precise determination of ρ0. d, Schematic illustrating the estimate of the size distribution from the exclusion of assemblies from beads with different cutoff sizes. If all assemblies can enter a bead, a fill fraction f = I/(Ioutρ0) of 1 (cyan) is expected. Exclusion of assemblies means lower f. We observe organization happening either on the 10 nm scale (jump of f) or spanning across scales (gradual increase f). e, f, Bead assays for the proteins analyzed in Fig. 4d. Color scale as in panel (d). In case of multiplexed assays, beads with different pore size are masked by the gray circles. Insets are provided to show more beads per image. Source numerical data are provided in Source Data and Supplementary Table 3.

Extended Data Fig. 9 Membrane-bound organelles do not exhibit the typical filtration behavior of BMCs.

a, Summary of liquid-like behavior, the integrated result of filtration and dilution experiments (n = 7 measurements pooled from N = 2 biological independent samples). While both distributions for MBOs and BMCs are wide, they are strongly centered on opposite sides of the scale. Scaffold proteins of BMCs are typically more shifted than co-proteins, termed clients and regulators24. Boxplots display data distribution with the center as the median, box limits as quartiles, gray dots as outliers, and whiskers to non-outlier extremes. b, c, ROCs of organelles. Legends display the AUC, color-code as in (a). (c) Most MBOs -with exemption of the Golgi- are depleted from the most liquid-like region of the proteome. (d) BMCs show a good recall characteristic. Typically, co-proteins have lower AUC, as expected from the picture that scaffolds get populated by them depending on the context. Source numerical data and proteomics data are provided in Source Data and Supplementary Table 3.

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Supplementary Figs. 1–5.

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Supplementary Tables 1–3.

Supplementary Table 1. LLPS database. Supplementary Table 2. Prediction score. Supplementary Table 3. MS and RNA-seq data.

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Keber, F.C., Nguyen, T., Mariossi, A. et al. Evidence for widespread cytoplasmic structuring into mesoscale condensates. Nat Cell Biol 26, 346–352 (2024). https://doi.org/10.1038/s41556-024-01363-5

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